Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection and Classification of Epileptiform Transients in EEG Signals Using Convolution Neural Network

EEG is the most common test done by neurologists to study a patient’s brainwaves for pre-epileptic conditions. This thesis explains an end-to-end deep learning approach for detecting segments of EEG which display abnormal brain activity (Yellow-Boxes) and further classifying them to AEP (Abnormal Epileptiform Paroxysmals) and Non-AEP. It is treated as a binary and a multi-class problem. 1-D Convolution Neural Networks are used to carry out the identification and classification. Detection of Yellow-Boxes and subsequent analysis is a tedious process which can be frequently misinterpreted by neurologists without neurophysiology fellowship training. Hence, an automated machine learning system to detect and classify will greatly enhance the quality of diagnosis. Two convolution neural network architectures are trained for the detection of Yellow-Boxes as well as their classification. The first step is detecting the Yellow-Boxes. This is done by training convolution neural networks on a training set containing both Yellow-Boxed and Non-Yellow Boxed segments treated as a 2 class problem, and is also treated as a class extension to the classification of the Yellow-Boxes problem. The second step is the classification of the Yellow-Boxes, where 2 different architectures are trained to classify the Yellow-Boxed data to 2 and 4 classes. The over-all system is validated with the entire 30s EEG segments of multiple patients, which the system classifies as Yellow-Boxes or Non-Yellow Boxes and subsequent classification to AEP or Non-AEP, and is compared with the annotated data by neurologists.
The Adoption of Technology-Enhanced Instruction to Support Education for All

Abstract This study aimed at investigating the efforts of academic tutors at Arab Open University in Jordan (AOU) to implement technology enhanced instruction through using the learning management system software Moodle (LMS). The AOU has adopted an open learning approach. The aim is to support its vision to reach students in different parts of Jordan and other countries; and to overcome the barriers of communication between students and tutors. The LMS is currently used to deliver online materials to supplement traditional face-to-face teaching, and to increase learning opportunities in coordinated network enhanced classrooms. The LMS used at the AOU integrates communication tools, including a bulletin board, chat room, private-e-mails and calendars on the Websites for courses offered at the university. In addition, graphics, video, and audio files can be incorporated into the website. Such features can facilitate interaction between faculty members and students. A survey method for data collection was used in this study and a questionnaire was developed and analyzed that revealed implementing LMS at the appropriate level not only enhanced learning and teaching skills of students and tutors, but also helped students to participate at the expanding and evolving e-based international academic level. Factors such as ease, convenience and accessibility were important factors in influencing academic use of the system. This study also revealed some challenges and obstacles faced in implementing LMS to support the vision of education for all at the university level.
Stroke Risk Prediction Model

The purpose of this study is to improve the existing stroke risk prediction model for the next 10 years. Current existing risk prediction model was done based on the data obtained mostly from America and Africa. There are a few risk prediction models done based on the data from Asian participants. Hence, this paper will predict the risk for stroke for the next 10 years using data collected from Asia. Data from Korean and China risk prediction model were obtained and sorted according to categories. The weightage of each risk factors is then calculated. This study also used the artificial intelligence as a comparison to predict the risk of stroke. Data from a few studies obtained and compared to estimate the probability of stroke that may occur in 10 years of time. Then, a set of data will be used to train the Artificial Neural Network (ANN) and compared to the results obtained using conventional calculation method. The usage of ANN to predict and learn about the risk factors for stroke will give a significant benefit in the future. This study developed a personalized stroke risk prediction which expected to be better and relevant to Asian people compared to other risk prediction model.
Measuring the reactivity of intelligent agent programs

The booming of intelligent agent technology over past few decades brings a surging number of agent applications in various areas. There also have a large number of designs as well as programming languages been introduced in the literature in the agent oriented area. However, very little work has been dedicated to define quality measures for the development of an agent-based system. Previous efforts mostly focus on adopting classical measures such as using coupling (degree of program dependency) and cohesion (degree of function relationship in single module) to measure the quality of agent programs. I maintain that its time to work on a set of software quality measures that are specific to the distinct characteristics of agent-based systems. In this thesis, two methods are purposed to measure the reactivity of agent systems, which provide indications on how agent systems respond to changes in their environment in a timely fashion. A prototype tool is developed integrated with Jason, a well-known agent-oriented programming platform, to calculate reactivity of each agent in agent system based on their plan libraries, and conducted several experiments to demonstrate the reliability of reactivity measure. In addition, an agent behavioural profile is introduced which is an overview of relationships of actions in agent plan library. Based on agent behavioural profile, definitions of agent behavioral profile identity, entailment as well as preservation were proposed, which ensure original agent’s behaviours could be preserved while performing reactivity enhancement.
Modelling and simulation of combat operations in SimCombCalculator application

The basis of the article is mathematical discrete problem connected with local military battle. The problem consists of two dependent problems. One of them shows how to find our troops allocation to enemy’s troops. The battle damage assessment function for the problem is proposed. The second problem is connected with finding global strategy of attack. Local battle is described by modified Lanchester model. Two level logistic model is built for describing materials and munitions resupply management processes. This idea was used in computer simulator SimCombCalculator. The paper shows how to use this simulator for finding attack strategy from the platoon to brigade level. Keywords— battlefield model, decision support, mathematical modelling, computer simulation.
A New Feature Descriptor for Face Recognition

The idea behind this paper is to use a new statistical feature for the face recognition problem. To the best of our knowledge, no one has attempted to implement this approach before. The idea is simple and straight forward. For each face image, a feature vector is formed by concatenating 4 vectors together namely; row-wise sum, column-wise sum, diagonalwise sum and antidiagonal-wise sum. The generated feature vector is then used directly for classification or is can be first projected to another space such as eigenspace or fisherspace. The preliminary simulation results on ORL database show that the proposed approach outperforms the well-known principal component analysis (PCA) algorithm for face recognition.
Combating Cyber Security Breaches in Digital World Using Misuse Detection Methods: Misuse Detection

Intrusion Detection Systems (IDS) play a major role in the area of combating security breaches for information security. Current IDS are developed with Machine learning techniques like Artificial Neural Networks, C 4.5, KNN, Naïve Bayes classifiers, Genetic algorithms Fuzzy logic and SVMs. The objective of this paper is to apply Artificial Neural Networks and Support Vector Machines for intrusion detection. Artificial Neural Networks are applied along with faster training methods like variable learning rate and scaled conjugate gradient. Support Vector Machines use various kernel functions to improve the performance. From the kddcup’99 dataset 45,657 instances are taken and used in our experiment. The speed is compared for various training functions. The performance of various kernel functions is assessed. The detection rate of Support Vector Machines is found to be greater than Artificial Neural Networks with less number of false positives and with less time of detection.
Exploring hospital patients' satisfaction with cook‐chill foodservice systems: a preliminary study using a validated questionnaire

Decline in nutritional status during hospitalisation highlights the importance of achieving high-quality foodservice. This research aimed to explore patient satisfaction with hospital foodservices from on-site and off-site cook-chill production systems. Two cross-sectional surveys of hospital inpatients were conducted in an acute medical and a subacute rehabilitation ward before and after a change to off-site cook-chill production. The survey used a validated instrument (Acute Care Hospital Foodservice Patient Satisfaction Questionnaire). Sixty-six patients in 2006 and 51 patients in 2007 recorded unchanged results across factors of food quality, staffing service quality, meal service quality and physical environment quality. Satisfaction with foodservice was rated ‘good’ or ‘very good’ by the majority of patients in both surveys. The research instrument was able to deconstruct various issues relating to patients' satisfaction and six individual items correlated with overall satisfaction with foodservice. To maximise patient satisfaction, further research into these, and additional dimensions of foodservice quality is needed to determine how improvements may be achieved.
Diversity Combining via Universal Dimension- Reducing Space–Time Transformations

Receiver diversity combining methods play a key role in combating the detrimental effects of fading in wireless communication and other applications. A novel diversity combining method is proposed, where a universal, i.e., channel independent, orthogonal dimension-reducing space-time transformation is applied prior to quantization of the signals. The scheme may be considered as the counterpart of Alamouti modulation, and more generally of orthogonal space–time block codes.
Diseño de un array lineal de parches sobre guía rectangular para banda KU

The aim of this project is to design a radiant system consisting of a linear patch array over a rectangular waveguide, with linear polarization, operating in the beginning of the Ku-band (11 GHz 12 GHz). The elements placement along the array has been optimized to achieve two goals: reduce the input reflection of the whole design and conform the desired radiation pattern. Coupling lines have been used to feed the radiating elements from the waveguide. The radiating elements used, consist of a double stacked patch structure in printed circuit technology. Furthermore, microstrip transmission lines have been integrated into the patches, these lines modify the phase of each element obtaining control over the direction of maximun radiation of the antenna. Additionally, several transitions to feed the linear array have been designed. Among all the designs, it has been chosen the transition with the best performance: normalized waveguide WR90 transition. Finally, the linear array is integrated with the designed transition to form the complete antenna. A mutual coupling model between adjacent elements called radiated near electric field compensation model has been used to adjust this final design. In order to check the simulations results, a prototype of the antenna will be manufactured. The construction of the metalic parts will be carry out by a company outside the university. The remaining pieces will be manufactured and assembled in laboratory of printed circuits at Escuela Politécnica Superior (EPS) at U.A.M. Once the prototype is finished, it will be measured in reflection and radiation pattern in the Anechoic Chamber at EPS (U.A.M.).
Automatic Video Management System Using Face Recognition and MPEG‐7 Visual Descriptors

The main goal of this research is automatic video analysis using a face recognition technique. In this paper, an automatic video management system is introduced with a variety of functions enabled, such as index, edit, summarize, and retrieve multimedia data. The automatic management tool utilizes MPEG‐7 visual descriptors to generate a video index for creating a summary. The resulting index generates a preview of a movie, and allows non‐linear access with thumbnails. In addition, the index supports the searching of shots similar to a desired one within saved video sequences. Moreover, a face recognition technique is utilized to personal‐based video summarization and indexing in stored video data.
Knowledge Editor: A Tool for Interactive Modeling and Analyzing Biological PathwaysBased on Microarray Data

DNA microarray experiment, though it has acquired its position as one of the standard methods in the field of biology, produce a huge amount of raw data that requires multiple processes to extract profitable information. It is one of the ultimate goals of biologists to reflect such extracted results to existing data, and to improve well-established biological knowledge, i.e., metabolic pathways. KnowledgeEditor is the software to provide biologists to import a probe information from microarray data to a biomolecular network, and to modify a known metabolic pathway based on novel microarray experiments. The drawn network on Knowledge editor can be exported in XML format, which is suitable to organize and share data among scientists. It also enables users to publish the modeled network on the web with the plug-in module “GSCope Viewer. ”
Passive Variable Compliance for Dynamic Legged Robots

Recent developments in legged robotics have found that constant stiffness passive compliant legs are an effective mechanism for enabling dynamic locomotion. In spite of its success, one of the limitations of this approach is reduced adaptability. The final leg mechanism usually performs optimally for a small range of conditions such as the desired speed, payload, and terrain. For many situations in which a small locomotion system experiences a change in any of these conditions, it is desirable to have a tunable stiffness leg for effective gait control. To date, the mechanical complexities of designing usefully robust tunable passive compliance into legs has precluded their implementation on practical running robots. In this thesis we present an overview of tunable stiffness legs, and introduce a simple leg model that captures the spatial compliance of our tunable leg. We present experimental evidence supporting the advantages of tunable stiffness legs, and implement what we believe is the first autonomous dynamic legged robot capable of automatic leg stiffness adjustment. Finally we discuss design objectives, material considerations, and manufacturing methods that lead to robust passive compliant legs. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Mechanical Engineering & Applied Mechanics First Advisor Dr. Mark Yim Second Advisor Dr. Daniel E. Koditschek Third Advisor Dr. Jonathan E. Clark
Compensating for Low Topic Interest and Long Surveys

Certain survey characteristics proven to affect response rates, such as a survey's length and topic, are often under limited control of the researcher. Therefore, survey researchers sometimes seek to compensate for such undesired effects on response rates by employing countermeasures such as material or nonmaterial incentives. The scarce evidence on those factors' effects in web survey contexts is far from being conclusive. This study is aimed at filling this gap by examining the effects of four factors along with selected interactions presumed to affect response rates in web surveys. Requests to complete a web-based, self-administered survey were sent to 2,152 owners of personal websites. The 2 × 2 × 2 × 2 fully crossed factorial design encompassed the experimental conditions of (a) high versus low topic salience, (b) short versus long survey, (c) lottery incentive versus no incentive, and (d) no feedback and general feedback (study results) versus personal feedback (individual profile of results). As expected, highly salient and shorter surveys yielded considerably higher unit-response rates. Moreover, partial support was found for interaction hypotheses derived from the leverage-salience theory of survey participation. Offering personalized feedback compensated for the negative effects of low topic salience on response rates. Also, the lottery incentive tended to evoke more responses only if the survey was short (versus long), but this interaction effect was only marginally significant. The results stress the usefulness of a multifactorial approach encompassing interaction effects to understand participation differences in web surveys.
Computational techniques for modeling non-player characters in games

Modeling of non-player characters (NPCs) is an important research area in the development of computer games. Autonomous NPCs by emulating the behavior of human beings, with realistic performance and believable affective variations, in simulated environment, make the games more challenging and enjoyable. Modeling of NPCs is essentially the problem of creating autonomous agents, which are expected to function and adapt by themselves in a complex environment. The motivation behind this research is thus to create “realistic” and “believable” NPCs with the abilities of autonomy, interactivity, situatedness, learning, and adaptation. Three key problems are considered in this research: (1) how behavior models of NPCs may be learned by mimicking behavior patterns of other players? (2) how behavior models of NPCs may be adapted through interaction and feedback in a dynamic environment? (3) how emotion of NPCs may be modeled and integrated with the behavior system and to create variations of NPCs? For learning behavior models, this research investigates two classes of self-organizing neural networks. Firstly, the self-generating neural network (SGNN) is investigated to learn behavior rules from specific sample bots in a supervised manner. Further optimization of SGNN is also proposed via a pruning method which improves its performance. Our empirical experiments based on a first person shooting game environment called the Unreal Tournament show that SGNN is able to learn behaviors effectively from their prototype. Secondly, another class of self-organizing neural networks, known as Fusion Architecture for Learning, COgnition, and Navigation (FALCON), is adapted for imitative learning to learn behavior patterns in the Unreal Tournament game. Benchmark
'I just relexified this one': translation processes in language revival texts

In language revival, text creation is one of the key areas where language development work is focussed. As texts are predominantly developed from the starting point of a text in English, methods used for translation are crucially important in determining the form of the end product. In this paper, we present a representative selection of texts to illustrate different strategies used within translation processes. The salience of lexical gaps creates a pressure on translators to develop alternatives where a word cannot readily be found. In contrast, the lower salience of grammar allows translators greater flexibility in their focus on grammatical elements. Priorities, resource availability, and salient elements for individual translators at particular times function as drivers in relation to both grammar and lexicon. In studying language revival outputs, we note the intrinsic connection of language and sociopolitical context. To understand the use of forms and structures in these texts requires an view of revival languages as inherently in-process, being researched, learned and developed in a single complex process.
Application of kernel spherical k-means for intrusion detection systems

Operational system can be threatened by malicious network activities from intruders or hackers. Consequently, security of a system is indeed become an important subject to tackle this matter. Intrusion Detection System (IDS) is a system which can prevent network traffic and observe suspicious activities in network systems. Therefore, IDS can solve multiple privacy concerns. This paper will propose new method called Kernel Spherical K-Means (KSPKM) that has been modified from Spherical K-Means (SPKM) algorithm by using RBF and polynomial kernel. For our empirical study, we will be using the dataset from KDD Cup 1999 then classified types of attacks into five classes. In the end, we will see which one will produce better results in terms of classification accuracy. We found out that KSPKM succeed to improve clustering accuracy with the highest rate being 98,31% compared to SPKM.
An Improved Method for Radar Signal Sorting Based on SDIF Histogram Algorithm

Radar signal sorting is an important part in modern radar reconnaissance system.The sequential difference(SDIF) histogram algorithm is a typical algorithm of radar signal sorting.Based on subtraction operation,the algorithm can perform fast processing to the arrival time of pulses,is good at real time performance,but the algorithm has some defects to the jittered signal sorting.In order to solve the problem,this paper introduces the idea of overlapping pulse repetition interval(PRI) bins in the improved PRI transform into SDIF histogram algorithm,proposes an improved SDIF histogram algorithm,studies the sorting results of the method in the state of different jittered upper limits,and compares it with the SDIF sorting result.The results show that the algorithm improves the sorting ability of SDIF to the jittered signal evidently.
Alinhamento sentencial de textos paralelos português-inglês

As the first attempt at automatic parallel text alignment involving Brazilian Portuguese, in this research we implemented five well-known automatic sentence alignment methods, including empirical, linguistic and hybrid techniques, and evaluated them as applied to Brazilian Portuguese-English parallel texts. The results are in accordance with those reported for other pairs of languages, even in that highest precisions (above 94%) were obtained for corpora without noise (i.e. grammatical or translation errors), as expected. Furthermore, the results point to a virtual tie between the methods, it being impossible to elect one as the best. In addition to the implementations of those methods and the parallel corpora built to evaluate them, other linguistic and computational resources were built during this work which are of great value to PLN research.
Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization

The information contained within Call Details records (CDRs) of mobile networks can be used to study the operational efficacy of cellular networks and behavioural pattern of mobile subscribers. In this study, we extract actionable insights from the CDR data and show that there exists a strong spatiotemporal predictability in real network traffic patterns. This knowledge can be leveraged by the mobile operators for effective network planning such as resource management and optimization. Motivated by this, we perform the spatiotemporal analysis of CDR data publicly available from Telecom Italia. Thus, on the basis of spatiotemporal insights, we propose a framework for mobile traffic classification. Experimental results show that the proposed model based on machine learning technique is able to accurately model and classify the network traffic patterns. Furthermore, we demonstrate the application of such insights for resource optimisation.