Miracle-dz/UniFAR-FTUs huggingface dataset:
Asynchronous Training of Word Embeddings for Large Text Corpora

Word embeddings are a powerful approach for analyzing language and have been widely popular in numerous tasks in information retrieval and text mining. Training embeddings over huge corpora is computationally expensive because the input is typically sequentially processed and parameters are synchronously updated. Distributed architectures for asynchronous training that have been proposed either focus on scaling vocabulary sizes and dimensionality or suffer from expensive synchronization latencies. In this paper, we propose a scalable approach to train word embeddings by partitioning the input space instead in order to scale to massive text corpora while not sacrificing the performance of the embeddings. Our training procedure does not involve any parameter synchronization except a final sub-model merge phase that typically executes in a few minutes. Our distributed training scales seamlessly to large corpus sizes and we get comparable and sometimes even up to 45% performance improvement in a variety of NLP benchmarks using models trained by our distributed procedure which requires $1/10$ of the time taken by the baseline approach. Finally we also show that we are robust to missing words in sub-models and are able to effectively reconstruct word representations.
Optimal Clustering Algorithm for Maximizing Lifetime in Wireless Sensor Networks

Energy efficient clustering protocols are deeply studied for low power, multi-functional wireless sensors networks (WSNs), Clustering have to ensure connectivity and reliability in WSN even in large scale environment. In this paper, we present a new clustering approach called the Fixed Competition-based Clustering Approach (FCBA) based routing algorithm to fairly use the energy of the sensors to maximize the network lifetime. The Selecting of cluster heads with FCBA is performed based upon a residual energy and the distances among the cluster heads. The simulation results are given to validate the analytical results. The experimental results indicate that proposed protocol leads to reduction of sensor's nodes energy consumption and prolongs the network lifetime, significantly.
An Experimental Study on Robustness and Responsiveness Testing of Iranian Android Games

During the last decade, many Iranian software and game developers have targeted Android as their main development framework, resulting in the availability of a large number of Android applications. Despite the opportunities that Android provides for application developers, it introduces new challenges for the development of high quality applications. In this paper, we have focused on responsiveness and fault-tolerance as two interesting quality attributes of Android applications, lack of which might result in users' dissatisfaction and leaving the application. Hence, we have conducted an experimental study, using monkey testing technique, on a set of Iranian Android games to assess their robustness and responsiveness. The results demonstrate that much more attention needs to be paid by Iranian Android developers to the responsiveness and fault-tolerance of their applications, since about 85% of the analyzed games have shown to have at least one responsiveness or robustness error.
On Intelligent Traffic Control for Large-Scale Heterogeneous Networks: A Value Matrix-Based Deep Learning Approach

Recently, deep learning has emerged as an attractive technique to intelligently control network traffic. However, the contemporary researches only focused on small-/medium-scale networks, since the computational complexity of deep learning based traffic control algorithm significantly increases with the network size. In this paper, we address this issue and envision a reward-based deep learning structure, which jointly employs deep convolutional neural network (CNN) and a deep belief network (DBN) to predict the traffic load value matrix and construct the final action matrix, respectively. In our proposal, the deep CNN is used to construct the award prediction network, while the deep DBN constructs the action decision network. Thus, the final action space is simplified to a next destination action matrix, and the computational complexity is substantially reduced. Computer-based simulation results demonstrate that our proposal is able to achieve an improved performance in the large-scale network in terms of the packets loss rate and throughput in contrast with those in the conventional routing method.
La evaluación en la biblioteca universitaria brasileña: evolución y propuesta de mejora

La presente tesis estudia la evolucion de la evaluacion de la biblioteca universitaria en Brasil. En la actualidad, el unico sistema aplicable se realiza en el marco de la evaluacion de las titulaciones academicas por parte del Ministerio de Educacion, a traves de Instituto Nacional de Estudos e Pesquisas Educacionais Anisio Teixeira (INEP), entidad responsable de la coordinacion y ejecucion del proceso de la evaluacion de todo el Sistema de Educacion brasileno. El punto de partida para abordar esta investigacion fueron los resultados de un estudio de caso desarrollado en la Universidad Federal de Bahia en 2001, bajo la hipotesis de que el Ministerio no contaba con criterios e instrumentos adecuados para reflejar el papel de la biblioteca como un recurso pedagogico para las titulaciones. La revision de la literatura brasilena pertinente, la anglosajona y la espanola respecto al tema, especialmente la referida a la experiencia espanola con la Red de Bibliotecas Universitarias (REBIUN), fueron imprescindibles para conocer las tendencias y el estado de la cuestion. Asimismo, se llevo a cabo una labor de campo mediante cuestionarios en 7 paises y 31 universidades iberoamericanas, con la finalidad de conocer la situacion actual de los sistemas de evaluacion utilizados en dicha region. El bajo indice de respuesta, en torno al 50% de las instituciones encuestadas, se trato de compensar con los trabajos del Simposio Electronico sobre Evaluacion y Calidad en Biblioteca (Sociedad Argentina de Informacion, 2002), evento de gran trascendencia al que acudieron mas de 450 bibliotecarios y docentes. Esta iniciativa permitio elaborar un estudio comparativo que sirvio para situar a la realidad brasilena y tener en cuenta la casuistica mas similar. Todo lo anterior permitio cumplir con el objetivo general de esta tesis: elaborar un modelo para evaluar las bibliotecas universitarias en Brasil, a partir del estado de la cuestion de la biblioteca universitaria brasilena y de las experiencias localizadas en paises iberoamericanos. El referido modelo se basa en el Instrumento del INEP (2006), pero su contenido esta planteado teniendo en cuenta distintos trabajos: Indicadores de rendimiento para la evaluacion de la biblioteca universitaria, de REBIUN (2000), uno de los documentos del PNBU, elaborado por Maria Carmen Romcy de Carvalho (1995), los Estandares para las bibliotecas universitarias chilenas (2001) y la Guia metodologica para evaluar las bibliotecas de las instituciones de educacion superior (Mexico, 2000). La pretension final es que el Ministerio brasileno disponga de un sistema de evaluacion mas completo, que se vaya aplicando y mejorando conforme a los datos que vaya aportando y, a la vez, se pueda ir construyendo un banco de informaciones gerenciales y un sistema de indicadores de rendimiento.
Dynamic Pricing in One-Sided Autonomous Ride-Sourcing Markets

Dynamic pricing has been used by Transportation Network Companies (TNCs) to achieve a balance between the volume of ride requests with numbers of available drivers on two-sided TNC markets. Given the desire to reduce operating costs and the emergence of Autonomous Vehicles (AVs), the introduction of TNC-owned AV fleets could convert such services into one-sided markets, where operators have full control of service supply. In this paper we investigate the impact of utility-based dynamic pricing for Autonomous TNCs (ATNCs) in one-sided markets. We test the method using an Agent-Based Model (ABM) of Greater London in conditions of monopoly and competition, focusing on a statically priced ATNC service that offers a mix of private and shared ride services. Public transport is considered as an alternative mode of transportation in both scenarios. Results indicate that in monopoly, dynamic pricing provides higher revenues than static pricing at non-peak hours when average waiting times are low. On the contrary, in competition, dynamic pricing is superior at peak hours where increased waiting times are observed, thus increasing the value of low waiting time rides. Overall, in both market structures, it is found that shared trips are more popular in dynamic pricing compared to static pricing.
Real-time Stereo Reconstruction Failure Detection and Correction using Deep Learning

This paper introduces a stereo reconstruction method that besides producing accurate results in real-time, is capable to detect and conceal possible failures caused by one of the cameras. A classification of stereo camera sensor faults is initially introduced, the most common types of defects being highlighted. We next present a stereo camera failure detection method in which various additional checks are being introduced, with respect to the aforementioned error classification. Furthermore, we propose a novel error correction method based on CNNs (convolutional neural networks) that is capable of generating reliable disparity maps by using prior information provided by semantic segmentation in conjunction with the last available disparity. We highlight the efficiency of our approach by evaluating its performance in various driving scenarios and show that it produces accurate disparities on images from Kitti stereo and raw datasets while running in real-time on a regular GPU.
Onboard Battery Chargers for Plug-in Electric Vehicles With Dual Functional Circuit for Low-Voltage Battery Charging and Active Power Decoupling

This paper proposes a single-phase onboard battery charger (OBC) for plug-in electric vehicles, where the low-voltage (LV) battery charging circuit is utilized for an active power decoupling function. The OBC is operated in three different modes by sharing the transformer, switches, and capacitors. For a grid-to-vehicle mode or a vehicle-to-grid mode, the LV battery charging circuit serves as an active filter to eliminate the low-frequency power ripple at the DC link. Thus, the small film capacitors can be employed instead of large capacitors at the DC link. For the third operating mode, where the LV battery is charged from the HV battery, the isolation is provided by the dual active bridge (DAB) DC-DC converter. Since some components in the proposed OBC are used in common, the size and cost of the OBC can be reduced significantly. The simulation and experimental results have verified the validity of the proposed system.
Driver Identification via Brake Pedal Signals — A Replication and Advancement of Existing Techniques

Driver identification is a growing topic which offers a streamlined user experience in the connected car, but potentially also highlights privacy issues of our interconnected lives. Recent studies have reported the ability for individuals to be reliably identified out of a group based on their driving behavior. In particular, the state-of-the-art study claims that, in a controlled setting, data collected on how a driver operated the brake pedal could separately identify each of the 15 drivers from the others. The paper at hand was not able to validate these strong scientific claims using naturalistic driving data. In line with the results of other studies using similar data, the replicated identification accuracy dropped to values between 40% and 70% by applying the outlined methods. Nevertheless, this paper further contributes to the field by presenting and evaluating an alternative feature collection technique in order to achieve identification results between 80% and 99.5% in this challenging setting, thus advancing the state-of-the-art. These findings demonstrate the real-world capabilities of data-enabled driver identification, which both facilitates new use-cases and potentially raises privacy questions. As such, important key features from the identification models are presented to assist both researchers and practitioners in this rapidly developing topic.
Intelligence Information Retrieval in Corporate Information Systems

Local searching systems development is not a trivial task. The amount of data is too large for an exhaustive search, but not enough for the Bigdata algorithms. Some local searching systems built using existing searching engines could have low relevance for a specific user. Therefore, we need to integrate special algorithms for increasing the quality of searching. In this paper, we consider the ways of applying the machine-learning methods such as topic modeling and neural networks and demonstrate how we can increase search quality, based on the actual statistics collected. In addition, we demonstrate the evaluation criteria, based on the collected statistics, which we developed because of the unavailability of standard ways of evaluating the quality of the search engine.
Discovering physics and design trends from visual temporal structures

Living in a constantly changing world, we cannot help but notice the temporal regularities of visual changes around us. These changes can be irreversible governed by physical laws, such as glass bottles broken into pieces, or influenced by design trends, such as web pages adopting templates with larger background images. In this dissertation, we build computational models to discover and apply the knowledge of the physics for arrow of time, and the design trends for web pages from image sequences. In the first part of the thesis, I train models to learn the visual cues that are indicative of the arrow of time from large real world video datasets. In the second part of the thesis, I investigate the evolution of visual cues and layout in web page design through screenshots over time. The knowledge of these visual temporal structures are not only of scientific interest by themselves, but also of practical uses demonstrated in this thesis.
A Pilot Study on Footprint Posture Classification of Passengers in Light Rail Public Transport via Deep Convolutional Neural Networks

The public transportation industry is an essential mean of commuting within major cities and rural towns alike. Due to the high number of daily users, crowded environments can lead to limited seating requiring passengers to stand during their commute. Standing passengers are at risk during heavy braking scenarios and can also become a hazard to other seated passengers. Similarly, those who may not have a strong balance, or physically impaired such as the elderly endure a risk when vehicle braking is sudden. In this research, a method of analysing posture via simplistic pressure mat data is achieved. The integration of identifying a passenger's posture in relation to their balance may have the potential to inform public transport operators on the levels of braking that can be used based on their passengers at the time. Additionally, the identification of passengers using hand rail supports derived from floor mats only will inform operators of the potential risk during sudden braking. The results show an average classification accuracy of 99%, precision of 98%, recall of 98% and F-score performance of 98%.
The AGXX® Antimicrobial Coating Causes a Thiol-Specific Oxidative Stress Response and Protein S-bacillithiolation in Staphylococcus aureus

Multidrug-resistant pathogens, such as methicillin-resistant Staphylococcus aureus (MRSA) pose an increasing health burden and demand alternative antimicrobials to treat bacterial infections. The surface coating AGXX® is a novel broad-spectrum antimicrobial composed of two transition metals, silver and ruthenium that can be electroplated on various surfaces, such as medical devices and implants. AGXX® has been shown to kill nosocomial and waterborne pathogens by production of reactive oxygen species (ROS), but the effect of AGXX® on the bacterial redox balance has not been demonstrated. Since treatment options for MRSA infections are limited, ROS-producing agents are attractive alternatives to combat multi-resistant strains. In this work, we used RNA-seq transcriptomics, redox biosensor measurements and phenotype analyses to study the mode of action of AGXX® microparticles in S. aureus USA300. Using growth and survival assays, the growth-inhibitory amount of AGXX® microparticles was determined as 5 μg/ml. In the RNA-seq transcriptome, AGXX® caused a strong thiol-specific oxidative stress response and protein damage as revealed by the induction of the PerR, HypR, QsrR, MhqR, CstR, CtsR, and HrcA regulons. The derepression of the Fur, Zur, and CsoR regulons indicates that AGXX® also interferes with the metal ion homeostasis inducing Fe2+- and Zn2+-starvation responses as well as export systems for toxic Ag+ ions. The induction of the SigB and GraRS regulons reveals also cell wall and general stress responses. AGXX® stress was further shown to cause protein S-bacillithiolation, protein aggregation and an oxidative shift in the bacillithiol (BSH) redox potential. In phenotype assays, BSH and the HypR-controlled disulfide reductase MerA were required for protection against ROS produced under AGXX® stress in S. aureus. Altogether, our study revealed a strong thiol-reactive mode of action of AGXX® in S. aureus USA300 resulting in an increased BSH redox potential and protein S-bacillithiolation.
Interaction-Aware Long-term Driving Situation Prediction

Automated vehicles require a comprehensive understanding of the current traffic situation and their future evolution to perform safe and comfortable actions. To enable reliable long-term predictions of traffic participants the interaction among each other cannot be neglected. This contribution tackles the problem of interaction-based trajectory prediction with limited information of the situation as delivered by most on-board perception systems of nowadays automated vehicles. A Monte Carlo simulation based approach which utilizes well-known sensor models in a probabilistic manner to model the interaction between traffic participants is presented. Besides uncertainty in maneuver decisions, the maneuver execution is modeled with a probabilistic model learned from realworld data. Furthermore, the problem of limited information is addressed by a combination of the interaction-aware model with a maneuver classification algorithm providing information on the short-term maneuver. The approach is evaluated on two meaningful simulation scenarios demonstrating the advantages of interaction-based prediction in general and the handling of limited perception capabilities in specific.
A Unified STARIMA based Model for Short-term Traffic Flow Prediction

This paper proposes a unified spatio-temporal model on the basis of STARIMA (Space-Time AutoRegressive Integrated Moving Average) for short-term road traffic prediction. The contributions of this paper are as follows. First, we develop a physically intuitive approach to traffic prediction that captures the time-varying spatio-temporal correlation between traffic at different measurement points. The spatio-temporal correlation is affected by the road network topology, time-varying speed, and time-varying trip distribution. Distinctly different from previous black-box approaches to road traffic modeling and prediction, parameters of the proposed approach have physically intuitive meanings which make them readily amenable to suit changing road and traffic conditions. Second, unlike some existing techniques which capture the variation of spatio-temporal correlation by a complete redesign and calibration of the model, the proposed approach uses a unified model which incorporates the physical factors potentially affecting the variation of spatio-temporal correlation into a series of parameters. These parameters are relatively easy to control and adjust when road and traffic conditions change, thereby greatly reducing the computational complexity. Experiments using two set of real traffic traces demonstrate that the proposed approach has superior accuracy compared with the widely used ARIMA (AutoRegressive Integrated Moving Average) and is only marginally inferior to that obtained by constructing multiple STARIMA models for different time of the day, however with a much reduced computational and implementation complexity.
Environment Estimation with Dynamic Grid Maps and Self-Localizing Tracklets

Dynamic environment representation is an important and demanding topic in the field of autonomous driving. One of the generic ways to estimate the surrounding world of an intelligent vehicle is to use dynamic grid maps. However, there are still several unsolved challenges in the grid-based tracking solutions like the ability to converge faster and providing a more efficient way to fuse multi-sensorial information. In this work, we address both of these challenges as a single probabilistic estimator. First, we treat the grid map estimation process as a multi-channel tracking mechanism. In particular, we use a particle filter based solution to integrate both the occupancy and semantic grids. Second, we adapt the idea of simultaneous grid cell tracking and object shape estimation into the grid map domain and propose “self-localizing tracklets”, which are individual particle filter based estimators that are used for two main tasks: stabilizing the position estimation accuracy of dynamic cells with respect to the object boundary, and estimating a better object shape. The presented concepts offer an improved representation flexibility and a faster algorithm convergence.
Supervised Trajectory Tracking Control

This work deals with the design of a supervisory module for the motion control of autonomous vehicles. The supervisor's goal is to monitor the behavior of a trajectory tracking controller (TTC), introducing corrective control actions to prevent violations of safety and input constraints. It relies on the combination of control barrier functions and optimization methods, and considers Lyapunov-based constraints and costs to penalize performance degradation due to supervisor's corrective actions. Simulation results of a collision avoidance scenario demonstrate the effectiveness of the proposed supervised TTC.
Optimal Conditions for Signal Reconstruction Based on STFT Magnitude Spectrum

Signal reconstruction based on magnitude spectrum of short term Fourier Transform (STFT) only has been a challenging topic since many years. Whereas theory proves that's completely possible to reconstruct a signal from its magnitude spectrum and minimal phase data, practical implementations are still not able to do that quite accurately. Therefore, this study aims to set the optimal initial conditions required to reconstruct a signal from the STFT magnitude spectrum with an arbitrary or zero initial phase. Results prove that an optimal selection of the frame length, the shift rate and the iterations number allows enhancing the quality of the reconstructed signals.
Multi-pattern Matching Algorithm based on Variable Step Size

By analyzing the existing pattern matching algorithms, we found AC_BM algorithm or AC_SUNDAY algorithm combine the advantages of AC algorithm and the single-pattern matching algorithms. When a mismatch occurs, the moving distance on matching trees become larger. Based on this idea, an improved jump step matching algorithm named AC_ID is proposed. When occurs the mismatch or the scanning is over, the moving distance on the matching tree is determined by the first four characters of the current matching window. It make the maximum jump distance on matching tree be equal to the shortest pattern string length plus four in the pattern string set. Furthermore, two fields (pattern string length, initial position) are added to each node to process the pattern string with anchoring. Finally, the experiment results show that either the number of moving or matching time, the proposed algorithm is better than AC_BM algorithm and AC_SUNDAY algorithm.