Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Technologies and e-Learning: towards an entity-centric approach for Learning Management Systems

In the past, the application of semantic technologies to educational settings attracted a lot of attention, in particular regarding those approaches and software tools able to enrich, categorize and retrieve learning objects. In this paper we present a different perspective, specifically the evolution of a traditionally engineered learning platform into a semantic-enabled application. The semantic enrichment addresses two issues: a) the unique identification of entities inside the contents and b) adding new features to the platform without refactoring it through the usage of semantic representation of information. We believe that these two modifications are missing elements for semantic learning, and in order to apply these changes, we integrated our Virtual communities platform with the entity-centric tools developed in the Okkam EU-funded project. Okkam tools provide a solution to uniquely identifying entities inside data and contents, specifically using the Entity Name System (ENS), which supplies a persistent identifier, called OKKAMid, to any entity included in the dataset and advanced entity matching methods for detecting the occurrence of the same entity in different contexts. This allows a deeper connection between entities’ information available inside content, inside the platform and outside the platform. Once the entity in the LMS has been profiled, it is possible to connect any content where the different occurrences of the same entity have been used, and also to connect any other content outside the learning platform where that entity has been identified, for example web pages or social networks. Furthermore, we extended a domain ontology (SIOC) for the conceptual representation needs of the application, and created an RDF graph mapped onto the database in order to add new functionalities to our virtual community platform, taking advantage of the inference processes available through a reasoner and substituting some parts of the business logic of the application.
Marmousi2 An elastic upgrade for Marmousi

The original Marmousi model was created by a consortium led by the Institut Francais du Petrole (IFP) in 1988. Since its creation, the model and its acoustic finite-difference synthetic data have been used by hundreds of researchers throughout the world for a multitude of geophysical purposes, and to this day remains one of the most published geophysical data sets. The advancement in computer hardware capabilities since the late 1980s has made it possible to perform a major upgrade to the model and data set, thereby extending the usefulness of the model for, hopefully, some time to come. This paper outlines the creation of an updated and upgraded Marmousi model and data set which we have named Marmousi2.
Algorithms and Architectures for Secure Embedded Multimedia Systems

Embedded multimedia systems provide real-time video support for applications in e tertainment (mobile phones, Internet video websites), defense (video-surveillanc e d tracking) and public-domain (tele-medicine, remote and distant learning, traffic monitoring and management) . With the widespread deployment of such real-time embedded systems, there has been an increas ing concern over the security and authentication of concerned multimedia data. While several (software) algorithms and hardware architectures have b een proposed in the research literature to support multimedia security, these fail to address embedded applic ations whose performance specifications have tighter constraints on computational power and av ailable hardware resources. The goals of this dissertation research are two fold: 1. To develop novel algorithms for joint video compression and encryption . The proposed algorithms reduce the computational requirements of multimedia encryption algorithms. W e propose an approach that uses the compression parameters instead of compresse d bitstr am for video encryption. 2. Hardware acceleration of proposed algorithms over reconfigurable computing platforms such as FPGAs and over VLSI circuits. We use signal processing knowledge to ma ke the algorithms suitable for hardware optimizations and reduce the critical path of circuits using hardware-specific optimizations. The proposed algorithms ensure a considerable level of security for low -power embedded systems such as portable video players and surveillance cameras. These scheme have zero or little compression losses and preserve the desired properties of compressed data in encr ypt d bitstream to ensure secure and scalable transmission of videos over heterogeneous networks.
Human Gait Based Gender Classification Using Various Transformation Techniques

Gender classification such as classifying human face is only challenging for computer, but even hard for human in some cases. In our work a new novel approach is proposed to recognize gender from the face image. Continuous Wavelet Transforms, Discrete Wavelet Transforms, Radon Transforms are used for features selections for each face images of male and female. These selected features will be used to classify the face images of each Gender using Support Vector Machine with Linear Kernel. Our work use ORL database contain 100 images include both Male and Female Gender . The experimental result shows that the proposed approach (Continuous wavelet Transform and Support Vector Machine). Proposed work achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head, hair and back are more discriminative than other components. All the above prove that gait-based gender classification is feasible in controlled environments. We use both static and dynamic data sets ( images & video) for the gender classification process through SVM classifier.
Behavioral Statistics: Logic and Methods

Darlington and Carlson are frank about the organization of their new statistics text, Behavioral Statistics: Logic and Methods. In the opening chapter they include a note to the more mathematically oriented student regarding the subject of organizing statistical methods: "Although mathematicians tend to prefer organization by type of derivation, we have found that most students who do not go on to advanced statistics find that organization according to use is of much more lasting benefit. " Their book, they continue, "is organized primarily for these people" (p. 11). This statement, as much as any of the particulars of their approach, is likely to shape the reactions of many to this book. One purpose of this review is to examine
Fully-connected non-hierarchical decentralized detection networks

Fully connected or 'bantering' decentralized detection networks-that is, networks in which a decision is reached by iterated broadcasting by the constituent sensors of tentative local decisions, these being continually revised on the basis of group opinion-are examined. It is shown that such a network will also converge in an unsupervised manner to a consensus or common decision among all sensors, and that this process occurs very quickly even in large networks. Additionally, a modified structure which, at the expense of slower convergence, reaches the optimal (i.e. centralized) decision is presented. <<ETX>>
Modèles syntaxiques probabilistes non-génératifs

This work deals with models used, or usable in the domain of Automatic Natural Language Processing, when one seeks a syntactic interpretation of a statement. This interpretation can be used as additional information for subsequent treatments, that can aim for instance at producing a semantic representation of the statement. It can also be used as a filter to select utterances belonging to a specific language, among several hypotheses, as done in Automatic Speech Recognition. As the syntactic interpretation of a statement is generally ambiguous with natural languages, the probabilisation of the space of syntactic trees can help in the analysis task : when several analyses are competing, one can then extract the most probable interpretation, or classify interpretations according to their probabilities. We are interested here in the probabilistic versions of Context-Free Grammars (PCFGs) and Substitution Tree Grammar (PTSGs). Syntactic treebanks, which as much as possible account for the language we wish to model, serve as the basis for defining the probabilistic parameters of such grammars. First, we exhibit in this thesis some drawbacks of the usual learning paradigms, due to the use of arbitrary heuristics (STSG DOP model), or to the use of learning criteria that consider these grammars as generative ones (creation of sentences from the grammar) rather than dedicated to analysis (creation of analyses from the sentence). In a second time, we propose new methods for training grammars, based on the traditional Maximum Entropy and Maximum Likelihood criteria. These criteria are instanciated so that they correspond to a syntactic analysis task rather than a language generation task. Specific training algorithms are necessary for their implementation, but traditional algorithms can cope with those models for the task of syntactic analysis. Lastly, we invest the problem of time complexity of syntactic analysis, which is a real issue for the effective use of PTSGs. We describe classes of PTSGs that allow the analysis of a sentence in polynomial complexity. We finally describe a method that enable the extraction of such a PTSG from the set of subtrees of a treebank. The PTSG produced by this method allows us to test our non-generative learning criterium on "realistic" data, and to give a statistical comparison between this criterium and the usual heuristic criterium in term of analysis performance.
Encoding and Decoding Schemes for MIMO System Combined with Convolutional Code

To introduce extra coding gain to Multi-input Multi-output (MIMO) system, how to combine MIMO and convolutional code (CC) was studied in two cases. In the first case, the type of MIMO was limited to pure spatial multiplexing. Then in the second case, the type was extended to the hybrid of spatial multiplexing and transmission diversity. Several encoding-decoding schemes were designed. For each scheme, its advantage and disadvantage were analyzed, and bit error rate performance was evaluated by simulation. Finally the scheme with best performance and acceptable complexity was elected from candidates for each case.
Comparative Study of Virtual Machine Software Packages with Real Operating System

Virtualization allows computer users to utilize their resources more efficiently and effectively. Operating system that runs on top of the Virtual Machine or Hypervisor is called guest OS. The Virtual Machine is an abstraction of the real physical machine. The main aim of this thesis work was to analyze different kinds of virtualization software packages and to investigate their advantages and disadvantages. In addition, we analyzed the performance of the virtual software packages with a real operating system in terms of web services. Web Servers play an important role on the Internet. The response time and throughput for a web server are different for different virtualization software packages and between a real host and a virtual host. In this thesis, we analyzed the web server performance on Linux. We compared the throughput for three different virtualization software packages (VMware, QEMU, and Virtual Box). The performance results clearly indicate that the real machine performance is better than the performance of the virtual machines. VMware has the better performance compared to other virtual software packages.
Synchronizing RTP Packet Reflector

In contemporary Internet the most of the networks have consistent problems with reliable multicast support which is demanded not only by multimedia applications that use such infrastructure for efficient content distribution among clients. We have developed a tool that replicates (or reflects) RTP/UDP traffic from each connected client to all of them and thus it simulates multicast functionality on unicast network at cost of efficiency loss compared to true multicast. Original reflector implementation called rum by Julian Highfield [rum] has been further developed and enhanced by our group [TNC]. When using reflectors data may be sent over one link in multiple copies while in multicast data go over one link in maximum of one copy only. Advantage of reflector based networks lies in independence on underlying infrastructure capabilities and in possible implementation of many interesting features [ICN]. Efficiency drawbacks can be partially eliminated by creating overlay network using tunneling between reflectors. In this report we describe reflector implementation that performs synchronization of multiple reflected RTP packet streams for synchronized, timely, and in-order delivery to connected clients. Examples of applications for such reflector are described at the end of the report.
Improving prediction of customer behavior in nonstationary environments

Customer churn, switching from one service provider to another, costs the wireless telecommunications industry $4 billion each year in North America and Europe. To proactively build lasting relationships with customers, it is thus crucial to predict customer behavior. Machine learning has been applied to churn prediction, using historical data such as usage, billing, customer service, and demographics. However, because customer behavior is often nonstationary, training a model based on data extracted from a window of time in the past yields poor performance on the present. We propose two distinct approaches, using more historical data or new, unlabeled data, to improve the results for this real-world, large-scale, nonstationary problem. A new ensemble classification method, with combination weights learned from both labeled and unlabeled data, is also proposed, and it outperforms bagging and mixture of experts.
ADEPT: a general purpose numerical device simulator for modeling solar cells in one-, two-, and three-dimensions

ADEPT (a device emulation program and toolbox), a numerical simulation code for modeling solar cells composed of a variety of semiconductor materials in one, two, and three spatial dimensions, is described. Material models for silicon, GaAs, AlGaAs, CuInSe/sub 2/, CdTe, CdS, and thin film Si:H have been implemented. One-dimensional simulations can be run easily in a personal computer environment, and recent advances in sparse matrix solvers make it possible to run 2D simulations on small workstations. <<ETX>>
GMDH-type neural networks: with radial basis functions and their application to medical image recognition of the brain

In this paper, the group method of data handling (GMDH)-type neural networks with radial basis functions are proposed. Such networks can automatically organize themselves by using a heuristic self-organization method. In this algorithm, the network architecture can be automatically adjusted according to the complexity of the approximated nonlinear system. The number of hidden layers and the number of neurons in the hidden layers are selected so as to minimize an error criterion defined as Akaike's information criterion (AIC). Furthermore, various types of nonlinear combinations of variables are initially generated in each layer and only the useful combinations are selected by using AIC. In this study, the GMDH-type neural networks with radial basis functions are applied to medical image recognition of the brain. It is shown that this algorithm is simple and useful in medical image recognition of the brain.
Grammar Acquisition and Processing Instruction: Secondary and Cumulative Effects

Research on Processing Instruction has so far investigated the primary effects of Processing Instruction. In this book, the results of a series of experimental studies investigating possible secondary and cumulative effects of Processing Instruction on the acquisition of French, Italian and English as a second language will be presented. The results of the three experiments have demonstrated that Processing Instruction not only provides learners the direct or primary benefit of learning to process and produce the morphological form on which they received instruction, but also a secondary benefit in that they transferred that training to processing and producing another morphological form on which they had received no instruction.
Data association and tracking using hidden Markov models and dynamic programming

The problem of target tracking from distributed sensors in a cluttered environment is addressed. An algorithm that achieves target tracking and target motion analysis is introduced. This technique uses the formalism of hidden Markov models (HMMs) and is based on two successive steps: the first one consists of a spatial fusion of the measurements obtained at a given time, and the second achieves temporal association, thus leading to the target trajectory. This approach basically differs from classic ones (PDA, JPDA, etc.) because it requires no initialization and no a priori hypothesis for target motion. Simulation results are shown in which the multiple target and maneuvering target cases are given particular attention. <<ETX>>
Knowledge Sharing Processes And Tools In U.S.A Primary Health Care: Analysis Of Four Case Studies

AbstractThis paper focuses on knowledge sharing as an important area in the study of knowledge management (KM). The paper's primary goal is to examine how health-care practices employ knowledge sharing tools and processes to the benefit of their daily work processes. In addition, the study seeks to understand how knowledge sharing would contribute to practices' performance. Adopting a qualitative approach, four health care practices were selected from within a research network to represent larger and smaller practice with different performance levels, two high-performing practices (HP) and two low-performing practices (LP), using standard clinical and health care practice measures. Data collection and analysis involved several iterative steps including interviews, field notes of observations. Four case studies were developed reflecting how each practice implemented knowledge sharing tools and processes and how this implementation influenced performance. The study found that all of the four practices manifested and implemented knowledge sharing tools including knowledge artifacts; sharing social tools-meetings and communication channels; and processes including training and apprenticeship and communities of practices; however, the level of implementation varied among the four practices. A comparison of the high-performing and low-performing practices showed that even though the high-performing practices implemented and invested more in knowledge sharing, knowledge sharing processes and tools were not completely integrated throughout the practices including the high performing ones. Moreover, in all practices there was little evidence of encouragement to efficiently use knowledge sharing tools either among staff members or between patients and the practice.
Insight To Glaucomatous Image Classification Using Probabilistic Neural Network

Features within retina images has been analysed to identify efficiently glaucoma in its early stages. Image features like average and energy were used to classify glaucoma. In this paper we extracted the image features using daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters and then classified the images using PNN (Probabilistic neural network Algorithm) and Structural Similarity Measurement. The classified glaucomatous image was later processed using Fuzzy C means and K means algorithm to get exudates of the image. We observed high efficiency glaucomatic feature extraction from the images with high accuracy. Further analysis is underway to propose a method to identify initial phases of glaucoma and the percentage of accuracy. Keywords— daubechies; biorthogonal; glaucomatous image.
High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting

Foreword Preface 1 Introduction 2 Light And Color 3 HDR Image Encodings 4 HDR Image Capture 5 Display Devices 6 The Human Visual System and HDR Tone Mapping 7 Spatial Tone Reproduction 8 Frequency Domain and Gradient Domain Tone Reproduction 9 Image-Based Lighting List of Symbols References Index
A Graph Coloring Approach to Dynamic Slicing of Object-Oriented Programs

Program slicing is a decomposition technique, which produces a subprogram from the parent program relevant to a particular computation. Hence slicing is also regarded as a program transformation technique. A dynamic program slice is an executable part of a program whose behavior is identical, for the same program input, to that of the original program with respect to a variable of interest at some execution position. Dynamic slices are smaller than static slice, which can be used eciently in dierent software engineering activities like program testing, debugging, software maintenance, program comprehension etc. In this dissertation, we present our work concerned with the dynamic slicing of object-oriented programs. We have developed a novel algorithm, which incorporates graph coloring technique to compute dynamic slice of object-oriented programs. But in order to achieve the goal efficiently, we have contradicted the constraints of the traditional graph coloring theory. Moreover, the state restriction of the slicing criterion is taken into consideration, in addition to the dependence analysis. The advantage of our algorithm is that, it is more time ecient than the existing algorithms. We have named this algorithm, as Contradictory Graph Coloring Algorithm (CGCA).
Dynamic Load Generator: Synthesising dynamic hardware load characteristics

In this thesis we proposed and tested a new method for creating synthetic workloads. Our method takes the dynamic behaviour into consideration, whereas previous studies only consider the static behaviour. This was done by recording performance monitor counters (PMC) events from a reference application. These events were then used to calculate the hardware load characteristics, in our case cache miss ratios, that were stored for each sample and used as input to a load regulator. A signalling application was then used together with a load regulator and a cache miss generator to tune the hardware characteristics until they were similar to those of the reference application. For each sample, the final parameters from the load regulator were stored in order to be able to simulate it. By simulating all samples with the same sampling period with which they were recorded, the dynamic behaviour of the reference application could be simulated. Measurements show that this was successful for L1 D$ miss ratio, but not for L1 I$ miss ratio and only to a small extent for L2 D$ miss ratio. We were also able to show that the total convergence time for the regulator could be reduced by using case-based reasoning to select the initial parameters from similar samples.
Systemic text generation as problem solving

From the Publisher: An exploration of a new approach to text generation that interprets systemic grammar as a computational representation. Patten demonstrates that systemic grammar can be easily and automatically translated into current AI-knowledge representations and efficiently processed by the same knowledge-based techniques currently exploited by expert systems. Thus, the fundamental methodological problem of interfacing specialized computational representations with equally specialized linguisticrepresentations can be resolved. The study provides a detailed discussion of the formal model proposed and of a substantial implementation including a relatively large systemic grammar. It represents a fundamental and productive contribution to the literature of text generation.