Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary-inspired approach to compare trust models in agent simulations

In many dynamic open systems, agents have to interact with one another to achieve their goals. These interactions pose challenges in relation to the trust modeling of agents which aim to facilitate an agent's decision making regarding the uncertainty of the behaviour of its peers. A lot of literature has focused on describing trust models, but less on evaluating and comparing them. The most extensive way to evaluate trust models is executing simulations with different conditions and a given combination of different types of agents (honest, altruist, etc.). Trust models are then compared according to efficiency, speed of convergence, adaptability to sudden changes, etc. Our opinion is that such evaluation measures do not represent a complete way to determine the best trust model, since they do not include testing which one is evolutionarily stable. Our contribution is the definition of a new way to compare trust models observing their ability to become dominant. It consists of finding out the right equilibrium of trust models in a multiagent system that is evolutionarily stable, and then observing which agent became dominant. We propose a sequence of simulations where evolution is implemented assuming that the worst agent in a simulation would replace its trust model with the best one in such simulation. Therefore the ability to become dominant could be an interesting feature for any trust model. Testing this ability through this evolutionary-inspired approach is then useful to compare and evaluate trust models in agent systems. Specifically we have applied our evaluation method to the Agent Reputation and Trust competitions held at 2006, 2007 and 2008 AAMAS conferences. We observe then that the resulting ranking of comparing the agents ability of becoming dominant is different from the official one where the winner was decided running a game with a representative of all participants several times. Since it is a new evaluation method that, as our application to the ART competition showed, gives additional information on the quality of trust models, it would improve the way they are compared. The application of our proposal is not restricted to the ART domain, we suggest that this kind of evolutionary approach has to be taken into account in any evaluation of trust models in agent systems.
Path Planning in the Presence of Dynamically Moving Obstacles with Uncertainty

In this paper, the problem of path-planning with dynamically moving elliptical obstacles is addressed. A new analytical result for computing the axes aligned bounding box for the ellipses with bounded uncertainty in the position of the centre and the orientation is presented. Genetic algorithm is utilised for finding the shortest path from the initial to goal position avoiding the moving obstacles. Defence Science Journal, 2010, 60(1), pp.55-60 , DOI:http://dx.doi. org/10.14429/dsj.60. 107
A Novel Beamforming and Combining Scheme for Two-Way AF Satellite Systems

We propose a novel beamforming and combining scheme for a two-way amplify-and-forward (AF) satellite system. Exchange of the data in between two multiple-antenna-based Earth stations (ESs) via a single-antenna-based satellite is performed. The transmitting ES uses beamforming for transmission of the data, and the receiving ES utilizes the combining vector for detecting the transmitted signal. By using maximum eigenvalue criterion, the beamforming and combining vectors are calculated. The satellite channels (uplink and downlink) are assumed to be shadowed Rician (SR) fading. First, we assume independent and identically distributed (i.i.d.) SR fading channels and derive the probability density function (pdf) of maximum eigenvalue to obtain the expression of moment-generating function (mgf). The closed-form expression for the symbol error rate (SER) of the scheme for $M$ - ary phase-shift keying (MPSK) is obtained by using an mgf-based approach. The diversity order of the studied scheme is also derived by exploiting the asymptotic property of the Meijer G-function. Next, we consider correlated SR fading channels and derive expressions of SER and diversity order. It is shown by simulations that the proposed scheme outperforms the existing beamforming and combining based scheme in two-way AF satellite systems. It is concluded on the basis of the diversity order analysis that the diversity of the proposed scheme linearly depends upon the minimum of the number of antennas of two ESs. Furthermore, it is also shown by simulation and analysis that correlation does not affect the diversity order of the considered scheme.
OpenCL Cryptographic Library

Modern GPUs are devices with very high parallelism for a very low cost. Integer and logic instruction support enable us to use them for many workloads unrelated to rendering. Cryptographic algorithms like AES or Blowfish can benefit from being executed on the system’s GPU. Such execution off-loads work from the main CPU, freeing it to do other tasks on a server system. For bulk encryption and decryption the whole operation can often be faster as well. This thesis describes implementation of an OpenCL library of commonly used ciphers — AES-ECB, AES-CTR, AES-GCM and Blowfish-ECB. Integrations of the library with existing software are included. The library provides abstractions that enable easy implementation of additional ciphers in the future.
Embedding N-Detect Test Set into Controlled Bits Stream

N-detect testing suffers from the drawback with its test set size and test application time. A Build-In Self-Test (BIST) N-detect test patterns generation method based on controlled linear shifter is proposed, which embeds test set into the controlled bits stream and provide high defect coverage with low storage area overhead and test application time. In this method, corresponding N test patterns are generated for each fault, by using the bits which are directly stored in ROM to control the first bit of the linear shifter, dynamically generate the complete N-detect test set. Simulation results for benchmark circuits show that the proposed method with power consumption considering provides considerably lower area overhead, shorter test application time and higher transition-fault coverage compared to other recently-published methods.
Distributed Graph Layout with Spark

This paper presents a novel way to draw very large graphs, especially those too big to fit the memory of a single computer. This new method takes advantage of the recent progress in distributed computing, notably using the Apache MapReduce library called Spark. Our implementation of a force-directed graph drawing algorithm and the way to compute repulsive forces in MapReduce are exhibited. We demonstrate the horizontal scalability of this algorithm and show layouts computed on a Hadoop cluster with our method.
Pre-trained Deep Neural Network using Sparse Autoencoders and Scattering Wavelet Transform for Musical Genre Recognition

Research described in this paper tries to combine the approach of Deep Neural Networks (DNN) with the novel audio features extracted using the Scatter- ing Wavelet Transform (SWT) for classifying musical genres. The SWT uses a sequence of Wavelet Transforms to compute the modulation spectrum coef- ficients of multiple orders, which has already shown to be promising for this task. The DNN in this work uses pre-trained layers using Sparse Autoencoders (SAE). Data obtained from the Creative Commons website jamendo.com is used to boost the well-known GTZAN database, which is a standard bench- mark for this task. The final classifier is tested using a 10-fold cross validation to achieve results similar to other state-of-the-art approaches.
Privacy-driven Design of Learning Analytics Applications - Exploring the Design Space of Solutions for Data Sharing and Interoperability

Studies have shown that issues of privacy, control of data, and trust are essential to implementation of learning analytics systems. If these issues are not addressed appropriately systems will tend to collapse due to legitimacy crisis, or they will not be implemented in the first place due to resistance from learners, their parents, or their teachers. This paper asks what it means to give priority to privacy in terms of data exchange and application design and offers a conceptual tool, a Learning Analytics Design Space model, to ease the requirement solicitation and design for new learning analytics solutions. The paper argues the case for privacy-driven design as an essential part of learning analytics systems development. A simple model defining a solution as the intersection of an approach, a barrier, and a concern is extended with a process focussing on design justifications to allow for an incremental development of solutions. This research is exploratory of nature, and further validation is needed to prove the usefulness of the Learning Analytics Design Space model.
Building a Paraphrase Corpus Based on Class-oriented Candidate Generation

語彙・構文的言い換えの中には, 形態・構文的パターンに基づいて一括りにできるものの, 表現を構成する語の統語・意味的な特性に依存して言い換えの可否や言い換え方が決まる現象が少なくない. 本論文では, そのような言い換えを語彙構成的言い換えと呼ぶ. たとえば, 複合語を構成語に分解するような言い換え, 機能動詞構文の言い換え, 態や格の交替, 種々の動詞交替, 語彙的派生などは語彙構成的言い換えの範疇に含まれる. 我々は現在, これら語彙構成的言い換えに関わる語の統語・意味的な特性を明らかにするため, および言い換え生成技術の定量的評価のために, 個々の言い換えクラスごとに言い換え事例集 (言い換えコーパス) を構築している. 本論文では, 言い換え前後の表現の形態・構文的パターンと既存の言い換え生成システムを用いて言い換え事例を半自動的に収集する手法について述べる. また, 日本語の機能動詞構文の言い換え, 動詞の自他交替を対象とした予備試行の結果を報告する
Relación Estructural entre la Enterobacteria Escherichia Coli y el Sistema Operativo Linux Structural Relationship between Enterobacter Escherichia Coli and the Linux OS

RESUMEN Los sistemas operativos son elementos inherentes a cualquier plataforma computacional; estos permiten la gestion de recursos de hardware y software que manipula el usuario. En particular, el sistema operativo Linux, en su kernel y en la shell, busca organizar su estructura interna de tal manera que facilite la comprension e incremente la portabilidad y extension con el fin de favorecer su mantenimiento. Por consiguiente, frente a esta estructura, bacterias como la Escherichia Coli presentan cierta similitud con respecto a la funcionalidad, pero mejor adaptada, debido a la dinamica interna que presenta su ADN, minimizando los problemas rutinarios que se presentan en un computador. Palabras Claves: Sistema operativo, Linux, E. Coli.
Survey on Background Identification Using Various Algorithms

— The background identification is one of the important task in determining the moving object. The tracking of moving object and persons is commonly used in traffic monitoring. The common method for real-time segmentation of moving regions in image sequences involves background subtraction or thresholding. This paper discuss about various algorithms used for identifying the background. This results in a stable, real time outdoor tracker which reliably deals with changes in the lighting, repetitive motions from clutter, and changes in long-term scene.
Power Proficient Data Gathering in Wireless Sensor Networks Using Huddling and Prediction

- The data gathering in sensor network is done periodically to mine the unprocessed data readings. This data gathering which makes the data analysis composite. From the wireless sensor networks users are in need to mine the data constantly from the network, in this case exacting the data is not easy-and it is very expensive. It is important to frame a new data gathering scheme by integrating the huddling and predictions techniques. A power proficient for huddling based data gathering in wireless sensor network Scheme incorporates the active/inactive prediction techniques is proposed. In the group of sensor nodes ,a node called huddle head is represented as to collect a data values for readings. To apply this techniques very efficiently in WSN, The framed algorithms are used to utilize the advantages of active/inactive prediction techniques. In this frame work the designed algorithm is more adequate to have sophisticated features with slumber/alert schedule . It avoids the uncontrolled data transmission among node-node, but rather than it apply a faster ,added efficient huddle -to huddle transmission
GeoTime Visualization for Peace Support Operations

Visualization display techniques allow people to see, use and interact with large amounts of multidimensional data in a natural and easy manner, particularly in complex, time sensitive situations. Information visualization techniques amplify cognition by increasing human mental resources, reducing search times, improving recognition of patterns, increasing inference making, and increasing monitoring scope [1, 6]. These benefits translate into significant system and task related performance objectives [7].
Algorithmic Advances in Allocation and Scheduling

We design new and improved approximation algorithms for classical problems in machine scheduling and resource allocation. In the machine scheduling problem we consider, we are given a set of jobs J with processing times pj for each j ∈ J. Further, a job j may be scheduled only on some subset of machines Sj ⊆ M. The goal is to find an assignment of jobs to machines to minimize the time by which all jobs can be processed. The input for the resource allocation problem we consider is identical after treating the jobs as resources and machines as players respectively. The goal, however, is different: we want to assign resources to the players while ensuring that every player receives resources totaling value at least τ for the largest possible τ. This corresponds to maximizing a natural notion of fairness. Our techniques for improving the state of the art for both problems are versatile. We demonstrate their applicability in developing an efficient hypergraph generalization of the Hungarian algorithm for finding perfect matchings in bipartite graphs.
Applications of Semantic Web Technologies for Web-based ITS

This chapter emphasizes integration of the Semantic Web technologies in intelligent learning systems by giving a proposal for an intelligent learning management system (ILMS) architecture we named Multitutor. This system is a Web-based environment for the development the e-learning courses and for the use of them by the students. Multitutor is designed as a Web-classroom client-server system, ontologically founded, and is built using modern intelligent and Web-related technologies. This system enables the teachers to develop tutoring systems for any course. The teacher has to define the metadata of the course: chapters, the lessons and the tests, the references of the learning materials.
DEVELOPMENTS IN TEACHER KNOWLEDGE FOR TEACHING ALGEBRA

The growth of deep and well-connected mathematical understanding by students is an important goal for instruction. It is widely accepted that teachers need to bring a repertoire of robust content and pedagogical content knowledge of their own to the design and delivery of such instruction (Ball, 1990; Hill & Ball, 2004; Shulman, 1986). While this issue has been given considerable attention in instructional studies of primary mathematics, there is limited work in that involves secondary mathematics and the knowledge that supports the teaching of secondary mathematics (Koirala, Johnson & Davis, 2012). Algebra is a key strand of secondary mathematics and it presents numerous challenges to both students and teachers (Chinnappan, 2010; Sultan & Artzt, 2011). Our present study is motivated by the desire to examine teachers’ knowledge for teaching a range of algebra topics and related concepts such as functions and matrices. In the case of matrices, we focused on teacher knowledge that is relevant to supporting students’ ability at a) understanding the dimensions of matrices and b) representing problems with the aid of matrices. The results show that our participating teacher has a strong body of content knowledge of matrices that was necessary to teach students perform operations with a given set of matrices. However, this knowledge was neither sufficiently developed nor pliable in assisting students to convert a real-life problem and solve that problem using matrices. We argue that the latter situation is reflective of the teacher’s pedagogical content knowledge that is developing in this specific area of secondary mathematics.
THE ART AND SCIENCE OF GPU AND MULTI-CORE PROGRAMMING

This paper examines the computational programming issues that arise from the introduction of GPUs and multi-core computer systems. The discussions and analyses examine the implication of two principles (spatial and temporal locality) that provide useful metrics to guide programmers in designing and implementing efficient sequential and parallel application programs. Spatial and temporal locality represents a science of information flow and is relevant in the development of highly efficient computational programs. The art of high performance programming is to take combinations of these principles an d unravel the bottlenecks and latencies as sociate with the architecture for each manufacturer computer system, and develop appropriate coding and/or task scheduling schemes to mitigate or eliminate these latencies.
An in vitro assay for enzymatic studies on human ALG13/14 heterodimeric UDP-N-acetylglucosamine transferase

The second step of eukaryotic lipid-linked oligosaccharide (LLO) biosynthesis is catalyzed by the conserved ALG13/ALG14 heterodimeric UDP-N-acetylglucosamine transferase (GnTase). In humans, mutations in ALG13 or ALG14 lead to severe neurological disorders with a multisystem phenotype, known as ALG13/14-CDG (congenital disorders of glycosylation). How these mutations relate to disease is unknown because to date, a reliable GnTase assay for studying the ALG13/14 complex is lacking. Here we describe the development of a liquid chromatography/mass spectrometry-based quantitative GnTase assay using chemically synthesized GlcNAc-pyrophosphate-dolichol as the acceptor and purified human ALG13/14 dimeric enzyme. This assay enabled us to demonstrate that in contrast to the literature, only the shorter human ALG13 isoform 2, but not the longer isoform 1 forms a functional complex with ALG14 that participates in LLO synthesis. The longer ALG13 isoform 1 does not form a complex with ALG14 and therefore lacks GnTase activity. Importantly, we further established a quantitative assay for GnTase activities of ALG13- and ALG14-CDG variant alleles, demonstrating that GnTase deficiency is the cause of ALG13/14-CDG phenotypes.
Detecting User Emotions in Twitter through Collective Classification

The explosion in the use of social networks has generated a big amount of data including user opinions about varying subjects. For classifying the sentiment of user postings, many text-based techniques have been proposed in the literature. As a continuation of sentiment analysis, there are also studies on the emotion analysis. Due to the fact that many different emotions are needed to be dealt with at this point, the problem gets more complicated as the number of emotions to be detected increases. In this study, a different user-centric approach for emotion detection is considered such that connected users may be more likely to hold similar emotions; therefore, leveraging relationship information can complement emotion inference task in social networks. Employing Twitter as a source for experimental data and working with the proposed collective classification algorithm, emotions of the users are predicted in a collaborative setting.
Optimal Control to Stabilize the Supply of Essential Commodities

This paper discusses the significant role of quantative methods in decision making processes and the ability of these methods to predict results and make available tools to the decision maker to control a critical situation. In this aspect, this paper presents a mathematical model to the consumption of an important commodity in a nation, such a commodity is considered as strategic. Usually, it has strong effects socially and economically. Hence, it has a determinately political impact within a nation. The model is based on the time series methods. Real data of consumption, population, price, and income have been used. The paper presents the methods used to satisfy conditions of ARIMA models and shows results obtained for reliable forecasting. The model is validated and tested, hence, it is used to study the dynamics of the commodity consumption as an output affected by a number of inputs. The stability of the system is analysed and an optimum control algorithm has been developed to optimize stability in consumption and availability of the commodity. The method above has been applied to the commodity of Sugar and the consumption in Sudan. The interaction of variables like the number of population, price of sugar and the level of income with the rate of consumption has been reflected through the predicative model. The model has performed satisfactorily for the purposes of decision making and control.
Morphological segmentation of sport scenes using color information

We propose a new method for segmenting image sequences in sports scenes containing brisk movement. An important feature of this method is the computation of color histograms in the areas for the turf, which is done interactively during rehearsal. Another important feature is automatic morphological segmentation during broadcast. The morphological segmentation consists of two operations: coarse segmentation by binary reconstruction based on the areas detected by thresholding the color histogram, and fine segmentation by watershed transformation with markers. It is shown that this new method achieves accurate segmentation in sport scenes.
A Survey on Face Recognition Techniques

Face recognition is considered as one of the emerging topics in computer vision application. Currently biometric research is mainly focused on automated face recognition in various applications such as surveillance application in various public security measures. Face recognition is a biometric system used to categorize or authenticate a person from a digital image by extracting its features and then distinguishing it, regardless external conditions. This learning version has mainly two motives: first one is the extensive assortment of profitable and regulation implementation claims, second is the convenience of possible skills. This paper is organised as three sections. The first section describes brief introduction to face recognition. The second section types of face recognition and various problems affecting the process and finally third section describes a comparative study of different face recognition methods.
Virtual Learning Environments: Definitions and Singularities

Article presents and discusses different definitions and characteristics of the systems involved in the creation of Virtual Learning Environments found in literature. It also presents particularities and main characteristics of Hypertext Systems, Hypermedia, Hyper video e Multimedia. Key Words: Learning Management Systems (LMS), Content Management Systems (CMS), Hypertext, Hypermedia, Hyper video e Multimedia.
Exoskeleton Robot for Upper Limb Rehabilitation: Design Analysis and Control

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