Distributed intelligent management,of active networks

This paper focuses on improving computer network management by the adoption of artificial intelligence techniques. A logical inference system has being devised to enable automated isolation, diagnosis, and even repair of network problems, thus enhancing the reliability, performance, and security of networks. We propose a distributed multi-agent architecture for network management, where a logical reasoner acts as an external managing entity capable of directing, coordinating, and stimulating actions in an active management architecture. The active networks technology represents the lower level layer which makes possible the deployment of code which implement teleo-reactive agents, distribut…

Separation of Concerns and Role Implementation in the PASSI Design Process

The use of design patterns proved successful in lowering the development time and number of errors when producing software with the object-oriented paradigm. In previous works we engaged the production of a tool for the reuse of patterns for multi-agent systems. Now we are fronting a new problem: automatic code generation for agents, designed with a specific methodology, with the support of design patterns and using an aspect oriented approach. In this work we present our preliminary experiences in the identification, description, production and use of aspects for multi agent systems and a tool for code production.

Una Comunità di Chatbot per i Beni Culturali

L'intuizione occupa un ruolo fondamentale nella capacità di ragionamento degli esseri umani. L'obiettivo del lavoro è la progettazione di un'infrastruttura in grado di fornire informazioni che oltre ad appoggiarsi ad un insieme di conoscenze basato su regole sia dotata anche di capacità intuitive. A tal fine è presentato un sistema preliminare che rappresenta il primo passo verso la realizzazione di tale infrastruttura. Il sistema permette ad un utente di interagire con una comunità di chat-bot aventi competenze specifiche in modo da navigare in uno spazio concettuale generato automaticamente con il paradigma di analisi della semantica latente (LSA). La base di conoscenza di ciascun chat-bo…

Fast Fingerprints Classification Only Using the Directional Image

The classification phase is an important step of an automatic fingerprint identification system, where the goal is to restrict only to a subset of the whole database the search time. The proposed system classifies fingerprint images in four classes using only directional image information. This approach, unlike the literature approaches, uses the acquired fingerprint image without enhancement phases application. The system extracts only directional image and uses three concurrent decisional modules to classify the fingerprint. The proposed system has a high classification speed and a very low computational cost. The experimental results show a classification rate of 87.27%.

Goal-Oriented Development of BDI Agents: The PRACTIONIST Approach

The representation of goals and the ability to reason about them play an important role in goal-oriented requirements analysis and modelling techniques, especially in agent-oriented software engineering, as goals are more stable than other abstractions (e.g. user stories). In PRACTIONIST, a framework for developing agent systems according to the Belief-Desire-Intention (BDI) model, goals play a central role. Thus, in this paper we describe the structure of the goal model in the PRACTIONIST framework and how agents use their goal model to reason about goals, desires, and intentions during their deliberation process and means-ends reasoning as well as while performing their activities.

Detection of User Activities in Intelligent Environments

Research on Ambient Intelligence (AmI) focuses on the development of smart environments adaptable to the needs and preferences of their inhabitants. For this reason it is important to understand and model user preferences. In this chapter we describe a system to detect user behavior patterns in an intelligent workplace. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science at the University of Palermo (Italy). © Springer International Publishing Switzerland 2014.

Multi-sensor Fusion through Adaptive Bayesian Networks

Common sensory devices for measuring environmental data are typically heterogeneous, and present strict energy constraints; moreover, they are likely affected by noise, and their behavior may vary across time. Bayesian Networks constitute a suitable tool for pre-processing such data before performing more refined artificial reasoning; the approach proposed here aims at obtaining the best trade-off between performance and cost, by adapting the operating mode of the underlying sensory devices. Moreover, self-configuration of the nodes providing the evidence to the Bayesian network is carried out by means of an on-line multi-objective optimization.

A Framework for Parallel Assessment of Reputation Management Systems

Several distributed applications running over the Internet use Reputation Management Systems (RMSs) to guarantee reliable interactions among unknown agents. Because of the heterogeneity of the existing RMSs, their assessment in terms of correctness and resistance to security attacks is not a trivial task. This work addresses this issue by presenting a novel parallel simulator aimed to support researchers in evaluating the performances of a RMS since the design phase. Preliminary results obtained by simulating two different attacks confirm the suitability of the proposed framework to evaluate different RMSs.

Studio e Progettazione di un linguaggio ad alto livello per la costruzione automatica di basi di conoscenza AIML

A Collaborative Tool for Designing and Enacting Design Processes

Today several approaches using Situational Method Engineering paradigm exist, each of them proposes methods and techniques for developing ad-hoc design processes. In this context heavy efforts were spent in the construction of appropriate tools that could help method engineers in producing a specific design process and in using it. We developed a tool called Metameth for supporting the design process definition and its enactment. Metameth is implemented as a multi-agent system, where each agent is capable of reasoning and adapting itself in order to support the designer in performing different kinds of design activities.

An Adaptive Bayesian System for Context-Aware Data Fusion in Smart Environments

The adoption of multi-sensor data fusion techniques is essential to effectively merge and analyze heterogeneous data collected by multiple sensors, pervasively deployed in a smart environment. Existing literature leverages contextual information in the fusion process, to increase the accuracy of inference and hence decision making in a dynamically changing environment. In this paper, we propose a context-aware, self-optimizing, adaptive system for sensor data fusion, based on a three-tier architecture. Heterogeneous data collected by sensors at the lowest tier are combined by a dynamic Bayesian network at the intermediate tier, which also integrates contextual information to refine the infe…

Knowledge Extraction from Environmental Data Through a Cognitive Architecture

Wireless Sensor Networks represent a novel technology which is expected to experience a dramatic diffusion thanks to the promise to be a pervasive sensory means; however, one of the issues limiting their potential growth relies in the difficulty of managing and interpreting huge amounts of collected data. This paper proposes a cognitive architecture for the extraction of high-level knowledge from raw data through the representation of processed data in opportune conceptual spaces. The presented framework interposes a conceptual layer between the subsymbolic one, devoted to sensory data processing, and the symbolic one, aimed at describing the environment by means of a high level language. T…

Sign Languages Recognition Based on Neural Network Architecture

In the last years, many steps forward have been made in speech and natural languages recognition and were developed many virtual assistants such as Apple’s Siri, Google Now and Microsoft Cortana. Unfortunately, not everyone can use voice to communicate to other people and digital devices. Our system is a first step for extending the possibility of using virtual assistants to speech impaired people by providing an artificial sign languages recognition based on neural network architecture.

An Ambient Intelligence System for Assisted Living

Nowadays, the population's average age is constantly increasing, and thus the need for specialized home assistance is on the rise. Smart homes especially tailored to meet elderly and disabled people's needs can help them maintaining their autonomy, whilst ensuring their safety and well-being. This paper proposes a complete context-aware system for Ambient Assisted Living (AAL), which infers user's actions and context, analyzing its past and current behavior to detect anomalies and prevent possible emergencies. The proposed system exploits Dynamic Bayesian Networks to merge raw data coming from heterogeneous sensors and infer user's behavior and health conditions. A rule-based reasoner is ab…

Real-time detection of twitter social events from the user's perspective

Over the last 40 years, automatic solutions to analyze text documents collection have been one of the most attractive challenges in the field of information retrieval. More recently, the focus has moved towards dynamic, distributed environments, where documents are continuously created by the users of a virtual community, i.e., the social network. In the case of Twitter, such documents, called tweets, are usually related to events which involve many people in different parts of the world. In this work we present a system for real-time Twitter data analysis which allows to follow a generic event from the user's point of view. The topic detection algorithm we propose is an improved version of…

Simulated Annealing Technique for Fast Learning of SOM Networks

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensi…

Sentence Induced Transformations in Conceptual Spaces

The proposed work illustrates how "primitive concepts" can be automatically induced from a text corpus. The primitive concepts are identified by the orthonormal axis of a "conceptual" space induced by a methodology inspired to the latent semantic analysis approach. The methodology represents a natural language sentence by means of a set of rotations of an orthonormal basis in the "conceptual"space. The rotations, triggered by the sequence of words composing the sentence and realized by means of geometric algebra rotors, allow to highlight "conceptual" relations that can arise among the primitive concepts.

Fast Training of Self Organizing Maps for the Visual Exploration of Molecular Compounds

Visual exploration of scientific data in life science\ud area is a growing research field due to the large amount of\ud available data. The Kohonen’s Self Organizing Map (SOM) is\ud a widely used tool for visualization of multidimensional data.\ud In this paper we present a fast learning algorithm for SOMs\ud that uses a simulated annealing method to adapt the learning\ud parameters. The algorithm has been adopted in a data analysis\ud framework for the generation of similarity maps. Such maps\ud provide an effective tool for the visual exploration of large and\ud multi-dimensional input spaces. The approach has been applied\ud to data generated during the High Throughput Screening\ud of mo…

An Adaptive Routing Mechanism for Efficient Resource Discovery in Unstructured P2P Networks

The widespread adoption of large-scale decentralized peer-to-peer (P2P) systems imposes huge challenges on distributed search and routing. Decentralized and unstructured P2P networks are very attractive because they require neither centralized directories, nor precise control over network topology or data placement. However their search mechanisms are extremely unscalable, generating large loads on the network participants. In this paper, to address this major limitation, we propose and evaluate the adoption of an innovative algorithm for routing user queries. The proposed approach aims at dynamically adapting the network topology to peer interests, on the basis of query interactions among …

An Intelligent System for Building Bioinformatics Workflows

In this paper a new intelligent system designed to support the researcher in the development of a workflow for bio informatics experiments is presented. The proposed system is capable to suggest one or more strategies in order to resolve the selected problem and to support the user in the assembly of a workflow for complex experiments, using a a Knowledge base, representing the expertise about the application domain, and a Rule-Based system for decision-making activity. Moreover, the system can represent this workflow at different abstraction layers, freeing the user from implementation details and assisting him in the correct configuration of the algorithms. A sample workflow for protein c…

Exploiting the Human Factor in a WSN-Based System for Ambient Intelligence

Practical applications of ambient intelligence cannot leave aside requirements about ubiquity, scalability, and transparency to the user. An enabling technology to comply with this goal is represented by wireless sensor networks (WSNs); however, although capable of limited in-network processing, they lack the computational power to act as a comprehensive intelligent system. By taking inspiration from the sensory processing model of complex biological organisms, we propose here a cognitive architecture able to perceive, decide upon, and control the environment of which the system is part. WSNs act as a transparent interface that allows the system to understand human requirements through impl…

Gesture Recognition for Improved User Experience in a Smart Environment

Ambient Intelligence (AmI) is a new paradigm that specifically aims at exploiting sensory and context information in order to adapt the environment to the user's preferences; one of its key features is the attempt to consider common devices as an integral part of the system in order to support users in carrying out their everyday life activities without affecting their normal behavior. Our proposal consists in the definition of a gesture recognition module allowing users to interact as naturally as possible with the actuators available in a smart office, by controlling their operation mode and by querying them about their current state. To this end, readings obtained from a state-of-the-art…

Human Activity Recognition Process Using 3-D Posture Data

In this paper, we present a method for recognizing human activities using information sensed by an RGB-D camera, namely the Microsoft Kinect. Our approach is based on the estimation of some relevant joints of the human body by means of the Kinect; three different machine learning techniques, i.e., K-means clustering, support vector machines, and hidden Markov models, are combined to detect the postures involved while performing an activity, to classify them, and to model each activity as a spatiotemporal evolution of known postures. Experiments were performed on Kinect Activity Recognition Dataset, a new dataset, and on CAD-60, a public dataset. Experimental results show that our solution o…

Interoperable real-time symbolic programming for smart environments

Smart environments demand novel paradigms offering easy configuration, programming and deployment of pervasive applications. To this purpose, different solutions have been proposed ranging from visual paradigms based on mashups to formal languages. However, most of the paradigms proposed in the literature require further external tools to turn application description code into an executable program before the deployment on target devices. Source code generation, runtime upgrades and recovery, and online debugging and inspection are often cumbersome in these programming environments. In this work we describe a methodology for real-time and on-line programming in smart environments that is co…

A Quantum Planner for Robot Motion

The possibility of integrating quantum computation in a traditional system appears to be a viable route to drastically improve the performance of systems endowed with artificial intelligence. An example of such processing consists of implementing a teleo-reactive system employing quantum computing. In this work, we considered the navigation of a robot in an environment where its decisions are drawn from a quantum algorithm. In particular, the behavior of a robot is formalized through a production system. It is used to describe the world, the actions it can perform, and the conditions of the robot’s behavior. According to the production rules, the planning of the robot activities is processe…

Context-Aware Visual Exploration of Molecular Datab

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the res…

Sub-Symbolic Encoding of Words in WordNet Environment

Fast Volumetric Reconstruction of Human Body through Superquadrics

This paper describes a technique to reconstruct the volumes of the human body. For this purpose, are introduced mathematical objects able to represent 3d shapes, called super quadrics. These objects are positioned in the space according the captures made by a Microsoft Kinect device and are composed to represent the volumes of the human body. The employment of quaternions provides a relevant speedup for the rotation of the volumes and allows to follow the human movements in real time and reduced computational cost.

Conceptual spaces for computer vision representations

A framework for high-level representations in computer vision architectures is described. The framework is based on the notion of conceptual space. This approach allows us to define a conceptual semantics for the symbolic representations of the vision system. In this way, the semantics of the symbols can be grounded to the data coming from the sensors. In addition, the proposed approach generalizes the most popular frameworks adopted in computer vision.

An architecture for autonomous agents exploiting conceptual representations

An architecture for autonomous agents is proposed that integrates the functional and the behavioral approaches to robotics. The integration is based on the introduction of a conceptual level, linking together a subconceptual, behavioral, level, and a linguistic level, encompassing symbolic representation and data processing. The proposed architecture is described with reference to an experimental setup, in which the robot task is that of building a significant description of its working environment. © 1998 Elsevier Science B.V. All rights reserved.

Latent Semantic Description of Iconic Scenes

It is proposed an approach for the automatic description of scenes using a LSA–like technique. The described scenes are composed by a set of elements that can be geometric forms or iconic representation of objects. Every icon is characterized by a set of attributes like shape, colour and position. Each scene is related to a set of sentences describing their content. The proposed approach builds a data driven vector semantic space where the scenes and the sentences are mapped. A new scene can be mapped in this created space accordingly to a suitable metric. Preliminary experimental results show the effectiveness of the procedure.

A Software System for Automatic Signed Italian Recognition

The paper shows a system for automatic recognition of Signed It alian sentences. The proposed system is based on a multi-level architecture that m odels and manages the knowledge involved in the recognition process in a simple and robust way, integrating a common sense engine in order to deal with sentences in their context. In this architecture, the higher abstraction level introd uces a semantic control and an analysis of the correctness of a sentence given a sequence of previously recognized signs. Experimenta tions are presented using a set of signs from the Italian Sign Language (LIS) and a sentence template usef ul for domotic applications, an d show a high recognition rate that encou…

Sub-symbolic Mapping of Cyc Microtheories in Data-Driven “Conceptual” Spaces

The presented work aims to combine statistical and cognitive-oriented approaches with symbolic ones so that a conceptual similarity relationship layer can be added to a Cyc KB microtheory. Given a specific microtheory, a LSA-inspired conceptual space is inferred from a corpus of texts created using both ad hoc extracted pages from the Wikipedia repository and the built-in comments about the concepts of the specific Cyc microtheory. Each concept is projected in the conceptual space and the desired layer of subsymbolic relationships between concepts is created. This procedure can help a user in finding the concepts that are "sub-symbolically conceptually related" to a new concept that he want…

A Multimodal Guide for Virtual 3D Models of Cultural Heritage Artifacts

The area of cultural heritage preservation and fruition has drawn an ever growing attention of artificial intelligence and human-computer interaction research in the last decades. The common aim is to develop systems that can interact with the user in a variety of modes and in the most natural way. In this paper, a multimodal guide for virtual 3D environment navigation is presented. The proposed system integrates X3D environment with a multimodal interface. The application scenario is to provide a visitor assistance and guidance during the visit of one of the halls in the historical Palazzo Steri, the headquarters of the University of Palermo.

Understanding dynamic scenes

We propose a framework for the representation of visual knowledge in a robotic agent, with special attention to the understanding of dynamic scenes. According to our approach, understanding involves the generation of a high level, declarative description of the perceived world. Developing such a description requires both bottom-up, data driven processes that associate symbolic knowledge representation structures with the data coming out of a vision system, and top-down processes in which high level, symbolic information is in its turn employed to drive and further refine the interpretation of a scene. On the one hand, the computer vision community approached this problem in terms of 2D/3D s…

A Middleware to Develop and Test Vehicular Sensor Network Applications

The Smart city ecosystem is composed of several networked devices that provide services to citizens and improve their quality of life. Basic services, which must be exposed by the underlying software infrastructure, require efficient networking and communication protocols to coordinate and manage all the system components. In particular, Vehicular Sensor Networks (VSNs) are envisioned as key components of smart cities. Verification is crucial in such a highly dynamic scenario to ensure operation correctness and to reduce the development cost of smart applications. However, the rigidity of existing middlewares makes development, reconfiguration, and testing rather difficult. In this work, we…

Notice of Violation of IEEE Publication Principles<BR>An adaptive routing mechanism for P2P resource discovery

The key to the usability of large-scale decentralize peer-to-peer (P2P) systems, and one of the most challenge design aspects, is efficient mechanism for distributed resource discovery. Unstructured P2P networks are very attractive because they do not suffer the limitations of centralized systems an the drawbacks of highly structured approaches. However the search algorithms are usually based on simple flooding scheme generating large loads on the network participants. In this paper to address this major limitation, we present the design an evaluation of an innovative searching protocol in unstructured P2P networks. The approach aims at dynamically adapting the network topology to peers' in…

MODULAR KNOWLEDGE REPRESENTATION IN ADVISOR AGENTS FOR SITUATION AWARENESS

A modular knowledge representation framework for conversational agents is presented. The approach has been realized to suit the situation awareness paradigm. The modularity of the framework makes possible the composition of specific modules that deal with particular features, simplifying both the chatbot design process and its smartness. As a proof of concepts we have developed a modular, situation awareness oriented, KB for a conversational agent, which plays the role of an advisor aimed at helping a user to be in charge of a virtual town, inspired to the SimCity series game. The agent makes an extensive use of semantic computing techniques and is able to perceive, comprehend and project c…

Clinical Anatomy and information technology.

Exploiting multimodality for intelligent mobile access to pervasive services in cultural heritage sites

In this chapter the role of multimodality in intelligent, mobile guides for cultural heritage environments is discussed. Multimodal access to information contents enables the creation of systems with a higher degree of accessibility and usability. A multimodal interaction may involve several human interaction modes, such as sight, touch and voice to navigate contents, or gestures to activate controls. We first start our discussion by presenting a timeline of cultural heritage system evolution, spanning from 2001 to 2008, which highlights design issues such as intelligence and context-awareness in providing information. Then, multimodal access to contents is discussed, along with problems an…

An Intelligent Multimodal Site-guide for the "Parco Archeologico della Valle dei Templi di Agrigento

A framework for sign language sentence recognition by common sense context

This correspondence proposes a complete framework for sign language recognition that integrates a commonsense engine in order to deal with sentence recognition. The proposed system is based on a multilevel architecture that allows modeling and managing of the knowledge of the recognition process in a simple and robust way. The final abstraction level of this architecture introduces the semantic context and the analysis of the correctness of a sentence given in a sequence of recognized signs. Experimentations are presented using a set of signs from the Italian sign language (LIS) for domotic applications. The implemented system maintains a high recognition rate when the set of signs grows, c…

THE METAMODEL: A STARTING POINT FOR DESIGN PROCESSES CONSTRUCTION

The construction of ad-hoc design processes following the Situational Method Engineering (SME) paradigm is currently carried out by adopting a set of phases for which, until now, no well defined techniques and guidelines have been established. The consequence is that organizations are very dependent on method designers' skills. In this paper, we propose an approach based on SME for constructing customized agent oriented design processes. Our approach adopts the metamodel as the most important factor leading to the selection and assembly of method fragments and an algorithm for establishing the instantiation order of metamodel elements. The algorithm makes the proposed approach repeatable a…

Smartphone data analysis for human activity recognition

In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide the user with more and more functions, so that anyone is encouraged to carry one during the day, implicitly producing that can be analysed to infer knowledge of the user’s context. In this work we present a novel framework for Human Activity Recognition (HAR) using smartphone data captured by means of embedded triaxial accelerometer and gyroscope sensors. Some statistics over the captured sensor data are computed to model each activity, then real-time classification is performed by means of an efficient supervised learning technique. The system we propose also adopts a …

<title>HAP: a hybrid system for reasoning about actions and plans in robotics</title>

The paper describes the main ideas and principles of HAP (Hybrid representation of Actions and Plans), a system for hybrid representation and reasoning in advanced robotics. In this context, hybrid representation refers to the integration of both symbolic and analogic knowledge representation paradigms. In particular, the logic/symbolic component is based on a KL-ONE-like representation language. The system embeds "analogic experts", that are concurrent procedures operating in a direct and fast way on the world representation. These "experts" help the system in planning a correct temporal sequence of actions. As a reference scenario, assembly (and disassembly) problems are considered. The a…

Towards a conceptual representation of actions

An autonomous robot involved in missions should be able to generate, update and process its own actions. It is not plausible that the meaning of the actionsus ed by the robot isgiv en form the outside of the system itself. Rather, this meaning should be anchored to the world through the perceptual abilitiesof the robot. We present an approach to conceptual action representation based on a "conceptual" level that actsasan intermediate level between symbolsand data coming form sensors. Symbolic representations are interpreted by mapping them on the conceptual level through a mapping mechanism based on artificial neural networks.

A Logical Architecture for Active Network Management

This paper focuses on improving network management by exploiting the potential of “doing” of the Active Networks technology, together with the potential of “planning,” which is typical of the artificial intelligent systems. We propose a distributed multiagent architecture for Active Network management, which exploits the dynamic reasoning capabilities of the Situation Calculus in order to emulate the reactive behavior of a human expert to fault situations. The information related to network events is generated by programmable sensors deployed across the network. A logical entity collects this information, in order to merge it with general domain knowledge, with a view to identifying the roo…

Geometric and conceptual knowledge representation within a generative model of visual perception

A representation scheme of knowledge at both the geometric and conceptual levels is offered which extends a generative theory of visual perception. According to this theory, the perception process proceeds through different scene representations at various levels of abstraction. The geometric domain is modeled following the CSG (constructive solid geometry) approach, taking advantage of the geometric modelling scheme proposed by A. Pentland, based on superquadrics as representation primitives. Recursive Boolean combinations and deformations are considered in order to enlarge the scope of the representation scheme and to allow for the construction of real-world scenes. In the conceptual doma…

Self-Organizing Architectures for Digital Signal Processing

A Subsymbolic Approach to Word Modeling for Domain Specific Speech Recognition

Verification of Symbolic Distributed Protocols for Networked Embedded Devices

The availability of versatile and interconnected embedded devices makes it possible to build low-cost networks with a large number of nodes running even complex applications and protocols in a distributed manner. Common tools used for modeling and verification, such as simulators, present some limitations as application correctness is checked off-board and only focuses on source code. Execution in the real network is thus excluded from the early stages of design and verification. In this paper, a system for modeling and verification of symbolic distributed protocols running on embedded devices is introduced. The underlying methodology is rooted in a symbolic programming paradigm that makes …

Information technology in clinical anatomy

Word Mapping in Semantic Hyperspaces

Vision and emotional flow in a cognitive architecture for human-machine interaction

The detection and recognition of a human face should meet the need for social interaction that drives a humanoid robot, and it should be consistent with its cognitive model and the perceived scene. The paper deals with the description of the potential of having a system of emotional contagion, and proposes a simple implementation of it. An emotional index allows to build a mechanism which tends to align the emotional states of the robot and the human when a specific object is detected in the scene. Pursuing the idea of social interaction based on affect recognition, a first practical application capable of managing the emotional flow is described, involving both conceptual spaces and an emo…

Context-awareness for multi-sensor data fusion in smart environments

Multi-sensor data fusion is extensively used to merge data collected by heterogeneous sensors deployed in smart environments. However, data coming from sensors are often noisy and inaccurate, and thus probabilistic techniques, such as Dynamic Bayesian Networks, are often adopted to explicitly model the noise and uncertainty of data. This work proposes to improve the accuracy of probabilistic inference systems by including context information, and proves the suitability of such an approach in the application scenario of user activity recognition in a smart home environment. However, the selection of the most convenient set of context information to be considered is not a trivial task. To thi…

Use of Soft Topographic Maps for Clustering Bacteria on the Basis of their 16S rRNA Gene Sequence

An automatic system for humanoid dance creation

Abstract The paper describes a novel approach to allow a robot to dance following musical rhythm. The proposed system generates a dance for a humanoid robot through the combination of basic movements synchronized with the music. The system made up of three parts: the extraction of features from audio file, estimation of movements through the Hidden Markov Models and, finally, the generation of dance. Starting from a set of given movements, the robot choices sequence of movements a suitable Hidden Markov Model, and synchronize them processing musical input. The proposed approach has the advantage that movement execution probabilities could be changed according evaluation of the dance executi…

Exploiting Cognitive Architectures to design Storytelling Activities for NarRob

In this work, we exploited the potential of a cognitive architecture to model the characters of a story in an interactive storytelling system. The system is accessible through NarRob, a humanoid social robot, able to manage storytelling activities aimed at improving the emotional and social skills of children, also adding expressiveness to the narration by using proper associate gestures and emotional expressions. Our main goal was to implement the cognitive processes of the agents interpreted by the robot within an environment coinciding with a narrative context. The narrated story is largely inspired by the "FearNot!" game, where in our system, we modeled the cognitive processes elaborate…

Gaining insight by structural knowledge extraction

The availability of increasingly larger and more complex datasets has boosted the demand for systems able to analyze them automatically. The design and implementation of effective systems requires coding knowledge about the application domain inside the system itself; however, the designer is expected to intuitively grasp the most relevant features of the raw data as a. preliminary step. In this paper we propose a framework to get useful insight about a set of complex data, and we claim that a shift in perspective may be of help to tackle with the unaddressed goal of representing knowledge by means of the structure inferred from the collected samples. We will present a formulation of knowle…

Sub-symbolic Encoding of Words

A new methodology for sub-symbolic semantic encoding of words is presented. The methodology uses the WordNet lexical database and an ad hoc modified Sammon algorithm to associate a vector to each word in a semantic n-space. All words have been grouped according to the WordNet lexicographers’ files classification criteria: these groups have been called lexical sets. The word vector is composed by two parts: the first one, takes into account the belonging of the word to one of these lexical sets; the second one is related to the meaning of the word and it is responsible for distinguishing the word among the other ones of the same lexical set. The application of the proposed technique over all…

Intelligent Energy Management System

Energy management is nowadays a subject of great importance and complexity. It consists in choosing among a set of sources able to produce energy that will give energy to a set of loads by minimising losses and costs. The sources and loads are heterogeneous, distributed and the reaction of the system, the choice of sources, must be done in real-time to avoid power outage. The goal of this paper is to present a system able to self-regulate a heterogeneous set of power sources and loads organised as a coherent group of entities that is called micro-grid, in order to optimize several criteria such as: cost and efficiency. This system is based upon the Multi-Agent Systems paradigm. Each micro-g…

A Fast and Interactive Approach to Application Development on Wireless Sensor and Actuator Networks

In Wireless Sensor and Actuator Networks (WSANs) sensor and actuator devices are connected through radio links to perform tasks in many different contexts. Conven- tionally, applications for WSANs are developed using traditional operating systems which application code is linked with at the end of a cross-compilation process. We propose instead an alternative approach for building applications on WSANs that is based on interactivity and does not require time consuming cross-compilation phases. In our development methodology, it is possible to define procedures and services according to the application target, simultaneously test them and reprogram the nodes interactively when needed, even a…

A Web-Oriented Java3D Talking Head

Facial animation denotes all those systems performing speech synchro- nization with an animated face model. These kinds of systems are named Talking Heads or Talking Faces. At the same time simple dialogue systems called chatbots have been developed. Chatbots are software agents able to interact with users through pattern-matching based rules. In this paper a Talking Head oriented to the creation of a Chatbot is presented. An answer is generated in form of text trig- gered by an input query. The answer is converted into a facial animation using a 3D face model whose lips movements are synchronized with the sound produced by a speech synthesis module. Our Talking Head exploits the naturalnes…

A multi‐agent system for itinerary suggestion in smart environments

Abstract Modern smart environments pose several challenges, among which the design of intelligent algorithms aimed to assist the users. When a variety of points of interest are available, for instance, trajectory recommendations are needed to suggest users the most suitable itineraries based on their interests and contextual constraints. Unfortunately, in many cases, these interests must be explicitly requested and their lack causes the so‐called cold‐start problem. Moreover, lengthy travelling distances and excessive crowdedness of specific points of interest make itinerary planning more difficult. To address these aspects, a multi‐agent itinerary suggestion system that aims at assisting t…

A Multimodal Guide for the Augmented Campus

The use of Personal Digital Assistants (PDAs) with ad-hoc built-in information retrieval and auto-localization functionalities can help people navigating an environment in a more natural manner compared to traditional audio/visual pre-recorded guides. In this work we propose and discuss a user-friendly, multi-modal guide system for pervasive context-aware service provision within augmented environments. The proposed system is adaptable to the user needs of mobility within a given environment; it is usable on different mobile devices and in particular on PDAs, which are used as advanced adaptive HEI (human-environment interaction) interfaces. An information retrieval service is provided that…

A symbolic distributed event detection scheme for Wireless Sensor Networks

Due to the possibility of extensive and pervasive deployment of many tiny sensor devices in the area of interest, Wireless Sensor Networks (WSNs) result particularly suitable to detect significant events and to react accordingly in industrial and home scenarios. In this context, fuzzy inference systems for event detection in WSNs have proved to be accurate enough in treating imprecise sensory readings to decrease the number of false alarms. Besides reacting to event occurrences, the whole network may infer more information to enrich the event semantics resulting from reasoning processes carried out on the individual nodes. Contextual knowledge, including spatial and temporal relationships, …

An Intelligent Multimodal Site-guide for the “Parco Archeologico della Valle dei Templi” in Agrigento

Fast Fingerprints Classification only using the Directional Image

The classification phase is an important step of an automatic fingerprint identification system, where the goal is to restrict only to a subset of the whole database the search time. The proposed system classifies fingerprint images in four classes using only directional image information. This approach, unlike the literature approaches, uses the acquired fingerprint image without enhancement phases application. The system extracts only directional image and uses three concurrent decisional modules to classify the fingerprint. The proposed system has a high classification speed and a very low computational cost. The experimental results show a classification rate of 87.27%.

Structural Knowledge Extraction from Mobility Data

Knowledge extraction has traditionally represented one of the most interesting challenges in AI; in recent years, however, the availability of large collections of data has increased the awareness that “measuring” does not seamlessly translate into “understanding”, and that more data does not entail more knowledge. We propose here a formulation of knowledge extraction in terms of Grammatical Inference (GI), an inductive process able to select the best grammar consistent with the samples. The aim is to let models emerge from data themselves, while inference is turned into a search problem in the space of consistent grammars, induced by samples, given proper generalization operators. We will …

Notice of Violation of IEEE Publication Principles: Reinforcement learning for P2P searching

For a peer-to-peer (P2P) system holding a massive amount of data, an efficient and scalable search for resource sharing is a key determinant to its practical usage. Unstructured P2P networks avoid the limitations of centralized systems and the drawbacks of a highly structured approach, because they impose few constraints on topology and data placement, and they support highly versatile search mechanisms. However their search algorithms are usually based on simple flooding schemes, showing severe inefficiencies. In this paper, to address this major limitation, we propose and evaluate the adoption of a local adaptive routing protocol. The routing algorithm adopts a simple reinforcement learni…

A M3G Talking Head for Smartphones

Often customer information services or virtual support guides make use of friendly interface to facilitate human-machine interaction. Indeed, virtual guided tours or helpdesks use a talking anthropomorphic head to communicate with the user. In this paper, we present a talking head for Smart phones, PDAs and, in general, all the mobile devices able to support J2ME and MIDP protocol. The objective of this article is to illustrate how to make such an interface as portable as possible by maximizing the limited computational resources of these devices.

A Decision Support System for Reverse Engineering Gene Regulatory Networks

In this paper we present a knowledge-based system that aims at helping scientists in the reverse engineering process of gene regulatory networks. The main motivation of the proposed approach is to support scientists in the choice of the wide variety of algorithms and methods currently applied in the literature to infer Gene Regulatory Networks starting from gene expression measured using microarray technology. The Decision Support System (DSS) architecture is based on an ontology to model the knowledge base, a logical reasoner that builds the workflow of tasks to be done starting from the user’s request and a set of rules, and, finally, an agenda that runs the algorithms and software schedu…

An Ontology Design Methodology for Knowledge-Based Systems with Application to Bioinformatics

Ontologies are formal knowledge representation models. Knowledge organization is a fundamental requirement in order to develop Knowledge-Based systems. In this paper we present Data-Problem-Solver (DPS) approach, a new ontological paradigm that allows the knowledge designer to model and represent a Knowledge Base (KB) for expert systems. Our approach clearly distinguishes among the knowledge about a problem to resolve (answering the what to do question), the solver method to resolve it (answering the how to do question) and the type of input data required (answering the what I need question). The main purpose of the proposed paradigm is to facilitate the generalization of the application do…

Autonomic behaviors in an Ambient Intelligence system

Ambient Intelligence (AmI) systems are constantly evolving and becoming ever more complex, so it is increasingly difficult to design and develop them successfully. Moreover, because of the complexity of an AmI system as a whole, it is not always easy for developers to predict its behavior in the event of unforeseen circumstances. A possible solution to this problem might lie in delegating certain decisions to the machines themselves, making them more autonomous and able to self-configure and self-manage, in line with the paradigm of Autonomic Computing. In this regard, many researchers have emphasized the importance of adaptability in building agents that are suitable to operate in real-wor…

Tecniche LSA e Chat-bot per il recupero automatico di informazioni

Enabling peer-to-peer User-Preference-Aware Energy Sharing Through Reinforcement Learning

Renewable, heterogeneous and distributed energy resources are the future of power systems, as envisioned by the recent paradigm of Virtual Power Plants (VPPs). Residential electricity generation, e.g., through photovoltaic panels, plays a fundamental role in this paradigm, where users are able to participate in an energy sharing system and exchange energy resources among each other. In this work, we study energy sharing systems and, differently from previous approaches, we consider realistic user behaviors by taking into account the user preferences and level of engagement in the energy trades. We formulate the problem of matching energy resources while contemplating the user behavior as a …

A computer support system to support diagnosis by imaging and its experimental application to images of patients affected by multiple sclerosis

Application of an intelligent study and research support system for clinical anatomy in a cooperation scenario

Scientific research and teaching are strongly interrelated. A student should be educated both to the fundamentals of a discipline and to the research tasks as the future development of a discipline is entrusted to the students of today. Computer based tutoring systems already showed useful in pursuing the former target while the Intelligent Study and Research Support System developed at DINFO may be used to fulfill both in an integrated manner. This paper introduces the possible application of the ISRSS to training of Clinical Anatomy in a scenario of international cooperation among academic institutions.

A MAS metamodel-driven approach to process composition

Exploiting Deductive Processes for Automated Network Management

This paper focuses on improving network management by the adoption of artificial intelligence techniques. We propose a distributed multiagent architecture for network management, which exploits the dynamic reasoning capabilities of the situation calculus in order to emulate the reactive behavior of a human expert to fault situations. The information related to network events is generated by programmable sensors deployed on the network devices and is collected by a logical entity for network managing where it is merged with general domain knowledge, with a view to identifying the root causes of faults and to decide on reparative actions. The logical inference system has been devised to carry…

Geometric Algebra Rotors for Sub-Symbolic Coding of Natural Language Sentences

A sub-symbolic encoding methodology for natural language sentences is presented. The procedure is based on the creation of an LSA-inspired semantic space and associates rotation operators derived from Geometric Algebra to word bigrams of the sentence. The operators are subsequently applied to an orthonormal standard basis of the created semantic space according to the order in which words appear in the sentence. The final rotated basis is then coded as a vector and its orthogonal part constitutes the sub-symbolic coding of the sentence. Preliminary experimental results for a classification task, compared with the traditional LSA methodology, show the effectiveness of the approach.

A Kinect-Based Gesture Acquisition and Reproduction System for Humanoid Robots

The paper illustrates a system that endows an humanoid robot with the capability to mimic the motion of a human user in real time, serving as a basis for further gesture based human-robot interactions. The described approach uses the Microsoft Kinect as a low cost alternative to expensive motion capture devices.

An efficient distributed algorithm for generating and updating multicast trees

As group applications are becoming widespread, efficient network utilization becomes a growing concern. Multicast transmission represents a necessary lower network service for the wide diffusion of new multimedia network applications. Multicast transmission may use network resources more efficiently than multiple point-to-point messages; however, creating optimal multicast trees (Steiner Tree Problem in networks) is prohibitively expensive. This paper proposes a distributed algorithm for the heuristic solution of the Steiner Tree Problem, allowing the construction of effective distribution trees using a coordination protocol among the network nodes. Furthermore, we propose a novel distribut…

The Computational Correlates of Artificial Qualia

Development of an IoT Environmental Monitoring Application with a Novel Middleware for Resource Constrained Devices

In this paper the development of a Mobile Health monitoring system is described. The system combines user location data with air quality information provided by a heterogeneous sensing infrastructure providing users with advises about their daily exposure to air pollutants. The highly dynamic integration of different kind of nodes, mostly characterized by rather constrained resources, of this application is crucial to implement the Internet of Things vision, and requires powerful and effective programming methodologies to abstract implementation of high-level distributed processing from hardware dependencies. We then describe our programming methodology and our novel middleware supporting d…

Chatbots as Interface to Ontologies

Chatbots are simple conversational agents using 'pattern matching rules' to carry out the dialogue with the user and various expedients to improve their credibility. However, the rules on which they are based on are too restrictive and their language understanding capability is very limited. Nevertheless chatbots are widespread in several applications, especially to provide information to users in a new and enjoyable way. In this chapter we describe different chatbot architectures, exploiting the use of ontologies in order to create clever information suppliers overcoming the main limits of chatbots: The knowledge base building and the rigidness of the dialogue mechanism. © Springer Interna…

Cognitive meta-learning of syntactically inferred concepts

This paper outlines a proposal for a two-level cognitive architecture reproducing the process of abstract thinking in human beings. The key idea is the use of a level devoted to the extraction of compact representation for basic concepts, with additional syntactic inference carried on at a meta-level, in order to provide generalization. Higher-level concepts are inferred according to a principle of simplicity, consistent with Kolmogorov complexity, and merged back into the lower level in order to widen the underlying knowledge base.

User Activity Recognition for Energy Saving in Smart Homes

Abstract Energy demand in typical home environments accounts for a significant fraction of the overall consumption in industrialized countries. In such context, the heterogeneity of the involved devices, and the non negligible influence of the human factor make the optimization of energy use a challenging task; effective automated approaches must take into account basic information about users, such as the prediction of their course of actions. Our proposal consists in learning customized structural models for common user activities for predicting the trend of energy consumption; the approach aims to lower energy demand in the proximity of predicted peak loads so as to keep the overall cons…

Smart Assistance for Students and People Living in a Campus

Being part of one of the fastest growing area in Artificial Intelligence (AI), virtual assistants are nowadays part of everyone's life being integrated in almost every smart device. Alexa, Siri, Google Assistant, and Cortana are just few examples of the most famous ones. Beyond these off-the-shelf solutions, different technologies which allow to create custom assistants are available. IBM Watson, for instance, is one of the most widely-adopted question-answering framework both because of its simplicity and accessibility through public APIs. In this work, we present a virtual assistant that exploits the Watson technology to support students and staff of a smart campus at the University of Pa…

Clustering Quality and Topology Preservation in Fast Learning SOMs

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper, we describe Fast Learning SOM (FLSOM) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that FLSOM also improves the quality of the map by providing better clustering quality and topology preservation of multidimensional input data. Several tests have been carried out on different multidimensional datasets, which demonstrate better performances of the algorithm in comparison with the original …

A Multimodal Interaction Guide for Pervasive Services Access

A pervasive, multimodal virtual guide for a cultural heritage site tour is illustrated. The guide is based on the integration of different technologies such as conversational agents, commonsense reasoning knowledge bases, multimodal interfaces and self-location detection systems. The aim of the work is to offer a more natural, context sensitive access to information with respect to traditional audio/visual pre-recorded guides. A prototype has been developed and implemented on a Qtek 9090 with Windows Mobile 2003 in order to deal with the "Museo Archeologico Regionale di Agrigento" domain.

Geometric Algebra Rotors for Sub-symbolic Coding of Natural Language Sentences

A sub-symbolic encoding methodology for natural language sentences is presented. The procedure is based on the creation of an LSA-inspired semantic space and associates rotation operators derived from Geometric Algebra to word bigrams of the sentence. The operators are subsequently applied to an orthonormal standard basis of the created semantic space according to the order in which words appear in the sentence. The final rotated basis is then coded as a vector and its orthogonal part constitutes the sub-symbolic coding of the sentence. Preliminary experimental results for a classification task, compared with the traditional LSA methodology, show the effectiveness of the approach.

Sub-Symbolic Knowledge Representation for Evocative Chat-Bots

A sub-symbolic knowledge representation oriented to the enhancement of chat bot interaction is proposed. The result of the technique is the introduction of a semantic sub-symbolic layer to a traditional ontology-based knowledge representation. This layer is obtained mapping the ontology concepts into a semantic space built through Latent Semantic Analysis (LSA) technique and it is embedded into a conversational agent. This choice leads to a chat-bot with “evocative” capabilities whose knowledge representation framework is composed of two areas: the rational and the evocative one. As a standard ontology we have chosen the well-founded WordNet lexical dictionary, while as chat-bot the ALICE a…

Using and Extending the SPEM Specifications to Represent Agent Oriented Methodologies

Situational Method Engineering used for constructing ad-hoc agent oriented design processes is grounded on a well defined set of phases that are principally based on reuse of components coming from existing agent design processes; these components have to be stored in a repository. The identification and extraction of these components could take large advantages from the existence of a standardized representation of the design processes they come from. In this paper we illustrate our solution based on SPEM 2.0 specifications for modelling agent design processes and extending them when necessary to meet the specific needs we faced in our experiments.

An Emotional Talking Head for a Humoristic Chatbot

The interest about enhancing the interface usability of applications and entertainment platforms has increased in last years. The research in human-computer interaction on conversational agents, named also chatbots, and natural language dialogue systems equipped with audio-video interfaces has grown as well. One of the most pursued goals is to enhance the realness of interaction of such systems. For this reason they are provided with catchy interfaces using humanlike avatars capable to adapt their behavior according to the conversation content. This kind of agents can vocally interact with users by using Automatic Speech Recognition (ASR) and Text To Speech (TTS) systems; besides they can c…

EXPERT CHAT-BOTS FOR CULTURAL HERITAGE

A Decisional Multi-Agent Framework for Automatic Supply Chain Arrangement

In this work, a multi-agent system (MAS) for supply chain dynamic configuration is proposed. The brain of each agent is composed of a Bayesian Decision Network (BDN); this choice allows the agent for taking the best decisions estimating benefits and potential risks of different strategies, analyzing and managing uncertain information about the collaborating companies. Each agent collects information about customer's orders and current market prices, and analyzes previous experiences of collaborations with trading partners. The agent therefore performs a probabilistic inferential reasoning to filter information modeled in its knowledge base in order to achieve the best performance in the sup…

An Intelligent Agent to Support City Policies Decisions

Automatic Dictionary Creation by Sub-symbolic Encoding of Words

This paper describes a technique for automatic creation of dictionaries using sub-symbolic representation of words in cross-language context. Semantic relationship among words of two languages is extracted from aligned bilingual text corpora. This feature is obtained applying the Latent Semantic Analysis technique to the matrices representing terms co-occurrences in aligned text fragments. The technique allows to find the “best translation” according to a properly defined geometric distance in an automatically created semantic space. Experiments show an interesting correctness of 95% obtained in the best case.

Sensor9k : A testbed for designing and experimenting with WSN-based ambient intelligence applications

Ambient Intelligence systems are typically characterized by the use of pervasive equipment for monitoring and modifying the environment according to users' needs, and to globally defined constraints. Our work describes the implementation of a testbed providing the hardware and software tools for the development and management of AmI applications based on wireless sensor and actuator networks, whose main goal is energy saving for global sustainability. A sample application is presented that addresses temperature control in a work environment, through a multi-objective fuzzy controller taking into account users' preferences and energy consumption.

Human-ambient interaction through Wireless Sensor Networks

Recent developments in technology have permitted the creation of cheap, and unintrusive devices that may be effectively employed for instrumenting an intelligent environment. The present work describes a modular framework that makes use of a class of those devices, namely wireless sensors, in order to monitor relevant physical quantities and to collect users' requirements through implicit feedback. A central intelligent unit extracts higher-level concepts from raw sensory inputs, and carries on symbolic reasoning based on them. The aim of the reasoning is to plan a sequence of actions that will lead the environment to a state as close as possible to the users' desires, taking into account b…

Knowledge-based verification of concatenative programming patterns inspired by natural language for resource-constrained embedded devices

We propose a methodology to verify applications developed following programming patterns inspired by natural language that interact with physical environments and run on resource-constrained interconnected devices. Natural language patterns allow for the reduction of intermediate abstraction layers to map physical domain concepts into executable code avoiding the recourse to ontologies, which would need to be shared, kept up to date, and synchronized across a set of devices. Moreover, the computational paradigm we use for effective distributed execution of symbolic code on resource-constrained devices encourages the adoption of such patterns. The methodology is supported by a rule-based sys…

A gesture recognition framework for exploring museum exhibitions

In this paper we present a gesture recognition framework for providing the visitors of a museum exhibition with a non intrusive interface for the multimedia enjoyment of digital contents. Early experiments were carried out at the Computer History Museum Exhibition of the University of Palermo.

A Dynamic Reasoning Architecture for Computer Network Management

This paper focuses on improving network management and monitoring by the adoption of Artificial Intelli- gence techniques. In order to allow automated reasoning on networking concepts, we defined an accurate ontologi- cal model capable of describing as better as possible the networking domain. The thorough representation of the do- main knowledge is used by a Logical Reasoner, which is an expert system capable of performing high-level manage- ment tasks.

A hybrid architecture for autonomous agents

A new hybrid approach for autonomous agents is described. The approach integrates in a principled way the functional and the behavioral approaches of agent design. The integration is based on the introduction of a conceptual space representation that links the subsymbolic level, which is a repository of reactive modules, with the symbolic level, in which rich symbolic descriptions of the agent environment take place. Results are reported obtained by an experimental implementation of the agent.

Conquering Fine-Grained Blends of Design Patterns

The reuse of design patterns in realistic software systems is often a result of blending multiple pattern elements together rather than instantiating them in an isolated manner. The explicit description of pattern compositions is the key for (i) documenting the structure and the behavior of blended patterns and, (ii) more importantly, supporting the reuse of composite patterns across different software projects. In this context, this paper proposes a fine-grained composition language for describing varying blends of design patterns based on their structural and behavioural semantics. The reusability and expressiveness of the proposed language are assessed through its application to 32 compo…

A Knowledge Management System using Bayesian Network

In today's world, decision support and knowledge management processes are strategic and interdependent activities in many organizations. The companies' interest on a correct knowledge management is grown, more than interest on the mere knowledge itself. This paper proposes a Knowledge Management System based on Bayesian networks. The system has been tested collecting and using data coming from projects and processes typical of ICT companies, and provides a Document Management System and a Decision Support system to share documents and to plan how to best use firms' knowledge.

Creativity in Conceptual Spaces

The main aim of this paper is contributing to what in the last few years has been known as computational creativity. This will be done by showing the relevance of a particular mathematical representation of G"ardenfors's conceptual spaces to the problem of modelling a phenomenon which plays a central role in producing novel and fruitful representations of perceptual patterns: analogy.

An Approach to Enhance Chatbot Semantic Power and Maintainability: Experiences within the FRASI Project

The paper illustrates the implementation and semantic enhancement of a domain-oriented Question-Answering system based on a pattern-matching chat bot technology, developed within an industrial project, named FRASI. The main difficulty in building a KB for a chat bot is to handwrite all possible question-answer pairs that constitute the KB. The proposed approach simplifies the chat bot realization thanks to two solutions. The first one uses an ontology, which is exploited in a twofold manner: to construct dynamic answers as a result of an inference process about the domain, and to automatically populate, off-line, the chat bot KB with sentences that can be derived from the ontology, describi…

Panel Summary: Planning and Problem Solving

I will sketch some ideas that have guided the psychological approaches to problem solving. Traditionally, psychologists have considered the ability to solve problems “one of the most important manifestations of human thinking”. 1 Moreover, “problem” has been defined as a search that starts when we have a goal, but our habitual means are not sufficient to achieve it.

Is our robot self conscious?

Knowledge organization for modelling workflows in Taverna environment

Today Workflow Management Systems (WFMS), like Taverna and Kepler, have a very important place in the everyday work of the scientist. These tools support the access to computational resources and act as interface for building complex data processing chains. The next step is to support decisions of the researcher on autonomously developing workflow parts guided by requirements of the scientist while she/he is working on the high-level goal of the experiment. To this aim, it is necessary an ontology to store the knowledge related to the experiments and tools used, and to make this knowledge available not only to the scientist, but also to a suitable artificial intelligent system. In this pape…

A cognitive architecture for robot self-consciousness

Objective: One of the major topics towards robot consciousness is to give a robot the capabilities of self-consciousness. We propose that robot self-consciousness is based on higher order perception of the robot, in the sense that first-order robot perception is the immediate perception of the outer world, while higher order perception is the perception of the inner world of the robot. Methods and material: We refer to a robot cognitive architecture that has been developed during almost 10 years at the RoboticsLab of the University of Palermo. The architecture is organized in three computational areas. The subconceptual area is concerned with the low level processing of perceptual data comi…

SESAMO: An integrated framework for gathering, managing and sharing environmental data

ICT systems are widely adopted for environmental management, but existing solutions address limited tasks and compose a plethora of heterogeneous tools, which impose a great additional effort on the operators. This work presents SESAMO, a novel framework to provide the operators with a unique tool for gathering, managing and merging environmental and territorial data. SESAMO uses WSNs for providing pervasive monitoring of environmental phenomena and exploits a multi-tier infrastructure in order to integrate data coming from heterogeneous information sources.

Using the Hermite Regression Formula to Design a Neural Architecture with Automatic Learning of the “Hidden” Activation Functions

The value of the output function gradient of a neural network, calculated in the training points, plays an essential role for its generalization capability. In this paper a feed forward neural architecture (αNet) that can learn the activation function of its hidden units during the training phase is presented. The automatic learning is obtained through the joint use of the Hermite regression formula and the CGD optimization algorithm with the Powell restart conditions. This technique leads to a smooth output function of αNet in the nearby of the training points, achieving an improvement of the generalization capability and the flexibility of the neural architecture. Experimental results, ob…

An Efficient Retransmission Strategy for Data Gathering in Wireless Sensor Networks

This paper introduces a new strategy for data gathering in wireless sensor networks that takes into account the need for both energy saving and for a reasonable tradeoff between robustness and efficiency. The proposed algorithm implements an efficient strategy for retransmission of lost packets by discovering alternative routes and making clever use of multiple paths when necessary; in order to do that we use duplicate and order insensitive aggregation functions, and by taking advantage of some intrinsic characteristics of the wireless sensor networks, we exploit implicit acknowledgment of reception and smart caching of the data.

Social Practices based characters in a Robotic Storytelling System

In this work, we present a robotic storytelling system, where the characters have been modelled as cognitive agents embodied in Pepper and NAO robots. The characters have been designed by exploiting the ACT-R architecture, taking into account knowledge, behaviours, norms, and expectations typical of social practices and desires resulting from their personality. The characters explain their reasoning processes during the narration, through a sort of internal dialogue that generate a high level of credibility experienced over the audience.

Sensor Mining for User Behavior Profiling in Intelligent Environments

The proposed system exploits sensor mining methodologies to profile user behaviors patterns in an intelligent workplace. The work is based in the assumption that users’ habit profiles are implicitly described by sensory data, which explicitly show the consequences of users’ actions over the environment state. Sensor data are analyzed in order to infer relationships of interest between environmental variables and the user, detecting in this way behavior profiles. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science of Palermo University.

Adaptive distributed outlier detection for WSNs.

The paradigm of pervasive computing is gaining more and more attention nowadays, thanks to the possibility of obtaining precise and continuous monitoring. Ease of deployment and adaptivity are typically implemented by adopting autonomous and cooperative sensory devices; however, for such systems to be of any practical use, reliability and fault tolerance must be guaranteed, for instance by detecting corrupted readings amidst the huge amount of gathered sensory data. This paper proposes an adaptive distributed Bayesian approach for detecting outliers in data collected by a wireless sensor network; our algorithm aims at optimizing classification accuracy, time complexity and communication com…

Virtual conversation with a real talking head

A talking head is system performing an animated face model synchronized with a speech synthesis module. It is used as a presentation layer of a conversational Agent which provide an answer. It provides an answer when a query is written as an input by the user. The textual answer is converted into facial movements of a 3D face model whose lips and tongue movements are synchronized with the sound of the synthetic voice. The Client-Server paradigm has been used for the WEB infrastructure delegating the animation and synchronization to the client, so that the server can satisfy multiple requests from clients; while the Chatbot, the Digital Signal Processing and the Natural language Processing a…

Deep learning network for exploiting positional information in nucleosome related sequences

A nucleosome is a DNA-histone complex, wrapping about 150 pairs of double-stranded DNA. The role of nucleosomes is to pack the DNA into the nucleus of the Eukaryote cells to form the Chromatin. Nucleosome positioning genome wide play an important role in the regulation of cell type-specific gene activities. Several biological studies have shown sequence specificity of nucleosome presence, clearly underlined by the organization of precise nucleotides substrings. Taking into consideration such advances, the identification of nucleosomes on a genomic scale has been successfully performed by DNA sequence features representation and classical supervised classification methods such as Support Vec…

ML-Based Radiomics Analysis for Breast Cancer Classification in DCE-MRI

Breast cancer is the most common malignancy that threatening women’s health. Although Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) for breast lesions characterization is widely used in the clinical practice, physician grading performance is still not optimal, showing a specificity of about 72%. In this work Radiomics was used to analyze a dataset acquired with two different protocols in order to train Machine-Learning algorithms for breast cancer classification. Original radiomic features were expanded considering Laplacian of Gaussian filtering and Wavelet Transform images to evaluate whether they can improve predictive performance. A Multi-Instant features selection invo…

Daily Peak Temperature Forecasting with Elman Neural Networks

This work presents a forecaster based on an Elman artificial neural network trained with resilient backpropagation algorithm for predicting the daily peak temperatures one day ahead. The available time series was recorded at Petrosino (TP), in the west coast of Sicily, Italy and it is composed by temperature (min and max values), the humidity (min and max values) and the rainfall value between January 1st, 1995 and May 14th, 2003. Performances and reliabilities of the proposed model were evaluated by a number of measures, comparing different neural models. Experimental results show very good prediction performances.

An Artificial Soft Somatosensory System for a Cognitive Robot

The paper proposes an artificial somatosensory system loosely inspired by human beings' biology and embedded in a cognitive architecture (CA). It enables a robot to receive the stimulation from its embodiment, and use these sensations, we called roboceptions, to behave according to both the external environment and the internal robot status. In such a way, the robot is aware of its body and able to interpret physical sensations can be more effective in the task while maintaining its well being. The robot's physiological urges are tightly bound to the specific physical state of the robot. Positive and negative physical information can, therefore, be processed and let the robot behave in a mo…

A system for sign language sentence recognition based on common sense context

The paper proposes a complete framework for sign language recognition that integrates common sense in order to deal with sentences. The proposed system is based on a cognitive architecture allows modeling and managing the knowledge of the recognition process in a simple and robust way. The final abstraction level of this architecture introduces the semantic context and the analysis of the correctness of a sentence given a sequence of recognized signs. Experimentations are presented using the Italian sign language (LIS), and shows that the system maintains the recognition rate high when set of sign grows, correcting erroneous recognized single sign using the context

Rule based reasoning for network management

This paper focuses on improving network management by the adoption of artificial intelligence techniques. We propose a distributed multi-agent architecture for network management, where a logical reasoner acts as a managing entity capable of directing, coordinating, and triggering monitoring and management actions in the proposed architecture. The logical inference system has been devised to enable automated isolation, diagnosis, and to repair network anomalies, thus enhancing the reliability, performance, and security of the network. The measurements of network events are captured by programmable sensors deployed on the network devices and are collected by the network management entity whe…

An Efficient Approach for Constructing Dynamic Multicast Trees

Clifford Rotors for Conceptual Representation in Chatbots

In this abstract we introduce an unsupervised sub-symbolic natural language sentences encoding procedure aimed at catching and representing into a Chatbot Knowledge Base (KB) the concepts expressed by an user interacting with a robot. The chatbot KB is coded in a conceptual space induced from the application of the Latent Semantic Analysis (LSA) paradigm on a corpus of documents. LSA has the effect of decomposing the original relationships between elements into linearly-independent vectors. Each basis vector can be considered therefore as a "conceptual coordinate", which can be tagged by the words which better characterize it. This tagging is obtained by performing a (TF-IDF)-like weighting…

Hybrid architecture for shape reconstruction and object recognition

The proposed architecture is aimed to recover 3-D- shape information from gray-level images of a scene; to build a geometric representation of the scene in terms of geometric primitives; and to reason about the scene. The novelty of the architecture is in fact the integration of different approaches: symbolic reasoning techniques typical of knowledge representation in artificial intelligence, algorithmic capabilities typical of artificial vision schemes, and analogue techniques typical of artificial neural networks. Experimental results obtained by means of an implemented version of the proposed architecture acting on real scene images are reported to illustrate the system capabilities.

An Ambient Intelligence Architecture for Extracting Knowledge from Distributed Sensors

Precisely monitoring the environmental conditions is an essential requirement for AmI projects, but the wealth of data generated by the sensing equipment may easily overwhelm the modules devoted to higher-level reasoning, clogging them with irrelevant details. The present work proposes a new approach to knowledge extraction from raw data that addresses this issue at different levels of abstraction. Wireless sensor networks are used as the pervasive sensory tool, and their computational capabilities are exploited to remotely perform preliminary data processing. A central intelligent unit subsequently extracts higher-level concepts represented in a geometrical space and carries on symbolic re…

Simulation and Test of UAV Tasks With Resource-Constrained Hardware in the Loop

Simulations are indispensable to reduce costs and risks when developing and testing algorithms for unmanned aerial vehicles (UAV) especially for applications in high risk scenarios like search and rescue (SAR) operations and post-disaster damage assessment. Many UAV applications require real-time tasks for which the timeliness of computations is fundamental. However, standard simulation tools are not guaranteed to run in sync with real-time events, leading to unreliable assessments of the ability of the target hardware to perform specific tasks. In this work we present a simulation and test system able to run UAV tasks on resource-constrained target hardware possibly adopted in these applic…

Improved SOM Learning using Simulated Annealing

Self-Organizing Map (SOM) algorithm has been extensively used for analysis and classification problems. For this kind of problems, datasets become more and more large and it is necessary to speed up the SOM learning. In this paper we present an application of the Simulated Annealing (SA) procedure to the SOM learning algorithm. The goal of the algorithm is to obtain fast learning and better performance in terms of matching of input data and regularity of the obtained map. An advantage of the proposed technique is that it preserves the simplicity of the basic algorithm. Several tests, carried out on different large datasets, demonstrate the effectiveness of the proposed algorithm in comparis…

Quantum planning for swarm robotics

Computational resources of quantum computing can enhance robotic motion, decision making, and path planning. While the quantum paradigm is being applied to individual robots, its approach to swarms of simple and interacting robots remains largely unexplored. In this paper, we attempt to bridge the gap between swarm robotics and quantum computing, in the framework of a search and rescue mission. We focus on a decision-making and path-planning collective task. Thus, we present a quantum-based path-planning algorithm for a swarm of robots. Quantization enters position and reward information (measured as a robot’s proximity to the target) and path-planning decisions. Pairwise information-exchan…

A Modular Framework for Versatile Conversational Agent Building

This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible.

An AMI System for User Daily Routine Recognition and Prediction

Ambient Intelligence (AmI) defines a scenario involving people living in a smart environment enriched by pervasive sensory devices with the goal of assisting them in a proactive way to satisfy their needs. In a home scenario, an AmI system controls the environment according to a user’s lifestyle and daily routine. To achieve this goal, one fundamental task is to recognize the user’s activities in order to generate his daily activities profile. In this chapter, we present a simple AMI system for a home scenario to recognize and predict users’ activities. With this predictive capability, it is possible to anticipate their actions and improve their quality of life. Our approach uses a Hidden M…

Artificial Pleasure and Pain Antagonism Mechanism in a Social Robot

The goal of the work is to build some Python modules that allow the Nao robot to emulate a somatosensorial system similar to the human one. Assuming it can perceive some feelings similar to the ones recognized by the human system, it will be possible to make it react appropriately to the external stimuli. The idea is to have a group of software sensors working simultaneously, providing some feedback to show how the robot is feeling at a particular time. It will be able to feel articular pain and stress, to perceive people in his surroundings (and in a future work to react according to the knowledge of them with face recognition), feel pleasure by recognizing caresses on his head and respond…

Humorist Bot: Bringing Computational Humour in a Chat-Bot System

A conversational agent, capable to have a ldquosense of humourrdquo is presented. The agent can both generate humorous sentences and recognize humoristic expressions introduced by the user during the dialogue. Humorist Bot makes use of well founded techniques of computational humor and it has been implemented using the ALICE framework embedded into an Yahoo! Messenger client. It includes also an avatar that changes the face expression according to humoristic content of the dialogue.

Understanding the Environment through Wireless Sensor Networks

This paper presents a new cognitive architecture for extracting meaningful, high-level information from the environment, starting from the raw data collected by a Wireless Sensor Network. The proposed framework is capable of building rich internal representation of the sensed environment by means of intelligent data processing and correlation. Furthermore, our approach aims at integrating the connectionist, data-driven model with the symbolic one, that uses a high-level knowledge about the domain to drive the environment interpretation. To this aim, the framework exploits the notion of conceptual spaces, adopting a conceptual layer between the subsymbolic one, that processes sensory data, a…

Image Segmentation through a Hierarchy of Minimum Spanning Trees

Many approaches have been adopted to solve the problem of image segmentation. Among them a noticeable part is based on graph theory casting the pixels as nodes in a graph. This paper proposes an algorithm to select clusters in the images (corresponding to relevant segments in the image) corresponding to the areas induced in the images through the search of the Minimum Spanning Tree (MST). In particular is is based on a clustering algorithm that extracts clusters computing a hierarchy of Minimum Spanning Trees. The main drawback of this previous algorithm is that the dimension of the cluster is not predictable and a relevant portion of found clusters can be composed by micro-clusters that ar…

A Modular Architecture for Adaptive ChatBots

We illustrate an architecture for a conversational agent based on a modular knowledge representation. This solution provides intelligent conversational agents with a dynamic and flexible behavior. The modularity of the architecture allows a concurrent and synergic use of different techniques, making it possible to use the most adequate methodology for the management of a specific characteristic of the domain, of the dialogue, or of the user behavior. We show the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation techniques and capable to differently manage conversation features has been developed. Each module is automatically trigg…

Programming distributed applications with symbolic reasoning on WSNs

Programming Wireless Sensor Networks (WSNs) is a complex task for which existing approaches adopt rigid architectures that are only suitable for specific application fields. In previous papers we introduced a programming methodology and a lightweight middleware based on high-level programming and executable code exchange for distributed processing on WSNs. In this paper, we show how high-level programming can be effectively used on WSNs to implement symbolic reasoning. In order to prove the feasibility of our approach, we present a Fuzzy Logic system where the value updates and the rule evaluations are performed in a distributed way. Through the proposed methodology, we discuss the developm…

The Conscious Robotic Arm-Hand Project

Clustering Bacteria Species Using Neural Gas: Preliminary Study

In this work a method for clustering and visualization of bacteria taxonomy is presented. A modified version of the Batch Median Neural Gas (BNG) algorithm is proposed. The BNG algorithm is able to manage non vectorial data given as a dissimilarity matrix. We tested the modified BNG on the dissimilarity matrix obtained from sequences alignment and computing distances using bacteria genotype information regarding the16S rRNA housekeeping gene, which represents a stable part of bacteria genome. The dataset used for the experiments is obtained from the Ribosomal Database Project II, and it is made of 5159 sequences of 16S rRNA genes. Preliminary results of the experiments show a promising abil…

A Knowledge Management System based on Ontologies

Recently the companies’ interest on a correct knowledge management is grown, more than interest on the mere knowledge itself. In the last few years, several projects have been carried out, with the aim of the development of innovative systems capable of collecting and sharing information. This paper proposes a Knowledge Management System, whose main feature is an ontological knowledge representation. The ontological representation of data allows of specializing the reasoning capabilities and of providing ad hoc behaviors. The system has been tested collecting and using data coming from projects and processes typical of ICT companies, and provides a Document Management System and an Expert S…

Closing the sensing-reasoning-actuating loop in resource-constrained WSANs through distributed symbolic processing

Many issues in creating complex applications for pervasive environments are primarily due to the effort required to integrate perception, reasoning and actuating tasks in an efficient and homogeneous way, especially when the underlying infrastructure consists of wirelessly networked embedded devices. To mitigate the complexity of the actual implementation, satisfactory programming paradigms supporting the integration and coordination among heterogeneous devices are required. In this paper we show how a distributed symbolic processing approach that is particularly suited for resource constrained devices, such as the nodes of a Wireless Sensor and Actuator Network (WSAN), may be apt to the pu…

Composition of SIFT features for robust image representation

In this paper we propose a novel feature based on SIFT (Scale Invariant Feature Transform) algorithm1 for the robust representation of local visual contents. SIFT features have raised much interest for their power of description of visual content characterizing punctual information against variation of luminance and change of viewpoint and they are very useful to capture local information. For a single image hundreds of keypoints are found and they are particularly suitable for tasks dealing with image registration or image matching. In this work we stretched the spatial coverage of descriptors creating a novel feature as composition of keypoints present in an image region while maintaining…

MAGA: A Mobile Archaeological Guide at Agrigento

A MAS metamodel-driven approach to process fragments selection

The construction of ad-hoc design processes is more and more required today. In this paper we present our approach for the construction of a new design process following the Situational Method Engineering paradigm. We mainly focus on the selection and assembly activities on the base of what we consider a key element in agent design processes: the MAS metamodel. The paper presents an algorithm establishing a priority order in the realization (instantiation) of MAS metamodel elements by the fragments that will compose the new process.

Antibiotic Resistance Profiling, Analysis of Virulence Aspects and Molecular Genotyping of Staphylococcus aureus Isolated in Sicily, Italy

Abstract Staphylococcus aureus is the major cause of foodborne diseases worldwide. In this retrospective study, 84 S. aureus strains were characterized. The collection comprises 78 strains isolated during 1998 and 2014 from dairy products and tissue samples from livestock bred for dairy production in Sicily. One isolate was obtained from a pet (dog), one from an exotic animal (a circus elephant), and four human isolates were obtained during a severe food poisoning outbreak that occurred in Sicily in 2015. All the strains were characterized by pulsed-field gel electrophoresis (PFGE), for antibiotic resistance and presence of toxin genes. PFGE results showed 10 different pulsotypes, with thre…

A Lightweight Middleware Platform for Distributed Computing on Wireless Sensor Networks

Abstract The peculiar features of Wireless Sensor Networks (WSNs) suggest to exploit the distributed computing paradigm to perform complex tasks in a collaborative manner, in order to overcome the constraints related to sensor nodes limited capabilities. In this context, we describe a lightweight middleware platform to support the development of distributed applications on WSNs. The platform provides just a minimal general-purpose software layer, while the application components, including communication and processing algorithms, as well as the exchanged data, are described symbolically, with neither preformed syntax nor strict distinction between data and code. Our approach allows for inte…

Distributed Symbolic Network Quality Assessment for Resource-constrained Devices

After a Wireless Sensor Network (WSN) is deployed it is subject to significant variations of the quality of its radio links during its lifetime. Knowledge of the condition of the wireless links can be useful to optimize power consumption and increase the reliability of the network. However, resource-constrained nodes may not be able to spare the storage space for network monitoring code. Also, reprogramming deployed nodes can be costly or unfeasible. In this work, we show how an approach based on the exchange of symbolic executable code among nodes enables the assessment of the network status in terms of Packet Reception Rate (PRR) with no extra storage requirements on deployed networks. We…

A Context-Aware System for Ambient Assisted Living

In the near future, the world's population will be characterized by an increasing average age, and consequently, the number of people requiring for a special household assistance will dramatically rise. In this scenario, smart homes will significantly help users to increase their quality of life, while maintaining a great level of autonomy. This paper presents a system for Ambient Assisted Living (AAL) capable of understanding context and user's behavior by exploiting data gathered by a pervasive sensor network. The knowledge inferred by adopting a Bayesian knowledge extraction approach is exploited to disambiguate the collected observations, making the AAL system able to detect and predict…

An algebra for the manipulation of conceptual spaces in cognitive agents

According to Gärdenfors, the theory of conceptual spaces describes a level of representation present in some cognitive agents between a sub-conceptual and a symbolic level of representation. In contrast to a large part of contemporary philosophical speculation on these matters for which concepts and conceptual content are propositional, conceptual spaces provide a geometric framework for the representation of concepts. In this paper we introduce an algebra for the manipulation of different conceptual spaces in order to formalise the process whereby an artificial agent rearranges its internal conceptual representations as a consequence of its perceptions, which are here rendered in terms of …

The ALSWEB Framework: A Web-based Framework for the Linguistic Atlas of Sicily Project

In this work the ALSWEB framework is presented. The ALSWEB is a virtual linguistic laboratory for linguistic research developed as a web application. The purpose of the framework is to model the entire process regarding the different steps of data acquisition, data transformation, information acquisition from different data and research hypotheses verification in the ALS (Linguistic Atlas of Sicily) project. The nature of the ALS research involves different type of data. The socio-linguistic researcher that is the main actor of the proposed framework has to acquire information in many formats: multimedia data, audio data, question-answer (textual) from particular questionnaires. In this wor…

An ontological-based knowledge organization for bioinformatics workflow management system

Una comunità di Chat-bot per i beni culturali - Expert Chat-Bots for Cultural Heritage

Learning of Actions and Goals through Observation and Imitation

Interacting with Augmented Environments

Pervasive systems augment environments by integrating information processing into everyday objects and activities. They consist of two parts: a visible part populated by animate (visitors, operators) or inanimate (AI) entities interacting with the environment through digital devices, and an invisible part composed of software objects performing specific tasks in an underlying framework. This paper shows an ongoing work from the University of Palermo''s Department of Computer Science and Engineering that addresses two issues related to simplifying and broadening augmented environment access.

Roboception and adaptation in a cognitive robot

In robotics, perception is usually oriented at understanding what is happening in the external world, while few works pay attention to what is occurring in the robot’s body. In this work, we propose an artificial somatosensory system, embedded in a cognitive architecture, that enables a robot to perceive the sensations from its embodiment while executing a task. We called these perceptions roboceptions, and they let the robot act according to its own physical needs in addition to the task demands. Physical information is processed by the robot to behave in a balanced way, determining the most appropriate trade-off between the achievement of the task and its well being. The experiments show …

A knowledge-based decision support system in bioinformatics: An application to protein complex extraction

Abstract Background We introduce a Knowledge-based Decision Support System (KDSS) in order to face the Protein Complex Extraction issue. Using a Knowledge Base (KB) coding the expertise about the proposed scenario, our KDSS is able to suggest both strategies and tools, according to the features of input dataset. Our system provides a navigable workflow for the current experiment and furthermore it offers support in the configuration and running of every processing component of that workflow. This last feature makes our system a crossover between classical DSS and Workflow Management Systems. Results We briefly present the KDSS' architecture and basic concepts used in the design of the knowl…

Time-Constrained Node Visit Planning for Collaborative UAV-WSN Distributed Applications.

Unmanned Aerial Vehicles (UAVs) are often studied as tools to perform data collection from Wireless Sensor Networks (WSNs). Path planning is a fundamental aspect of this endeavor. Works in the current literature assume that data are always ready to be retrieved when the UAV passes. This operational model is quite rigid and does not allow for the integration of the UAV as a computational object playing an active role in the network. In fact, the UAV could begin the computation on a first visit and retrieve the data later. Potentially, the UAV could orchestrate the distributed computation to improve its performance, change its parameters, and even upload new applications to the sensor network…

A Conversational Agent to Support Decisions in SimCity like Games

Computational intelligent techniques applied to economics have played an important role in the last years. In this paper we propose a framework based on an intelligent conversational agent embedded with a decision support system, aimed at suggesting the best managing strategies for a game-based model of a virtual town. The agent tries to prospect the future evolutions of particular choices taken by the user. Interaction is conducted through a natural language interface built as an Alice-based conversational agent.

A Dynamic Distributed Algorithm for Multicast Path Setup

In the past few years, there has been a considerable work on multicast route selection techniques, with the aim to design scalable protocols which can guarantee an efficient use of network resources. Steiner tree-based multicast algorithms produce optimal trees, but they are prohibitively expensive. For this reason, heuristic methods are generally employed. Conventional centralized Steiner heuristics provide effective solutions, but they are unpractical for large networks, since they require a complete knowledge of the network topology. In this paper, we propose a new distributed approach that is efficient and suitable for real network adoption. Performance evaluation indicates that it outp…

An Efficient Distributed Algorithm for Generating Multicast Distribution Trees

Multicast transmission may use network resources more efficiently than multiple point-to-point messages; however, creating optimal multicast trees (Steiner Tree Problem in Networks) is prohibitively expensive. For this reason, heuristic methods are generally employed. Conventional centralized Steiner heuristics provide effective solutions, but they are unpractical for large networks, since they require complete knowledge of the network topology. This paper proposes a distributed algorithm for the heuristic solution of the Steiner Tree Problem. The algorithm allows the construction of effective distribution trees using a coordination protocol among the network nodes. The algorithm has been i…

A framework for real-time Twitter data analysis

A framework for real-time Twitter data analysisWe propose improvements to the Soft Frequent Pattern Mining (SFPM) algorithmThe stream of tweets is organized in dynamic windows whose size depends both on the volume of tweets and timeThe set of keywords used to query Twitter is progressively refined to highlight the user's point of viewComparisons with two state of the art systems Twitter is a popular social network which allows millions of users to share their opinions on what happens all over the world. In this work we present a system for real-time Twitter data analysis in order to follow popular events from the user's perspective. The method we propose extends and improves the Soft Freque…

Modeling and Verification of Symbolic Distributed Applications Through an Intelligent Monitoring Agent

Wireless Sensor Networks (WSNs) represent a key component in emerging distributed computing paradigms such as IoT, Ambient Intelligence, and Smart Cities. In these contexts, the difficulty of testing, verifying, and monitoring applications in their intended scenarios ranges from challenging to impractical. Current simulators can only be used to investigate correctness at source code level and with limited accuracy. This paper proposes a system and a methodology to model and verify symbolic distributed applications running on WSNs. The approach allows to complement the distributed application code at a high level of abstraction in order to test and reprogram it, directly, on deployed network…

A Hybrid Neural Network Architecture for Dynamic Scene Understanding

A hyprdid (neural and symbolic) architecture allowing for a deep understanding of moving scenes is described. The architecture is based on a working and effective integration among three levels of representation of data coming out from external sensors.

A Geometric Approach to Automatic Description of Iconic Scenes

It is proposed a step towards the automatic description of scenes with a geometric approach. The scenes considered are composed by a set of elements that can be geometric forms or iconic representation of objects. Every icon is characterized by a set of attributes like shape, colour, position, orientation. Each scene is related to a set of sentences describing its content. The proposed approach builds a data driven vector semantic space where the scenes and the sentences are mapped. Sentences and scene with the same meaning are mapped in near vectors and distance criteria allow retrieving semantic relations.

A Sub-Symbolic Approach to Word Modelling for Domain Specific Speech Recognition

In this work a sub-symbolic technique for automatic, data driven language models construction is presented. Such a technique can be used to arrange a language-modelling module, which can be easily integrated in existing speech recognition architectures, such as the well-found HTK architecture. The proposed technique takes advantages from both the traditional LSA approach and from a novel application of a probability space metric known as "Hellinger's distance". Experimental trials are also presented, in order to validate the proposed approach.

A general theoretical framework for designing cognitive architectures: Hybrid and meta-level architectures for BICA

In this paper, we will discuss hybrid architectures in which different processing modules coexist and cooperate in a principled way. A fundamental and essential role is played by modules performing meta-computation, i.e., computation about computation itself. Meta-level architectures, therefore, become an essential complement of hybrid architectures for biologically inspired cognitive architectures (BICA). Engineering and modeling BICAs is a hard task due to the lack of techniques for developing and implementing their features. We propose a new concept of intelligent agent as a useful abstraction for developing BICAs and having means for representing all the involved entities together with …

A cognitive architecture for artificial vision

Abstract A new cognitive architecture for artificial vision is proposed. The architecture, aimed at an autonomous intelligent system, is cognitive in the sense that several cognitive hypotheses have been postulated as guidelines for its design. The first one is the existence of a conceptual representation level between the subsymbolic level, that processes sensory data, and the linguistic level, that describes scenes by means of a high level language. The conceptual level plays the role of the interpretation domain for the symbols at the linguistic levels. A second cognitive hypothesis concerns the active role of a focus of attention mechanism in the link between the conceptual and the ling…

Method fragments for agent design methodologies: from standardisation to research

The method engineering paradigm enables designers to reuse portions of design processes (called method fragments or chunks in literature) to build processes that are expressly tailored for realising a system that is specific for some problem or development context. This paper initially reports on the standardisation attempt carried out by the FIPA Methodology Technical Committee (TC) and then presents the research activities we did starting from that work; these resulted in a slightly different definition of some of the most important elements of the approach in order to support a multiview representation of the fragment (the views are process, reuse, storing and implementation). The paper …

A hybrid system for malware detection on big data

In recent years, the increasing diffusion of malicious software has encouraged the adoption of advanced machine learning algorithms to timely detect new threats. A cloud-based approach allows to exploit the big data produced by client agents to train such algorithms, but on the other hand, poses severe challenges on their scalability and performance. We propose a hybrid cloud-based malware detection system in which static and dynamic analyses are combined in order to find a good trade-off between response time and detection accuracy. Our system performs a continuous learning process of its models, based on deep networks, by exploiting the growing amount of data provided by clients. The prel…

An ASSOM neural network to represent actions performed by an autonomous agent

An ASSOM neural network to describe the action performed by an autonomous reactive agent is proposed. The neural network receives in input the sequences of data acquired by the agent internal sensors and it classifies them by generating the corresponding symbolic assertions. Experimental results performed on a RWI B12 autonomous robot are reported.

Intelligent Advisor Agents in Distributed Environments

The chapter presents a Distributed Expert System based on a multi-agent-architecture. The system is composed of a community of intelligent conversational agents playing the role of specialized advisors for the government of a virtual town, inspired to the SimCity game. The agents are capable to handle strategic decision under uncertainty conditions. They interact in natural language with their owners, obtain information on the current status of the town and give suggestions about the best strategies to apply in order to govern the town. © 2010 Springer-Verlag Berlin Heidelberg.

High-level Programming and Symbolic Reasoning on IoT Resource Constrained Devices

While the vision of Internet of Things (IoT) is rather inspiring, its practical implementation remains challenging. Conventional programming approaches prove unsuitable to provide IoT resource constrained devices with the distributed processing capabilities required to implement intelligent, autonomic, and self-organizing behaviors. In our previous work, we had already proposed an alternative programming methodology for such systems that is characterized by high-level programming and symbolic expressions evaluation, and developed a lightweight middleware to support it. Our approach allows for interactive programming of deployed nodes, and it is based on the simple but effective paradigm of …

A Web-based Intelligent Tutoring System for Clinical Anatomy

Anchoring symbols to conceptual spaces: the case of dynamic scenarios.

In recent years, there have been several proposals for the realization of models inspired to biological solutions for pattern recognition. In this work we propose a new approach, based on a hierarchical modular structure, to realize a system capable to learn by examples and recognize objects in digital images. The adopted techniques are based on multiresolution image analysis and neural networks. Performance on two different data sets and experimental timings on a single instruction multiple data (SIMD) machine are also reported.

Normalised compression distance and evolutionary distance of genomic sequences: comparison of clustering results

Genomic sequences are usually compared using evolutionary distance, a procedure that implies the alignment of the sequences. Alignment of long sequences is a time consuming procedure and the obtained dissimilarity results is not a metric. Recently, the normalised compression distance was introduced as a method to calculate the distance between two generic digital objects and it seems a suitable way to compare genomic strings. In this paper, the clustering and the non-linear mapping obtained using the evolutionary distance and the compression distance are compared, in order to understand if the two distances sets are similar.

An expert system hybrid architecture to support experiment management

Specific expert systems are used for supporting, speeding-up and adding precision to in silico experimentation in many domains. In particular, many experimentalists exhibit a growing interest in workflow management systems for making a pipeline of experiments. Unfortunately, these type of systems does not integrate a systematic approach or a support component for the workflow composition/reuse. For this reason, in this paper we propose a knowledge-based hybrid architecture for designing expert systems that are able to support experiment management. This architecture defines a reference cognitive space and a proper ontology that describe the state of a problem by means of three different per…

A Semantic Layer on Semi-structured Data Sources for Intuitive Chatbots

The main limits of chatbot technology are related to the building of their knowledge representation and to their rigid information retrieval and dialogue capabilities, usually based on simple "pattern matching rules". The analysis of distributional properties of words in a texts corpus allows the creation of semantic spaces where represent and compare natural language elements. This space can be interpreted as a "conceptual" space where the axes represent the latent primitive concepts of the analyzed corpus. The presented work aims at exploiting the properties of a data-driven semantic/conceptual space built using semi-structured data sources freely available on the web, like Wikipedia. Thi…

An RFID framework for multimodal service provision

In recent years there has been a growing interest toward the development of pervasive and context-aware services, and RFID technology played a relevant role in the context sensing task. We propose the use of RFID technology together with a conversational agent in order to implement a multimodal information retrieval service we call SensorMesh. The information acquired from RFID tags about the nearest point of interest is processed by the conversational agent that carries a more natural interaction with the user, also exploiting a common sense ontology. The service is accessible using a multimodal browser on Personal Digital Assistants (PDAs); the browser allows the user to interact with the…

Interaction Capabilities of a Robotic Receptionist

A system aimed at facilitating the interaction between a human user and an humanoid robot is presented. The system is suited to answer questions about laboratories activities, people involved, projects, research themes and collaborations among employees. The task is accomplished by the HermiT reasoner invoked by a speech recognition module. The system is capable of navigating a specific ontology making inference on it. The presented system is part of a broader social robot framework whose goal is to give the user a fulfilling social interaction experience, driven by the perception of the robot internal state and involving intuitive and computational creativity capabilities.

Synthetic phenomenology and high-dimensional buffer hypothesis

Synthetic phenomenology typically focuses on the analysis of simplified perceptual signals with small or reduced dimensionality. Instead, synthetic phenomenology should be analyzed in terms of perceptual signals with huge dimensionality. Effective phenomenal processes actually exploit the entire richness of the dynamic perceptual signals coming from the retina. The hypothesis of a high-dimensional buffer at the basis of the perception loop that generates the robot synthetic phenomenology is analyzed in terms of a cognitive architecture for robot vision the authors have developed over the years. Despite the obvious computational problems when dealing with high-dimensional vectors, spaces wit…

Symposia

Panel Summary: Knowledge Model Representations

Following the usual classifications of cognitive psychologists, we can say that the problem of representation spans three domains: the environment, the brain, and cognitive processes, which are usually studied by different scientists: the physicists, the neurobiologists and the psychologists. With the development of computer science and artificial intelligence new approaches have been introduced, which make possible simulation and implementation of cognitive processes through neural networks and symbolic systems. But the contribution of new methods is not limited to simulation, because they try to provide new models which consider cognitive process as information processing, not as reaction…

Conceptual representations of actions for autonomous robots

An autonomous robot involved in long and complex missions should be able to generate, update and process its own plans of action. In this perspective, it is not plausible that the meaning of the representations used by the robot is given from outside the system itself. Rather, the meaning of internal symbols must be firmly anchored to the world through the perceptual abilities and the overall activities of the robot. According to these premises, in this paper we present an approach to action representation that is based on a "conceptual" level of representation, acting as an intermediate level between symbols and data coming from sensors. Symbolic representations are interpreted by mapping …

An ontological-based knowledge organization for bioinformatics workflow management system

Motivation and Objectives In the field of Computer Science, ontologies represent formal structures to define and organize knowledge of a specific application domain (Chandrasekaran et al., 1999). An ontology is composed of entities, called classes, and relationships among them. Classes are characterized by features, called attributes, and they can be arranged into a hierarchical organization. Ontologies are a fundamental instrument in Artificial Intelligence for the development of Knowledge-Based Systems (KBS). With its formal and well defined structure, in fact, an ontology provides a machine-understandable language that allows automatic reasoning for problems resolution. Typical KBS are E…

Symbolic and conceptual representation of dynamic scenes: Interpreting situation calculus on conceptual spaces

In (Chella et al. [1,2]) we proposed a framework for the representation of visual knowledge, with particular attention to the analysis and the representation of scenes with moving objects and people. One of our aims is a principled integration of the models developed within the artificial vision community with the propositional knowledge representation systems developed within symbolic AI. In the present note we show how the approach we adopted fits well with the representational choices underlying one of the most popular symbolic formalisms used in cognitive robotics, namely the situation calculus.

A Knowledge Management and Decision Support Model for Enterprises

We propose a novel knowledge management system (KMS) for enterprises. Our system exploits two different approaches for knowledge representation and reasoning: a document-based approach based on data-driven creation of a semantic space and an ontology-based model. Furthermore, we provide an expert system capable of supporting the enterprise decisional processes and a semantic engine which performs intelligent search on the enterprise knowledge bases. The decision support process exploits the Bayesian networks model to improve business planning process when performed under uncertainty. Copyright © 2011 Patrizia Ribino et al.

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a …

Adversarial Machine Learning in e-Health: Attacking a Smart Prescription System

Machine learning (ML) algorithms are the basis of many services we rely on in our everyday life. For this reason, a new research line has recently emerged with the aim of investigating how ML can be misled by adversarial examples. In this paper we address an e-health scenario in which an automatic system for prescriptions can be deceived by inputs forged to subvert the model's prediction. In particular, we present an algorithm capable of generating a precise sequence of moves that the adversary has to take in order to elude the automatic prescription service. Experimental analyses performed on a real dataset of patients' clinical records show that a minimal alteration of the clinical record…

An Autonomic System for Estimating Human Presence through Bayesian Networks

In the Ambient Intelligence (AmI) context, a relevant research topic is represented by the methods for determining users' presence in order to design context-aware systems capable of monitoring the environment in which they operate, and of timely reacting to changes. This work describes an autonomic software agent comprising a double-level reasoning. At the lower level, a Bayesian network merges the available sensory information related to the users' presence, whereas the upper level performs a meta-reasoning on the system performance and configuration in order to enable the system self-assessment. Experimental results show the validity of the proposed method on a sample scenario.

A Hybrid Recommender System for Cultural Heritage Promotion

Assisting users during their cultural trips is paramount in promoting the heritage of a territory. Recommender Systems offer the automatic tools to guide users in their decision process, by maximizing the adherence of the proposed contents with the particular preferences of every single user. However, traditional recommendation paradigms suffer from several drawbacks which are exacerbated in Cultural Heritage scenarios, due to the extremely wide range of users behaviors, which may also depend on their different educational backgrounds. In this paper, we propose a Hybrid recommender system which combines the four most common recommendation paradigms, namely collaborative filtering, popularit…

Regulation of the biosynthesis of the dalbavancin precursor A40926

A machine learning approach for user localization exploiting connectivity data

The growing popularity of Location-Based Services (LBSs) has boosted research on cheaper and more pervasive localization systems, typically relying on such monitoring equipment as Wireless Sensor Networks (WSNs), which allow to re-use the same instrumentation both for monitoring and for localization without requiring lengthy off-line training. This work addresses the localization problem, exploiting knowledge acquired in sample environments, and extensible to areas not considered in advance. Localization is turned into a learning problem, solved by a statistical algorithm. Additionally, parameter tuning is fully automated thanks to its formulation as an optimization problem based only on co…

A Virtual Shopper Customer Assistant in Pervasive Environments

In this work we propose a smart, human-like PDA-based personal shopper assistant. The system is able to understand the user needs through a spoken natural language interaction and then stores the preferences of the potential customer. Subsequently the personal shopper suggests the most suitable items and shops that match the user profile. The interaction is given by automatic speech recognition and text-to-speech technologies; localization is allowed by the use of Wireless technologies, while the interaction is performed by an Alice-based chat-bot endowed with reasoning capabilities. Besides, being implemented on a PDA, the personal shopper satisfies the user needs of mobility and it is als…

Characterization of two Streptomyces coelicolor A3(2) small orfs from the major locus of histidine biosynthesis.

LSA-Enhanced Ontologies for Information Exploration System on Cultural Heritage

A computer support system to support diagnosis by imaging and its experimental application in Images of patients affected by multiple sclerosis

Dal web al web semantico

An Efficient Retransmission Strategy for Data Gathering in Wireless Sensor Networks Authors

This paper introduces a new strategy for data gathering in wireless sensor networks that takes into account the need for both energy saving and for a reasonable tradeoff between robustness and efficiency. The proposed algorithm implements an efficient strategy for retransmission of lost packets by discovering alternative routes and making clever use of multiple paths when necessary; in order to do that we use duplicate and order insensitive aggregation functions, and by taking advantage of some intrinsic characteristics of the wireless sensor networks, we exploit implicit acknowledgment of reception and smart caching of the data.

A New Linear Initialization in SOM for Biomolecular Data

In the past decade, the amount of data in biological field has become larger and larger; Bio-techniques for analysis of biological data have been developed and new tools have been introduced. Several computational methods are based on unsupervised neural network algorithms that are widely used for multiple purposes including clustering and visualization, i.e. the Self Organizing Maps (SOM). Unfortunately, even though this method is unsupervised, the performances in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. In this paper we present a new initialization technique based on a totally connected undirected graph, that report relat…

A Study on Classification Methods Applied to Sentiment Analysis

Sentiment analysis is a new area of research in data mining that concerns the detection of opinions and/or sentiments in texts. This work focuses on the application and the comparison of three classification techniques over a text corpus composed of reviews of commercial products in order to detect opinions about them. The chosen domain is about "perfumes", and user opinions composing the corpus are written in Italian language. The proposed approach is completely data-driven: a Term Frequency / Inverse Document Frequency (TFIDF) terms selection procedure has been applied in order to make computation more efficient, to improve the classification results and to manage some issues related to t…

Deep learning models for bacteria taxonomic classification of metagenomic data.

Background An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions.…

LSA for Intuitive Chat-Agents Tutoring System

The PASSI and Agile PASSI MAS Meta-models Compared with a Unifying Proposal

A great number of processes for multi-agent systems design have been presented in last years to support the different approaches to agent-oriented design; each process is specific for a particular class of problems and it instantiates a specific MAS meta-model. These differences produce inconsistences and overlaps: a MAS meta-model may define a term not referred by another, or the same term can be used with a different meaning. We think that the lack of a standardization may cause a significant delay to the diffusion of the agent paradigm outside research context. Working for this unification goal, it is also necessary to define in unambiguous way the terms of the agent model and their rela…

Immagini della Emergente Società in Rete

Agent-Oriented Software Patterns for Rapid and Affordable Robot Programming

Robotic systems are often quite complex to develop: they are huge, heavily constrained from the nonfunctional point of view and they implement challenging algorithms. The lack of integrated methods with reuse approaches leads robotic developers to reinvent the wheel each time a new project starts. This paper proposes to reuse the experience done when building robotic applications, by catching it into design patterns. These represent a general mean for (i) reusing proved solutions increasing the final quality, (ii) communicating the knowledge about a domain and (iii) reducing the development time and effort. Despite of this generality, the proposed repository of patterns is specific for mult…

An ACT-R Based Humanoid Social Robot to Manage Storytelling Activities

This paper describes an interactive storytelling system, accessible through the SoftBank robotic platforms NAO and Pepper. The main contribution consists of the interpretation of the story characters by humanoid robots, obtained through the definition of appropriate cognitive models, relying on the ACT-R cognitive architecture. The reasoning processes leading to the story evolution are based on the represented knowledge and the suggestions of the listener in critical points of the story. They are disclosed during the narration, to make clear the dynamics of the story and the feelings of the characters. We analyzed the impact of such externalization of the internal status of the characters t…

Conceptual Spaces and Artificial Consciousness

Panel Summary Perceptual Learning and Discovering

The problem of learning and discovering in perception is addressed and discussed with particular reference to present machine learning paradigms. These paradigms are briefly introduced by S. Gaglio. The subsymbolic approach is addressed by S. Nolfi, and the role of symbolic learning is analysed by F. Esposito. Many of the open problems, that are evidentiated in the course of the panel, show how this is an important field of research that still needs a lot of investigation. In particular, as a result of the whole discussion, it seems that a suitable integration of different approaches must be accurately investigated. It is observed, in fact, that the weakness of the most part of the existing…

Knowledge representation for robotic vision based on conceptual spaces and attentive mechanisms

A new cognitive architecture for artificial vision is proposed. The architecture is aimed for an autonomous intelligent system, as several cognitive hypotheses have been postulated as guidelines for its design. The design is based on a conceptual representation level between the subsymbolic level processing the sensory data, and the linguistic level describing scenes by means of a high-level language. The architecture is also based on the active role of a focus of attention mechanism in the link between the conceptual and the linguistic level. The link between the conceptual level and the linguistic level is modelled as a time-delay attractor neural network.

A Virtual Shopper Customer Assistant in Pervasive Environments.

A Conversational Agent Based on a Conceptual Interpretation of a Data Driven Semantic Space

In this work we propose an interpretation of the LSA framework which leads to a data-driven “conceptual” space creation suitable for an “intuitive” conversational agent. The proposed approach allows overcoming the limitations of traditional, rule-based, chat-bots, leading to a more natural dialogue.

EHeBby: An evocative humorist chat-bot

A conversational agent, capable to have a "sense of humor" is presented. The agent can both generate humorous sentences and recognize humoristic expressions introduced by the user during the dialogue. EHeBby is an entertainment oriented conversational agent implemented using the ALICE framework embedded into an Yahoo! Messenger client. It is characterized by two areas: a rational, rule-based area and an evocative area. The first one is based on well founded techniques of computational humor and a standard AIML KB. The second one is based on a conceptual space, automatically induced by a corpus of funny documents, where KB items and user sentences are mapped. This area emulates an associativ…

A New SOM Initialization Algorithm for Nonvectorial Data

Self Organizing Maps (SOMs) are widely used mapping and clustering algorithms family. It is also well known that the performances of the maps in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. This drawback is common to all the SOM algorithms, and critical for a new SOM algorithm, the Median SOM (M-SOM), developed in order to map datasets characterized by a dissimilarity matrix. In this paper an initialization technique of M-SOM is proposed and compared to the initialization techniques proposed in the original paper. The results show that the proposed initialization technique assures faster learning and better performance in terms…

A Knowledge Based Decision Support System for Bioinformatics and System Biology

In this paper, we present a new Decision Support System for Bioinformatics and System Biology issues. Our system is based on a Knowledge base, representing the expertise about the application domain, and a Reasoner. The Reasoner, consulting the Knowledge base and according to the user’s request, is able to suggest one or more strategies in order to resolve the selected problem. Moreover, the system can build, at different abstraction layers, a workflow for the current problem on the basis of the user’s choices, freeing the user from implementation details and assisting him in the correct configuration of the algorithms. Two possible application scenarios will be introduced: the analysis of …

Subsymbolic Approach to Word Modeling for Domain Specific Speech Recognition

Topographic Map of Gammaproteobacteria using 16S rRNA gene sequences

The Random Neural Network Model for the On-line Multicast Problem

In this paper we propose the adoption of the Random Neural Network Model for the solution of the dynamic version of the Steiner Tree Problem in Networks (SPN). The Random Neural Network (RNN) is adopted as a heuristic capable of improving solutions achieved by previously proposed dynamic algorithms. We adapt the RNN model in order to map the network characteristics during a multicast transmission. The proposed methodology is validated by means of extensive experiments.

A Rule-Based System for Hardware Configuration and Programming of IoT Devices

Simplifying programming, deployment, and configuration of heterogeneous networked IoT devices requires networking, hardware, representation of knowledge and concepts, design and programming skills. In fact, IoT applications are mostly built by adopting different existing paradigms and technologies on a case-by-case basis. As a result, programming tools hinder adaptability and interoperability of applications with their rigidity. In this paper, we propose a rule-based system that configures and programs IoT devices automatically. The rule base holds formal specifications about hardware platforms, networking protocols, physical world concepts, and applications. Provided with a high-level appl…

On-board Energy Consumption Assessment for Symbolic Execution Models on Embedded Devices

Internet of Things (IoT) applications operate in several domains while requiring seamless integration among heterogeneous objects. Regardless of the specific platform and context, IoT applications demand high energy efficiency. Adopting resource-constrained embedded devices for IoT applications means ensuring low power consumption, low maintenance costs and possibly longer battery life. Meeting these requirements is particularly arduous as programmers are not able to monitor the energy consumption of their own software during development or when applications are finally deployed. In this paper, we discuss on-board real-time energy evaluation of both hardware and software during the developm…

Automatic Dictionary Creation by Sub-symbolicEncoding of Words

Wordnet and semidiscrete decomposition for sub-symbolic representation of words

A methodology for sub-symbolic semantic encoding of words is presented. The methodology uses the standard, semantically highly-structured WordNet lexical database and the SemiDiscrete matrix Decomposition to obtain a vector representation with low memory requirements in a semantic n-space. The application of the proposed algorithm over all the WordNet words would lead to a useful tool for the sub-symbolic processing of texts.

An Expert System for the Design of Agents

The growing interest for the design and development of multi-agent systems has brought to the creation of a specific research area called Agent-Oriented Software Engineering (AOSE), specifically conceived for the development of complex systems. The development of such systems needs the support of appropriate tools that could help the designer in producing the design artefacts. We developed a tool called Metameth that may be used to define a new (agent-oriented) design process as well as to apply it. In this paper, we describe only a slice of this complex tool, specifically addressing the interaction with human actors (the designers). This subsystem is conceived as a collaborative multi-agen…

Soft Topographic Map for Clustering and Classification of Bacteria

In this work a new method for clustering and building a topographic representation of a bacteria taxonomy is presented. The method is based on the analysis of stable parts of the genome, the so-called “housekeeping genes”. The proposed method generates topographic maps of the bacteria taxonomy, where relations among different type strains can be visually inspected and verified. Two well known DNA alignement algorithms are applied to the genomic sequences. Topographic maps are optimized to represent the similarity among the sequences according to their evolutionary distances. The experimental analysis is carried out on 147 type strains of the Gammaprotebacteria class by means of the 16S rRNA…

Conceptual spaces for anchoring

Comparison of genomic sequences clustering using Normalized Compression Distance and Evolutionary Distance

Genomic sequences are usually compared using evolutionary distance, a procedure that implies the alignment of the sequences. Alignment of long sequences is a long procedure and the obtained dissimilarity results is not a metric. Recently the normalized compression distance was introduced as a method to calculate the distance between two generic digital objects, and it seems a suitable way to compare genomic strings. In this paper the clustering and the mapping, obtained using a SOM, with the traditional evolutionary distance and the compression distance are compared in order to understand if the two distances sets are similar. The first results indicate that the two distances catch differen…

A Structural Approach to Infer Recurrent Relations in Data

Extracting knowledge from a great amount of collected data has been a key problem in Artificial Intelligence during the last decades. In this context, the word "knowledge" refers to the non trivial new relations not easily deducible from the observation of the data. Several approaches have been used to accomplish this task, ranging from statistical to structural methods, often heavily dependent on the particular problem of interest. In this work we propose a system for knowledge extraction that exploits the power of an ontology approach. Ontology is used to describe, organise and discover new knowledge. To show the effectiveness of our system in extracting and generalising the knowledge emb…

A Lightweight Network Discovery Algorithm for Resource-constrained IoT Devices

Although quite simple, existing protocols for the IoT suffer from the inflexibility of centralized infrastructures and require several configuration stages. The implementation of these protocols is often prohibitive on resource-constrained devices. In this work, we propose a distributed lightweight implementation of network discovery for simple IoT devices. Our approach is based on the exchange of symbolic executable code among nodes. Based on this abstraction, we propose an algorithm that makes even IoT resource-constrained nodes able to construct the network topology graph incrementally and without any a priori information about device positioning and presence. The minimal set of executab…

WordNet and SemiDiscrete Decomposition for Sub-symbolic Encoding of Words

Topographic maps for clustering and fast identification of bacteria using 16s housekeeping gene

In microbial identification the standard method to attribute a specific name to a bacterial isolate relays on the comparison of morphologic and phenotypic characters to those described for type or typical strains. In the last years a new standard for identifying bacteria using genotypic information began to be developed. In this new approach phylogenetic relationships of bacteria could be determined by comparing a stable part of the bacteria genetic code, the so called "housekeeping genes". The most commonly used gene for taxonomic purposes for bacteria is the 16S rRNA. The goal of this chapter is to show that genotypic features can be used to build a topographic map for clustering of a lar…

Convergence of Web 2.0 and Semantic Web: A Semantic Tagging and Searching System for Creating and Searching Blogs

The work presented in this paper aims to combine Latent Semantic Analysis methodology, common sense and traditional knowledge representation in order to improve the dialogue capabilities of a conversational agent. In our approach the agent brain is characterized by two areas: a "rational area", composed by a structured, rule-based knowledge base, and an "associative area", obtained through a data- driven semantic space. Concepts are mapped in this space and their mutual geometric distance is related to their conceptual similarity. The geometric distance between concepts implicitly defines a sub-symbolic relationship net, which can be seen as a new "sub- symbolic semantic layer" automaticall…

DC4CD

In this article, we present Distributed Computing for Constrained Devices (DC4CD), a novel software architecture that supports symbolic distributed computing on wireless sensor networks. DC4CD integrates the functionalities of a high-level symbolic interpreter, a compiler, and an operating system, and includes networking abstractions to exchange high-level symbolic code among peer devices. Contrarily to other architectures proposed in the literature, DC4CD allows for changes at runtime, even on deployed nodes of both application and system code. Experimental results show that DC4CD is more efficient in terms of memory usage than existing architectures, with which it also compares well in te…

WSN Design and Verification Using On-Board Executable Specifications

The gap between informal functional specifications and the resulting implementation in the chosen programming language is notably a source of errors in embedded systems design. In this paper, we discuss a methodology and a software platform aimed at coping with this issue in programming resource-constrained wireless sensor network nodes (WSNs). Whereas the typical development model for the WSNs is based on cross compilation, the proposed approach supports high-level symbolic coding of abstract models and distributed applications, as well as their test and their execution, directly on the target hardware. As a working example, we discuss the application of our methodology to specify the func…

Artificial qualia: in search of computational correlates

A Java 3D Talking Head for a Chatbot

Facial animation is referred to all those systems per- forming the speech synchronization with an animated face model. This kind of systems are called ”Talking Head” or ”Talking Face”. In this paper a Talking Head oriented to the creation of a Chatbot is presented. It requires an in- put query and an answer is generated in form of text. The answer is transduced into a facial animation using a 3D face model whose lips movements are synchronized with the sound produced by a speech synthesis module. Our ”Talk- ing Head” explores the naturalness of the facial animation and provides a real-time interactive interface to the user. The WEB infrastructure has been realized using the Client- Server m…

New shape from Shading methods

Shape from Shading is perhaps the most difficult topic to deal with in Artificial Vision: several researchers have faced it using different approaches. The most part of these methods are based on the Horn algorithm so they require very heavy regularity assumptions about the perceived objects' shape and are computationally expensive.

Additional file 1 of Deep learning models for bacteria taxonomic classification of metagenomic data

Preliminary classification results. Preliminary classification results obtained training a model with a kind of input data, e.g. SG, and testing it with the other type of input data, e.g. AMP. (XLSX 9.52 kb)