Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer Based Behavioral Biometric Authentication via Multi-Modal Fusion

Abstract : Biometric computer authentication has an advantage over password and access card authentication in that it is based on something you are, which is not easily copied or stolen. One way of performing biometric computer authentication is to use behavioral tendencies associated with how a user interacts with the computer. However, behavioral biometric authentication accuracy rates are much larger then more traditional authentication methods. This thesis presents a behavioral biometric system that fuses user data from keyboard, mouse, and Graphical User Interface (GUI) interactions. Combining the modalities results in a more accurate authentication decision based on a broader view of the user's computer activity while requiring less user interaction to train the system than previous work. Testing over 30 users, shows that fusion techniques significantly improve behavioral biometric authentication accuracy over single modalities on their own. Two fusion techniques are presented, feature fusion and decision level fusion. Using an ensemble based classification method the decision level fusion technique improves the FAR by 0.86% and FRR by 2.98% over the best individual modality.
Generating IDS Attack Pattern Automatically Based on Attack Tree

Generating attack pattern automatically based on attack tree is studied. The extending definition of attack tree is proposed. And the algorithm of generating attack tree is presented. The method of generating attack pattern automatically based on attack tree is shown, which is tested by concrete attack instances. The results show that the algorithm is effective and efficient. In doing so, the efficiency of generating attack pattern is improved and the attack trees can be reused.
XDL-Based Hard Macro Generator

XDL Based Hard Macro Generator Subhrashankha Ghosh Department of Electrical and Computer Engineering Master of Science In a conventional hardware design flow, the compilation process to create the physical circuit on the FPGA takes a long time. HMFlow is a design flow that reduces the compilation time by using pre-compiled modules called hard macros. HMFlow uses System Generator to create the designs, which are then converted to hard macros. The hard macro creation process takes a long time and a possible solution is a hard macro generator called XdlCoreGen, which is described in this thesis. XdlCoreGen can quickly create fully mapped and placed hard macros using XDL. XDL is a human readable design format that describes an FPGA and can be manipulated to configure the FPGA. XdlCoreGen also provides a framework to configure a Xilinx Virtex4 FPGA using XDL. In addition to XdlCoreGen, this thesis also describes the FPGA configuration methodology using XDL. This thesis also describes a cache based router, where instead of finding a route, a pre-generated route is used to route the hard macros generated by XdlCoreGen. This thesis also presents test results using XdlCoreGen. However, the main focus of this thesis will be the speed of hard macro generation by XdlCoreGen.
The framework of image recognition based on modified freeman chain code

Image recognition of line drawing involves feature extraction and feature comparison; works on the extraction required the representation of the image to be compared and analysed. Combining these two requirements, a framework that implements a new extraction algorithm of a chain code representation is presented. In addition, new corner detection is presented as pre-processing to the line drawing input in order to derive the chain code. This paper presents a new framework that consists of five steps namely pre-processing and image processing, new corner detection algorithm, chain code generator, feature extraction algorithm, and recognition process. Heuristic approach that is applied in the corner detection algorithm accepts thinned binary image as input and produces a modified thinned binary image containing J characters to represent corners in the image. Using the modified thinned binary image, a new chain code scheme that is based on Freeman chain code is proposed and an algorithm is developed to generate a single chain code series that is representing the line drawing input. The feature extraction algorithm is then extracts the three pre-defined features of the chain code for recognition purpose. The features are corner properties, distance between corners, and angle from a corner to the connected corner. The explanation of steps in the framework is supported with two line drawings. The results show that the framework successfully recognizes line drawing into five categories namely not similar line drawing, and four other categories that are similar but with attributes of rotation angle and scaling ratio.
NOVEL DIGITAL CONTROLLER FOR MULTI FULL-BRIDGE DC/DC CONVERTER

Distributed generation that utilizes 5-10kW Solid Oxide Fuel Cells requires power electronics to optimize the overall system efficiency while reducing the cost. The Adaptive Energy Zero-Voltage-Switching Phase-Shift-Modulated Full-Bridge (AE-ZVSPSM-FB) topology meets these criteria under all loading conditions, but suffers from complexity associated with an analog control implementation. This thesis presents a novel Look-Up-Table (LUT) based digital controller required for such converter. The applied design approach also reduces the design time and controller requirements, which in turn decreases the overall system cost. Steady-state analysis for the AE-ZVS-PSM-FB converter is performed using a piece-wise equivalent circuit model. This analysis is used to verify the LUT concept that forms the basis for the proposed LUT-based digital controller. The proposed LUT-based digital control algorithm is developed and verified using Field Programmable Gate Array (FPGA) Logic platform. Design procedures and operational function under steady state and step change conditions are presented. Simulation results demonstrate the LUT concept in the AE-ZVS-PSM-FB converter, and the simplicity of the proposed LUT-based digital controller in producing the expected switching sequence. Simulation results were also produced showing successful dynamic response of LUT-based digital controller interconnected with the converter under different operating conditions. A Xilinx FPGA demonstration board was used to generate experimental switching sequence results to demonstrate the simplicity of the proposed controller.
Pragmatic strategies in Catalan and English complaints : A comparative study of native and EFL speakers

L'A. propose une etude comparative des strategies utilisees par des locuteurs catalans et des locuteurs anglais pour exprimer un mecontentement en anglais. Il s'agit de determiner • les formules semantiques utilisees, • les situations ou le locuteur reste silencieux, • s'il existe un transfert pragmatique du comportement verbal chez les locuteurs catalans
Multimodal Medical Image Fusion Under Redundant Transforms

Multimodal medical image fusion gives the complementary information present in each imaging sensor combined into a single image. This is done to reduce the data, better visualization and clinical diagnosis. In this paper, an efficient fusion approach based on redundant transforms, which, in particular, restores and shift-invariant the image as fused image contains artifacts when reconstructed. Different wavelets like Daubechies, orthogonal, Biorthogonal, Symlets and Curvelets are compared with the proposed technique. Medical image fusion results are determined using subjective and objective analysis with the existing state of the art techniques which include wavelets and curvelets. The comparative analysis of the obtained fusion results are performed with RMS error, Entropy, Correlation coefficient, PSNR, Standard deviation, Spatial Frequency and Median frequency. The combined subjective and objective evaluation of the proposed fusion method shows the accuracy and increased robustness.
Consensus in Communication Networks under Bayesian Updating

This paper studies the evolution of beliefs of a group of Bayesian updaters who are connected through a social network that enables them to listen to the opinions of others. Each agent observes a sequence of private signals about the value of an unknown parameter. In addition, the agent receives private messages from others according to her connections in the network. A message conveys some information about the signal observed by the sender. Both signals and messages are independent and identically distributed across time but not necessarily across agents. Messages cannot be transmitted through indirect connections in the network. We first characterize the long-run behavior of an agent's beliefs in terms of the relative entropies of the conditional distributions of signals and messages available to the agent. Then, under some mild assumptions on the distributions of signals and messages, we identify a condition under which the agents reach a consensus in their opinions even when they begin with different priors. Finally, in contrast with most results in the existing literature, we show that a consensus need not be reached in a strongly connected network.
Metabolic Network Alignments and their Applications

The accumulation of high-throughput genomic and proteomic data allows for the reconstruction of the increasingly large and complex metabolic networks. In order to analyze the accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks. But even finding similar networks becomes computationally challenging. The dissertation addresses these challenges with discrete optimization and the corresponding algorithmic techniques. Based on the property of the gene duplication and function sharing in biological network, we have formulated the network alignment problem which asks the optimal vertex-to-vertex mapping allowing path contraction, vertex deletion, and vertex insertions. We have proposed the first polynomial time algorithm for aligning an acyclic metabolic pattern pathway with an arbitrary metabolic network. We also have proposed a polynomial-time algorithm for patterns with small treewidth and implemented it for series-parallel patterns which are commonly found among metabolic networks. We have developed the metabolic network alignment tool for free public use. We have performed pairwise mapping of all pathways among five organisms and found a set of statistically significant pathway similarities. We also have applied the network alignment to identifying inconsistency, inferring missing enzymes , and finding potential candidates. INDEX WORDS: Network alignment, Graph pattern matching
A new Ranking Technique for Integration among Higher-Level and Lower-Level Domain Ontologies and its Application to the Electromagnetic Domain

Nowadays ontologies represent a largely adopted information codification technique in many knowledge domains. Complex ontological frameworks have been developed in diverse areas, presenting the association of different levels of logic and semantic abstraction. These structures gather general contents (Domain Ontologies) and integrate them with more specific concepts (Subdomain and Application Ontologies). However, integration procedures may bring about complex issues such as semantic overlaps and knowledge base modifications. In order to minimize the occurrence of such events, an accurate selection and evaluation phase is advisable. In this paper we propose a methodology to evaluate higher-level domain ontologies in order to determine which candidate ontology would perform better if integrated with lower-level ones. The methodology is based on the computation of a set of multi-purpose ad-hoc metrics that are used as evaluation criteria in a multi-decisional ranking process. The methodology is applied to a real-life integration case study in the Electromagnetic knowledge domain. Two well-known scientific ontology frameworks are selected and evaluated in order to determine their suitability to provide a mid-level to proprietary Electromagnetic ontologies.
Films mésoporeux hybrides organiques-inorganiques : synthèse, organisation des pores et application en optique ophtalmique.

Le premier chapitre de ce manuscrit expose les differentes voies d'elaboration de films poreux par le procede sol-gel et montre les avantages apportes par l'utilisation de couches mesoporeuses structurees par des tensioactifs. Ainsi, tout au long de ce travail, nous nous sommes attaches a preparer des films les mieux ordonnes possible. Par ailleurs, nous avons choisi des procedes d'elaboration compatibles avec la faible stabilite thermique des substrats ophtalmiques organiques. La partie suivante decrit la synthese de films de silice mesoporeuse presentant des morphologies poreuses variees et le choix, parmi l'ensemble de ces structures, de celles dont la robustesse et la porosite finale conviennent pour l'elaboration de couches a bas indice de refraction. Le troisieme chapitre a pour objectif la preparation de couches dont le vieillissement est limite. Cet objectif s'inscrit dans le cadre plus general de la synthese de couches mesoporeuses les plus hydrophobes et les mieux organisees possible. Cette etude aboutit sur l'elaboration d'un anti-reflets integrant une couche mesoporeuse, deposee sur un empilement ophtalmique reel. Le systeme ainsi realise est caracterise en termes de performances optiques et de tenue mecanique.
Example-based Outlier Detection with Relevance Feedback

Detecting outliers is an important, but tricky problem, since the preference of outlier-ness often depends on the user and/or the dataset. Existing techniques traditionally detect outliers based on some prescribed definitions of outlier. However, it is very difficult for a user to decide the definition of outliers in prior. We have developed a system of detecting outliers that match users’ intentions implied by outlier examples in prior work. In this paper, we propose a new refined method of interactive outlier detection adaptive to users’ intentions. Our experiments on both synthetic and real datasets show that the new interactive method surpass the former method in detecting outliers that match the users’ intentions.
Introduction to computer science and programming

Organization of sequential, digital machines: CPU, I/O, storage, communications devices. Function of operating systems: translators, editors, peripheral control, utilities. The development, description and analysis of elementary algorithms. The goals for the course include an introduction to the concepts of programming in the Java language, elementary algorithms, top-down techniques of program construction, and include an introduction to operating systems, editors, translators, and debuggers. An introduction to Secure Coding Practices and Conncepts – Federal and CERT guidance
Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software

The book aims to introduce the reader to DEA in the most accessible manner possible. It is specifically aimed at those who have had no prior exposure to DEA and wish to learn its essentials, how it works, its key uses, and the mechanics of using it. The latter will include using DEA software. Students on degree or training courses will find the book especially helpful. The same is true of practitioners engaging in comparative efficiency assessments and performance management within their organisation. Examples are used throughout the book to help the reader consolidate the concepts covered. Table of content: List of Tables. List of Figures. Preface. Abbreviations. 1. Introduction to Performance Measurement. 2. Definitions of Efficiency and Related Measures. 3. Data Envelopment Analysis Under Constant Returns to Scale: Basic Principles. 4. Data Envelopment Analysis under Constant Returns to Scale: General Models. 5. Using Data Envelopment Analysis in Practice. 6. Data Envelopment Analysis under Variable Returns to Scale. 7. Assessing Policy Effectiveness and Productivity Change Using DEA. 8. Incorporating Value Judgements in DEA Assessments. 9. Extensions to Basic DEA Models. 10. A Limited User Guide for Warwick DEA Software. Author Index. Topic Index. References.
Gerusalemme: La collina del Golgota prima della costruzione delle fabbriche cristiane

L’analisi della Fabbrica del Santo Sepolcro di Gerusalemme (2007–2009), iniziata in occasione del rilievo per la valutazione del rischio sismico del complesso architettonico gerosolimitano, ha evidenziato la necessita di approfondire le conoscenze del luogo, il Golgota, nelle sue fasi piu antiche. Lo studio della geomorfologia di superficie e la verifica dei dati archeologici hanno suggerito alcune osservazioni circa l’evoluzione dell’impianto della chiesa dal IV secolo in poi, con elementi di approfondimento delle sue prime forme. L’analisi e la messa in relazione dei dati, desunti dai precedenti saggi di scavo e dalle recenti indagini effettuate, hanno dato la possibilita di avanzare alcune ipotesi ricostruttive del sito dalle sue prime frequentazioni fino alla costruzione della prima basilica cristiana intrapresa da Costantino. Sono, quindi, stati elaborati alcuni modelli tridimensionali del sito, in funzione della sua prima destinazione a cava di materiale lapideo e delle modificazioni successive volute da Adriano con la costruzione del tempio pagano realizzato dopo la terza guerra giudaica.
Unified behavior framework in discrete event simulation systems

Intelligent agents provide simulations a means to add lifelike behavior in place of manned entities. When implemented, typically a single intelligent agent model (or approach to defining decision making), such as rule-based, behavior trees, neural networks, etc., is selected. This choice introduces restrictions into what behaviors agents can manifest, and can require significant testing in edge cases. This paper presents the incorporation and application of the Unified Behavior Framework (UBF) into the Advanced Framework for Simulation, Integration, and Modeling environment. The UBF provides the flexibility to implement any and all behavior-based systems, allowing the developer to rapidly assemble a decision making agent that leverages multiple paradigms or approaches. The UBF achieves this by leveraging several key software engineering principles: modular design, scalability through reduced code complexity, simplified development and testing through abstraction, and the promotion of code reuse. The use of UBF to define intelligent agents within a 2v2 Integrated Air Defense System is demonstrated.
Swarm Based Fault Tolerant Routing in MPLS Networks

In MPLS/GMPLS networks, during fault prevalence, heavy data loss occurs within minimum duration of time. Also it is difficult to handle these faults as it may cause critical network survivability issue. Most of the existing literature works estimates the failure probability, bandwidth and delay of all routes using the traditional shortest path algorithm which leads to huge delay and overhead. In order to overcome these issues, in this paper, we propose ant colony optimization (ACO) algorithm for routing in GMLPS/MPLS. In this technique, swarm intelligence based fault tolerant routing is performed. This involves the selection of optimal path with maximum available bandwidth and minimum delay. In order to enhance the security of MPLS network, the threshold secret sharing mechanism is considered. By simulation results, we show that the proposed approach minimizes the delay and route failures while increasing the bandwidth utilization and throughput.
Automatic performance monitoring tools

The LRDE has been mainly developing, for more than five years, two libraries dedicated to scientific computing in the field of images and automata processing. These two libraries aim at being both generic and efficient. The generic aspect has been ensured, since the beginning, by a good test suite to avoid functional regression. However, the performance of these libraries has decreased. As a solution, we have built some tools to automatically track the performances. This report introduces the tools set up to gather any kind of measures. It also describes the tools used to draw chart based on the data.
Exercise and Type 2 Diabetes

Although physical activity (PA) is a key element in the prevention and management of type 2 diabetes mellitus (T2DM), many with this chronic disease do not become or remain regularly active. High-quality studies establishing the importance of exercise and fitness in diabetes were lacking until recently, but it is now well established that participation in regular PA improves blood glucose control and can prevent or delay T2DM, along with positively affecting lipids, blood pressure, cardiovascular events, mortality, and quality of life. Structured interventions combining PA and modest weight loss have been shown to lower T2DM risk by up to 58% in high-risk populations. Most benefits of PA on diabetes management are realized through acute and chronic improvements in insulin action, accomplished with both aerobic and resistance training. The benefits of physical training are discussed, along with recommendations for varying activities, PA-associated blood glucose management, diabetes prevention, gestational diabetes, and safe and effective practices for PA with diabetes-related complications.
Using Information Extraction and Text Classification in an Effort to Support Systematic Literature Reviews

Systematic literature reviews are an important tool in Evidence-based Software Engineering, but require a large amount of effort and time from the researchers. Data extraction is an important step in these reviews, but current practice requires the researchers to manually extract large amounts of data. This thesis investigates the possibility of developing a prototype for automatic extraction, so to reduce the time spent on manually extracting this data. By reviewing related research, and experimenting with different features and machine learning models, two different models were implemented in the prototype: Conditional Random Fields for information extraction and Maximum Entropy for text classification. The models achieved average F1 performance score of 67.02% and 73.82%, respectively. These results can be characterized as good results, and show that it is possible to automate the data extraction process, by annotating a small part of the dataset and training machine learning models to perform the extraction.
Optimization System of Block Division Using Genetic Algorithm and Product Model

Block construction method is generally used in modern shipyards. In this method, interim products are built by assembling parts, and the ship is built from these interim products. This process can be referred as “Assembling Hierarchy” in this paper. In order to construct the ship effectively, an appropriate Assembling Hierarchy plan which considering the configurations of interim products is required. So in this paper, an optimization system of ship Assembling Hierarchy using Genetic Algorithms (GA) and Product Model is discussed. In order to realize the optimization using GA, it is necessary to define the chromosomes and to examine the GA operations. Therefore, Joint Hierarchy Chromosome is newly introduced. Joint hierarchy chromosome is genotype or encoded solution of Assembling Hierarchy. In this chromosome, the level of Assembling Hierarchy on each joint is set as design variable. By using this chromosome, various plans of ship Assembling Hierarchy can be generated. Effective GA operations such as selection, crossover and mutation are also shown. Moreover, proposed system is integrated with Product Model. Therefore, fitness with penalty of the plan is calculated by the use of Product Model data. Finally, an example of the optimization is shown in the paper to confirm the validity of the developed method.