Following the WCAG 2.0 techniques: Experiences from designing a WCAG 2.0 checking tool

2012

Published version of a chapter in the book: Computers Helping People with Special Needs. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31522-0_63 This paper presents a conceptual analysis of how the Web Content Accessibility Guidelines (WCAG) 2.0 and its accompanying documents can be used as a basis for the implementation of an automatic checking tool and the definition of a web accessibility metric. There are two major issues that need to be resolved to derive valid and reliable conclusions from the output of individual tests. First, the relationship of Sufficient Techniques and Common Failures has to be taken into account. Second, the logical combination of the…

A Tsetlin Machine with Multigranular Clauses

2019

The recently introduced Tsetlin Machine (TM) has provided competitive pattern recognition accuracy in several benchmarks, however, requires a 3-dimensional hyperparameter search. In this paper, we introduce the Multigranular Tsetlin Machine (MTM). The MTM eliminates the specificity hyperparameter, used by the TM to control the granularity of the conjunctive clauses that it produces for recognizing patterns. Instead of using a fixed global specificity, we encode varying specificity as part of the clauses, rendering the clauses multigranular. This makes it easier to configure the TM because the dimensionality of the hyperparameter search space is reduced to only two dimensions. Indeed, it tur…

Adaptive sparse representation of continuous input for tsetlin machines based on stochastic searching on the line

2021

This paper introduces a novel approach to representing continuous inputs in Tsetlin Machines (TMs). Instead of using one Tsetlin Automaton (TA) for every unique threshold found when Booleanizing continuous input, we employ two Stochastic Searching on the Line (SSL) automata to learn discriminative lower and upper bounds. The two resulting Boolean features are adapted to the rest of the clause by equipping each clause with its own team of SSLs, which update the bounds during the learning process. Two standard TAs finally decide whether to include the resulting features as part of the clause. In this way, only four automata altogether represent one continuous feature (instead of potentially h…

Deep CNN-ELM Hybrid Models for Fire Detection in Images

2018

In this paper, we propose a hybrid model consisting of a Deep Convolutional feature extractor followed by a fast and accurate classifier, the Extreme Learning Machine, for the purpose of fire detection in images. The reason behind using such a model is that Deep CNNs used for image classification take a very long time to train. Even with pre-trained models, the fully connected layers need to be trained with backpropagation, which can be very slow. In contrast, we propose to employ the Extreme Learning Machine (ELM) as the final classifier trained on pre-trained Deep CNN feature extractor. We apply this hybrid model on the problem of fire detection in images. We use state of the art Deep CNN…

Smart load prediction analysis for distributed power network of Holiday Cabins in Norwegian rural area

2020

Abstract The Norwegian rural distributed power network is mainly designed for Holiday Cabins with limited electrical loading capacity. Load prediction analysis, within such type of network, is necessary for effective operation and to manage the increasing demand of new appliances (e. g. electric vehicles and heat pumps). In this paper, load prediction of a distributed power network (i.e. a typical Norwegian rural area power network of 125 cottages with 478 kW peak demand) is carried out using regression analysis techniques for establishing autocorrelations and correlations among weather parameters and occurrence time in the period of 2014–2018. In this study, the regression analysis for loa…

An Iterative Information Retrieval Approach from Social Media in Crisis Situations

2019

During to past few years, social media have gained a pivotal role in crisis communication. Its usage has ranged from informing the public about the status of a crisis and what precaution need to be taken, to family members checking on the safety of loved ones. Despite the widespread use of social media in crises situations and the clear potential benefit from collecting potential critical information from social media, emergency management services (EMSs) are still reluctant to use social media as a source of information to improve their situational awareness. One of the reasons for the reluctance is that crises management are typically overloaded with information. Adding social media will …

The regression Tsetlin machine: a novel approach to interpretable nonlinear regression

2019

Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. Thi…

Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook

2022

The deep learning revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. These new methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid. Off-the-shelf algorithms can find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. Using these opportunities requires collaboration across ecological and data science disciplines, which can be challenging to initiate. To facilitate these collaborations and promote the use of deep learning towards ecosystem-based management…

Comparing Recurrent Neural Networks using Principal Component Analysis for Electrical Load Predictions

2021

Electrical demand forecasting is essential for power generation capacity planning and integrating environment-friendly energy sources. In addition, load predictions will help in developing demand-side management in coordination with renewable power generation. Meteorological conditions influence urban area load pattern; therefore, it is vital to include weather parameters for load predictions. Machine Learning algorithms can effectively be used for electrical load predictions considering impact of external parameters. This paper explores and compares the basic Recurrent Neural Networks (RNN); Simple Recurrent Neural Networks (Vanilla RNN), Gated Recurrent Units (GRU), and Long Short-Term Me…

Using Tsetlin Machine to discover interpretable rules in natural language processing applications

2021

Tsetlin Machines (TM) use finite state machines for learning and propositional logic to represent patterns. The resulting pattern recognition approach captures information in the form of conjunctive clauses, thus facilitating human interpretation. In this work, we propose a TM-based approach to three common natural language processing (NLP) tasks, namely, sentiment analysis, semantic relation categorization and identifying entities in multi-turn dialogues. By performing frequent itemset mining on the TM-produced patterns, we show that we can obtain a global and a local interpretation of the learning, one that mimics existing rule-sets or lexicons. Further, we also establish that our TM base…

Increasing sample efficiency in deep reinforcement learning using generative environment modelling

2020

Relative evaluation of regression tools for urban area electrical energy demand forecasting

2019

Abstract Load forecasting is the most fundamental application in Smart-Grid, which provides essential input to Demand Response, Topology Optimization and Abnormally Detection, facilitating the integration of intermittent clean energy sources. In this work, several regression tools are analyzed using larger datasets for urban area electrical load forecasting. The regression tools which are used are Random Forest Regressor, k-Nearest Neighbour Regressor and Linear Regressor. This work explores the use of regression tool for regional electric load forecasting by correlating lower distinctive categorical level (season, day of the week) and weather parameters. The regression analysis has been do…

MapAI: Precision in BuildingSegmentation

2022

MapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) in collaboration with Centre for Artificial Intelligence Research at the University of Agder (CAIR), the Norwegian Mapping Authority, AI:Hub, Norkart, and the Danish Agency for Data Supply and Infrastructure. The competition will be held in the fall of 2022. It will be concluded at the Northern Lights Deep Learning conference focusing on the segmentation of buildings using aerial images and laser data. We propose two different tasks to segment buildings, where the first task can only utilize aerial images, while the second must use laser data (LiDAR) with…

Towards safe reinforcement-learning in industrial grid-warehousing

2020

Abstract Reinforcement learning has shown to be profoundly successful at learning optimal policies for simulated environments using distributed training with extensive compute capacity. Model-free reinforcement learning uses the notion of trial and error, where the error is a vital part of learning the agent to behave optimally. In mission-critical, real-world environments, there is little tolerance for failure and can cause damaging effects on humans and equipment. In these environments, current state-of-the-art reinforcement learning approaches are not sufficient to learn optimal control policies safely. On the other hand, model-based reinforcement learning tries to encode environment tra…

Development of a Simulator for Prototyping Reinforcement Learning-Based Autonomous Cars

2022

Autonomous driving is a research field that has received attention in recent years, with increasing applications of reinforcement learning (RL) algorithms. It is impractical to train an autonomous vehicle thoroughly in the physical space, i.e., the so-called ’real world’; therefore, simulators are used in almost all training of autonomous driving algorithms. There are numerous autonomous driving simulators, very few of which are specifically targeted at RL. RL-based cars are challenging due to the variety of reward functions available. There is a lack of simulators addressing many central RL research tasks within autonomous driving, such as scene understanding, localization and mapping, pla…

Towards a Deep Reinforcement Learning Approach for Tower Line Wars

2017

There have been numerous breakthroughs with reinforcement learning in the recent years, perhaps most notably on Deep Reinforcement Learning successfully playing and winning relatively advanced computer games. There is undoubtedly an anticipation that Deep Reinforcement Learning will play a major role when the first AI masters the complicated game plays needed to beat a professional Real-Time Strategy game player. For this to be possible, there needs to be a game environment that targets and fosters AI research, and specifically Deep Reinforcement Learning. Some game environments already exist, however, these are either overly simplistic such as Atari 2600 or complex such as Starcraft II fro…

Temperate Fish Detection and Classification: a Deep Learning based Approach

2021

A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) …

A Novel Tsetlin Automata Scheme to Forecast Dengue Outbreaks in the Philippines

2018

Being capable of online learning in unknown stochastic environments, Tsetlin Automata (TA) have gained considerable interest. As a model of biological systems, teams of TA have been used for solving complex problems in a decentralized manner, with low computational complexity. For many domains, decentralized problem solving is an advantage, however, also may lead to coordination difficulties and unstable learning. To combat this negative effect, this paper proposes a novel TA coordination scheme designed for learning problems with continuous input and output. By saving and updating the best solution that has been chosen so far, we can avoid having the overall system being led astray by spur…

Improving prostate whole gland segmentation in t2-weighted MRI with synthetically generated data

2021

Whole gland (WG) segmentation of the prostate plays a crucial role in detection, staging and treatment planning of prostate cancer (PCa). Despite promise shown by deep learning (DL) methods, they rely on the availability of a considerable amount of annotated data. Augmentation techniques such as translation and rotation of images present an alternative to increase data availability. Nevertheless, the amount of information provided by the transformed data is limited due to the correlation between the generated data and the original. Based on the recent success of generative adversarial networks (GAN) in producing synthetic images for other domains as well as in the medical domain, we present…

A Relational Tsetlin Machine with Applications to Natural Language Understanding

2021

TMs are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns. In addition to being natively interpretable, they have provided competitive accuracy for various tasks. In this paper, we increase the computing power of TMs by proposing a first-order logic-based framework with Herbrand semantics. The resulting TM is relational and can take advantage of logical structures appearing in natural language, to learn rules that represent how actions and consequences are related in the real world. The outcome is a logic program of Horn clauses, bringing in a structured view of unstructured data. In closed-domain question-answering, th…

Automatic Sleep Stage Identification with Time Distributed Convolutional Neural Network

2021

Polysomnography (PSG), the gold standard for sleep stage classification, requires a sleep expert for scoring and is both resource-intensive and expensive. Many researchers currently focus on the real-time classification of the sleep stages based on biomedical signals, such as Electroencephalograph (EEG) and electrooculography (EOG). However, most of the research work is based on machine learning models with multiple signal inputs or hand-engineered features requiring prior knowledge of the sleep domain. We propose a novel encoded Time-Distributed Convolutional Neural Network (TDConvNet) to automatically classify sleep stages based on a single raw PSG signal. The TDConvNet can infer sleep st…

The Dreaming Variational Autoencoder for Reinforcement Learning Environments

2018

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and plannin…

Emergency Analysis: Multitask Learning with Deep Convolutional Neural Networks for Fire Emergency Scene Parsing

2021

In this paper, we introduce a novel application of using scene semantic image segmentation for fire emergency situation analysis. To analyse a fire emergency scene, we propose to use deep convolutional image segmentation networks to identify and classify objects in a scene based on their build material and their vulnerability to catch fire. We introduce our own fire emergency scene segmentation dataset for this purpose. It consists of real world images with objects annotated on the basis of their build material. We use state-of-the-art segmentation models: DeepLabv3, DeepLabv3+, PSPNet, FCN, SegNet and UNet to compare and evaluate their performance on the fire emergency scene parsing task. …

A Novel Multi-step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning

2020

Due to the high energy consumption and scalability challenges of deep learning, there is a critical need to shift research focus towards dealing with energy consumption constraints. Tsetlin Machines (TMs) are a recent approach to machine learning that has demonstrated significantly reduced energy usage compared to neural networks alike, while performing competitively accuracy-wise on several benchmarks. However, TMs rely heavily on energy-costly random number generation to stochastically guide a team of Tsetlin Automata (TA) to a Nash Equilibrium of the TM game. In this paper, we propose a novel finite-state learning automaton that can replace the TA in TM learning, for increased determinis…

Indoor Space Classification Using Cascaded LSTM

2020

Author's accepted manuscript. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Indoor space classification is an important part of localization that helps in precise location extraction, which has been extensively utilized in industrial and domestic domain. There are various approaches that employ Bluetooth Low Energy (BLE), Wi-Fi, magnetic field, object detecti…

The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems

2019

The recently introduced Tsetlin Machine (TM) has provided competitive pattern classification accuracy in several benchmarks, composing patterns with easy-to-interpret conjunctive clauses in propositional logic. In this paper, we go beyond pattern classification by introducing a new type of TMs, namely, the Regression Tsetlin Machine (RTM). In all brevity, we modify the inner inference mechanism of the TM so that input patterns are transformed into a single continuous output, rather than to distinct categories. We achieve this by: (1) using the conjunctive clauses of the TM to capture arbitrarily complex patterns; (2) mapping these patterns to a continuous output through a novel voting and n…

A Multi-layer Feed Forward Neural Network Approach for Diagnosing Diabetes

2018

Diabetes is one of the worlds major health problems according to the World Health Organization. Recent surveys indicate that there is an increase in the number of diabetic patients resulting in an increase in serious complications such as heart attacks and deaths. Early diagnosis of diabetes, particularly of type 2 diabetes, is critical since it is vital for patients to get insulin treatments. However, diagnoses could be difficult especially in areas with few medical doctors. It is, therefore, a need for practical methods for the public for early detection and prevention with minimal intervention from medical professionals. A promising method for automated diagnosis is the use of artificial…

A Spatio-temporal Probabilistic Model of Hazard and Crowd Dynamics in Disasters for Evacuation Planning

2013

Published version of a chapter in the book: Recent Trends in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-38577-3_7 Managing the uncertainties that arise in disasters – such as ship fire – can be extremely challenging. Previous work has typically focused either on modeling crowd behavior or hazard dynamics, targeting fully known environments. However, when a disaster strikes, uncertainty about the nature, extent and further development of the hazard is the rule rather than the exception. Additionally, crowd and hazard dynamics are both intertwined and uncertain, making evacuation planning extremely difficult. To address this chal…

Positionless aspect based sentiment analysis using attention mechanism.

2021

Abstract Aspect-based sentiment analysis (ABSA) aims at identifying fine-grained polarity of opinion associated with a given aspect word. Several existing articles demonstrated promising ABSA accuracy using positional embedding to show the relationship between an aspect word and its context. In most cases, the positional embedding depends on the distance between the aspect word and the remaining words in the context, known as the position index sequence. However, these techniques usually employ both complex preprocessing approaches with additional trainable positional embedding and complex architectures to obtain the state-of-the-art performance. In this paper, we simplify preprocessing by …

Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments

2017

This paper deals with the extremely fascinating area of “fusing” the outputs of sensors without any knowledge of the ground truth. In an earlier paper, the present authors had recently pioneered a solution, by mapping it onto the fascinating paradox of trying to identify stochastic liars without any additional information about the truth. Even though that work was significant, it was constrained by the model in which we are living in a world where “the truth prevails over lying”. Couched in the terminology of Learning Automata (LA), this corresponds to the Environment (Since the Environment is treated as an entity in its own right, we choose to capitalize it, rather than refer to it as an “…

Deep Learning for Classifying Physical Activities from Accelerometer Data

2021

Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify the physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the proposed models on two phy…

Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks

2021

Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weath…

Causality-based Social Media Analysis for Normal Users Credibility Assessment in a Political Crisis

2019

Information trustworthiness assessment on political social media discussions is crucial to maintain the order of society, especially during emergent situations. The polarity nature of political topics and the echo chamber effect by social media platforms allow for a deceptive and a dividing environment. During a political crisis, a vast amount of information is being propagated on social media, that leads up to a high level of polarization and deception by the beneficial parties. The traditional approaches to tackling misinformation on social media usually lack a comprehensive problem definition due to its complication. This paper proposes a probabilistic graphical model as a theoretical vi…

A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks

2019

In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting. The TM is interpretable because it is based on manipulating expressions in propositional logic, leveraging a large team of Tsetlin Automata (TA). Apart from being interpretable, this approach is attractive due to its low computational cost and its capacity to handle noise. To attack the problem of forecasting, we introduce a preprocessing method that extends the TM so that it can handle continuous input. Briefly stated, we convert continuous input into a binary representation based on thresholding. The resulting extended TM is evaluated and analyzed…

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization With Medical Applications

2019

Medical applications challenge today's text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap ahead in accuracy, this leap comes at the sacrifice of interpretability. To address this accuracy-interpretability challenge, we here introduce, for the first time, a text categorization approach that leverages the recently introduced Tsetlin Machine. In all brevity, we represent the terms of a text as propositional variables. From these, we capture categories using simple propositional formulae, such as: if "rash" and "reaction" and "penicillin" then Allergy. The Tsetlin Machine learns these formulae from a labelled tex…

Adaptive Continuous Feature Binarization for Tsetlin Machines Applied to Forecasting Dengue Incidences in the Philippines

2020

The Tsetlin Machine (TM) is a recent interpretable machine learning algorithm that requires relatively modest computational power, yet attains competitive accuracy in several benchmarks. TMs are inherently binary; however, many machine learning problems are continuous. While binarization of continuous data through brute-force thresholding has yielded promising accuracy, such an approach is computationally expensive and hinders extrapolation. In this paper, we address these limitations by standardizing features to support scale shifts in the transition from training data to real-world operation, typical for e.g. forecasting. For scalability, we employ sampling to reduce the number of binariz…

Evaluating Anomaly Detection Algorithms through different Grid scenarios using k-Nearest Neighbor, iforest and Local Outlier Factor

2022

Author's accepted manuscript © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage. The available advanced information and communicating platform and computational capability renders smart grid prone to attacks with extreme social, financial an…

Neuroevolution of Actively Controlled Virtual Characters - An Experiment for an Eight-Legged Character

2018

Physics-based character animation offers an attractive alternative for traditional animations. However, it is often strenuous for a physics-based approach to incorporate active user control of different characters. In this paper, a neuroevolutionary approach is proposed using HyperNEAT to combine individually trained neural controllers to form a control strategy for a simulated eight-legged character, which is a previously untested character morphology for this algorithm. It is aimed to evaluate the robustness and responsiveness of the control strategy that changes the controllers based on simulated user inputs. The experiment result shows that HyperNEAT is able to evolve long walking contr…

Interpretability in Word Sense Disambiguation using Tsetlin Machine

2021

Modelling of Compressors in an Industrial CO $$_2$$ -Based Operational Cooling System Using ANN for Energy Management Purposes

2019

Large scale cooling installations usually have high energy consumption and fluctuating power demands. There are several ways to control energy consumption and power requirements through intelligent energy and power management, such as utilizing excess heat, thermal energy storage and local renewable energy sources. Intelligent energy and power management in an operational setting is only possible if the time-varying performance of the individual components of the energy system is known. This paper presents an approach to model the compressors in an industrial, operational two-stage cooling system, with CO\(_2\) as the working fluid, located in an advanced food distribution warehouse in Norw…

MedAI: Transparency in Medical Image Segmentation

2021

MedAI: Transparency in Medical Image Segmentation is a challenge held for the first time at the Nordic AI Meet that focuses on medical image segmentation and transparency in machine learning (ML)-based systems. We propose three tasks to meet specific gastrointestinal image segmentation challenges collected from experts within the field, including two separate segmentation scenarios and one scenario on transparent ML systems. The latter emphasizes the need for explainable and interpretable ML algorithms. We provide a development dataset for the participants to train their ML models, tested on a concealed test dataset.

On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth

2015

In many applications, data from different sensors are aggregated in order to obtain more reliable information about the process that the sensors are monitoring. However, the quality of the aggregated information is intricately dependent on the reliability of the individual sensors. In fact, unreliable sensors will tend to report erroneous values of the ground truth, and thus degrade the quality of the fused information. Finding strategies to identify unreliable sensors can assist in having a counter-effect on their respective detrimental influences on the fusion process, and this has has been a focal concern in the literature. The purpose of this paper is to propose a solution to an extreme…

CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning

2020

Reinforcement Learning (RL) is a general framework concerned with an agent that seeks to maximize rewards in an environment. The learning typically happens through trial and error using explorative methods, such as \(\epsilon \)-greedy. There are two approaches, model-based and model-free reinforcement learning, that show concrete results in several disciplines. Model-based RL learns a model of the environment for learning the policy while model-free approaches are fully explorative and exploitative without considering the underlying environment dynamics. Model-free RL works conceptually well in simulated environments, and empirical evidence suggests that trial and error lead to a near-opti…

On Solving the Problem of Identifying Unreliable Sensors Without a Knowledge of the Ground Truth: The Case of Stochastic Environments.

2017

The purpose of this paper is to propose a solution to an extremely pertinent problem, namely, that of identifying unreliable sensors (in a domain of reliable and unreliable ones) without any knowledge of the ground truth. This fascinating paradox can be formulated in simple terms as trying to identify stochastic liars without any additional information about the truth. Though apparently impossible, we will show that it is feasible to solve the problem, a claim that is counterintuitive in and of itself. One aspect of our contribution is to show how redundancy can be introduced, and how it can be effectively utilized in resolving this paradox. Legacy work and the reported literature (for exam…

SleepXAI: An explainable deep learning approach for multi-class sleep stage identification

2022

AbstractExtensive research has been conducted on the automatic classification of sleep stages utilizing deep neural networks and other neurophysiological markers. However, for sleep specialists to employ models as an assistive solution, it is necessary to comprehend how the models arrive at a particular outcome, necessitating the explainability of these models. This work proposes an explainable unified CNN-CRF approach (SleepXAI) for multi-class sleep stage classification designed explicitly for univariate time-series signals using modified gradient-weighted class activation mapping (Grad-CAM). The proposed approach significantly increases the overall accuracy of sleep stage classification …

Safer Reinforcement Learning for Agents in Industrial Grid-Warehousing

2020

In mission-critical, real-world environments, there is typically a low threshold for failure, which makes interaction with learning algorithms particularly challenging. Here, current state-of-the-art reinforcement learning algorithms struggle to learn optimal control policies safely. Loss of control follows, which could result in equipment breakages and even personal injuries.

Towards Multilevel Ant Colony Optimisation for the Euclidean Symmetric Traveling Salesman Problem

2015

Ant Colony Optimization ACO metaheuristic is one of the best known examples of swarm intelligence systems in which researchers study the foraging behavior of bees, ants and other social insects in order to solve combinatorial optimization problems. In this paper, a multilevel Ant Colony Optimization MLV-ACO for solving the traveling salesman problem is proposed, by using a multilevel process operating in a coarse-to-fine strategy. This strategy involves recursive coarsening to create a hierarchy of increasingly smaller and coarser versions of the original problem. The heart of the approach is grouping the variables that are part of the problem into clusters, which is repeated until the size…

A pattern recognition approach for peak prediction of electrical consumption

2016

Predicting and mitigating demand peaks in electrical networks has become a prevalent research topic. Demand peaks pose a particular challenge to energy companies because these are difficult to foresee and require the net to support abnormally high consumption levels. In smart energy grids, time-differentiated pricing policies that increase the energy cost for the consumers during peak periods, and load balancing are examples of simple techniques for peak regulation. In this paper, we tackle the task of predicting power peaks prior to their actual occurrence in the context of a pilot Norwegian smart grid network.

Distributed Learning Automata-based S-learning scheme for classification

2019

This paper proposes a novel classifier based on the theory of Learning Automata (LA), reckoned to as PolyLA. The essence of our scheme is to search for a separator in the feature space by imposing an LA-based random walk in a grid system. To each node in the grid, we attach an LA whose actions are the choices of the edges forming a separator. The walk is self-enclosing, and a new random walk is started whenever the walker returns to the starting node forming a closed classification path yielding a many-edged polygon. In our approach, the different LA attached to the different nodes search for a polygon that best encircles and separates each class. Based on the obtained polygons, we perform …

Vector representation of non-standard spellings using dynamic time warping and a denoising autoencoder

2017

The presence of non-standard spellings in Twitter causes challenges for many natural language processing tasks. Traditional approaches mainly regard the problem as a translation, spell checking, or speech recognition problem. This paper proposes a method that represents the stochastic relationship between words and their non-standard versions in real vectors. The method uses dynamic time warping to preprocess the non-standard spellings and autoencoder to derive the vector representation. The derived vectors encode word patterns and the Euclidean distance between the vectors represents a distance in the word space that challenges the prevailing edit distance. After training the autoencoder o…

A multi-step finite-state automaton for arbitrarily deterministic Tsetlin Machine learning

2021

Adaptive Task Assignment in Online Learning Environments

2016

With the increasing popularity of online learning, intelligent tutoring systems are regaining increased attention. In this paper, we introduce adaptive algorithms for personalized assignment of learning tasks to student so that to improve his performance in online learning environments. As main contribution of this paper, we propose a a novel Skill-Based Task Selector (SBTS) algorithm which is able to approximate a student's skill level based on his performance and consequently suggest adequate assignments. The SBTS is inspired by the class of multi-armed bandit algorithms. However, in contrast to standard multi-armed bandit approaches, the SBTS aims at acquiring two criteria related to stu…

Optimizing PolyACO Training with GPU-Based Parallelization

2016

A central part of Ant Colony Optimisation (ACO) is the function calculating the quality and cost of solutions, such as the distance of a potential ant route. This cost function is used to deposit an opportune amount of pheromones to achieve an apt convergence, and in an active ACO implementation a significant part of the runtime is spent in this part of the code. In some cases, the cost function accumulates up towards 94 % in its run time making it a performance bottle neck.

The Use of Artificial Intelligence in Disaster Management - A Systematic Literature Review

2019

Whenever a disaster occurs, users in social media, sensors, cameras, satellites, and the like generate vast amounts of data. Emergency responders and victims use this data for situational awareness, decision-making, and safe evacuations. However, making sense of the generated information under time-bound situations is a challenging task as the amount of data can be significant, and there is a need for intelligent systems to analyze, process, and visualize it. With recent advancements in Artificial Intelligence (AI), numerous researchers have begun exploring AI, machine learning (ML), and deep learning (DL) techniques for big data analytics in managing disasters efficiently. This paper adopt…

Deep 3D Convolution Neural Network for Alzheimer’s Detection

2020

One of the most well-known and complex applications of artificial intelligence (AI) is Alzheimer’s detection, which lies in the field of medical imaging. The complexity in this task lies in the three-dimensional structure of the MRI scan images. In this paper, we propose to use 3D Convolutional Neural Networks (3D-CNN) for Alzheimer’s detection. 3D-CNNs have been a popular choice for this task. The novelty in our paper lies in the fact that we use a deeper 3D-CNN consisting of 10 layers. Also, with effectively training our model consisting of Batch Normalization layers that provide a regularizing effect, we don’t have to use any transfer learning. We also use the simple data augmentation te…

On Obtaining Classification Confidence, Ranked Predictions and AUC with Tsetlin Machines

2020

Tsetlin machines (TMs) are a promising approach to machine learning that uses Tsetlin Automata to produce patterns in propositional logic, leading to binary (hard) classifications. In many applications, however, one needs to know the confidence of classifications, e.g. to facilitate risk management. In this paper, we propose a novel scheme for measuring TM confidence based on the logistic function, calculated from the propositional logic patterns that match the input. We then use this scheme to trade off precision against recall, producing area under receiver operating characteristic curves (AUC) for TMs. Empirically, using four real-world datasets, we show that AUC is a more sensitive meas…

Information Abstraction from Crises Related Tweets Using Recurrent Neural Network

2016

Social media has become an important open communication medium during crises. The information shared about a crisis in social media is massive, complex, informal and heterogeneous, which makes extracting useful information a difficult task. This paper presents a first step towards an approach for information extraction from large Twitter data. In brief, we propose a Recurrent Neural Network based model for text generation able to produce a unique text capturing the general consensus of a large collection of twitter messages. The generated text is able to capture information about different crises from tens of thousand of tweets summarized only in a 2000 characters text.

PolyACO+: a multi-level polygon-based ant colony optimisation classifier

2017

Ant Colony Optimisation for classification has mostly been limited to rule based approaches where artificial ants walk on datasets in order to extract rules from the trends in the data, and hybrid approaches which attempt to boost the performance of existing classifiers through guided feature reductions or parameter optimisations. A recent notable example that is distinct from the mainstream approaches is PolyACO, which is a proof of concept polygon-based classifier that resorts to ant colony optimisation as a technique to create multi-edged polygons as class separators. Despite possessing some promise, PolyACO has some significant limitations, most notably, the fact of supporting classific…

Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games

2018

Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to the vast capabilities of convolutional neural networks, that can extract useful features from noisy and complex data. Games are excellent tools to test and push the boundaries of novel RL algorithms because they give valuable insight into how well an algorithm can perform in isolated environments without the real-life consequences. Real-time strategy games (RTS) is a genre that has tremendous complexity and challenges the player in short and long-term planning. The…

The Role of Artificial Intelligence in Social Media Big data Analytics for Disaster Management -Initial Results of a Systematic Literature Review

2018

When any kind of disaster occurs, victims who are directly and indirectly affected by the disaster often post vast amount of data (e.g., images, text, speech, video) using numerous social media platforms. This is because social media has recently become a primary communication channel among people to report either to public or to emergency responders (ERs). ERs, who are from various emergency response organizations (EROs), usually consider to gain awareness of the situation in order to respond to occurred disaster. However, with the occurrence of the disaster, within minutes, the social media platforms are flooded with various kinds of data which become overwhelmed for ERs with big data. Fu…

Road Detection for Reinforcement Learning Based Autonomous Car

2020

Human mistakes in traffic often have terrible consequences. The long-awaited introduction of self-driving vehicles may solve many of the problems with traffic, but much research is still needed before cars are fully autonomous.In this paper, we propose a new Road Detection algorithm using online supervised learning based on a Neural Network architecture. This algorithm is designed to support a Reinforcement Learning algorithm (for example, the standard Proximal Policy Optimization or PPO) by detecting when the car is in an adverse condition. Specifically, the PPO gets a penalty whenever the virtual automobile gets stuck or drives off the road with any of its four wheels.Initial experiments …

Towards automatic assessment of government web sites

2013

This paper presents an approach for automatic assessment of web sites in large scale e-Government surveys. The approach aims at supplementing and to some extent replacing human evaluation which is typically the core part of these surveys.The heart of the solution is a colony inspired algorithm, called the lost sheep, which automatically locates targeted governmental material online. The algorithm centers around classifying link texts to determine if a web page should be downloaded for further analysis.The proposed algorithm is designed to work with minimum human interaction and utilize the available resources as best possible. Using the lost sheep, the people carrying out a survey will only…

On Utilizing Stochastic Non-linear Fractional Bin Packing to Resolve Distributed Web Crawling

2014

This paper deals with the extremely pertinent problem of web crawling, which is far from trivial considering the magnitude and all-pervasive nature of the World-Wide Web. While numerous AI tools can be used to deal with this task, in this paper we map the problem onto the combinatorially-hard stochastic non-linear fractional knapsack problem, which, in turn, is then solved using Learning Automata (LA). Such LA-based solutions have been recently shown to outperform previous state-of-the-art approaches to resource allocation in Web monitoring. However, the ever growing deployment of distributed systems raises the need for solutions that cope with a distributed setting. In this paper, we prese…

Integer Weighted Regression Tsetlin Machines

2020

The Regression Tsetlin Machine (RTM) addresses the lack of interpretability impeding state-of-the-art nonlinear regression models. It does this by using conjunctive clauses in propositional logic to capture the underlying non-linear frequent patterns in the data. These, in turn, are combined into a continuous output through summation, akin to a linear regression function, however, with non-linear components and binary weights. However, the resolution of the RTM output is proportional to the number of clauses employed. This means that computation cost increases with resolution. To address this problem, we here introduce integer weighted RTM clauses. Our integer weighted clause is a compact r…

Deep Q-Learning With Q-Matrix Transfer Learning for Novel Fire Evacuation Environment

2021

We focus on the important problem of emergency evacuation, which clearly could benefit from reinforcement learning that has been largely unaddressed. Emergency evacuation is a complex task which is difficult to solve with reinforcement learning, since an emergency situation is highly dynamic, with a lot of changing variables and complex constraints that makes it difficult to train on. In this paper, we propose the first fire evacuation environment to train reinforcement learning agents for evacuation planning. The environment is modelled as a graph capturing the building structure. It consists of realistic features like fire spread, uncertainty and bottlenecks. We have implemented the envir…

Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability

2020

Despite significant effort, building models that are both interpretable and accurate is an unresolved challenge for many pattern recognition problems. In general, rule-based and linear models lack accuracy, while deep learning interpretability is based on rough approximations of the underlying inference. Using a linear combination of conjunctive clauses in propositional logic, Tsetlin Machines (TMs) have shown competitive performance on diverse benchmarks. However, to do so, many clauses are needed, which impacts interpretability. Here, we address the accuracy-interpretability challenge in machine learning by equipping the TM clauses with integer weights. The resulting Integer Weighted TM (…

Automated Dental Identification with Lowest Cost Path-Based Teeth and Jaw Separation

2016

Abstract Teeth are some of the most resilient tissues of the human body. Because of their placement, teeth often yield intact indicators even when other metrics, such as finger prints and DNA, are missing. Forensics on dental identification is now mostly manual work which is time and resource intensive. Systems for automated human identification from dental X-ray images have the potential to greatly reduce the necessary efforts spent on dental identification, but it requires a system with high stability and accuracy so that the results can be trusted. This paper proposes a new system for automated dental X-ray identification. The scheme extracts tooth and dental work contours from the X-ray…

Genetic Algorithm Modeling for Photocatalytic Elimination of Impurity in Wastewater

2019

The existence of C.I. Acid Yellow 23 (AY23) in water causes a great danger to people and society. Here, we suggest an advanced technique which predicts the photochemical deletion of AY23. The genetic algorithm (GA) technique is suggested in order to predict the photocatalytic removal of AY23 by implementing the Ag-TiO\(_{2}\) nanoparticles provided under appropriate conditions.

Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation

2019

Abstract Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works in three stages. First, the ExtraTrees classifier is used to select relevant features for each type of attack individually for each (ELM). Then, an ensemble of ELMs is used to detect each type of attack separately. Finally, the results of all ELMs are combined using a softmax layer to refine the results and increase the accuracy further. The intuition behind our system is that multi-class classification is quite difficult compared to binary classification. So, we…

Emergency Detection with Environment Sound Using Deep Convolutional Neural Networks

2020

In this paper, we propose a generic emergency detection system using only the sound produced in the environment. For this task, we employ multiple audio feature extraction techniques like the mel-frequency cepstral coefficients, gammatone frequency cepstral coefficients, constant Q-transform and chromagram. After feature extraction, a deep convolutional neural network (CNN) is used to classify an audio signal as a potential emergency situation or not. The entire model is based on our previous work that sets the new state of the art in the environment sound classification (ESC) task (Our paper is under review in the IEEE/ACM Transactions on Audio, Speech and Language Processing and also avai…

Mining Interpretable Rules for Sentiment and Semantic Relation Analysis Using Tsetlin Machines

2020

Tsetlin Machines (TMs) are an interpretable pattern recognition approach that captures patterns with high discriminative power from data. Patterns are represented as conjunctive clauses in propositional logic, produced using bandit-learning in the form of Tsetlin Automata. In this work, we propose a TM-based approach to two common Natural Language Processing (NLP) tasks, viz. Sentiment Analysis and Semantic Relation Categorization. By performing frequent itemset mining on the patterns produced, we show that they follow existing expert-verified rule-sets or lexicons. Further, our comparison with other widely used machine learning techniques indicates that the TM approach helps maintain inter…

Ant Colony Optimisation-Based Classification Using Two-Dimensional Polygons

2016

The application of Ant Colony Optimization to the field of classification has mostly been limited to hybrid approaches which attempt at boosting the performance of existing classifiers (such as Decision Trees and Support Vector Machines (SVM)) — often through guided feature reductions or parameter optimizations.

ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse

2020

Author's accepted manuscript Industrial cooling systems consume large quantities of energy with highly variable power demand. To reduce environmental impact and overall energy consumption, and to stabilize the power requirements, it is recommended to recover surplus heat, store energy, and integrate renewable energy production. To control these operations continuously in a complex energy system, an intelligent energy management system can be employed using operational data and machine learning. In this work, we have developed an artificial neural network based technique for modelling operational CO2 refrigerant based industrial cooling systems for embedding in an overall energy management s…

Ant colony optimisation for planning safe escape routes

2013

Published version of a chapter from the volume: Recent Trends in Applied Artificial Intelligence. Also available on SpringerLink: http://dx.doi.org/10.1007/978-3-642-38577-3_6 An emergency requiring evacuation is a chaotic event filled with uncertainties both for the people affected and rescuers. The evacuees are often left to themselves for navigation to the escape area. The chaotic situation increases when a predefined escape route is blocked by a hazard, and there is a need to re-think which escape route is safest. This paper addresses automatically finding the safest escape route in emergency situations in large buildings or ships with imperfect knowledge of the hazards. The proposed so…

Distributed learning automata-based scheme for classification using novel pursuit scheme

2020

Learning Automata (LA) is a popular decision making mechanism to “determine the optimal action out of a set of allowable actions” (Agache and Oommen, IEEE Trans Syst Man Cybern-Part B Cybern 2002(6): 738–749, 2002). The distinguishing characteristic of automata-based learning is that the search for the optimising parameter vector is conducted in the space of probability distributions defined over the parameter space, rather than in the parameter space itself (Thathachar and Sastry, IEEE Trans Syst Man Cybern-Part B Cybern 32(6): 711–722, 2002). Recently, Goodwin and Yazidi pioneered the use of Ant Colony Optimisation (ACO) for solving classification problems (Goodwin and Yazidi 2016). In th…

Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation

2019

Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environment is a promising tool which would revolutionize video and image analysis for a wide range of applications in marine ecology. However, classifying fish from images captured by underwater cameras is in general very challenging due to noise and illumination variations in water. Previous classification methods in the literature relies on filtering the images to separate the fish from the background or sharpening the images by removing background noise. This pre-filtering process may negatively impact the classificat…

Distributed learning automata for solving a classification task

2016

In this paper, we propose a novel classifier in two-dimensional feature spaces based on the theory of Learning Automata (LA). The essence of our scheme is to search for a separator in the feature space by imposing a LA based random walk in a grid system. To each node in the gird we attach an LA, whose actions are the choice of the edges forming the separator. The walk is self-enclosing, i.e, a new random walk is started whenever the walker returns to starting node forming a closed classification path yielding a many edged polygon. In our approach, the different LA attached at the different nodes search for a polygon that best encircles and separates each class. Based on the obtained polygon…

Towards Open Domain Chatbots—A GRU Architecture for Data Driven Conversations

2018

Understanding of textual content, such as topic and intent recognition, is a critical part of chatbots, allowing the chatbot to provide relevant responses. Although successful in several narrow domains, the potential diversity of content in broader and more open domains renders traditional pattern recognition techniques inaccurate. In this paper, we propose a novel deep learning architecture for content recognition that consists of multiple levels of gated recurrent units (GRUs). The architecture is designed to capture complex sentence structure at multiple levels of abstraction, seeking content recognition for very wide domains, through a distributed scalable representation of content. To …

Convolutional Regression Tsetlin Machine: An Interpretable Approach to Convolutional Regression

2021

The Convolutional Tsetlin Machine (CTM), a variant of Tsetlin Machine (TM), represents patterns as straightforward AND-rules, to address the high computational complexity and the lack of interpretability of Convolutional Neural Networks (CNNs). CTM has shown competitive performance on MNIST, Fashion-MNIST, and Kuzushiji-MNIST pattern classification benchmarks, both in terms of accuracy and memory footprint. In this paper, we propose the Convolutional Regression Tsetlin Machine (C-RTM) that extends the CTM to support continuous output problems in image analysis. C-RTM identifies patterns in images using the convolution operation as in the CTM and then maps the identified patterns into a real…

A Deep Reinforcement Learning scheme for Battery Energy Management

2020

Deep reinforcement learning is considered promising for many energy cost optimization tasks in smart buildings. How-ever, agent learning, in this context, is sometimes unstable and unpredictable, especially when the environments are complex. In this paper, we examine deep Reinforcement Learning (RL) algorithms developed for game play applied to a battery control task with an energy cost optimization objective. We explore how agent behavior and hyperparameters can be analyzed in a simplified environment with the goal of modifying the algorithms for increased stability. Our modified Deep Deterministic Policy Gradient (DDPG) agent is able to perform consistently close to the optimum over multi…

Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network

2020

In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN) with Attention mechanism. The novelty of the paper lies in using multiple feature channels consisting of Mel-Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), the Constant Q-transform (CQT) and Chromagram. Such multiple features have never been used before for signal or audio processing. And, we employ a deeper CNN (DCNN) compared to previous models, consisting of spatially separable convolutions working on time and feature domain separately. Alongside, we use atten…

A Learning Automata Local Contribution Sampling Applied to Hydropower Production Optimisation

2017

Learning Automata (LA) is a powerful approach for solving complex, non-linear and stochastic optimisation problems. However, existing solutions struggle with high-dimensional problems due to slow convergence, arguably caused by the global nature of feedback. In this paper we introduce a novel Learning Automata (LA) scheme to attack this challenge. The scheme is based on a parallel form of Local Contribution Sampling (LCS), which means that the LA receive individually directed feedback, designed to speed up convergence. Furthermore, our scheme is highly decentralized, allowing parallel execution on GPU architectures. To demonstrate the power of our scheme, the LA LCS is applied to hydropower…

Robust Interpretable Text Classification against Spurious Correlations Using AND-rules with Negation

2022

The state-of-the-art natural language processing models have raised the bar for excellent performance on a variety of tasks in recent years. However, concerns are rising over their primitive sensitivity to distribution biases that reside in the training and testing data. This issue hugely impacts the performance of the models when exposed to out-of-distribution and counterfactual data. The root cause seems to be that many machine learning models are prone to learn the shortcuts, modelling simple correlations rather than more fundamental and general relationships. As a result, such text classifiers tend to perform poorly when a human makes minor modifications to the data, which raises questi…

Deep Convolutional Neural Networks for Fire Detection in Images

2017

Detecting fire in images using image processing and computer vision techniques has gained a lot of attention from researchers during the past few years. Indeed, with sufficient accuracy, such systems may outperform traditional fire detection equipment. One of the most promising techniques used in this area is Convolutional Neural Networks (CNNs). However, the previous research on fire detection with CNNs has only been evaluated on balanced datasets, which may give misleading information on real-world performance, where fire is a rare event. Actually, as demonstrated in this paper, it turns out that a traditional CNN performs relatively poorly when evaluated on the more realistically balance…

Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise

2022

Authors accepted manuscript Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Q-learning is one of the most well-known Reinforcement Learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped Deep Q-Learning Network is amongst them. It utilizes multiple neural network heads to introduce diversity into Q-learning. Dive…

Deep Neural Networks for Prediction of Exacerbations of Patients with Chronic Obstructive Pulmonary Disease

2018

Chronic Obstructive Pulmonary Disease (COPD) patients need help in daily life situations as they are burdened with frequent risks of acute exacerbation and loss of control. An automated monitoring system could lead to timely treatments and avoid unnecessary hospital (re-)admissions and home visits by doctors or nurses. Therefore we present a Deep Artificial Neural Networks for approach prediction of exacerbations, particularly Feed-Forward Neural Networks (FFNN) for classification of COPD patients category and Long Short-Term Memory (LSTM), for early prediction of COPD exacerbations and subsequent triage. The FFNN and LSTM models are trained on data collected from remote monitoring of 94 pa…

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

2014

Predicting and mitigating demand peaks in electrical networks has become a prevalent research topic. Demand peaks pose a particular challenge to energy companies because these are difficult to foresee and require the net to support abnormally high consumption levels. In smart energy grids, time-differentiated pricing policies that increase the energy cost for the consumers during peak periods, and load balancing are examples of simple techniques for peak regulation. In this paper, we tackle the task of predicting power peaks prior to their actual occurrence in the context of a pilot Norwegian smart grid network.

A novel strategy for solving the stochastic point location problem using a hierarchical searching scheme

2014

Stochastic point location (SPL) deals with the problem of a learning mechanism (LM) determining the optimal point on the line when the only input it receives are stochastic signals about the direction in which it should move. One can differentiate the SPL from the traditional class of optimization problems by the fact that the former considers the case where the directional information, for example, as inferred from an Oracle (which possibly computes the derivatives), suffices to achieve the optimization-without actually explicitly computing any derivatives. The SPL can be described in terms of a LM (algorithm) attempting to locate a point on a line. The LM interacts with a random environme…

Accurate Wound and Lice Detection in Atlantic Salmon Fish Using a Convolutional Neural Network

2022

The population living in the coastal region relies heavily on fish as a food source due to their vast availability and low cost. This need has given rise to fish farming. Fish farmers and the fishing industry face serious challenges such as lice in the aquaculture ecosystem, wounds due to injuries, early fish maturity, etc. causing millions of fish deaths in the fish aquaculture ecosystem. Several measures, such as cleaner fish and anti-parasite drugs, are utilized to reduce sea lice, but getting rid of them entirely is challenging. This study proposed an image-based machine-learning technique to detect wounds and the presence of lice in the live salmon fish farm ecosystem. A new equally di…

Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing

2020

The Tsetlin Machine (TM) is a machine learning algorithm founded on the classical Tsetlin Automaton (TA) and game theory. It further leverages frequent pattern mining and resource allocation principles to extract common patterns in the data, rather than relying on minimizing output error, which is prone to overfitting. Unlike the intertwined nature of pattern representation in neural networks, a TM decomposes problems into self-contained patterns, represented as conjunctive clauses. The clause outputs, in turn, are combined into a classification decision through summation and thresholding, akin to a logistic regression function, however, with binary weights and a unit step output function. …

Learning Automata-based Misinformation Mitigation via Hawkes Processes

2021

AbstractMitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Processes (MHP) have become a fundamental tool because they model social network dynamics, which facilitates execution and evaluation of mitigation policies. In this paper, we propose a novel light-weight intervention-based misinformation mitigation framework using decentralized Learning Automata (LA) to control the MHP. Each automaton is associated with a single user and learns to what degree that user should be involved in the mitigation strategy by interacting with a corresponding MHP, and performing a joint ra…

Deregulated Electric Energy Price Forecasting in NordPool Market using Regression Techniques

2019

Deregulated electricity market day-ahead electrical energy price forecasting is important. It is influenced by external parameters and it is a complicated function. In this work two neighboring regions in the NordPool market are analyzed to provide day-ahead electrical price forecasting using regression techniques. The characteristics of the NordPool market trading behavior leads to unanticipated price peaks at daily, weekly and annual level. The considered two Nordic regions have different energy generation sources (e.g Norway has controllable hydro power, Denmark has non-controllable wind-power) therefore day-ahead electrical energy price forecasting in deregulated market for these two ne…

Load Demand Analysis of Nordic Rural Area with Holiday Resorts for Network Capacity Planning

2019

Most of the Nordic holiday resorts are in rural area with low capacity distributed network. The rural area network is weak and needs capacity expansion planning as the load demand of this area are going to increase due to penetration of electric vehicles and heat pumps. Such type of rural network can also be operated as a micro-grid, and therefore load analysis is required for appropriate operation. The load analysis will also be useful for finding proper sizing of distributed energy resources including energy storage. In this work, load demand analysis of a typical Nordic holiday resorts, connected in rural grid, is presented to find out the load variation during the usage periods. The loa…

Combining a context aware neural network with a denoising autoencoder for measuring string similarities

2020

Abstract Measuring similarities between strings is central for many established and fast-growing research areas, including information retrieval, biology, and natural-language processing. The traditional approach to string similarity measurements is to define a metric with respect to a word space that quantifies and sums up the differences between characters in two strings; surprisingly, these metrics have not evolved a great deal over the past few decades. Indeed, the majority of them are still based on making a simple comparison between character and character distributions without considering the words context. This paper proposes a string metric that encompasses similarities between str…

AIs for Dominion Using Monte-Carlo Tree Search

2015

Dominion is a complex game, with hidden information and stochastic elements. This makes creating any artificial intelligence AI challenging. To this date, there is little work in the literature on AI for Dominion, and existing solutions rely upon carefully tuned finite-state solutions. This paper presents two novel AIs for Dominion based on Monte-Carlo Tree Search MCTS methods. This is achieved by employing Upper Confidence Bounds UCB and Upper Confidence Bounds applied to Trees UCT. The proposed solutions are notably better than existing work. The strongest proposal is able to win 67% of games played against a known, good finite-state solution, even when the finite-state solution has the u…

Teaching Programming to Large Student Groups through Test Driven Development - Comparing Established Methods with Teaching based on Test Driven Devel…

2016

This paper presents an approach for teaching programming in large university classes based on test driven development (TDD) methods. The approach aims at giving the students an industry-like environment already in their education and introduces full automation and feedback programming classes through unit testing. The focus for this paper is to compare the novel approach with existing teaching methods. It does so by comparing introduction to programming classes in two institutions. One university ran a TDD teaching process with fully automated assessments and feedback, while the other ran a more traditional on-line environment with manual assessments and feedback. The TDD approach has clear…

Escape planning in realistic fire scenarios with Ant Colony Optimisation

2014

Published version of an article from the journal:Applied Intelligence Also available on Springerlink: http://dx.doi.org/10.1007/s10489-014-0538-9 An emergency requiring evacuation is a chaotic event, filled with uncertainties both for the people affected and rescuers. The evacuees are often left to themselves for navigation to the escape area. The chaotic situation increases when predefined escape routes are blocked by a hazard, and there is a need to re-think which escape route is safest. This paper addresses automatically finding the safest escape routes in emergency situations in large buildings or ships with imperfect knowledge of the hazards. The proposed solution, based on Ant Colony …

Towards Model-Based Reinforcement Learning for Industry-Near Environments

2019

Deep reinforcement learning has over the past few years shown great potential in learning near-optimal control in complex simulated environments with little visible information. Rainbow (Q-Learning) and PPO (Policy Optimisation) have shown outstanding performance in a variety of tasks, including Atari 2600, MuJoCo, and Roboschool test suite. Although these algorithms are fundamentally different, both suffer from high variance, low sample efficiency, and hyperparameter sensitivity that, in practice, make these algorithms a no-go for critical operations in the industry.

Interpretable Option Discovery Using Deep Q-Learning and Variational Autoencoders

2021

Deep Reinforcement Learning (RL) is unquestionably a robust framework to train autonomous agents in a wide variety of disciplines. However, traditional deep and shallow model-free RL algorithms suffer from low sample efficiency and inadequate generalization for sparse state spaces. The options framework with temporal abstractions [18] is perhaps the most promising method to solve these problems, but it still has noticeable shortcomings. It only guarantees local convergence, and it is challenging to automate initiation and termination conditions, which in practice are commonly hand-crafted.

A Hierarchical Learning Scheme for Solving the Stochastic Point Location Problem

2012

Published version of a chapter in the book: Advanced Research in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31087-4_78 This paper deals with the Stochastic-Point Location (SPL) problem. It presents a solution which is novel in both philosophy and strategy to all the reported related learning algorithms. The SPL problem concerns the task of a Learning Mechanism attempting to locate a point on a line. The mechanism interacts with a random environment which essentially informs it, possibly erroneously, if the unknown parameter is on the left or the right of a given point which also is the current guess. The first pioneering work […

On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators

2020

The Tsetlin Machine (TM) is a recent machine learning algorithm with several distinct properties, such as interpretability, simplicity, and hardware-friendliness. Although numerous empirical evaluations report on its performance, the mathematical analysis of its convergence is still open. In this article, we analyze the convergence of the TM with only one clause involved for classification. More specifically, we examine two basic logical operators, namely, the "IDENTITY"- and "NOT" operators. Our analysis reveals that the TM, with just one clause, can converge correctly to the intended logical operator, learning from training data over an infinite time horizon. Besides, it can capture arbit…

Not a Target. A Deep Learning Approach for a Warning and Decision Support System to Improve Safety and Security of Humanitarian Aid Workers

2019

Humanitarian aid workers who try to provide aid to the most vulnerable populations in the Middle East or Africa are risking their own lives and safety to help others. The current lack of a collaborative real-time information system to predict threats prevents responders and local partners from developing a shared understanding of potentially threatening situations, causing increased response times and leading to inadequate protection. To solve this problem, this paper presents a threat detection and decision support system that combines knowledge and information from a network of responders with automated and modular threat detection. The system consists of three parts. It first collects te…

The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems

2019

The recently introduced Tsetlin Machine (TM) has provided competitive pattern classification accuracy in several benchmarks, composing patterns with easy-to-interpret conjunctive clauses in propositional logic. In this paper, we go beyond pattern classification by introducing a new type of TMs, namely, the Regression Tsetlin Machine (RTM). In all brevity, we modify the inner inference mechanism of the TM so that input patterns are transformed into a single continuous output, rather than to distinct categories. We achieve this by: (1) using the conjunctive clauses of the TM to capture arbitrarily complex patterns; (2) mapping these patterns to a continuous output through a novel voting and n…

The Convolutional Tsetlin Machine

2019

Convolutional neural networks (CNNs) have obtained astounding successes for important pattern recognition tasks, but they suffer from high computational complexity and the lack of interpretability. The recent Tsetlin Machine (TM) attempts to address this lack by using easy-to-interpret conjunctive clauses in propositional logic to solve complex pattern recognition problems. The TM provides competitive accuracy in several benchmarks, while keeping the important property of interpretability. It further facilitates hardware-near implementation since inputs, patterns, and outputs are expressed as bits, while recognition and learning rely on straightforward bit manipulation. In this paper, we ex…

FlashRL: A Reinforcement Learning Platform for Flash Games

2017

Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential in among others successfully playing computer games. However, there only exists a few game platforms that provide diversity in tasks and state-space needed to advance RL algorithms. The existing platforms offer RL access to Atari- and a few web-based games, but no platform fully expose access to Flash games. This is unfortunate because applying RL to Flash games have potential to push the research of RL algorithms. This paper introduces the Flash Reinforcement Learning platform (FlashRL) which attempts to fill this gap by providing an environment for thousands of…

A Neural Turing~Machine for Conditional Transition Graph Modeling

2019

Graphs are an essential part of many machine learning problems such as analysis of parse trees, social networks, knowledge graphs, transportation systems, and molecular structures. Applying machine learning in these areas typically involves learning the graph structure and the relationship between the nodes of the graph. However, learning the graph structure is often complex, particularly when the graph is cyclic, and the transitions from one node to another are conditioned such as graphs used to represent a finite state machine. To solve this problem, we propose to extend the memory based Neural Turing Machine (NTM) with two novel additions. We allow for transitions between nodes to be inf…

Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing

2020

The Tsetlin Machine (TM) is a machine learning algorithm founded on the classical Tsetlin Automaton (TA) and game theory. It further leverages frequent pattern mining and resource allocation principles to extract common patterns in the data, rather than relying on minimizing output error, which is prone to overfitting. Unlike the intertwined nature of pattern representation in neural networks, a TM decomposes problems into self-contained patterns, represented as conjunctive clauses. The clause outputs, in turn, are combined into a classification decision through summation and thresholding, akin to a logistic regression function, however, with binary weights and a unit step output function. …

Towards a Deep Reinforcement Learning Approach for Tower Line Wars

2017

There have been numerous breakthroughs with reinforcement learning in the recent years, perhaps most notably on Deep Reinforcement Learning successfully playing and winning relatively advanced computer games. There is undoubtedly an anticipation that Deep Reinforcement Learning will play a major role when the first AI masters the complicated game plays needed to beat a professional Real-Time Strategy game player. For this to be possible, there needs to be a game environment that targets and fosters AI research, and specifically Deep Reinforcement Learning. Some game environments already exist, however, these are either overly simplistic such as Atari 2600 or complex such as Starcraft II fro…

A Novel Multi-Step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning

2020

Due to the high energy consumption and scalability challenges of deep learning, there is a critical need to shift research focus towards dealing with energy consumption constraints. Tsetlin Machines (TMs) are a recent approach to machine learning that has demonstrated significantly reduced energy usage compared to neural networks alike, while performing competitively accuracy-wise on several benchmarks. However, TMs rely heavily on energy-costly random number generation to stochastically guide a team of Tsetlin Automata to a Nash Equilibrium of the TM game. In this paper, we propose a novel finite-state learning automaton that can replace the Tsetlin Automata in TM learning, for increased d…

A Regression Tsetlin Machine with Integer Weighted Clauses for Compact Pattern Representation

2020

The Regression Tsetlin Machine (RTM) addresses the lack of interpretability impeding state-of-the-art nonlinear regression models. It does this by using conjunctive clauses in propositional logic to capture the underlying non-linear frequent patterns in the data. These, in turn, are combined into a continuous output through summation, akin to a linear regression function, however, with non-linear components and unity weights. Although the RTM has solved non-linear regression problems with competitive accuracy, the resolution of the output is proportional to the number of clauses employed. This means that computation cost increases with resolution. To reduce this problem, we here introduce i…

The Dreaming Variational Autoencoder for Reinforcement Learning Environments

2018

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and plannin…

Identifying unreliable sensors without a knowledge of the ground truth in deceptive environments

2017

This paper deals with the extremely fascinating area of “fusing” the outputs of sensors without any knowledge of the ground truth. In an earlier paper, the present authors had recently pioneered a solution, by mapping it onto the fascinating paradox of trying to identify stochastic liars without any additional information about the truth. Even though that work was significant, it was constrained by the model in which we are living in a world where “the truth prevails over lying”. Couched in the terminology of Learning Automata (LA), this corresponds to the Environment (Since the Environment is treated as an entity in its own right, we choose to capitalize it, rather than refer to it as an “…

Contrastive Transformer: Contrastive Learning Scheme with Transformer innate Patches

2023

This paper presents Contrastive Transformer, a contrastive learning scheme using the Transformer innate patches. Contrastive Transformer enables existing contrastive learning techniques, often used for image classification, to benefit dense downstream prediction tasks such as semantic segmentation. The scheme performs supervised patch-level contrastive learning, selecting the patches based on the ground truth mask, subsequently used for hard-negative and hard-positive sampling. The scheme applies to all vision-transformer architectures, is easy to implement, and introduces minimal additional memory footprint. Additionally, the scheme removes the need for huge batch sizes, as each patch is t…

Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow

2020

An adaptive task difficulty assignment method which we reckon as balanced difficulty task finder (BDTF) is proposed in this paper. The aim is to recommend tasks to a learner using a trade-off between skills of the learner and difficulty of the tasks such that the learner experiences a state of flow during the learning. Flow is a mental state that psychologists refer to when someone is completely immersed in an activity. Flow state is a multidisciplinary field of research and has been studied not only in psychology, but also neuroscience, education, sport, and games. The idea behind this paper is to try to achieve a flow state in a similar way as Elo’s chess skill rating (Glickman in Am Ches…

Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling

2020

Using logical clauses to represent patterns, Tsetlin Machines (TMs) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning speed on several benchmarks. Each TM clause votes for or against a particular class, with classification resolved using a majority vote. While the evaluation of clauses is fast, being based on binary operators, the voting makes it necessary to synchronize the clause evaluation, impeding parallelization. In this paper, we propose a novel scheme for desynchronizing the evaluation of clauses, eliminating the voting bottleneck. In brief, every clause runs in its own thread for massive native parallelism. For each training…

Ant colony optimisation-based classification using two-dimensional polygons

2016

Deep Reinforcement Learning using Capsules in Advanced Game Environments

2018

Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to vast capabilities of Convolutional Neural Networks (ConvNet), enabling algorithms to extract useful information from noisy environments. Capsule Network (CapsNet) is a recent introduction to the Deep Learning algorithm group and has only barely begun to be explored. The network is an architecture for image classification, with superior performance for classification of the MNIST dataset. CapsNets have not been explored beyond image classification. This thesis introduce…

Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples

2022

In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims at incorporating semi-supervised learning into reinforcement learning through splitting Q-values into state values and action advantages. We require that an offline expert assesses the value of a state in a coarse manner using three discrete values. An expert network is designed in addition to the Q-network, which updates each time following the regular offline minibatch update whenever the expert example buffer is not empty. Using the board game Othello, we compare our algorithm with the baseline Q-learning algorithm, which is a…

Combining a Context Aware Neural Network with a Denoising Autoencoder for Measuring String Similarities

2018

Measuring similarities between strings is central for many established and fast growing research areas including information retrieval, biology, and natural language processing. The traditional approach for string similarity measurements is to define a metric over a word space that quantifies and sums up the differences between characters in two strings. The state-of-the-art in the area has, surprisingly, not evolved much during the last few decades. The majority of the metrics are based on a simple comparison between character and character distributions without consideration for the context of the words. This paper proposes a string metric that encompasses similarities between strings bas…

Hierarchical Object Detection applied to Fish Species

2022

Gathering information of aquatic life is often based on timeconsuming methods utilizing video feeds. It would be beneficial to capture more information cost-effectively from video feeds. Video based object detection has an ability to achieve this. Recent research has shown promising results with the use of YOLO for object detection of fish. As underwater conditions can be difficult and thus fish species are hard to discriminate. This study proposes a hierarchical structure-based YOLO Fish algorithm in both the classification and the dataset to gain valuable information. With the use of hierarchical classification and other techniques. YOLO Fish is a state-of-the-art object detector on Nordi…

Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling

2021

Using logical clauses to represent patterns, Tsetlin Machine (TM) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning speed on several benchmarks. Each TM clause votes for or against a particular class, with classification resolved using a majority vote. While the evaluation of clauses is fast, being based on binary operators, the voting makes it necessary to synchronize the clause evaluation, impeding parallelization. In this paper, we propose a novel scheme for desynchronizing the evaluation of clauses, eliminating the voting bottleneck. In brief, every clause runs in its own thread for massive native parallelism. For each training e…

Towards Model-based Reinforcement Learning for Industry-near Environments

2019

Deep reinforcement learning has over the past few years shown great potential in learning near-optimal control in complex simulated environments with little visible information. Rainbow (Q-Learning) and PPO (Policy Optimisation) have shown outstanding performance in a variety of tasks, including Atari 2600, MuJoCo, and Roboschool test suite. While these algorithms are fundamentally different, both suffer from high variance, low sample efficiency, and hyperparameter sensitivity that in practice, make these algorithms a no-go for critical operations in the industry. On the other hand, model-based reinforcement learning focuses on learning the transition dynamics between states in an environme…

A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks

2019

In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting. The TM is interpretable because it is based on manipulating expressions in propositional logic, leveraging a large team of Tsetlin Automata (TA). Apart from being interpretable, this approach is attractive due to its low computational cost and its capacity to handle noise. To attack the problem of forecasting, we introduce a preprocessing method that extends the TM so that it can handle continuous input. Briefly stated, we convert continuous input into a binary representation based on thresholding. The resulting extended TM is evaluated and analyzed…

Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation

2019

Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environment is a promising tool which would revolutionize video and image analysis for a wide range of applications in marine ecology. However, classifying fish from images captured by underwater cameras is in general very challenging due to noise and illumination variations in water. Previous classification methods in the literature relies on filtering the images to separate the fish from the background or sharpening the images by removing background noise. This pre-filtering process may negatively impact the classificat…