Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning analytics: a review of the literature

Many factors have reinforced institutions’ interest in learning analytics, which has become one of the most important optimal solutions to education problems and performance improvement, due to their ability to enable educators to quickly identify users behavior patterns and learn about the nature of students development stages, in addition to their ability to provide the best data, to collect data instantly, and to measure and analyze learners data and their contexts in order to understand the learning process and the environments in which learning takes place. Learning Analytics is a kind of intelligent use of data, one of the most important recent trends to improve accountability at all levels of education. Learning analysis programs provide a variety of methods for monitoring learners performance, in addition to providing tools that encourage continuous improvement. The main goal of learning analytics is to develop structures that help control content, levels of pedagogical support, and other personalized services by capturing, processing, reporting, and working with data. The aim of this article is to provide a view of the studies carried out on learning analytics in recent years.
End-to-End Analysis of Event Chains under the QNX Adaptive Partitioning Scheduler

Modern autonomous cars run classic AUTOSAR applications alongside advanced driving assistance systems on a single-vehicle computer. Ensuring safety and predictability in such a complex system is challenging and requires temporal isolation between the various components. A promising solution is the POSIX-compliant QNX operating system: it meets the automotive standards for functional safety at the highest level (ISO 26262 ASIL-D) and provides temporal isolation through the Adaptive Partitioning Scheduler (APS), a resource reservation algorithm that guarantees processor bandwidth to groups of threads. These guarantees make it an ideal platform for composing diverse and complex applications on centralized vehicle computers. However, so far, there is no precise description or analysis of the APS reservation mechanism in real-time literature. In this paper, we provide the first description of the behavior of the APS from a real-time point of view and validate the results by running experiments on a real QNX platform. Based on the derived scheduler rules, we develop a response-time analysis to bound the end-to-end latency of event chains under APS. Finally, we evaluate different design strategies on a case study based on a real autonomous construction vehicle.
Performance Analysis of Hybrid MIMO/FSO Architecture Based on Optical Network

To get around the logistics, we need to figure out a solution to the last-mile challenge in communication systems. Wireless optical communication (OWC) systems, one of which is discussed in this paper, offer a promising alternative to wired connections. An FSO implementation is the best option for this design. As a result, we propose a multi-input, multi-output (MIMO) based FSO for OWC communication. Our simulation's signal structure appeared to be in decent condition after ten iterations. As a result, the FSO's eye opening is bigger in all iterations for all channels. With a BER of 1.45E-06, this system achieves an average maximum Q-factor of (4.8539). (−65.2048dBm) average total power, but average signal power is (−65.2048dBm) (−65.2064dBm) In addition, the system's average noise power is (−103.223185dBm) and its average maximum eye height is (1.54E-05a.u.) all of which are excellent.
A Bad IDEa: Weaponizing uncontrolled online-IDEs in availability attacks

Botnets are an ongoing threat to the cyber world and can be utilized to carry out DDoS attacks of high magnitude. From the botmaster's perspective, there is a constant need for deploying more effective botnets and discovering new ways to bolster their bot ranks. Integrated Development Environments (IDEs) have been essential for software developers to write and compile source code. The increasing need for remote work and collaborative workspaces have led to the IDE-as-a-service paradigm that offers online code editing and compilation with multiple language support. In this paper, we show that a multitude of online IDEs do not run control checks on the user code and can be therefore lever-aged by a botnet. We examine the concept of uncontrolled execution environments and present a proof of concept to show how uncontrolled online-IDEs can be weaponized to perform large-scale attacks by a botnet. Overall, we detect a total of 719 online-IDEs with uncontrolled execution environments and limited sandboxing. Lastly, as ethical disclosure, we inform the IDE developers and service providers of the vulnerabilities and propose countermeasures.
Adaptive Sliding Mode Control of a Coupled Tank System

In this study, an adaptive sliding mode controller is designed to control liquid level on a single input single output coupled tank system. The dynamic model of the coupled tank system is explained and the conventional sliding mode control method is presented. One of the top challenging parts of the conventional sliding mode control design is defining the upper bound of the uncertainties on the system. An adaptive scheme can be used to overcome this problem. An adaptive sliding mode controller is proposed in this study to define the upper bound of the uncertainties on the coupled tank system. Both the conventional sliding mode control and the adaptive sliding mode control techniques are employed on a simulation comparatively. The simulation results prove that the proposed method provides a more effective performance to control liquid level compared to the conventional sliding mode control method.
Control of Dual Stator Induction Generator Based Wind Energy Conversion System

The work presents a robust Control scheme for a Dual Stator Induction Generator (DSIG) based Wind Energy Conversion System (WECS). This developed scheme mitigates deviations in torque and flux of the generator that occurs due to imbalance in the currents of the generator. The models developed comprises of a wind turbine system, a six phase SCIG, two – three phase converters for controlling the generator and a three-phase converter that connects the generator with the grid. A back-to-back voltage source converter was utilized for interfacing the DSIG with the grid. The non-linear model predictive control (NMPC) scheme was used for the control. All simulations were carried out in Matlab 2019b. The result obtained for the electromagnetic torque values obtained showed proper tracking of the reference torque, with an average percentage deviation of 17.11%. These minimal deviations were due to the large variations of the wind speed during these periods. While for the direct rotor flux, it was observed that, the flux settles at time 0.7 seconds which is well within the settling time limit. This showed that the developed scheme has properly decoupled the direct rotor flux from the quadratic rotor flux. For the grid side, the DC link voltage obtained has a settling time of 0.8 seconds, with a minimal overshoot.
MTTR Prediction of railway rolling stock using regression algorithms

Railway equipment is very important in terms of maintenance, due to the complexity of its mechanical and electrical systems and the number of interchangeable parts. Thus, in order to ensure reliable and safe performance, railway companies must carry out regular maintenance and replace faulty equipment in a timely manner, without disrupting train operations. In addition, maintenance facilities are spread over the entire rail network, so an intelligent solution is needed to predict maintainability following breakdowns and allow technicians to take the necessary measures to ensure the proper functioning of all their equipment. Mean Time To Repair (MTTR) is one of the performance indicators used to determine the maintainability of an asset. Using historical data, in combination with failure data and maintenance action data, we explore several methods: linear and polynomial regression algorithms, Lasso regression, Ridge regression and random forests to build MTTR prediction models for the rail system. The results obtained are very promising and show that the linear and polynomial algorithms outperform the others on the prediction of maintenance performance in terms of MTTR.
Analysis of the Effect of Off-chip Memory Access on the Performance of an NPU System

Numerous CNN accelerators, called neural processing units (NPUs), have been proposed and developed recently to accelerate CNN computation with a customized chip. To minimize the DRAM access volume, NPUs commonly have a large on-chip memory and try to reuse the fetched data from the off-chip DRAM maximally. While extensive researches have been conducted to minimize the effect of off-chip DRAM access on the performance in the NPU design, little attention is paid to the detailed analysis of the off-chip DRAM access overhead on the NPU performance. In this paper, we analyze the effects of off-chip DRAM access latency on the NPU performance and how the off-chip SRAM changes the DRAM access latency based on a cycle-accurate system simulation environment.
Guaranteeing Safety Despite Physical Errors in Cyber-Physical Systems

This paper considers a cyber-physical system with a so-called “self-looping” node that repeats the inner-loop for physical situation awareness, i.e., more loops for more harsh physical situations. Regarding such a self-looping node, we observe the existence of physical errors that make the looping useless and eventually cause a critical failure. To prevent such a critical failure despite a physical error, this paper proposes a novel mechanism by introducing “time wall” and “safety backup”. The time wall limits the time budget for the self-looping node so as to switch to the safety backup while still meeting the deadline to prevent critical failure despite physical errors. Our experiments through both simulation and actual implementation show that the proposed mechanism gives a comparable accuracy with the existing methods in normal cases while completely preventing the critical failure in physical error cases.
Frequency Response of Smartphones at the Upper Limit of the Audible Range

The lack of technical specifications for mobile phones limits their potential for use in new localization systems based on using audio signal processing. One of the most important parameters is sensitivity to the frequencies used for localization. In this paper, we examine the missing frequency response data of commercially available smartphones. Our main goal was to identify sensitivity in the upper limit of the audible range. We tested 14 smartphones and present the measurements using frequency response curves. The results show that most of the measured mobile phones may also be used in the systems utilizing frequencies above the hearing range.
Real-Time Walk Detection for Robotic Hip Exoskeleton Applications

Detection of the user’s walking is a critical part of exoskeleton technology for the full automation of smooth and seamless assistance during movement transitions. Researchers have taken several approaches in developing a walk detection system by using different kinds of sensors; however, only a few solutions currently exist which can detect these transitions using only the sensors embedded on a robotic hip exoskeleton (i.e., hip encoders and a trunk IMU), which is a critical consideration for implementing these systems in-the-loop of a hip exoskeleton controller. As a solution, we explored and developed two walk detection models that implemented a finite state machine as the models switched between walking and standing states using two transition conditions: stand-to-walk and walk-to-stand. One of our models dynamically detected the user’s gait cycle using two hip encoders and an IMU; the other model only used the two hip encoders. Our models were developed using a publicly available dataset and were validated online using a wearable sensor suite that contains sensors commonly embedded on robotic hip exoskeletons. The two models were then compared with a foot contact estimation method, which served as a baseline for evaluating our models. The results of our online experiments validated the performance of our models, resulting in 274 ms and 507 ms delay time when using the HIP+IMU and HIP ONLY model, respectively. Therefore, the walk detection models established in our study achieve reliable performance under multiple locomotive contexts without the need for manual tuning or sensors additional to those commonly implemented on robotic hip exoskeletons.
Supervised learning based intrusion detection for SCADA systems

Supervisory control and data acquisition (SCADA) systems play pivotal role in the operation of modern critical infrastructures (CIs). Technological advancements, innovations, economic trends, etc. have continued to improve SCADA systems effectiveness and overall CIs’ throughput. However, the trends have also continued to expose SCADA systems to security menaces. Intrusions and attacks on SCADA systems can cause service disruptions, equipment damage or/and even fatalities. The use of conventional intrusion detection models have shown trends of ineffectiveness due to the complexity and sophistication of modern day SCADA attacks and intrusions. Also, SCADA characteristics and requirement necessitate exceptional security considerations with regards to intrusive events’ mitigations. This paper explores the viability of supervised learning algorithms in detecting intrusions specific to SCADA systems and their communication protocols. Specifically, we examine four supervised learning algorithms: Random Forest, Naïve Bayes, J48 Decision Tree and Sequential Minimal Optimization-Support Vector Machines (SMO-SVM) for evaluating SCADA datasets. Two SCADA datasets were used for evaluating the performances of our approach. To improve the classification performances, feature selection using principal component analysis was used to preprocess the datasets. Using prominent classification metrics, the SVM-SMO presented the best overall results with regards to the two datasets. In summary, results showed that supervised learning algorithms were able to classify intrusions targeted against SCADA systems with satisfactory performances.
Kinematics and Autoregressive Model Analysis of a Differential Drive Mobile Robot

The usage areas of mobile robots continue to increase due to the wide variety of advantages they provide. In particular, differential drive mobile robots (DDMR) have been widely preferred in the last decade due to the benefits they offer in current studies and the potential they promise. Before using them in advanced studies, it is quite useful in many ways to conduct basic kinematic investigation that can provide key information about these robots. This not only saves time and money, but also directly affects the accuracy of these studies. In this study, kinematic analysis of a small DDMR driven by servo motors was carried out with basic locomotion tests and system identification. The mobility of the robot along the predefined and geometrically expressible routes was examined and its kinematic limits were tried to be determined. Through the system identification tests, the consistency of the robot's response to the input commands was investigated and the possible causes of errors were discussed. Moreover, the behavior of the robot was modeled and compared with parametric ARX (autoregressive with external input) model due to its simplicity and efficiency. As a result of the motion tests, it has been observed that the robot has some limitations in rotational movements. With the system diagnostic tests, it has been determined that the two main wheels of the robot produce different outputs to the system inputs.
Classification, Design, and Challenges of Unmanned Ships in Marine Transportation

Nowadays, there is a growing need for unmanned surface vehicles (USVs) for both military and civilian applications accompanied by a broader concept of smart or intelligent ships. In the past decade, various sizes and types of USVs have been developed to satisfy different marine purposes. However, most of the existing USVs are still in small sizes. In the future, large-scale unmanned ships for maritime transportation will be the final state of USVs. This paper focuses on the development phases, design components, and technology challenges of unmanned ships. After reviewing the state-of-the-art literature of research on USVs, the early stages of unmanned ships are categorized as conventional ships, enhanced ships, smart ships, remote-controlled ships, and autonomous ships. The design of unmanned ships is separated into ten components, which are the ground station, the ship station, the guidance, navigation, and control system, the communication system, the data collection system, the hull form, the maneuvering system, the propulsion system, the energy source, and test and measurements. Accordingly, the existing methods and challenges of the developments on unmanned ships are discussed.
A proposal learning strategy on CNN architectures for targets classification

Synthetic Aperture Radar (SAR) imagery has a great potential in remote sensing field in order to maintain aerial, land and maritime surveillance. To improve this potential for several goals, Machine Learning (ML) techniques are used for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality with several modality and to map classes with very complex and varied characteristics.We focus in this paper on the ship classification from SAR imagery using a deep learning algorithms with transfer leaning mechanisms. To improve the recognition performances, several learning strategies are proposed. We illustrate these issues through applying a transfer learning on deep learning ship classification with several learning strategies. We used in this work especially no huge labelled dataset available for training. The aim is thus to see how to leverage knowledge from models pre-trained on other tasks (source tasks) and use them for target classification (target task).
Bone age assessment using deep learning architecture: A Survey

Skeletal bone age evaluation with X-ray pictures is a routine clinical approach for detecting any abnormalities in bone development in children and neonates. An assessed bone age represents the development of the actual degree, and a substantial difference between the assessed and chronological ages may suggest a growth issue. As a result, skeletal bone age evaluation is used to check for growth anomalies, genetic issues, and endocrine illnesses. The most frequent method for calculating bone age is radiography of the hand and wrist. Additionally, automated approaches for evaluating x-ray of the left hand and wrist development, which reduces the variability between raters in comparison to human ways. No-radiation-based approaches for viewing hand and wrist bones, as in ultrasonography, had been suggested, but are not like accurate radiographic methods. Methods based on magnetic resonance imaging (MRI) are being developed, but further study is needed. Another study is dental age a method of determining bone age that also provides estimates of bone maturity. Precise age estimation was essential to use the appropriate treatment method in pediatric patients also for forensic purposes. The left-hand wrist radiographs are thought it’s the most accurate, more uniform skeleton evaluation approach in this survey as a classification problem, this work shall look at numerous hand and wrist exercises evaluation of skeletal maturity.
Impact of TQM/Six Sigma practices on company’s performance: Tunisian context

PurposeThe purpose of this paper is to explore the link between TQM/Six Sigma practices and their impact on performance.Design/methodology/approachBased on the literature review of research that investigated the TQM and Six Sigma best practices and performance, the TQM/Six Sigma practices are classified into three key categories: top management commitment and support (TMCS), infrastructure and core practices. This paper proposes a path model linking these practices and performance. The empirical data were obtained from a survey of 91 Tunisian companies in different sectors. The research model was tested using partial least squares (PLS) technique based on SmartPLS software.FindingsThe result shows that the implementation of Six Sigma practices is based majorly on the TQM practices. Also, it reveals the positive impact of TQM/Six Sigma practices on performance. Furthermore, the analysis of path model reveals the relative interdependence and significant link between TMCS, infrastructure, core practices and performance.Research limitations/implicationsThe path model tested in this study combines the TQM/Six Sigma practices and reveals their link with performance, which enhance the research theory of both two approaches. Moreover, it will be a useful support for the quality expert on the effective integration of TQM and Six Sigma methods.Originality/valueThis study is the first one which studies the link between TQM/Six Sigma practices and company’s performance in the Tunisian context based on PLS technique.
Adesão à terapêutica imunossupressora após o transplante de fígado: revisão integrativa

Objetivo: investigar as evidencias disponiveis na literatura sobre a nao adesao a terapeutica imunossupressora entre pacientes submetidos ao transplante de figado. Metodo: revisao integrativa da literatura, que incluiu investigacoes cuja amostra era composta por pacientes com idade igual ou superior a 18 anos, submetidos a transplante de figado. Excluiram-se as que continham pacientes submetidos a transplantes de multiplos orgaos. Para a selecao dos artigos foram consultadas as bases Medline/Pubmed, CINAHL, LILACS, Scopus e Embase. O periodo de busca determinado correspondeu a data inicial de indexacao das diferentes bases, ate a data limite de 10 de fevereiro de 2015, empregando-se os descritores controlados e nao controlados: liver transplantation, hepatic transplantation, liver orthotopic transplantation, medication adherence, medication non-adherence, medication compliance e patient compliance. Resultados: foram localizadas 191 investigacoes, das quais 10 atenderam aos objetivos do estudo e foram agrupadas em quatro categorias, a saber: processo educativo e ocorrencia de nao adesao; nao adesao relacionada ao numero de doses diarias dos medicamentos imunossupressores; metodos de deteccao da nao adesao e efeitos colaterais da terapeutica. Conclusao: verificou-se a existencia de fatores de risco relacionados ao servico de saude, como controle e reducao do numero de doses; relacionados ao individuo, como ser do sexo masculino, divorciado, usuario de alcool ou outras substâncias, expostos a menor suporte social e portadores de transtorno mental.
Magnetic gear with intersecting axes and straight stationary pole-pieces

Based on the magnetic field modulation effect, this article presents a magnetic gear with intersecting axes and straight stationary pole-pieces. Except for a higher utilization of permanent magnets and a greater output torque, the magnetic gear with intersecting axes and straight stationary pole-pieces provides several advantages, such as a smaller volume, higher torque density, and lower processing cost. The magnetic gear with intersecting axes and straight stationary pole-piece topology is herein introduced and the deduced constraints of the structural design are outlined. The transmission mechanism is demonstrated by analyzing the output torque in the middle of the air gaps. The effects of the main design parameters on the maximum static torque are evaluated using the finite element method. Based on the orthogonal experimental design and the response surface method, the optimum design results are provided. Prototype of the magnetic gear with intersecting axes and straight stationary pole-pieces has been realized and the static torque has been verified experimentally.