Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving La Redoute's CI/CD Pipeline and DevOps Processes by Applying Machine Learning Techniques

The complexity inherent to software development and maintenance – not only in technical terms, but also from a human perspective – entails challenges that can be addressed as learning problems. Machine learning techniques may be employed as tools to gain insight about strategies that can lead to the improvement of the quality of software processes and products. Defect proneness prediction, in particular, may be identified as an active research field. As stated by DevOps guidelines, the possibility of obtaining quick feedback allows teams to operate in an agile mode in which communication, decision taking and problem solving are expeditious, allowing companies to boost business value. This paper describes ongoing research for applying machine learning techniques to improve the quality of processes and products inside the DevOps pipeline of the La Redoute's IT department.
Location Aware Clustering and Trust Management in Mobile Ad Hoc Networks

Properly clustering a Mobile Ad Hoc Network (MANET) is an efficient method to ensure optimal network resources exploitation. A lot of clustering schemes have been proposed, most of them suffering from the same drawback i.e., cluster lifetime instability due to the mobility of the nodes. In order to cope with this particular problem, along with trust issues that arise and have been partially investigated previously, we propose a time-optimized clustering scheme which improves the lifespan of the network clusters, thus leading to increased trust efficiency, since malicious nodes are easier to locate. The malicious node spotting is achieved by combining individual node observations, as well as the collective cluster trust estimation regarding the reputation vector of a specific node.
Development of the OMUSE/AMUSE Modeling System

The Oceanographic Multipurpose Software Environment (OMUSE, [1]) is an open source framework developed for oceanographic and other earth system modelling applications. OMUSE provides a homogeneous environment to interface with numerical simulation codes. It was developed at the IMAU (Utrecht) using coupling technology developed for astrophysical applications in the AMUSE project at Leiden Observatory[2,3]. OMUSE simplifies the use and deployment of numerical simulations codes. Furthermore, the design of the OMUSE interfaces (figure 1) allow codes that represent different physics or span different ranges of physical scales to be easily combined in novel numerical experiments. The use cases for OMUSE range from running simple numerical experiments with single codes and the addition of data analysis tools in model runs, to setting up fairly complicated and strongly coupled solvers for problems that are intrinsically multi-scale and/or require different physics. Here, we will present the design of OMUSE as well as give examples of the types of the couplings that can be implemented using OMUSE. The example provided by AMUSE and OMUSE suggests that application of the same interfacing philosophy to a more extensive set of disciplines is possible. In order to facilitate this a better separation of the core framework and domain specific code is necessary. We will present ongoing work to support meteorological and hydrological applications and the use of the framework as the computational core in the eWatercycle project [4]. For this, adaptations are made to improve the interoperability with existing interface efforts (such as the BMI) and we discuss developments regarding the encapsulation of OMUSE/AMUSE and its component models in containers. This will facilitate the installation for first time users, removing a barrier in this respect. In addition to this we anticipate this to also offer more flexible deployment options for the framework.
Methods and Equipment for On-Line Testing of Hall Sensors Based on Semiconductor, Metal and Graphene Materials in the Nuclear Reactors' Neutron Fluxes

This article describes methods for investigating Hall sensors in neutron fluxes of research nuclear reactors. The work shows the equipment and results of on-line measurements of the electrophysical parameters of Hall sensors in the process of their irradiation with large doses of neutrons. The materials of the sensitive area of the Hall sensors were indium-containing materials of groups III -V, gold nanofilms and monoatomic graphene. The long-term stability of semiconductor sensors under continuous operation in one of the largest fusion reactors Joint European Torus is shown.
ScholAR: A Collaborative Learning Experience for Rural Schools Using Augmented Reality Application

Collaborative learning involves working in groups to solve a problem or perform a task. Collaborative learning is often encouraged in rural schools as due to lack of space and infrastructure, students are made to sit on floors and taught together within and between groups. Evidently, in rural schools, technologies have been introduced to support the existing teaching methods. Augmented Reality is one such technology that can provide a collaborative interactive experience. In our study, we provided an AR based application named 'ScholAR' to experimental group of 16 students of 7th grade as at that age they cultivate the ability to reason logically and develop conceptualizing skills. The application involved six tasks on the Mathematics topic of Introduction to 3D Solids targeted to enhance the spatial visualization skills of the students. We did a comparative study with the control group of 16 students who were taught the same topic using physical 3D models and the usual teaching method followed in their school. We report the results of this study, observations and analysis of the use of this AR application in a collaborative environment and the effect of collaboratively using the AR application on the students' performance.
Analysis of Oscillation Mechanism during Turn-on of SiC MOSFET

Silicon Carbide Metal Oxide Semiconductor Field Effect Transistor (SiC MOSFET) has recently been found to exhibit better electrical and thermal performance than the traditional Silicon devices due to superior material properties of SiC coupled with the fast switching speed of MOSFETs. Its application to power electronic systems is however accompanied by high switching ringing aggravated by its very fast switching speed. The analysis in this paper focuses on three aspects of turn-on oscillation: determination of dominant transition causing oscillations, derivation of transient and complete turn-on frequency and influence of voltage trajectory change on oscillations after complete turn-on. The ringing of SiC MOSFET during turn-on transient is analyzed and its influence on oscillations after complete turn-on investigated. It is found that the frequency of the turn-on transient oscillations is influenced by the rate of rise of drain current. It is further revealed that a high transient frequency can lead to a change in the trajectory of the falling drain voltage which consequently leads to a lower oscillation magnitude after complete turn-on. Furthermore, the voltage edge is found to be dominant over current edge in triggering oscillations after both complete turn-on and turn-off.
Interpreting Neural-Network Players for Game 2048

Game 2048 is a stochastic single-player game and development of strong computer players for 2048 has been based on N-tuple networks trained by reinforcement learning. In our previous study, we developed computer players for game 2048 based on convolutional neural networks (CNNs), and showed by experiments that networks with three or more convolution layers performed much better than that with two convolution layers. In this study, we analyze the inner working of our CNNs (i.e. white box approach) to identify the reasons of the performance. Our analyses include visualization of filters in the first layers and backward trace of the networks for some specific game states. We report several findings about inner working of our CNNs for game 2048.
Indoor Localisation using Aroma Fingerprints: Comparing Nearest Neighbour Classification Accuracy using Different Distance Measures

Measurements from an ion mobility spectrometry electronic nose (eNose) can be used for distinguishing different rooms in indoor localisation. An earlier study showed that the Nearest Neighbour classifier with Euclidean distance for features provides reasonable accuracy under certain conditions. In this paper 66 alternative distance measures are compared to the Euclidean distance and principal component analysis (PCA) is applied to the data. PCA shows that the measurements on the various channels of the eNose are correlated and that using principal components 1, 2 and 4 increases the accuracy considerably. Furthermore, the experiments revealed three Pareto optimal distance measures that reduce the misclassification rate between 9-10% while using only 82-88% of the search time compared with Euclidean distance.
Quality of Service Sensitive Routing for Software Defined Network Using Segment Routing

Communication networks are growing in size at an increasing rate with the traditional network. Quality of Service (QoS) is needed for new applications and new services on the Internet. Software Defined Network (SDN) techniques are appeared to be more flexible and programmable network. By utilizing the characteristics of SDN, this paper proposes QoS sensitive routing for available bandwidth by using segment routing (SR) to find the best path according to the QoS requirements. This paper focuses on monitoring of flow entries among switches and finding the feasible path over QoS-based routing scheme. To provide network throughput to the end hosts, the available bandwidth has to be accomplished. The routing algorithm finds the path which is feasible to meet the desired QoS of data flows. If the required QoS cannot provide to the requested flow, the controller determines how to calculate depending on the request from the switch. If the initial path will not be able to achieve the available bandwidth, the algorithm reroutes the higher bandwidth flow. This paper suggests improvements in QoS using Open Network Operating System (ONOS) controller with OpenFlow protocol and the simulation experiments with effective QoS are suitable for the future internet using SDN.
Mission Profile Emulator for Permanent Magnet Synchronous Machine in Voltage-Response Mode Based on Three-phase Power Electronic Converter

The mission profiles of Permanent Magnetic Synchronous Machine(PMSM) drive systems are becoming increasingly complicated in various emerging applications, such as wind power generation and electric vehicles, etc. Comprehensive tests and analysis of the motor drive system are getting crucial. Many aspects need to be taken into consideration such as machine parameters, load torques and control methods, etc. In this paper, a mission profile emulator based on power electronic converter for PMSM with voltage response is therefore proposed. The configuration scan be flexibly adjusted, and can generate similar electrical and mechanical behaviors as a real PMSM. The mission profile emulator avoids the use of real mechanical loads and fills the vacancy of study in mission profile emulators for PMSM working in voltage-response mode. The design of the emulator is also illustrated with PLECS simulation and experimental results.
Curation of Image Data for Medical Research

Microfocus X-ray computed tomography (µCT) and 3D microscopy scanning create scientific data in the form images. These images are each several tens of gigabytes in size. E-Scientists in medicine require a user-friendly way of storing the data and related metadata and accessing it. Existing management systems allow computer scientists to create automatic image workflows through the use of application programming interfaces (APIs) but do not offer an easy alternative for users less familiar with programming. We present a new approach to the management and curation of biomedical image data and related metadata. Our system, Mata, uses a network file share to give users direct access to their data and also provides access to metadata. Mata also enables a variety of visualization options as required by e-Scientists in medicine.
How Is Logging Practice Implemented in Open Source Software Projects? A Preliminary Exploration

Background: Logs are the footprints that software systems produce during runtime, which can be used to understand the dynamic behavior of these software systems. To generate logs, logging practice is accepted by developers to place logging statements in the source code of software systems. Compared to the great number of studies on log analysis, the research on logging practice is relatively scarce, which raises a very critical question, i.e. as the original intention, can current logging practice support capturing the behavior of software systems effectively? Aims: To answer this question, we first need to understand how logging practices are implemented these software projects. Method: In this paper, we carried out an empirical study to explore the logging practice in open source software projects so as to establish a basic understanding on how logging practice is applied in real world software projects. The density, log level (what to log? ) and context (where to log? ) are measured for our study. Results: Based on the evidence we collected in 28 top open source projects, we find the logging practice is adopted highly inconsistently among different developers both across projects and even within one project in terms of the density and log levels of logging statements. However, the choice of what context the logging statements to place is consistent to a fair degree. Conclusion: Both the inconsistency in density and log level and the convergence of context have forced us to question whether it is a reliable means to understand the runtime behavior of software systems via analyzing the logs produced by the current logging practice.
Personalized Content-Based Music Retrieval by User-Filtering and Query-Refinement

In recent years, music is an important media because it can relax us in our daily life. Therefore, most people listen to music frequently and current music websites offer online listening services. However, because the semantic gap, it is not easy to effectively retrieve the user preferred music especially from a huge amount of music data. For this issue, this paper presents a personalized content-based music retrieval system that integrates techniques of user-filtering and query-refinement to achieve high quality of music retrieval. In terms of user-filtering, the new user interest can be inferred by the user similarities. In terms of query-refinement, the user interest can be guided to the potential search space by iterative feedbacks. The experimental results show the proposed method does improve the retrieval quality significantly.
Code Reuse in Stack Overflow and Popular Open Source Java Projects

Solutions provided in Question and Answer (Q&A) websites such as Stack Overflow are regularly used in Open Source Software (OSS). However, many developers are unaware that both Stack Overflow and OSS are governed by licenses. Hence, developers reusing code from Stack Overflow for their OSS projects may violate licensing agreements if their attributions are not correct. Additionally, if code migrates from one OSS through Stack Overflow to another OSS, then complex licensing issues are likely to exist. Such forms of software reuse also have implications for future software maintenance, particularly where developers have poor understanding of copied code. This paper investigates code reuse between these two platforms (i.e., Stack Overflow and OSS), with the aim of providing insights into this issue. This study mined 151,946 Java code snippets from Stack Overflow, 16,617 Java files from 12 of the top weekly listed projects on SourceForge and GitHub, and 39,616 Java files from the top 20 most popular Java projects on SourceForge. Our analyses were aimed at finding the number of clones (indicating reuse) (a) within Stack Overflow posts, (b) between Stack Overflow and popular Java OSS projects, and (c) between the projects. Outcomes reveal that there was up to 3.3% code reuse within Stack Overflow, while 1.0% of Stack Overflow code was reused in recent popular Java projects and 2.3% in those projects that were more established. Reuse across projects was much higher, accounting for as much as 77.2%. Our outcomes have implication for strategies aimed at introducing strict quality assurance measures to ensure the appropriateness of code reuse, and licensing requirements awareness.
La traducción como acto ético-moral en los territorios fronterizos: o por qué la voz lírica de Demetria Martínez

This article contextualizes the question "why we translate that which we translate" in the tensioned territory shared by México and the United States. Said contextualization establishes a distance between the act of translation as a means for philosophical inquiry and the immediacy that is present in the act of interpretation as well as the inevitable contradictions that emerge from the act of translation. The act of translation that takes place in this article contributes to the tension within the U.S. side of the territory by translating two poems by Demetria Martínez, from English into Spanish, from a post-colonial and feminist perspective. Both poems in their original language and the act of translation shed further light on the inequity of the toposocial order that determines the experience of the subject within the territory as well as the ethical-moral dilemma generated by present border relations.
Analysis and Prediction of Employee Turnover Characteristics based on Machine Learning

Employee turnover indicates the staff decides to leave the company. Along with the fast development of economic and industries, employee turnover phenomenon becomes popular gradually in recent years. On one hand, the staff decides to leave the company due to various reasons. On the other hand, staff retention and job stability impact the normal operation of the company. Companies need to grasp the major factors of employee turnover, and then take relevant measures to deal with this problem. This paper employs machine learning technique to sort out the characteristics of employee turnover. Furthermore, we adopt GBDT algorithm and LR algorithm to fit the characteristic model which influences employee turnover. Finally, this paper implements the employee turnover prediction in realistic companies, which provides an effective reference for companies to reduce the turnover rate of employees.
GaN HEMT Driving Scheme of Totem-Pole Bridgeless PFC Converter

Due to the excellent reverse recovery characteristics, gallium nitride high electron mobility transistor (GaN HEMT) is the ideal device for totem-pole bridgeless PFC circuits. However, the driving technology of GaN HEMT deserves to be concerned when working in a complementary conduction mode in a half-bridge circuit. Because of the parasitic parameters, the driving signal may oscillate and even malfunction may occur, hence the device may be damaged. In this paper, the influence of the impedance of the driving circuit and Miller capacitance on the drive circuit is analyzed. Meanwhile, the corresponding solution is given and a feasible drive scheme is proposed. An experiment platform of the totem-pole bridgeless PFC converter is built and experimental results could verify the effectiveness of the proposed drive scheme.
Design of High Efficiency Inverter with Air Cooling for Electric Vehicle

This paper investigates a high efficiency inverter design for EV. It firstly compares the inverter with respect to topologies, device selections and modulation scheme at key operating points of a typical driving cycle. Secondarily the thermal design with air cooling is discussed. Then a two-level inverter prototype is built. Finally, testing of the inverter prototype with passive load is provided.
Load Balancing Method for IoT Sensor System Using Multi-access Edge Computing

In recent years, small terminals such as smartphones equipped with various sensor functions have become widespread, and studies on wireless sensor networks and edge computing are advancing from the use of the latest technology such as Internet of Things (IoT) and intelligent transport systems. However, there are still many problems such as cooperation between edges and clouds, off-load, and how to realize orchestration. In addition, research on offloading when load conditions dynamically fluctuate and load balancing among edge servers has not been investigated. In this paper, we propose a load balancing method for an IoT sensor system using multi-access edge computing to equalize the load of edge servers when events such as a traffic congestion and guerrilla rainstorms occur.
A New Method of Analysis of the Sign Algorithm Based on Gaussian Distributed Tap Weights

In this paper, we develop statistical analysis of an FIR-type adaptive filter employing the Sign Algorithm (SA) with a Gaussian regressor, based on a proposed assumption that the tap weights are Gaussian distributed. The prior analysis of the SA assumed jointly Gaussian error and regressor, for which theoretical convergence has a limited accuracy, particularly when the variance of the tap weights is not small enough. The proposed Gaussian distributed tap weight assumption has been proven to improve the accuracy of the analysis. We propose use of a Gaussian probability density function of the error given the tap weight misalignment under the Gaussian distributed tap weight assumption. In the analysis, we develop equations to theoretically calculate transient behavior of filter convergence in terms of Excess Mean Square Error. In experiments, we examine the proposed new method of analysis of the SA, where for some examples theoretical convergence is compared with simulation results, to find good agreement between simulation and theory, showing the validity of the proposed assumption and analysis.
Graphene Oxide-Chitosan Based Ultra-Flexible Electrochemical Sensor for Detection of Serotonin

This paper reports a Graphene Oxide (GO)-Chitosan based chemiresistor for in situ label-free electrochemical detection of serotonin. This ultra-flexible sensor consists of platinum (Pt) electrodes, separated by a 60 µm gap, patterned on 2.5 µm thick GO-Chitosan film which acts as the active sensing material. Due to its low film thickness, our proposed ultra-flexible sensor is conformable onto curvy substrates having radii of curvature (r) as low as the submillimeter range. By wrapping the sensor around a cylindrical probe of r = 500 µm this paper demonstrate their use as Sensor on Probe (SoP) platform for easy and fast detection of serotonin. The sensors were characterized with synthetic blood equivalent (Dulbecco's modified Eagle medium (DMEM) medium and showed good electrical stability over a dynamic range of 0. 2µM-2mM. With its biocompatible, biodegradable and eco-friendly properties, the GO-Chitosan based sensors could find application in rapid point-of-care (POC) determination of diseases such as carcinoid syndrome.