A Data Association Algorithm for People Re-Identification in Photo Sequences
In this paper, a new system is presented to support the user in the face annotation task. Every time a photo sequence becomes available, the system analyses it to detect and cluster faces in set corresponding to the same person. We propose to model the problem of people re-identification in photos as a data association problem. In this way, the system takes advantage from the assumption that each person can appear at most once in each photo. We propose a fully automated method for grouping facial images, the method does not require any initialization neither a priori knowledge of the number of persons that are in the photo sequence. We compare the results obtained with our method and with s…
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.
Face Processing on Low-Power Devices
The research on embedded vision-based techniques is considered nowadays as one of the most interesting matters of computer vision. In this work we address the scenario in which a real-time face processing system is needed to monitor people walking through some locations. Some face detection (e.g., Viola-Jones face detector) and face recognition (e.g., eigenfaces) approaches have reached a certain level of maturity, so we focused on the development of such techniques on embedded systems taking into account both hardware and software constraints. Our goal is to detect the presence of some known individuals inside some sensitive areas producing a compact description of the observed people. Cap…
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…
Human Mobility Simulator for Smart Applications
Several issues related to Smart City development require the knowledge of accurate human mobility models, such as in the case of urban development planning or evacuation strategy definition. Nevertheless, the exploitation of real data about users' mobility results in severe threats to their privacy, since it allows to infer highly sensitive information. On the contrary, the adoption of simulation tools to handle mobility models allows to neglect privacy during the design of location-based services. In this work, we propose a simulation tool capable of generating synthetic datasets of human mobility traces; then, we exploit them to evaluate the effectiveness of algorithms which aim to detect…
A Platform for the Evaluation of Distributed Reputation Algorithms
In distributed environments, where unknown entities cooperate to achieve complex goals, intelligent techniques for estimating agents' truthfulness are required. Distributed Reputation Management Systems (RMSs) allow to accomplish this task without the need for a central entity that may represent a bottleneck and a single point of failure. The design of a distributed RMS is a challenging task due to a multitude of factors that could impact on its performances. In order to support the researcher in evaluating the RMS robustness against security attacks since its beginning design phase, in this work we present a distributed simulation environment that allows to model both the agent's behaviors…
SmartBuildings: An AmI system for energy efficiency
Nowadays, the increasing global awareness of the importance of energy saving in everyday life acts as a stimulus to provide innovative ICT solutions for sustainability. In this scenario, the growing interest in smart homes has been driven both by socioeconomic and technological expectations. One of the key aspects of being smart is the efficiency of the urban apparatus, which includes, among others, energy, transportation and buildings. The present work describes SmartBuildings, a novel Ambient Intelligence system, which aims at reducing the energy consumption of "legacy" buildings by means of artificial intelligence techniques applied on heterogeneous sensor networks. A prototype has been …
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…
A Simulation Framework for Evaluating Distributed Reputation Management Systems
In distributed environments, where interactions involve unknown entities, intelligent techniques for estimating agents’ reputation are required. Reputation Management Systems (RMSs) aim to detect malicious behaviors that may affect the integrity of the virtual community. However, these systems are highly dependent of the application domain they address; hence the evaluation of different RMSs in terms of correctness and resistance to security attacks is frequently a tricky task. In this work we present a simulation framework to support researchers in the assessment of a RMS. The simulator is organized in two logic layers where network nodes are mapped to system processes that implement the i…
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 userâs 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 …
Probabilistic Corner Detection for Facial Feature Extraction
After more than 35 years of resarch, face processing is considered nowadays as one of the most important application of image analysis. It can be considered as a collection of problems (i.e., face detection, normalization, recognition and so on) each of which can be treated separately. Some face detection and face recognition techniques have reached a certain level of maturity, however facial feature extraction still represents the bottleneck of the entire process. In this paper we present a novel facial feature extraction approach that could be used for normalizing Viola-Jones detected faces and let them be recognized by an appearance-based face recognition method. For each observed featur…
A data association approach to detect and organize people in personal photo collections
In this paper we present a method to automatically segment a photo sequence in groups containing the same persons. Many methods in literature accom- plish to this task by adopting clustering techniques. We model the problem as the search for probable associations between faces detected in subsequent photos con- sidering the mutual exclusivity constraint: a person can not be in a photo two times, nor two faces in the same photo can be assigned to the same group. Associations have been found considering face and clothing descriptions. In particular, a two level architecture has been adopted: at the first level, associations are computed within meaningful temporal windows (situations); at the …
Enabling Technologies on Hybrid Camera Networks for Behavioral Analysis of Unattended Indoor Environments and Their Surroundings
This paper presents a layered network architecture and the enabling technologies for accomplishing vision-based behavioral analysis of unattended environments. Specifically the vision network covers both the attended environment and its surroundings by means of multi-modal cameras. The layer overlooking at the surroundings is laid outdoor and tracks people, monitoring entrance/exit points. It recovers the geometry of the site under surveillance and communicates people positions to a higher level layer. The layer monitoring the unattended environment undertakes similar goals, with the addition of maintaining a global mosaic of the observed scene for further understanding. Moreover, it merges …
Clustering techniques for personal photo album management
In this work we propose a novel approach for the automatic representation of pictures achieving at more effective organization of personal photo albums. Images are analyzed and described in multiple representation spaces, namely, faces, background and time of capture. Faces are automatically detected, rectified and represented projecting the face itself in a common low-dimensional eigenspace. Backgrounds are represented with low-level visual features based on RGB histogram and Gabor filter bank. Faces, time and background information of each image in the collection is automatically organized using a mean-shift clustering technique. Given the particular domain of personal photo libraries, wh…
A Fog-Based Application for Human Activity Recognition Using Personal Smart Devices
The diffusion of heterogeneous smart devices capable of capturing and analysing data about users, and/or the environment, has encouraged the growth of novel sensing methodologies. One of the most attractive scenarios in which such devices, such as smartphones, tablet computers, or activity trackers, can be exploited to infer relevant information is human activity recognition (HAR). Even though some simple HAR techniques can be directly implemented on mobile devices, in some cases, such as when complex activities need to be analysed timely, users’ smart devices can operate as part of a more complex architecture. In this article, we propose a multi-device HAR framework that exploits the fog c…
Smart Auctions for Autonomic Ambient Intelligence Systems
The main goal of Ambient Intelligence (AmI) is to support users in their daily activities by satisfying and anticipating their needs. To achieve such goal, AmI systems rely on physical infrastructures made of heterogenous sensing devices which interact in order to exchange information and perform monitoring tasks. In such a scenario, a full achievement of AmI vision would also require the capability of the system to autonomously check the status of the infrastructure and supervise its maintenance. To this aim, in this paper, we extend some previous works in order to allow the self-management of AmI devices enabling them to directly interact with maintenance service providers. In particular,…
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…
Automatic Generation of Subject-Based Image Transitions
This paper presents a novel approach for the automatic generation of image slideshows. Counter to standard cross-fading, the idea is to operate the image transitions keeping the subject focused in the intermediate frames by automatically identifying him/her and preserving face and facial features alignment. This is done by using a novel Active Shape Model and time-series Image Registration. The final result is an aesthetically appealing slideshow which emphasizes the subject. The results have been evaluated with a users’ response survey. The outcomes show that the proposed slideshow concept is widely preferred by final users w.r.t. standard image transitions.
Bio-inspired Sensory Data Aggregation
The Ambient Intelligence (AmI) research field focuses on the design of systems capable of adapting the surrounding environmental conditions so that they can match the users needs, whether those are consciously expressed or not [4][1].
Vulnerability evaluation of distributed reputation management systems
In distributed environments, Reputation Management Systems (RMSs) aim to estimate agents' trustworthiness by exploiting different sources of information. The distributed nature of these systems makes them vulnerable to several types of security attacks, and the response provided by a specific RMS depends on various factors, such as the algorithms adopted for estimating the reputation values and the communication protocols used to enable the cooperation among agents. This work examines the most important security attacks against RMSs and proposes a set of metrics for a quantitative evaluation of the RMS vulnerabilities. A parallel simulation framework is used to automatically give a vulnerab…
Uno Smart Campus per UniPA
L’accesso pervasivo alla rete ha avviato una profonda trasformazione del tessuto sociale e culturale, incidendo profondamente anche sui processi di erogazione dei servizi della Pubblica Amministrazione. L’Università degli Studi di Palermo si inserisce in tale contesto, avendo recentemente intrapreso un percorso di innovazione che mira ad espandere i confini della didattica, della divulgazione del sapere scientifico, della creazione di nuova scienza e cultura, al fine di creare uno Smart Campus pronto ad accogliere gli studenti ormai “nativi digitali”.
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…
3D Scene Reconstruction Using Kinect
The issue of the automatic reconstruction of 3D scenes has been addressed in several chapters over the last few years. Many of them describe techniques for processing stereo vision or range images captured by high quality range sensors. However, due to the high price of such input devices, most of the methods proposed in the literature are not suitable for real-world scenarios. This chapter proposes a method designed to reconstruct 3D scenes perceived by means of a cheap device, namely the Kinect sensor. The scene is efficiently represented as a composition of superquadric shapes so as to obtain a compact description of environment, however complex it may be. The approach proposed here is i…
User detection through multi-sensor fusion in an AmI scenario
Recent advances in technology, with regard to sensing and transmission devices, have made it possible to obtain continuous and precise monitoring of a wide range of qualitatively diverse environments. This has boosted the research on the novel field of Ambient Intelligence, which aims at exploiting the information about the environment state in order to adapt it to the user’s preference. In this paper, we analyze the issue of detecting the user’s presence in a given region of the monitored area, which is crucial in order to trigger subsequent actions. In particular, we present a comprehensive framework that turns data perceived by sensors of different nature, and with possible imprecision, …
SpADe: Multi-Stage Spam Account Detection for Online Social Networks
In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest of …
IMAGE AND FACE ANALYSIS FOR PERSONAL PHOTO ORGANIZATION
In recent years, digital cameras are becoming very commonplace and users need tools to manage large personal photo collections. In a typical scenario, a user acquires a certain number of pictures and then transfers this new photo sequence to his PC. Thus, before being added to the whole personal photo collection, it would be desirable that this new photo sequence is processed and organized. For example, users may be interested in using (i.e., browsing, saving, printing and so on) a subset of stored data according to some particular picture properties. For these reasons, automatic techniques for content-based description of personal photos are needed. Tools enabling an incremental organizati…
A Simulation Software for the Evaluation of Vulnerabilities in Reputation Management Systems
Multi-agent distributed systems are characterized by autonomous entities that interact with each other to provide, and/or request, different kinds of services. In several contexts, especially when a reward is offered according to the quality of service, individual agents (or coordinated groups) may act in a selfish way. To prevent such behaviours, distributed Reputation Management Systems (RMSs) provide every agent with the capability of computing the reputation of the others according to direct past interactions, as well as indirect opinions reported by their neighbourhood. This last point introduces a weakness on gossiped information that makes RMSs vulnerable to malicious agents’ intent …
Your friends mention It. What about visiting it? A mobile social-based sightseeing application
In this short poster paper, we present an application for suggesting attractions to be visited by users, based on social signal processing techniques.
A fog-based hybrid intelligent system for energy saving in smart buildings
In recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper proposes a hybrid intelligent system which exploits a fog-based architecture to achieve energy efficiency in smart buildings. Our proposal combines reactive intelligence, for quick adaptation to the ever-changing environment, and deliberative intelligence, for performing complex learning and optimization. Such hybrid nature allows our system to be adaptive, by reacting in real time to relevant events occurring in the environment and, at the same time, …
Assisted labeling for spam account detection on twitter
Online Social Networks (OSNs) have become increasingly popular both because of their ease of use and their availability through almost any smart device. Unfortunately, these characteristics make OSNs also target of users interested in performing malicious activities, such as spreading malware and performing phishing attacks. In this paper we address the problem of spam detection on Twitter providing a novel method to support the creation of large-scale annotated datasets. More specifically, URL inspection and tweet clustering are performed in order to detect some common behaviors of spammers and legitimate users. Finally, the manual annotation effort is further reduced by grouping similar u…
A Heterogeneous Sensor and Actuator Network Architecture for Ambient Intelligence
One of the most important characteristics of a typical ambient intelligence scenario is the presence of a number of sensors and actuators that capture information about user preferences and activities. Such nodes, i.e., sensors and actuators, are often based on different technologies so that types of networks which are typically different coexist in a real system, for example, in a home or a building. In this chapter we present a heterogeneous sensor and actuator network architecture designed to separate network management issues from higher, intelligent layers. The effectiveness of the solution proposed here was evaluated using an experimental scenario involving the monitoring of an office…
An Intelligent System for Energy Efficiency in a Complex of Buildings
Energy efficiency has nowadays become one of the most challenging task for both academic and commercial organizations, and this has boosted research on novel fields, such as Ambient Intelligence. In this paper we address the issue of timely and ubiquitous monitoring of building complexes in order to optimize their energy consumption, and present an intelligent system addressed to the typical end user, i.e. the administrator, or responsible operator, of the complex. A three-level architecture has been designed for detecting the presence of the building inhabitants user and understanding their preferences with respect to the environmental conditions in order to optimize the overall energy eff…
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…
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…
Automatic image representation and clustering on mobile devices.
In this paper a novel approach for the automatic representation of pictures on mobile devices is proposed. With the wide diffusion of mobile digital image acquisition devices, the need of managing a large number of digital images is quickly increasing. In fact the storage capacity of such devices allow users to store hundreds or even thousands, of pictures that, without a proper organization, become useless. Users may be interested in using (i.e., browsing, saving, printing and so on) a subset of stored data according to some particular picture properties. A content-based description of each picture is needed to perform on-board image indexing. In our work the images are analyzed and descri…
A Federated Learning Approach for Distributed Human Activity Recognition
In recent years, the widespread diffusion of smart pervasive devices able to provide AI-based services has encouraged research in the definition of new distributed learning paradigms. Federated Learning (FL) is one of the most recent approaches which allows devices to collaborate to train AI-based models, whereas guarantying privacy and lower communication costs. Although different studies on FL have been conducted, a general and modular architecture capable of performing well in different scenarios is still missing. Following this direction, this paper proposes a general FL framework whose validity is assessed by considering a distributed activity recognition scenario in which users' perso…
User Activity Recognition via Kinect in an Ambient Intelligence Scenario
The availability of an ever-increasing kind of cheap, unobtrusive, sensing devices has stressed the need for new approaches to merge raw measurements in order to realize what is happening in the monitored environment. Ambient Intelligence (AmI) techniques exploit information about the environment state to adapt the environment itself to the users’ preferences. Even if traditional sensors allow a rough understanding of the users’ preferences, ad-hoc sensors are required to obtain a deeper comprehension of users’ habits and activities. In this paper we propose a framework to recognize users’ activities via a depth and RGB camera device, namely the Microsoft Kinect. The proposed approach takes…
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…
Three-domain image representation for personal photo album management
In this paper we present a novel approach for personal photo album management. Pictures are analyzed and described in three representation spaces, namely, faces, background and time of capture. Faces are automatically detected and rectified using a probabilistic feature extraction technique. Face representation is then produced by computing PCA (Principal Component Analysis). Backgrounds are represented with low-level visual features based on RGB histogram and Gabor filter bank. Temporal data is obtained through the extraction of EXIF (Exchangeable image file format) data. Each image in the collection is then automatically organized using a mean-shift clustering technique. While many system…
Motion sensors for activity recognition in an ambient-intelligence scenario
In recent years, Ambient Intelligence (AmI) has attracted a number of researchers due to the widespread diffusion of unobtrusive sensing devices. The availability of such a great amount of acquired data has driven the interest of the scientific community in producing novel methods for combining raw measurements in order to understand what is happening in the monitored scenario. Moreover, due the primary role of the end user, an additional requirement of any AmI system is to maintain a high level of pervasiveness. In this paper we propose a method for recognizing human activities by means of a time of flight (ToF) depth and RGB camera device, namely Microsoft Kinect. The proposed approach is…
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…
Bayesian Modeling for Differential Cryptanalysis of Block Ciphers: A DES Instance
Encryption algorithms based on block ciphers are among the most widely adopted solutions for providing information security. Over the years, a variety of methods have been proposed to evaluate the robustness of these algorithms to different types of security attacks. One of the most effective analysis techniques is differential cryptanalysis, whose aim is to study how variations in the input propagate on the output. In this work we address the modeling of differential attacks to block cipher algorithms by defining a Bayesian framework that allows a probabilistic estimation of the secret key. In order to prove the validity of the proposed approach, we present as case study a differential att…
SMCP: a Secure Mobile Crowdsensing Protocol for fog-based applications
Abstract The possibility of performing complex data analysis through sets of cooperating personal smart devices has recently encouraged the definition of new distributed computing paradigms. The general idea behind these approaches is to move early analysis towards the edge of the network, while relying on other intermediate (fog) or remote (cloud) devices for computations of increasing complexity. Unfortunately, because both of their distributed nature and high degree of modularity, edge-fog-cloud computing systems are particularly prone to cyber security attacks that can be performed against every element of the infrastructure. In order to address this issue, in this paper we present SMCP…
DRESS: A Distributed RMS Evaluation Simulation Software
Distributed environments consist of a huge number of entities that cooperate to achieve complex goals. When interactions occur between unknown parties, intelligent techniques for estimating agent reputations are required. Reputation management systems (RMS's) allow agents to perform such estimation in a cooperative way. In particular, distributed RMS's exploit feedbacks provided after each interaction and allow prediction of future behaviors of agents. Such systems, in contrast to centralized RMSs, are sensitive to fake information injected by malicious users; thus, predicting the performance of a distributed RMS is a very challenging task. Although many existing works have addressed some c…
Gait Analysis Using Multiple Kinect Sensors
A gait analysis technique to model user presences in an office scenario is presented in this chapter. In contrast with other approaches, we use unobtrusive sensors, i.e., an array of Kinect devices, to detect some features of interest. In particular, the position and the spatio-temporal evolution of some skeletal joints are used to define a set of gait features, which can be either static (e.g., person height) or dynamic (e.g., gait cycle duration). Data captured by multiple Kinects is merged to detect dynamic features in a longer walk sequence. The approach proposed here was been evaluated by using three classifiers (SVM, KNN, Naive Bayes) on different feature subsets.
A Combined Fuzzy and Probabilistic Data Descriptor for Distributed CBIR
With the wide diffusion of digital image acquisition devices, the cost of managing hundreds of digital images is quickly increasing. Currently, the main way to search digital image libraries is by keywords given by the user. However, users usually add ambiguos keywords for large set of images. A content-based system intended to automatically find a query image, or similar images, within the whole collection is needed. In our work we address the scenario where medical image collections, which nowadays are rapidly expanding in quantity and heterogeneity, are shared in a distributed system to support diagnostic and preventive medicine. Our goal is to produce an efficient content-based descript…
Mimicking biological mechanisms for sensory information fusion
Current Artificial Intelligence systems are bound to become increasingly interconnected to their surrounding environment in the view of the newly rising Ambient Intelligence (AmI) perspective. In this paper, we present a comprehensive AmI framework for performing fusion of raw data, perceived by sensors of different nature, in order to extract higher-level information according to a model structured so as to resemble the perceptual signal processing occurring in the human nervous system. Following the guidelines of the greater BICA challenge, we selected the specific task of user presence detection in a locality of the system as a representative application clarifying the potentialities of …
SecureBallot: A secure open source e-Voting system
Abstract Voting is one of the most important acts through which a community can make a collective decision. In recent years, many works have focused on improving traditional voting mechanisms and, as a result, a wide range of electronic voting (e-Voting) systems have been proposed. Even though some approaches have achieved a proper level of usability, the main challenges of e-Voting are essentially still open: protect the privacy of participants, guarantee secrecy, anonymity, integrity, uniqueness, and authenticity of votes, while making e-Voting as trustful as voting. In order to address this issue, we present SecureBallot, a secure open-source e-Voting system that completely decouples the…
Malware detection through low-level features and stacked denoising autoencoders
In recent years, the diffusion of malicious software through various channels has gained the request for intelligent techniques capable of timely detecting new malware spread. In this work, we focus on the application of Deep Learning methods for malware detection, by evaluating their effectiveness when malware are represented by high-level, and lowlevel features respectively. Experimental results show that, when using high-level features, deep neural networks do not significantly improve the overall detection accuracy. On the other hand, when low-level features, i.e., small pieces of information extracted through a light processing, are chosen, they allow to increase the capability of corr…
A Java-based Wrapper for Wireless Communications
The increasing number of new applications for mobile devices in pervasive environments, do not cope with changes in the wireless communications. Developers of such applications have to deal with problems arising from the available wireless connections in the given environment. A middleware is a solution that allows to overcome some of these problems. It provides to the applications a set of functions that facilitate their development. In this paper we present a Java-based communication wrapper, called SmartTraffic, which allows programmers to seamlessly use TCP or UDP protocols over Bluetooth or any IP-based wireless network. Developers can use SmartTraffic within their Java applications, t…
Twitter Analysis for Real-Time Malware Discovery
In recent years, the increasing number of cyber-attacks has gained the development of innovative tools to quickly detect new threats. A recent approach to this problem is to analyze the content of Social Networks to discover the rising of new malicious software. Twitter is a popular social network which allows millions of users to share their opinions on what happens all over the world. The subscribers can insert messages, called tweet, that are usually related to international news. In this work, we present a system for real-time malware alerting using a set of tweets captured through the Twitter API’s, and analyzed by means of a Bayes naïve classifier. Then, groups of tweets discussing th…
Mobile Interface for Content-Based Image Management
People make more and more use of digital image acquisition devices to capture screenshots of their everyday life. The growing number of personal pictures raise the problem of their classification. Some of the authors proposed an automatic technique for personal photo album management dealing with multiple aspects (i. e., people, time and background) in a homogenous way. In this paper we discuss a solution that allows mobile users to remotely access such technique by means of their mobile phones, almost from everywhere, in a pervasive fashion. This allows users to classify pictures they store on their devices. The whole solution is presented, with particular regard to the user interface impl…
KARD - Kinect Activity Recognition Dataset
To cite this dataset, please refer to the following paper:Human Activity Recognition Process Using 3-D Posture Data. S. Gaglio, G. Lo Re, M. Morana. In IEEE Transactions on Human-Machine Systems. 2014 doi: 10.1109/THMS.2014.2377111******************************************************************KARD contains 18 Activities. Each activity is performed 3 times by 10 different subjects.1Horizontal arm wave2High arm wave3Two hand wave4Catch Cap5High throw6Draw X7Draw Tick8Toss Paper9Forward Kick10Side Kick11Take Umbrella12Bend13Hand Clap14Walk15Phone Call16Drink17Sit down18Stand upIn total, you have 4 (files) x 18 (activities) x 3 (repetitions) x 10 (subjects), that is 2160 files.Each filename …