Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Machine Translation of the Arabic Language

La traduction automatique de texte arabe a recu beaucoup d'attention au cours de la derniere decennie. La langue arabe, langue officielle de plus de 25 pays, est parlee par plus de 290 millions de personnes. Les changements politiques engendres par les revolutions arabes ont mis sur le devant de la scene cette langue et ses multiples dialectes. Ce travail s'inscrit dans le cadre du projet BOLT dont le but est d'ameliorer les performances des systemes de traduction arabe-anglais pour des domaines specifiques (SMS, parole conversationnelle, etc.)Dans cette these, j'ai enrichi le systeme de traduction a base de segments du LIUM a maints egards. Les systemes a base de segments fournissent actuellement les meilleures performances. Ces systemes sont bases sur deux modeles statistiques : le modele de traduction et le modele de langage. Dans l'objectif d’ameliorer la qualite de traduction de l'arabe, nous avons mis l'accent sur trois aspects. Le premier aspect est la reduction des mots inconnus dans la sortie de traduction. Le second aspect de mon travail de these est l'adaptation au domaine ou a la tâche de la table de traduction. Finalement, je me suis interesse a l'amelioration de la modelisation linguistique avec des reseaux de neurones. Ces modeles sont utilises pour re-evaluer les n-meilleures hypotheses de traduction.Toutes les techniques developpees ont ete minutieusement incorporees dans le systeme du LIUM et evaluees dans trois campagnes d’evaluation internationales dans le cadre du projet BOLT.
A hypothesis verification method using a regression tree for semiconductor yield analysis

Several researchers have reported on regression tree analysis for semiconductor yield. However, the scope of these analyses is restricted by the difficulty involved in applying regression tree analysis to a small number of samples with many attributes. It is often observed that splitting attributes in the root node do not indicate the hypothesized causes of a failure. We propose a method for verifying the hypothesized causes of a failure, which reduces the number of verification hypotheses. This method involves selecting sets of analysis data with the same cause of failure, extracting the hypothesis by applying regression tree analysis separately to each set of analysis data, and merging and sorting the attributes according to the t value. The results of an experiment conducted in a real environment show that the proposed method helps in widening the scope of applicability of regression tree analysis for semiconductor yield. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 183(3): 26–36, 2013; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22334
Optical flow estimation using steered-L1 norm

Motion is a very important part of understanding the visual picture of the surrounding environment. In image processing it involves the estimation of displacements for image points in an image sequence. In this context dense optical flow estimation is concerned with the computation of pixel displacements in a sequence of images, therefore it has been used widely in the field of image processing and computer vision. A lot of research was dedicated to enable an accurate and fast motion computation in image sequences. Despite the recent advances in the computation of optical flow, there is still room for improvements and optical flow algorithms still suffer from several issues, such as motion discontinuities, occlusion handling, and robustness to illumination changes. This thesis includes an investigation for the topic of optical flow and its applications. It addresses several issues in the computation of dense optical flow and proposes solutions. Specifically, this thesis is divided into two main parts dedicated to address two main areas of interest in optical flow. In the first part, image registration using optical flow is investigated. Both local and global image registration has been used for image registration. An image registration based on an improved version of the combined Local-global method of optical flow computation is proposed. A bi-lateral filter was used in this optical flow method to improve the edge preserving performance. It is shown that image registration via this method gives more robust results compared to the local and the global optical flow methods previously investigated. The second part of this thesis encompasses the main contribution of this research which is an improved total variation L1 norm. A smoothness term is used in the optical flow energy function to regularise this function. The L1 is a plausible choice for such a term because of its performance in preserving edges, however this term is known to be isotropic and hence decreases the penalisation near motion boundaries in all directions. The proposed improved L1 (termed here as the steered-L1 norm) smoothness term demonstrates similar performance across motion boundaries but improves the penalisation performance along such boundaries.
Generating Miss Rate Curves with Low Overhead Using Existing Hardware

Generating Miss Rate Curves with Low Overhead Using Existing Hardware Tom Walsh Master of Science Graduate Department of Computer Science University of Toronto 2009 Miss Rate Curves (MRCs) for main memory have been proposed as a representation of memory utilization for use in a range of optimizations in the area of memory management. Various techniques exist for their creation; however, all real-world methods of MRC generation must make trade-offs between overhead and accuracy. Proposals for new hardware techniques exist, but have yet to be implemented in actual hardware. We propose the use of the Intel PEBS (Precise Event-Based Sampling) performance monitoring capability for the task of MRC generation on existing commodity hardware. We use PEBS to generate MRCs and compare them against MRCs generated through instrumentation, finding the PEBS MRCs to be good, but imperfect approximations, while keeping average PEBS overheads below 5%. We were unable to show that PEBS is better or worse than existing techniques, but believe we have succeeded in showing the promise of the use of general purpose performance monitoring hardware for this task and in motivating future research and development in this area.
The flight object-a new flight data management concept for Europe

The drive for greater cost-effectiveness and improved safety/security in an environment of increasing air movements calls for improved availability of accurate and consistent flight data to stakeholder systems. Studies conducted by EUROCONTROL in 2001-3 indicate significant levels of inconsistency between flight data available to Aircraft Operators, Air Traffic Control (ATC), Air Traffic Flow Management (ATFM), Airports and Military systems, causing unnecessary workload, inefficient use of resources and unnecessary delays. EUROCONTROL's new Flight Data Interoperability Concept is intended to resolve this problem. Having passed through the initial feasibility phase, this Concept is now entering the development phase, in which it will become the basis for the development of a draft interoperability standard to be used in Europe for the specifications of new flight data processing systems deployed from 2007 onwards, and potentially to be proposed to the International Civil Aviation Organisation (ICAO) for global standardisation.
Power Components Measurement Using S-ADALINE

This paper presents the design and implementation of the two stage self-synchronized adaptive linear neural (S-ADALINE) network based novel approach for the measurement of electrical power components according to IEEE Standard 1459-2010. In the first stage, the current and voltage signals are processed to extract the harmonic information, whereas in the second stage, the power components are calculated using these results. The simulation results show that the proposed algorithm provides better performance than the conventional ADALINE and Newton type algorithm (NTA). A simple laboratory setup implemented by MATLAB and dedicated hardware is built to verify the performance of the proposed algorithm in real time applications. The framework of laboratory prototype has been given out and the necessary modification in proposed algorithm for on-line implementation has been discussed.
An Empirical comparison of rule based classification techniques in medical databases

Classification techniques have been widely applied in the field of medical databases and have gained a lot of success. At present various classification algorithms are available in the literature and the problem of choosing the best method for a particular data set is faced by many researchers. In this paper, we apply five well-known rule based classification techniques, Decision Tree, JRIP, NNGE, PART and RIDOR, on different medical databases and compare their relative merits & demerits. Subsequently, we interpret their applicability to segment patients into groups.
The Utility of Technacy Genre Theory in Technology Education: A Case Study into Food Technology Teaching

There are many and varied forces that shape school curriculum, but one that is of specific interest discussed in this paper concerns the perception of Food Technology secondary curriculum in Australia. Maintaining and fostering a coherent and accurate perception throughout the food technology career, from school leaver to professional undergraduate studies, is critical for both the evolution of the field of knowledge and the need to keep up with increasing world demand for food technologists and food innovation. Food Technology is a well-established secondary school elective in curriculum offerings, yet a contradiction has emerged between the ‘school view’ of Food Technology and the ‘professional view’ of the same – career pathways are confused due to the use of identical labelling to describe two different practices, causing a significant problem for the food industry profession. With both the school sector and the professional sector each asserting their respective perceptions of Food Technology as correct, a method for clarifying and classifying the nature of the disjuncture between the two claims has been illusive. This paper asserts that at the heart of the problem was the lack of a theoretically valid and reliable framework that may help clarify and articulate exactly what form of technology capability is being taught in secondary schooling according to current curriculum. The research reported here draws on an empirically tested framework – Technacy Genre Theory. The framework offers an indexing system that can define the nature of the degree of agreement between two forms of technological practice. The research confirmed that the label of ‘Food Technology’ is perceived significantly and substantially different between schoolteachers and the wider, relevant food profession. The paper concludes with the proposition that Technacy Genre Theory offers a new method for comparing and clarifying many combinations of technological typologies of practice.
New similarity measures for automatic short answer scoring in spontaneous non-native speech

This paper focuses on similarity measures about automatic short answer scoring in spontaneous non-native speech. We propose two new corpus-based measures of text similarity, which are Manhattan similarity measure and keywords coverage rate based on edit distance. Manhattan similarity measure is converted by Manhattan distance. Furthermore, automatic speech recognition (ASR) system and students' pronunciation may lead words' variation including pronunciation and morphology. Improved keywords coverage rate combined edit distance can distinguish students' grade and provide more objective scores. Experiment results show that these similarity measures are effective to our scoring system and performance of scoring system can achieve 96% of it by human raters.
Considering image structural properties while estimating compressed jpeg image quality

В роботі розглядаються питання кількісної оцінки впливу ефекту квантування коефіцієнтів дискретного косинусного перетворення (ДКП) на якість стиснених JPEG зображень. Для оцінки якості зображень використовується показник пікового відношення сигнал/шум, PSNR. Запропонована методика, яка дозволяє, ґрунтуючись на різних статистичних моделях ДКП коефіцієнтів, спрогнозувати величину результуючих спотворень JPEG зображення, спричинених компресією зображення із заданим рівнем якості.
Mining and Analyzing Subjective Experiences in User Generated Content

Chen, Lu. Ph.D., Department of Computer Science and Engineering, Wright State University, 2016. Mining and Analyzing Subjective Experiences in User Generated Content. This dissertation presents a unified framework that characterizes a subjective experience, such as sentiment, opinion, or emotion, in terms of an individual holding it, a target eliciting it, a set of expressions describing it, and a classification or assessment measuring it; it describes new algorithms that automatically identify and extract sentiment expressions and opinion targets from user generated content with minimal human supervision; it shows how to use social media data to predict election results and investigate religion and subjective well-being, by classifying and assessing subjective information in user generated content. Web 2.0 and social media enable people to create, share and discover information instantly anywhere, anytime. A great amount of this information is subjective information – the information about people’s subjective experiences, ranging from feelings of what is happening in our daily lives to opinions on a wide variety of topics. Subjective information is useful to individuals, businesses, and government agencies to support decision making in areas such as product purchase, marketing strategy, and policy making. However, much useful subjective information is buried in evergrowing user generated data on social media platforms, it is still difficult to extract high quality subjective information and make full use of it with current technologies. Current subjectivity and sentiment analysis research has largely focused on classifying the text polarity – whether the expressed opinion regarding a specific topic in a given text is positive, negative, or neutral. This narrow definition does not take into account the other types of subjective information such as emotion, intent, and preference, which may prevent their exploitation from reaching their full potential. This dissertation extends the definition and introduces a unified framework for mining
Assistive System Using Eye Gaze Estimation for Amyotrophic Lateral Sclerosis Patients

Amyotrophic lateral sclerosis (ALS) patients cannot control their muscle except eyes in the later stage of the disease progress. This paper aims to develop an eye-based assistive system that is controlled by the eye gaze to help ALS patients improve their life quality. Two main functions are proposed in this paper. The first one is called HelpCall that can detect the users’ eye gaze to active the corresponding events. ALS patients can “talk” with other people more easily by looking at specific buttons in the HelpCall system. The second one is an eye-control browser that allows the users browsing web pages in Internet. We design an interface that embeds the IE browser into several buttons controlled by the user eye gaze. ALS patients can visit the Internet only using their eyes in our proposed system. This paper discusses our ideas for the assistive system and then describes the design and implementation of our proposed system in details.
A MULTI -PERSPECTIVE EVALUATION OF MA AND GA FOR COLLABORATIVE FILTERING RECOMMENDER SYSTEM

The rising popularity of evolutionary algorithms to solve complex problems has inspired researchers to explore their utility in recommender systems. Recommender systems are intelligent web applications which generate recommendations keeping in view the user’s stated and unstated requirements. Evolutionary approaches like Genetic and memetic algorithms have been considered as one of the most successful approaches for combinatorial optimization. Memetic Algorithms (MAs) are enhanced genetic algorithms which incorporate local search in the evolutionary scheme. Local Search process on each solution after every generation helps in improving the convergence time of MA. This paper presents multi-perspective comparative evaluation of memetic and genetic evolutionary algorithms for model based collaborative filtering recommender system. Experimental study was conducted on MovieLens dataset to investigate the decision support and statistical efficiency of Memetic and genetic algorithms. Algorithms were analyzed from different perspectives like variation in number of clusters, effect of increasing the number of users, varying number of recommendations and using either one or more than one cluster for computing ratings of the unrated items. Results obtained demonstrated that from all perspectives memetic collaborative filtering algorithm has better predictive accuracy as compared genetic collaborative filtering algorithm.
A misclassification cost‐minimizing evolutionary–neural classification approach

Machine learning algorithms that incorporate misclassification costs have recently received considerable attention. In this paper, we use the principles of evolution to develop and test an evolutionary/genetic algorithm (GA)‐based neural approach that incorporates asymmetric Type I and Type II error costs. Using simulated, real‐world medical and financial data sets, we compare the results of the proposed approach with other statistical, mathematical, and machine learning approaches, which include statistical linear discriminant analysis, back‐propagation artificial neural network, integrated cost preference‐based linear mathematical programming‐based minimize squared deviations, linear integrated cost preference‐based GA, decision trees (C 5.0, and CART), and inexpensive classification with expensive tests algorithm. Our results indicate that the proposed approach incorporating asymmetric error costs results in equal or lower holdout sample misclassification cost when compared with the other statistical, mathematical, and machine learning misclassification cost‐minimizing approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.
Modeling Consumer's Trust in Blog based on Virtual Community Characteristics

We examined the formation of trust in blog by potential consumers of virtual community before they visit company online website; this trust belief in blog experience has been under investigated in trust literature. Since company web site is eager to attract blog consumer to its website based on trust in blog based on virtual community, out exploratory study investigated, (1) can consumer’s trust in a blog affect their trust in the virtual community? ; (2) what other factors might be influential on blog trust? A social relations and networks perspective was adopted to explain consumer’s trust during the visit-blog phase. Findings from 379 consumers who visited a blog revealed that loyalty, experience, reputation, and confiding personal information were significant in trust blog. Furthermore, findings showed that feedback and confiding personal information were more influential than trust based on a consumers’ prior experience with the virtual communities.
What are proteins teaching us on fundamental strategies for molecular nanotechnology?

Abstract Besides the fundamental competition between the top-down and bottom-up approaches in nanotechnology, there are some basic aspects for organizing structures and functions at the molecular level. The recent challenges to the development of nanotechnology are marked by a group of general requirements: selection of suited building units, overcoming the restrictions of planar technology, shrinking of nanofabrication facilities, sustainable production and management of life cycles, organization of autonomy and communication at the nano-level, and the optimization of power consumption and energy management. Looking at the natural principles in the construction, synthesis, and function of proteins helps in understanding the principal differences between the currently applied technologies and the characteristics of biomolecular mechanisms in cells. This view allows formulating seven basic rules to meet the general requirements for future developments in molecular nanotechnology.
Automatic Liver segmentation Using Vector Field Convolution and Artificial Neural Network in MRI Images

Accurate liver segmentation on Magnetic Resonance Images (MRI) is a challenging task especially at sites where surrounding tissues such as spleen and kidney have densities similar to that of the liver and lesions reside at the liver edges. The first and essential step for computer aided diagnosis (CAD) is the automatic liver segmentation that is still an open problem. Extensive research has been performed for liver segmentation; however it is still challenging to distinguish which algorithm produces more precise segmentation results to various medical images. In this paper, we have presented a new automatic system for liver segmentation in abdominal MRI images. Our method extracts liver regions based on several successive steps. The preprocessing stage is applied for image enhancement such as edge preserved and noise reduction. The proposed algorithm for liver segmentation is a combined algorithm which utilizes a contour algorithm with a Vector Field Convolution (VFC) field as its external force and perceptron neural network. By convolving the edge map generated from the image with the user-defined vector field kernel, VFC is calculated. We use trained neural networks to extract some features from liver region. The extracted features are used to find initial point for starting VFC algorithm. This system was applied to a series of test images to extract liver region. Experimental results showed the promise of the proposed algorithm.
FACEBOOK VS. MOODLE: WHAT DO STUDENTS REALLY THINK?

In order to evaluate the educational benefits of Facebook, which plays an increasingly important role in students’ social life as well as their academic life, and to compare it with Moodle, we surveyed students on their attitudes toward Facebook and Moodle as productive online tools for teaching and learning. An analysis of the results was carried out using the SPSS software package. The results of our study provide insight on the question of whether using Facebook as an educational tool is more effective than using Moodle and how it affects their everyday learning activities.
Bloom's Taxonomy of Educational Objectives in Information System Courses

The new edition of Bloom's taxonomy of educational objectives has been promoted for about 15 years. It is valuable for teaching reforms and courses construction. We used Bloom's taxonomy in information system courses. We also used web-based instruction in the courses. The six levels of cognitive process in Bloom’s taxonomy were emphasized in class teaching or on course website. And different assessment methods were selected to suit for Bloom’s taxonomy according to the teaching objectives. Our practices show that it is a good approach to combine class teaching and web-based instruction.