Miracle-dz/UniFAR-FTUs huggingface dataset:
A K-complementarity Technique for Forming Groups of Tutors in Intelligent Learning Environments

In e-learning environments, tutors perform the main function of tutoring. They follow up learners and answer their assistance requests which require different skills. These requests may not belong to the tutors’ skills and competencies, so collaboration among other tutors is expected. In fact, this collaboration can improve tutors’ skills and provide learners with effective monitoring. It can be permanent and adaptive, according to learners’ needs and requests. Grouping tutors with different skills is the aim of this research. In this paper, a new technique for grouping tutors is presented. The proposed technique is called K-complementarity. It is based on the complementarity of roles that are assigned to tutors. K-complementarity is based on tutor model and aims at obtaining K groups of tutors who have the most of the roles and skills. The proposed technique has been used by a Computer-Supported Collaborative Tutoring (CSCTT) system. This system had been tested in an Algerian University. The obtained results showed that the groups’ members are heterogeneous and the groups are homogeneous. Furthermore, each group had more than 80% of the roles by combining those of its members. So, these results can be considered as acceptable and very encouraging.
A kernel‐based method to determine optimal sampling times for the simultaneous estimation of the parameters of rival mathematical models

When several models are proposed for one and the same process, experimental design techniques are available to design optimal discriminatory experiments. However, because the experimental design techniques are model‐based, it is important that the required model predictions are not too uncertain. This uncertainty is determined by the quality of the already available data, since low‐quality data will result in poorly estimated parameters, which on their turn result in uncertain model predictions. Therefore, model discrimination may become more efficient and effective if this uncertainty is reduced first. This can be achieved by performing dedicated experiments, designed to increase the accuracy of the parameter estimates. However, performing such an additional experiment for each rival model may undermine the overall goal of optimal experimental design, which is to minimize the experimental effort. In this article, a kernel‐based method is presented to determine optimal sampling times to simultaneously estimate the parameters of rival models in a single experiment. The method is applied in a case study where nine rival models are defined to describe the kinetics of an enzymatic reaction (glucokinase). The results clearly show that the presented method performs well, and that a compromise experiment is found which is sufficiently informative to improve the overall accuracy of the parameters of all rival models, thus allowing subsequent design of an optimal discriminatory experiment. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009
Network-Based System for Face Recognition on Mobile Wireless Devices

This paper describes new internet-based face recognition system to be used in portable devices. In contrast to existing systems, which run computationally intensive facerecognition tasks at a mobile terminal shortening its battery lifetime, the proposed system uses mobile device only for image capturing and user-interface. All complex image processing tasks are performed by a remote high-powered network server to achieve robust and real time face recognition. The system is implemented in software and tested on Android-based Sony Tablet-S wireless terminal. According to measurements, it provides face recognition in images of 240x320 pixels in size at 10f/sec rate with very high accuracy. The paper discusses the proposed client-server architecture and the results of its experimental evaluation. Keywordsface recognition, face identification, network-based
A Novel Way to Relate Ontology Classes

The existing ontologies in the semantic web typically have anonymous union and intersection classes. The anonymous classes are limited in scope and may not be part of the whole inference process. The tools, namely, the pellet, the jena, and the protégé, interpret collection classes as (a) equivalent/subclasses of union class and (b) superclasses of intersection class. As a result, there is a possibility that the tools will produce error prone inference results for relations, namely, sub-, union, intersection, equivalent relations, and those dependent on these relations, namely, complement. To verify whether a class is complement of other involves utilization of sub- and equivalent relations. Motivated by the same, we (i) refine the test data set of the conference ontology by adding named, union, and intersection classes and (ii) propose a match algorithm to (a) calculate corrected subclasses list, (b) correctly relate intersection and union classes with their collection classes, and (c) match union, intersection, sub-, complement, and equivalent classes in a proper sequence, to avoid error prone match results. We compare the results of our algorithms with those of a candidate reasoner, namely, the pellet reasoner. To the best of our knowledge, ours is a unique attempt in establishing a novel way to relate ontology classes.
A novel inflatable robot with constant and continuous volume

Inflatable structures are already used in robotics to build stiff and light inflatable links. Moreover, those links are compliant and they can be safely used in robots interacting with critical installations. Nevertheless, until now, linkages with an inflatable structure have always been composed of independent inflatable links separated by joints. But the joints are heavier, less stiff, or less safe than the inflatable links. So in this paper, we present a new design of inflatable robots with joints having similar mechanical characteristics as the links. A first prototype, a model of the joints and some experimental validation will also be presented.
A Flexible Scalable Solution for All-Optical Multifiber Multicasting: SLAM

We previously proposed a mixed-integer linear-programming formulation, solved by CPLEX, which is a state-of-the-art optimization tool, and two heuristic solutions, layered all-optical multicasting algorithm (LAMA) and conservative fiber and wavelength assignment (C-FWA), for all-optical multicasting in wavelength-routed multifiber wavelength-division multiplexed networks with sparse wavelength conversion and light-splitting restrictions. However, LAMA was not suitable for large and dynamic multicasting problems. In this paper, we evaluate and propose scalable layered all-optical multicasting (SLAM), which is a scalable version of LAMA, for any size static or dynamic multicasting problems. We demonstrate that SLAM performs very close to the optimal (the lower bound/CPLEX) and LAMA, and significantly better than the existing work and C-FWA in terms of nearly all metrics, including the session- and group- blocking probabilities (SBPs and GBPs), since it does not separate routing and fiber-wavelength assignment steps as compared to the other candidates like the existing work and C-FWA. Additionally, transmitter-optimized SLAM, which is a version of SLAM that is specifically tuned to minimize the number of transmitters used, spends three times less extra transmitters than SLAM, without a performance loss in other metrics (one extra transmitter for 12 multicast sessions). Therefore, the adjustable parameters of SLAM make it very flexible to balance different objectives.
An investigation into the Pedagogical Trajectories of PGCE Trainees Using Espoused 'Beliefs'

Postgraduate trainee teachers undergo profound ‘shifts’ in their pedagogical understanding and practices through the year that they are taught at a UK Higher Education Institution. This study ‘investigated, in a paired pre–post design, the espoused pedagogical beliefs of three cohorts of PGCE trainees, at two time points – at the onset and toward the end of their studies in a teacher education department in a major HEI in the UK, with corroborative results from a fourth cohort and from a wider set of institutions. Using an adaptation of the ‘practices’ scale of Swan (2006), trainees’ pedagogical beliefs were charted and described on a continuum running from transmissionist to child-centred through answering 25 items, and shifts from pre-course to post course were investigated on two fronts – individually and for each item. Two general principles are represented in the data: trainees seem to either make rather more radical shifts towards child-centeredness (75/117 trainees [64.1%] at an average shift of +0.28) or more slight shifts towards a more teacher centred orientation (37/117 trainees [31.6%] at an average shift of -0.18. The average shift was +0.14 per trainee (from 3.35 to 3.49) - a significant trend towards a greater learner-centrism across the longitudinal study. I am currently studying the pedagogical beliefs of pre-and post-course postgraduate student teachers on the postgraduate Certificate of Education courses, which enable them to teach in the UK; charting shifts in these beliefs (Clarke and Hollingsworth, 2002) to try and understand the key levers for change (Smith, 2012). The beliefs under analysis are communicated through responses to questionnaires given on the day the trainees first arrive at University and again on the final day of the course. The questionnaires consist of 25 statements on English primary teaching practices in which the participants had to express a „belief‟ (scored 1 to 5). Analysis of each
Accurate tag estimation for dynamic framed-slotted ALOHA in RFID systems

Dynamic framed-slotted ALOHA (DFSA) is one of the most popular algorithms to resolve tag collision in RFID systems. In DFSA, it is widely known that the optimal performance is achieved when the frame size is equal to the number of tags. So, a reader dynamically adjusts the next frame size according to the current number of tags. Thus it is important to estimate the number of tags accurately. In this paper, we propose a novel tag estimation method for DFSA. We compare the performance of the proposed method with those of other existing methods. And, simulation results show that our scheme improves the accuracy of tag estimation and the speed of tag identification.
A self-learning musical grammar, or 'associative memory of the second kind'

A context-sensitive generative grammar that learns its production rules automatically from examples and optimizes the length of context for each individual production rule on the basis of conflicts occurring in the source material is described. The grammar has been applied to the generation of new melodic passages and counterpoint according to a certain style. This report describes some of the principal ideas and the work that was done. Music produced by this method generally sounds smooth, continuous, and pleasant. <<ETX>>
The Autopoietic Nature of the "Inner World" A Study with Evolved "Blind" Robots

In this paper we propose a model of anticipatory behavior in robots which lack any sort of external stimulation. It would seem that in order to foresee an event and produce an anticipatory action an organism should receive some input from the external environment as a basis to predict what comes next. We ask if, even in absence of external stimulation, the organism can derive this knowledge from an "inner" world which "resonates" with the external world and is built up by an autopoietic process. We describe a number of computer simulations that show how the be- havior of living organisms can reflect the particular characteristics of the environment in which they live and can be adaptive with respect to that environment even if the organism obtains extremely little information from the environment through its sensors, or no information at all. We use the Evorobot simulator to evolve a population of artificial organisms (software robots) with the ability to explore a square arena. Results indi- cate that sensor-less robots are able to accomplish this exploration task by exploiting three mechanisms: (1) they rely on the internal dynam- ics produced by recurrent connections; (2) they diversify their behavior by employing a larger number of micro-behaviors; (3) they self-generate an internal rhythm which is coupled to the external environment con- straints. These mechanisms are all mediated by the robot's actions.
ON THE LONG TIME BEHAVIOR OF GAS FLOWS BY EVAPORATION FROM A PLANE CONDENSED PHASE BY SHIGERU TAKATA

Evaporation from a plane condensed phase in the case of two species systems is studied on the basis of the kinetic theory of gases, with a special interest in the long time behavior of the mixture of vapors from initial equilibrium states. A standard finitedifference analysis by the use of the Garzó–Santos–Brey model Boltzmann equation is performed, and the long time behavior of the mixture and the condition for steady evaporation are clarified. Conventional gas-dynamic argument developed in [Y. Sone and H. Sugimoto, in Adiabatic Waves in Liquid-Vapor Systems, G.E. A. Meier and P.A. Thompson Eds., Springer, Berlin, 1990, pp. 293–304] is also applied, and the obtained numerical solutions are reported to be covered by the four-fold classification in this reference. It is, however, shown by the same argument that a certain class of the initial equilibrium states is not covered by that classification. The long time behavior of the mixture from this class of initial states is also discussed.
Classification into normal and abnormal breast tissues using NMF and SVM

We present a methodology that uses Nonnegative Matrix Factorization (NMF) for feature extraction from mammogram images. These measures are used as input features for a Support Vector Machine classifier with the purpose of distinguishing tissues between normal and abnormal cases. We compared our results with another popular technique of matrix factorization called Independent Component Analysis (ICA). We obtained better results with NMF, that prove to be a competitive technique as feature extraction and analysis.
A Genome-Wide Tethering Screen Reveals Novel Potential Post-Transcriptional Regulators in Trypanosoma brucei

In trypanosomatids, gene expression is regulated mainly by post-transcriptional mechanisms, which affect mRNA processing, translation and degradation. Currently, our understanding of factors that regulate either mRNA stability or translation is rather limited. We know that often, the regulators are proteins that bind to the 3′-untranslated region; they presumably interact with ribonucleases and translation factors. However, very few such proteins have been characterized in any detail. Here we describe a genome-wide screen to find proteins implicated in post-transcriptional regulation in Trypanosoma brucei. We made a library of random genomic fragments in a plasmid that was designed for expression of proteins fused to an RNA-binding domain, the lambda-N peptide. This was transfected into cells expressing mRNAs encoding a positive or negative selectable marker, and bearing the “boxB” lambda-N recognition element in the 3′-untranslated region. The screen identified about 300 proteins that could be implicated in post-transcriptional mRNA regulation. These included known regulators, degradative enzymes and translation factors, many canonical RNA-binding proteins, and proteins that act via multi-protein complexes. However there were also nearly 150 potential regulators with no previously annotated function, or functions unrelated to mRNA metabolism. Almost 50 novel regulators were shown to bind RNA using a targeted proteome array. The screen also provided fine structure mapping of the hit candidates' functional domains. Our findings not only confirm the key role that RNA-binding proteins play in the regulation of gene expression in trypanosomatids, but also suggest new roles for previously uncharacterized proteins.
The telomeric protein Pot1 from Schizosaccharomyces pombe binds ssDNA in two modes with differing 3′ end availability

Telomere protection and length regulation are important processes for aging, cancer and several other diseases. At the heart of these processes lies the single-stranded DNA (ssDNA)-binding protein Pot1, a component of the telomere maintenance complex shelterin, which is present in species ranging from fission yeast to humans. Pot1 contains a dual OB-fold DNA-binding domain (DBD) that fully confers its high affinity for telomeric ssDNA. Studies of S. pombe Pot1-DBD and its individual OB-fold domains revealed a complex non-additive behavior of the two OB-folds in the context of the complete Pot1 protein. This behavior includes the use of multiple distinct binding modes and an ability to form higher order complexes. Here we use NMR and biochemical techniques to investigate the structural features of the complete Pot1-DBD. These experiments reveal one binding mode characterized by only subtle alternations to the individual OB-fold subdomain structures, resulting in an inaccessible 3′ end of the ssDNA. The second binding mode, which has equivalent affinity, interacts differently with the 3′ end, rendering it available for interaction with other proteins. These findings suggest a structural switch that contributes to telomere end-protection and length regulation.
5-HT2A Gene Variants Moderate the Association between PTSD and Reduced Default Mode Network Connectivity

The default mode network (DMN) has been used to study disruptions of functional connectivity in a wide variety of psychiatric and neurological conditions, including posttraumatic stress disorder (PTSD). Studies indicate that the serotonin system exerts a modulatory influence on DMN connectivity; however, no prior study has examined associations between serotonin receptor gene variants and DMN connectivity in either clinical or healthy samples. We examined serotonin receptor single nucleotide polymorphisms (SNPs), PTSD, and their interactions for association with DMN connectivity in 134 White non-Hispanic veterans. We began by analyzing candidate SNPs identified in prior meta-analyses of relevant psychiatric traits and found that rs7997012 (an HTR2A SNP), implicated previously in anti-depressant medication response in the Sequenced Treatment Alternatives for Depression study (STAR*D; McMahon et al., 2006), interacted with PTSD to predict reduced connectivity between the posterior cingulate cortex (PCC) and the right medial prefrontal cortex and right middle temporal gyrus (MTG). rs130058 (HTR1B) was associated with connectivity between the PCC and right angular gyrus. We then expanded our analysis to 99 HTR1B and HTR2A SNPs and found two HTR2A SNPs (rs977003 and rs7322347) that significantly moderated the association between PTSD severity and the PCC-right MTG component of the DMN after correcting for multiple testing. Finally, to obtain a more precise localization of the most significant SNP × PTSD interaction, we performed a whole cortex vertex-wise analysis of the rs977003 effect. This analysis revealed the locus of the pre-frontal effect to be in portions of the superior frontal gyrus, while the temporal lobe effect was centered in the middle and inferior temporal gyri. These findings point to the influence of HTR2A variants on DMN connectivity and advance knowledge of the role of 5-HT2A receptors in the neurobiology of PTSD.
Semiautomated editing of computed tomography sections for visualization of vasculature

The goal of our work is to help radiologists remove obscuring structures from a large volume of computed tomography angiography (CTA) images by editing a small number of sections prior to three-dimensional (3D) reconstruction. We combine automated segmentation of the entire volume with manual editing of a small number of sections. The segmentation process uses a neural network to learn thresholds for multilevel thresholding and a constraint- satisfaction neural network to smooth the boundaries of labeled segments. Following segmentation, the user edits a small number of images by pointing and clicking, and then a connectivity procedure automatically selects corresponding segments from other sections by comparing adjacent voxels within and across sections for label identity. Our results suggest that automated segmentation followed by minimal manual editing is a promising approach to editing of CTA sequences. However, prerequisites to clinical utility are evaluation of segmentation accuracy and development of methods for resolution of label ambiguity.
Composite Event Speci cation in Active Database Systems : A Petri Nets Approach

Event detection is the rst and the most important step for Event-Condition-Action (ECA) rule execution in active database systems. Composite event detection is not easy for most existing active database systems. In this paper, a Conditional Colored Petri Net model (CCPN) is proposed for composite events speci cation and detection. Composite events are detected by checking composite transition enabling and verifying the temporal condition attached on the transition. On the other hand, rule execution are realized by verifying rule transition enabling and verifying the rule condition attached on the transition. In this way, both composite event detection and ECA rule execution are integrated in the same CCPN model. Furthermore, examples and implementation issues are discussed. Comparisons show that CCPN is a general model for active database system, and it can be used as an independent engine in many active database systems.
Measuring Outcomes for Children with Cerebral Palsy Who Use Gait Trainers

Gait trainers are walking devices that provide additional trunk and pelvic support. The primary population of children using gait trainers includes children with cerebral palsy (CP) functioning at Gross Motor Function Classification System (GMFCS) levels IV and V. A recent systematic review found that evidence supporting the effectiveness of gait trainer interventions for children was primarily descriptive and insufficient to draw firm conclusions. A major limitation identified was the lack of valid, sensitive and reliable tools for measuring change in body structure and function, activity and participation outcomes. Twelve different clinical tools were identified in the systematic review and in this paper we review and discuss the evidence supporting their reliability, validity and clinical utility for use with children using gait trainers. We also describe seven additional clinical measurement tools that may be useful with this intervention and population. The Pediatric Evaluation of Disability Inventory (PEDI) rated highest across all areas at this time. Individualized outcome measures, such as the Canadian Occupational Performance Measure (COPM) and Goal Attainment Scaling and measuring user satisfaction with tools, such as the Quebec User Evaluation of Satisfaction with assistive Technology, show potential for gait trainer outcomes research. Spatiotemporal measures appear to be less useful than functional measures with this intervention and population. All tools would benefit from further development for use with children with CP functioning at GMFCS levels IV and V.