Miracle-dz/UniFAR-FTUs huggingface dataset:
Contribution à la synthèse et l'optimisation multi-objectif par essaims particulaires de lois de commande robuste RST de systèmes dynamiques. (Contribution to the synthesis and multi-objective particle swarm optimization for robust RST control laws of dynamic systems)

This research focuses on the systematic synthesis and optimization of digital RST structure based controllers thanks to global metaheuristics approaches. The classic and hard problems of closed-loop poles placement and sensitivity functions shaping of RST control are well formulated as constrained multi-objective problems to be solved with proposed metaheuristics algorithms such as NSGA-II, MODE, MOPSO and especially epsilonMOPSO. Two formulations of the metaheuristics-tuned RST problem have been proposed. The first one, which is given in the time domain, deals with the minimization of several performance criteria like the Integral Absolute Error (IAE) and the Maximum Overshoot (MO) indices. These optimal criteria, related primarily to the step response of the controlled plant, are optimized under non-analytical constraints defined by temporal templates on the closed-loop dynamics. In the second approach, a formulation in the frequency domain is retained. The proposed strategy aims to optimize a desired output sensitivity function satisfying H∞ robustness constraints. The use of a suitable fixed parts of the optimized output sensitivity function will provide partial poles placement of the closed-loop dynamics of the digital RST controller. The opposite of such desired sensitivity function will define the associated H∞ weighting filter. The Multi-Objective Particle Swarm Optimization (MOPSO) technique is particularly retained for the resolution of all formulated multi-objective RST control problems. An adaptive grid based MOPSO algorithm is firstly proposed and then improved based on the epsilon-dominance concepts. Such proposed epsilon-MOPSO algorithm, with a good diversity of the provided Pareto solutions and fast convergence time, showed a remarkable superiority compared to the standard MOPSO, NSGA-II and MODE algorithms. Performance metrics, such as generational distance, error rate and spacing, are presented for the statistical analysis of the achieved multi-optimization results. An application to the variable speed RST control of an electrical DC drive is performed, also for the RST position control of a flexible transmission plant with varying loads. Demonstrative simulations Thèse de Doctorat Paris-Est 2016
Work with children with disabilities: The teachers' attitudes towards inclusion

Introduction. The aim of this paper was to determine the attitudes of physical education teachers and primary school teachers towards inclusive physical education in Serbia. Material and Methods. The research included 132 examinees of both genders: 59.09% females and 40.91% males. Data were collected using an anonymous questionnaire with a 4-point Likert scale. The questionnaire dealt with teachers' previous experience, their qualifications for implementation of inclusive physical education, support and additional training for realization of inclusive physical education. Data were processed using Statistical Package for the Social Sciences 20. Normality distribution was tested by Kolmogorov-Smirnov and Shapiro-Wilk test, whilst the differences between groups (sexual dimorphism, environment, professional orientation) were examined using Mann- Whitney and Kruskal-Wallis tests at the significance level of p ≤ 0.05. Results. The findings showed that the average scores were inclined towards positive attitudes to inclusive physical education. The findings also showed that there was no statistically significant gender-related difference of attitudes between physical education teachers and primary school teachers. However, there was a statistically significant difference related to geographical background in terms of qualification levels (p = 0.02) for work with children with disorders and disabilities, as well in terms of additional training (p = 0.02) and professional orientation, since physical education teachers have less experience working with students with developmental disorders. Conclusion. Both physical education teachers and primary school teachers have positive attitudes towards inclusion. Physical education teachers have less experience working with challenged students than primary school teachers, therefore additional training programs may be required.
Trust management and security solutions for vehicular networks. (Gestion de confiance et solutions de sécurité pour les réseaux véhiculaires)

VANETs (Vehicular Ad-hoc Networks) consist of vehicles capable of exchanging information by radio to improve road safety (alerts in case of accidents or in case of abnormal slowdowns, collaborative driving…) or allow internet access for passengers (collaborative networks, infotainment, etc.). Road safety messages exchanged between vehicles may be falsified or eliminated by malicious entities in order to cause accidents and endanger people life. In this thesis, we focus on defining, designing and evaluating a security solution for V2V communications in VANET, to ensure a secure communication and a good level of confidence between the different participating vehicles. Adopting a group-based model, we consider the Trustworthiness evaluation of vehicles participating in VANET and we develop a Trust Model to analyze the behavior of the vehicles in the group while preserving the privacy of the participants and maintaining low network overhead. We then propose a hierarchical and modular framework for Misbehavior Detection and Revocation Management
Extreme learning machine for handwritten Indic script identification in multiscript documents

Abstract. Unlike the conventional feedforward neural network, an emergent learning technique—which we call extreme learning machine (ELM)—provides a generalized performance of neural network with less user intervention and comparatively faster training. We study ELM with five different activation functions, sigmoidal, sine, hard limiter, triangular basis, and radial basis, for handwritten Indic script identification in multiscript documents. To describe scripts, both script dependent and independent features are computed. For validation, a dataset of 3300 handwritten line-level document images (300 samples per script) of 11 official Indic scripts is used. In our study, we observe that the sigmoidal activation function performs the best regardless of the number of scripts used, i.e., script identification cases: biscript, triscript, and multiscript.
Towards a strategy for recognition of collective emotions on social networking sites

Since the emergence of emotional theories and models, which explain individuals feelings and their emotional processes, diverse research areas have shown interest in studying these ideas in order to obtain relevant information about behavior, habits and preferences of people. However, there are some limitations on emotion recognition that have forced specialists to search ways to achieve it on particular cases. This article treats collective emotions recognition case focusing on social networking sites applying a particular strategy, as follow: Firstly, state of art investigation regard emotions representation models in individual and collectives. In addition, possible solutions are provided by computing areas regarding collective emotions problems. Secondly, a collective emotion strategy was designed where it was retrieved a collection of data from Twitter, in which some cleaning and processing steps were applied, in order to keep the expression as purest. Afterward, the collective emotion tagging step arrived, whither based on consensus theory approach, the majority tagged-feelings were grouped and recognized as collective emotions. Finally, prediction step was executed and resided on modeling collective data, wherein one part was supplied into the Machine Learning during training and the other one was served to test the machine accuracy. Thirdly, An evaluation was set to check the fit of the collective recognition strategy, where results obtained allow to place the proposed work in the right path as consequence of minor differences observed, that indicate higher precision according to the distances measures used during the study development.
Lactobacillus plantarum bacteriocin is associated with intestinal and systemic improvements in diet-induced obese mice and maintains epithelial barrier integrity in vitro

ABSTRACT We investigated the Lactobacillus plantarum bacteriocin plantaricin EF (PlnEF) system for its contributions to L. plantarum mediated benefits in a mouse model of diet-induced obesity. C57BL/6J mice on a high-fat diet (HFD) were administered a rifampicin resistant mutant of L. plantarum NCMIB8826 (NICMB8826-R) or an isogenic ΔplnEFI mutant strain, LM0419, every 48 h for nine weeks. Mice fed wild-type L. plantarum, but not LM0419, reduced their consumption of the HFD starting three weeks into the study and exhibited an overall 10% reduction in weight gain. The responses were independent of glucose homeostasis, as both NCMIB8826-R and LM0419 fed mice had improved oral glucose tolerance compared to sham controls. Although bacteriocins have antibacterial properties, the ileal, cecal, and fecal microbiota and cecocolic metabolomes were unchanged between mice fed either wild-type L. plantarum or the ΔplnEFI mutant. Instead, only mice fed NCMIB8826-R showed an increased production of ZO-1 in ileal tissues. To verify a potential role for the plantaricin EF system in supporting intestinal epithelial function, synthesized PlnEF peptides were applied to Caco-2 cell monolayers challenged with TNF-α and IFN-γ. The combination of PlnE and PlnF were required to prevent sustained cytokine-induced losses to Caco-2 cell para- and transcellular permeability and elevated IL-8 levels. In conclusion, this study shows that probiotic L. plantarum ameliorates the effects of obesogenic diets through a mechanism that involves the plantaricin EF system and likely includes L. plantarum – induced fortification of the intestinal epithelium.
Sinus floor elevation in sites with a perforated schneiderian membrane: What is the effect of placing a collagen membrane in a rabbit model?

OBJECTIVE To investigate the healing following sinus grafting in sites with a perforated schneiderian membrane repaired using a collagen membrane, compared to control sites without membrane perforation. MATERIALS & METHODS Following elevation of the sinus membrane in 16 rabbits, each sinus was assigned to one of the following groups: (a) intentional schneiderian membrane perforation, followed by the placement of a collagen membrane and bone grafting (group SMP) and (b) bone grafting without a perforation of the schneiderian membrane and without a collagen membrane placement (control group). At 2 and 4 weeks (n = 8 for each time-point), microcomputed tomographic (micro-CT) and histomorphometric analyses were performed. RESULTS Overall new bone formation in group SMP was significantly delayed compared to the control group at 2 and 4 weeks (1.58 ± 1.25% vs. 9.23 ± 2.69% at 2 weeks, 10.43 ± 3.55 vs. 17.86 ± 4.11% at 4 weeks, p < 0.05). At 2 weeks, new bone formation for the areas close to lateral (1.19 ± 2.02%) and medial sinus bone walls (3.17 ± 1.98%) was markedly delayed in group SMP compared to the control group (13.08 ± 6.13% and 12.75 ± 5.63%, respectively, p < 0.05), but there was no statistical difference in those areas at 4 weeks (p > 0.05). The augmented volumes at 2 and 4 weeks were not statistically significantly different in both groups. CONCLUSION The perforation of the schneiderian membrane and the repair using a collagen membrane delayed new bone formation in the augmented sinuses. However, the extension of the collagen membrane on the sinus bone walls was also attributable to this delayed bone formation.
Ehrlichia chaffeensis TRP47 enters the nucleus via a MYND-binding domain-dependent mechanism and predominantly binds enhancers of host genes associated with signal transduction, cytoskeletal organization, and immune response

Ehrlichia chaffeensis is an obligately intracellular bacterium that establishes infection in mononuclear phagocytes through largely undefined reprogramming strategies including modulation of host gene transcription. In this study, we demonstrate that the E. chaffeensis effector TRP47 enters the host cell nucleus and binds regulatory regions of host genes relevant to infection. TRP47 was observed in the nucleus of E. chaffeensis-infected host cells, and nuclear localization was dependent on a variant MYND-binding domain. An electrophoretic mobility shift assay (EMSA) demonstrated that TRP47 directly binds host DNA via its tandem repeat domain. Utilizing chromatin immunoprecipitation followed by high-throughput DNA sequencing (ChIP-seq) with E. chaffeensis-infected cells, TRP47 was found to bind at multiple sites in the human genome (n = 2,051 at p < 10−30). Ontology analysis identified genes involved in functions such as immune response, cytoskeletal organization, and signal transduction. TRP47-bound genes included RNA-coding genes, many of these linked to cell proliferation or apoptosis. Comparison of TRP47 binding sites with those of previously-identified E. chaffeensis nucleomodulins identified multiple genes and gene functional categories in common including intracellular transport, cell signaling, and transcriptional regulation. Further, motif analysis followed by EMSA with synthetic oligonucleotides containing discovered motifs revealed a conserved TRP47 DNA-binding motif. This study reveals that TRP47 is a nucleomodulin that enters the nucleus via a MYND-binding domain and appears to play a role in host cell reprogramming by regulation of transcription.
Experimental food supplementation increases reproductive effort in the Variable Antshrike in subtropical Brazil

Food limitation may interact with nest predation and influence nesting patterns, such as breeding season length and renesting intervals. If so, reproductive effort should change with food availability. Thus, when food is limited, birds should have fewer attempts and shorter seasons than when food is not limiting. Here we experimentally test that increased food availability results in increased reproductive effort in a fragmented landscape in the Variable Antshrike (Thamnophilus caerulescens) in southern Brazil. We followed nesting pairs in a naturally fragmented habitat and experimentally supplemented food for half of those pairs. Birds were seen, but evidence of nesting was never found in two small fragments, even though these fragments were larger than individual territories. Pairs with supplemented food were more likely to increase clutch size from two to three eggs and tended to renest sooner (20 d on average) than control pairs. Also, fragment size was associated with breeding patterns, although fragment replicates were unavailable. Nest duration, nest success and breeding season length were all greater, while renesting intervals were shorter, in the largest fragments. Simulations showed that only the largest fragments were able to have a net production of young. Food availability clearly influenced reproductive effort and as a consequence, because of the interaction with predation risk, forest fragments of varying sizes will have complex reproductive dynamics.
Multi-segment foot modeling for human animation

We present a multi-segment foot model and its control method for the simulation of realistic bipedal behaviors. The ground reaction force is the only source of control for a biped that stands and walks on its feet. The foot is the body part that interacts with the ground and produces appropriate actuation to the body. Foot anatomy features 26 bones and many more muscles that play an important role in weight transmission, balancing posture and assisting ambulation. Previously, the foot model was often simplified into one or two rigid bodies connected by a revolute joint. We propose a new foot model consisting of multiple segments to accurately reproduce human foot shape and its functionality. Based on the new model, we developed a foot pose controller that can reproduce foot postures that are generally not obtained in motion capture data. We demonstrate the validity of our foot model and the effectiveness of our foot controller with a variety of foot motions in a physics-based simulation.
DCCD: An Efficient and Scalable Distributed Code Clone Detection Technique for Big Code

Code clone detection is a very hot topic in the field of software maintenance, reuseability and security. There is still a lack of techniques to detect near-miss clones at different level of granularities, especially in big code. This paper presents Distributed Code Clone Detection (DCCD) technique, which detects clones from big code bases based on feature extraction. We performed preprocessing, indexing and clone detection for almost 27 TB of source code (324 billion LOC), DCCD is quite faster and efficient as compared to existing distributed indexing and clone detection techniques, i.e. 36 times faster than Benjamin technique, which is 86 times faster than CCFinder. These two techniques are also distributed and just detect Type-1 and Type2 clones, but our technique DCCD even detects Type-3 clones, efficiently. Our approach is faster, flexible, scalable and provides 87% accurate results with authenticity, ease of accessibility, upgradeability and maintainability. keyword Clone detection, Software maintenance, Software reuse, Big code, Similarity/Plagiarism detection
Tocilizumab treatment for new onset refractory status epilepticus

We investigated the therapeutic potential of the interleukin‐6 receptor inhibitor tocilizumab in 7 patients with new onset refractory status epilepticus (NORSE) who remained refractory to conventional immunotherapy with rituximab (n = 5) or without rituximab (n = 2). Status epilepticus (SE) was terminated after 1 or 2 doses of tocilizumab in 6 patients with a median interval of 3 days from the initiation. They had no recurrence of SE during the observation. However, 2 patients experienced severe adverse events related to infection during the tocilizumab therapy. Further prospective controlled studies are warranted to validate the efficacy and safety of tocilizumab in patients with NORSE. Ann Neurol 2018;84:940–945
Investigation of RNA‐RNA Interactions Using the RISE Database

RNA‐RNA interactions (RRIs) are essential to understanding the regulatory mechanisms of RNAs. Mapping RRIs in vivo in a transcriptome‐wide manner remained challenging until the recent development of several sequencing‐based technologies. However, RRIs generated from large‐scale studies had not been systematically collected and analyzed before. This article introduces RISE, a database of the RNA Interactome from Sequencing Experiments. RISE provides a comprehensive collection of RRIs in human, mouse, and yeast, derived from transcriptome‐wide sequencing experiments, as well as targeted sequencing studies and other public databases/datasets. To facilitate better understanding of the biological roles of these RRIs, RISE also offers rich functional annotations involving RNAs, and an interactive interface to explore the analysis results. Here, we provide a brief description of the RISE website and a step‐by‐step protocol for using RISE to study RRIs. © 2018 by John Wiley & Sons, Inc.
Cross-domain informativeness classification for disaster situations

Social Media services gain increasing importance as a new data source for achieving Situation Awareness in disaster management. One crucial prerequisite is to automatically filter social media messages towards informativeness commonly realized by supervised machine learning. Since disaster situations are different, most classification approaches focus on informativeness classification of similar disasters. Thus their use is limited to particular disaster types, for instance earthquakes or floods, lacking general applicability. At the same time, how to get accurate informativeness classification for new disaster events is not yet totally understood due to variations in training data, features, classification algorithms and their settings. To address these issues, our contribution is threefold: First, a systematic and in-depth analysis of an existing twitter crisis data set is provided along four different dimensions in order to gain a comprehensive understanding of those characteristics indicating informative Tweets in disaster situations. On basis of these insights, a cross domain classifier is engineered, which is applicable not only across different disaster events but also across disaster events of different types. Finally, systematic classification experiments are conducted, demonstrating that our classification approach is more accurate than other disaster type specific ones.
Optimal Economic Schedule for a Network of Microgrids With Hybrid Energy Storage System Using Distributed Model Predictive Control

In this paper, an optimal procedure for the economic schedule of a network of interconnected microgrids with hybrid energy storage system is carried out through a control algorithm based on distributed model predictive control (DMPC). The algorithm is specifically designed according to the criterion of improving the cost function of each microgrid acting as a single system through the network mode operation. The algorithm allows maximum economical benefit of the microgrids, minimizing the degradation causes of each storage system, and fulfilling the different system constraints. In order to capture both continuous/discrete dynamics and switching between different operating conditions, the plant is modeled with the framework of mixed logic dynamic. The DMPC problem is solved with the use of mixed integer linear programming using a piecewise formulation, in order to linearize a mixed integer quadratic programming problem.
Image-to-Text Transduction with Spatial Self-Attention

Attention mechanisms have been shown to improve recurrent encoder-decoder architectures in sequence-to-sequence learning scenarios. Recently, the Transformer model has been proposed which only applies dot-product attention and omits recurrent operations to obtain a source-target mapping [5]. In this paper we show that the concepts of self-and inter-attention can eﬀectively be applied in an image-to-text task. The encoder applies pre-trained convolution and pooling operations followed by self-attention to obtain an image feature representation. Self-attention combines image features of regions based on their similarity before they are made accessible to the decoder through inter-attention.