Miracle-dz/UniFAR-FTUs huggingface dataset:
Contribution to Perception and Artificial Bio-inspired Visual Attention for Acquisition and Conceptualization of Knowledge in Autonomous Robotics. (Contribution à la perception et l'attention visuelle artificielle bio-inspirée pour acquisition et conceptualisation de la connaissance en robotique aut

Dealing with the field of "Bio-inspired Perception", the present thesis focuses more particularly on Artificial Visual Attention and Visual Saliency. A concept of Artificial Visual Attention, inspired from the human mechanisms, providing a model of such artificial bio-inspired attention, was developed, implemented and tested in the context of autonomous robotics. Although there are several models of visual saliency, in terms of contrast and cognition, there is no hybrid model integrating both mechanisms of attention: the visual aspect and the cognitive aspect.To carryout such a model, we have explored existing approaches in the field of visual attention, as well as several approaches and paradigms in related fields (such as object recognition, artificial learning, classification, etc.). A functional architecture of a hybrid visual attention system, combining principles and mechanisms derived from human visual attention with computational and algorithmic methods, was implemented, explained and detailed.Another major contribution of this doctoral work is the theoretical modeling, development and practical application of the aforementioned Bio-inspired Visual Attention model, providing a basis for the autonomy of assistance-robotic systems.The carried out studies and experimental validation of the proposed models confirmed the relevance of the proposed approach in increasing the autonomy of robotic systems within a real environment
Multi-Dimensional QoS Prediction for Service Recommendations

Advances in mobile Internet technology have enabled the clients of Web services to be able to keep their service sessions alive while they are on the move. Since the services consumed by a mobile client may be different over time due to client location changes, a multi-dimensional spatiotemporal model is necessary for analyzing the service consumption relations. Moreover, competitive Web service recommenders for the mobile clients must be able to predict unknown quality-of-service (QoS) values well by taking into account the target client's service requesting time and location, e.g., performing the prediction via a set of multi-dimensional QoS measures. Most contemporary QoS prediction methods exploit the QoS characteristics for one specific dimension, e.g., time or location, and do not exploit the structural relationships among the multi-dimensional QoS data. This paper proposes an integrated QoS prediction approach which unifies the modeling of multi-dimensional QoS data via multi-linear-algebra based concepts of tensor and enables efficient Web service recommendation for mobile clients via tensor decomposition and reconstruction optimization algorithms. In light of the unavailability of measured multi-dimensional QoS datasets in the public domain, this paper also presents a transformational approach to creating a credible multi-dimensional QoS dataset from a measured taxi usage dataset which contains high dimensional time and space information. Comparative experimental evaluation results show that the proposed QoS prediction approach can result in much better accuracy in recommending Web services than several other representative ones.
Hydrogen Concentration in Photovoltaic a-Si:H Annealed at Different Temperatures Measured by Neutron Reflectometry

Amorphous hydrogenated silicon (a-Si:H) is an important material for surface defect passivation of photovoltaic silicon (Si) wafers in order to reduce their recombination losses. The material is, however, unstable with regard to hydrogen (H) desorption at elevated temperatures, which can be an issue during processing and device manufacturing. In this work, we determine the temperature stability of a-Si:H by structural characterization of a-Si:H/Si bilayers with neutron reflectometry and X-ray reflectometry combined with photoconductance measurements, yielding the minority carrier lifetime. The neutrons are sensitive to light elements such as H, while the X-rays, which are insensitive to the H concentration, provide an independent constraint on the layer structure. It is shown that H desorption takes place at a temperature of approximately <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> = <inline-formula><tex-math notation="LaTeX">$425\,^{\circ }\mathrm{C}$</tex-math></inline-formula>, and that the H content and minority carrier lifetimes have a strongly correlated linear relationship, which can be interpreted as one H atom passivating one defect.
Prostaglandin E2 Antagonizes TGF-β Actions During the Differentiation of Monocytes Into Dendritic Cells

Inflammatory dendritic cells (DCs) are a distinct subset of DCs that derive from circulating monocytes infiltrating injured tissues. Monocytes can differentiate into DCs with different functional signatures, depending on the presence of environment stimuli. Among these stimuli, transforming growth factor-beta (TGF-β) and prostaglandin E2 (PGE2) have been shown to modulate the differentiation of monocytes into DCs with different phenotypes and functional profiles. In fact, both mediators lead to contrasting outcomes regarding the production of inflammatory and anti-inflammatory cytokines. Previously, we have shown that human semen, which contains high concentrations of PGE2, promoted the differentiation of DCs into a tolerogenic profile through a mechanism dependent on signaling by E-prostanoid receptors 2 and 4. Notably, this effect was induced despite the huge concentration of TGF-β present in semen, suggesting that PGE2 overrides the influence exerted by TGF-β. No previous studies have analyzed the joint actions induced by PGE2 and TGF-β on the function of monocytes or DCs. Here, we analyzed the phenotype and functional profile of monocyte-derived DCs differentiated in the presence of TGF-β and PGE2. DC differentiation guided by TGF-β alone enhanced the expression of CD1a and abrogated LPS-induced expression of IL-10, while differentiation in the presence of PGE2 impaired CD1a expression, preserved CD14 expression, abrogated IL-12 and IL-23 production, stimulated IL-10 production, and promoted the expansion of FoxP3+ regulatory T cells in a mixed lymphocyte reaction. Interestingly, DCs differentiated in the presence of TGF-β and PGE2 showed a phenotype and functional profile closely resembling those induced by PGE2 alone. Finally, we found that PGE2 inhibited TGF-β signaling through an action exerted by EP2 and EP4 receptors coupled to cyclic AMP increase and protein kinase A activity. These results indicate that PGE2 suppresses the influence exerted by TGF-β during DC differentiation, imprinting a tolerogenic signature. High concentrations of TGF-β and PGE2 are usually found in infectious, autoimmune, and neoplastic diseases. Our observations suggest that in these scenarios PGE2 might play a mandatory role in the acquisition of a regulatory profile by DCs.
Semantic compression of episodic memories

Storing knowledge of an agent's environment in the form of a probabilistic generative model has been established as a crucial ingredient in a multitude of cognitive tasks. Perception has been formalised as probabilistic inference over the state of latent variables, whereas in decision making the model of the environment is used to predict likely consequences of actions. Such generative models have earlier been proposed to underlie semantic memory but it remained unclear if this model also underlies the efficient storage of experiences in episodic memory. We formalise the compression of episodes in the normative framework of information theory and argue that semantic memory provides the distortion function for compression of experiences. Recent advances and insights from machine learning allow us to approximate semantic compression in naturalistic domains and contrast the resulting deviations in compressed episodes with memory errors observed in the experimental literature on human memory.
Optimal Operation of Droop-Controlled Islanded Microgrids

Recently researchers have focused on the optimal operation of droop-controlled islanded microgrids (DCIMG). However, most of the papers consider only the economic objective. But, with increasing focus on environmental compliance, it has also become necessary to reduce emission from generation activities. Furthermore, most of the previous papers do not consider the heat demand in the microgrid and the uncertainties involved in load and renewable generation forecasting. This paper bridges all these gap areas by presenting a method for determining optimal droop settings of dispatchable distributed generation units in a DCIMG. The objectives are to minimize the operational cost and minimize the emission in the islanded droop-controlled microgrid while meeting all the operational constraints. The proposed formulation takes into account the electricity demand, the heat demand, load uncertainties, and renewable power uncertainties in the microgrid. Load and renewable power uncertainties are modeled by a stochastic scenario based approach. The multiobjective optimization problem is solved using fuzzified particle swarm optimization. The proposed method is validated on a 33-bus DCIMG test system. The results show the effectiveness of the proposed method.
Increase the efficiency of smart antennas by using fractals

In this research, the use of smart antennas was developed through the application of the Sierpinski-type fractal antenna, which is characterized by the precise characteristics of transmission and reception and the high frequency range of up to 6 kHz. The three matrices were compared with 324 points simulation in MATLAB . Work as fractals have been tested with a larger set of points. Which is made up of corrosive antennas. This study is characterized by the study of the characteristics of the various fractal antennas and multifractal antennas, where they were generated and analyzed. The results indicate the importance of using multiple precision antennas for accuracy. Sierpinski fractal antenna is suitable for WLAN applications.
Regulators of Long-Term Memory Revealed by Mushroom Body-Specific Gene Expression Profiling in Drosophila melanogaster

Memory formation is achieved by genetically tightly controlled molecular pathways that result in a change of synaptic strength and synapse organization. While for short-term memory traces, rapidly acting biochemical pathways are in place, the formation of long-lasting memories requires changes in the transcriptional program of a cell. Although many genes involved in learning and memory formation have been identified, little is known about the genetic mechanisms required for changing the transcriptional program during different phases of long-term memory (LTM) formation. With Drosophila melanogaster as a model system, we profiled transcriptomic changes in the mushroom body—a memory center in the fly brain—at distinct time intervals during appetitive olfactory LTM formation using the targeted DamID technique. We describe the gene expression profiles during these phases and tested 33 selected candidate genes for deficits in LTM formation using RNAi knockdown. We identified 10 genes that enhance or decrease memory when knocked-down in the mushroom body. For vajk-1 and hacd1—the two strongest hits—we gained further support for their crucial role in appetitive learning and forgetting. These findings show that profiling gene expression changes in specific cell-types harboring memory traces provides a powerful entry point to identify new genes involved in learning and memory. The presented transcriptomic data may further be used as resource to study genes acting at different memory phases.
The latest on the axon initial segment

This paper will review the latest results about the axon initial segment (AIS) revealing physiology details while focusing on the structural organization of the AIS. This highly specialized region of the axon is characterized, among others, by high concentrations of (particular) voltage-gated ion channels, and by highly specialized cytoskeletal networks. These repetitive and regular tiny structures (down to molecular motifs) act in synch for maintaining and modifying the functionalities associated to AIS: action potential initiation, threshold modulation, activity-dependent plasticity, assembly and maintenance, diffusion barrier (bi-directional selective filtering), etc. Significant progress in our understanding of the structural organization of the AIS has developed rapidly in recent years based on advances in high-resolution microscopy. Still, the precise ways through which the regular tiny structures observed are affecting functioning need significantly more effort. As an example here, one can look at the different voltage-gated ion channels and at their positioning, which are affecting the triggering of the action potential. When their variable densities are taken in conjunction with the regular spacing induced by the cytoskeleton, one cannot help but wonder about concepts akin to distributed amplification — which have never been put forward before in this biological context. That is why we conclude with quite a few open questions.
User Mobility Analysis in Disjoint-Clustered Cooperative Wireless Networks

Base station (BS) cooperation has been regarded as an effective solution to improve network coverage and throughput in next-generation wireless systems. However, it also introduces more complicated handoff patterns, which may potentially degrade user performance. In this work, we aim to theoretically quantify users' handoff performance in disjoint-clustered cooperative wireless networks. It is a challenging task due to spatial randomness of network topologies. We propose a stochastic geometric model on user mobility, and use it to derive a theoretical expression for the handoff rate experienced by an active user with arbitrary movement trajectory. As a study on the application of the handoff rate analysis, we furthermore characterize the average downlink user data rate under a common non-coherent joint-transmission scheme, which is then used to derive a tradeoff between handoff and data rates in a network with an optimal cooperative cluster size. Finally, extensive simulations are conducted to validate our analysis.
Injecting Relational Structural Representation in Neural Networks for Question Similarity

Effectively using full syntactic parsing information in Neural Networks (NNs) for solving relational tasks, e.g., question similarity, is still an open problem. In this paper, we propose to inject structural representations in NNs by (i) learning a model with Tree Kernels (TKs) on relatively few pairs of questions (few thousands) as gold standard (GS) training data is typically scarce, (ii) predicting labels on a very large corpus of question pairs, and (iii) pre-training NNs on such large corpus. The results on Quora and SemEval question similarity datasets show that NNs using our approach can learn more accurate models, especially after fine tuning on GS.
Effects of silver nanoparticle (Ag NP) on oxidative stress biomarkers in rat

Objective(s): Nanotechnology and nanoparticles are increasingly recognized for their potential applications in aerospace engineering, nanoelectronics, and environmental remediation, medicine and consumer products. More importantly is the potential for the application of silver nanoparticles (Ag NPs) in the treatment of diseases that require maintenance of circulating drug concentration o r targeting of specific cells or organs the aim of this study was to investigate the possible protective role of Ag NP antioxidative biomarkers in rats. Ag NPs are used to investigate the potential risks for the environment and health. Materials and Methods: Rats received Ag NP, 5, 50, 250 and 500 mg/kg/day IP. After two week of treatment, the activity of enzymatic scavengers such as glutathione peroxidase (GPx), superoxide dismutase (SOD) and total antioxidant capacity (TAC) of blood samples were measured. Results: Ag NP in 5, 50, 250 and 500 mg/kg reduced activities of CAT, SOD and increased TAC in plasma. Conclusion: In this study, Ag NP with 500mg/kg induced activities of CAT, SOD and decreased TAC. It is concluded that antioxidative properties of A g NP is dose dependent.
Research on fault location method of VSC-MTDC power grid

Fault location technology of multi-terminal flexible DC power grid is the key technology to ensure the safe and reliable operation of DC power grid. In this paper, multi-terminal flexible DC power gird consisting of two-level voltage source converter is regarded as an object. In the research of fault location method, fault section is confirmed firstly before fault location. Due to the fault circuit and DC side large capacitance, the voltage change rate of the DC capacitor at the outlet of the converter in the fault interval is different from none fault interval. According to the characteristics above, the method is used to determine the fault section, which compares the DC capacitor discharge voltage change rate of the VSC outlet side in fault interval with none fault one. The method is verified through the simulation in MATLAB/Simulink, and it's feasible and valid comparing with the existing ones.
MIMO underwater acoustic communications over shallow water channel

Underwater acoustic channels are typically bandlimited, reverberant and pose many obstacles to reliable highspeed digital communications. Recent progresses in digital modulation and channel decoding make current underwater acoustic modem fairly robust to time and frequency spreading of the channel. However the limited availability of underwater bandwidth prevents development of high data rate application. One approach for overcoming the limited bandwidth in the acoustic channel is the use of Multiple Input Multiple Output (MIMO) technology in which independent streams of information are transmitted from multiple transmit elements, simultaneously, all in the same bandwidth. In this paper we study the gain brought by MIMO techniques on underwater acoustic communications by considering the shallow water channel.
Fault diagnosis of motor using fuzzy logic technique

Electric motors are simple, robust and can be controlled easily through inverters. These features help in acceptance of electric motors for all kind of industrial applications. With condition monitoring technique, faults can be detected in early stage which helps in reduction of production cost of electric motor. This result can be achieved by means of increment in industrial efficiency and reduction in productive line stoppage. The main motive of this paper is to develop a computer based system by applying Fuzzy Logic. The system can be used to identify and estimate the condition of an induction motor. Several fault analysis techniques are available and vibration analysis techniques is one to detect the unusual vibrations in the motor which causes severe fault and responsible for outages. Analytical methods require in depth knowledge of motor model for fault detection so it is always feasible to implement. Therefore one important computer based technique is fuzzy logic interference system to monitor the health of motor through vibration analysis whether it is in good condition or bad. A simulation model is developed and implemented in Matlab/SIMULINK for Induction motor using Fuzzy-logic Controller.
Mining the Air - for Research in Social Science and Networking Measurement

Smartphone apps provide a vitally important opportunity for monitoring human mobility, human experience of ubiquitous information aids, and human activity in our increasingly well-instrumented spaces. As wireless data capabilities move steadily up in performance, from 2&3G to 4G (today's LTE) and 5G, it has become more important to measure human activity in this connected world from the phones themselves. The newer protocols serve larger areas than ever before and a wider range of data, not just voice calls, so only the phone can accurately measure its location. Access to the application activity permits not only monitoring the performance and spatial coverage with which the users are served, but as a crowd-sourced, unbiased background source of input on all these subjects, becomes a uniquely valuable resource for input to social science and government as well as telecom providers
Convolutional neural networks for text categorization with latent semantic analysis

The recent emphasis on intelligent systems has increased the focus on categorization techniques as it is an important step in information retrieval and natural language processing. The text categorization is largely achieved using machine learning techniques. In most of the approaches one-hot encoding or pre-trained word embedding such as word2vec or glove vectors are used. This study explores the feature vectors based encoding using Latent Semantic Analysis (LSA) technique along with the Convolutional Neural Network (CNN) being used as a classifier. It was found that applying LSA followed by CNN for text classification offers better accuracy than the conventional methods of CNN with other approaches. This research, thus, highlights the importance of Latent Semantic Analysis technique coupled with convolutional neural networks for text classification.
Predicting movie box office success using multiple regression and SVM

Hollywood is a multi-billion dollar industry which releases more than a hundred films a year, with large variations in the budgets and box office grosses of the movies. Identifying which factors are important to a movie's profitability and subsequently predicting the success of a movie given its relevant parameters could save movie studios hundreds of millions of dollars a year. This paper analyses the efficiency of using multiple linear regression and Support Vector Machine Classification to predict the box-office success of movies, while analysing the influence of variables like trailer views, Wikipedia page views, critic ratings and time of release.
Remote signal STATCOM controller for mitigation of sub-synchronous resonance in series compensated type 3 wind farm

Series compensation of transmission line increases its power transfer capability, however leads to serious destructive phenomenon known as Sub-Synchronous Resonance (SSR). Several SSR mitigation techniques in wind power plant (WPP) are found in literature however, very little information is available on mitigation of SSR in a series compensated type 3 WPP using STATCOM. This paper reports the performance of remote power signal based STATCOM SSRDC for mitigation of SSR due to induction generator effect (IGE) in series compensated type 3 WPP. A first bench mark (FBM) model is adopted with type 3 WPP for SSR studies. A STATCOM with SSRDC is employed at PCC. It is shown that STATCOM with damping controller is capable of mitigating SSR even at higher rating of WPP and higher series compensation level. Electromagnetic transient simulations using MATLAB/Simulink have been carried out to analyze the performance of STATCOM with and without SSRDC for mitigation of SSR in series compensated type 3 WPP.