Miracle-dz/UniFAR-FTUs huggingface dataset:
Educational Data Mining for Tutoring Support in Higher Education: A Web-Based Tool Case Study in Engineering Degrees

This paper presents a web-based software tool for tutoring support of engineering students without any need of data scientist background for usage. This tool is focused on the analysis of students’ performance, in terms of the observable scores and of the completion of their studies. For that purpose, it uses a data set that only contains features typically gathered by university administrations about the students, degrees and subjects. The web-based tool provides access to results from different analyses. Clustering and visualization in a low-dimensional representation of students’ data help an analyst to discover patterns. The coordinated visualization of aggregated students’ performance into histograms, which are automatically updated subject to custom filters set interactively by an analyst, can be used to facilitate the validation of hypotheses about a set of students. Classification of students already graduated over three performance levels using exploratory variables and early performance information is used to understand the degree of course-dependency of students’ behavior at different degrees. The analysis of the impact of the student’s explanatory variables and early performance in the graduation probability can lead to a better understanding of the causes of dropout. Preliminary experiments on data of the engineering students from the 6 institutions associated to this project were used to define the final implementation of the web-based tool. Preliminary results for classification and drop-out were acceptable since accuracies were higher than 90% in some cases. The usefulness of the tool is discussed with respect to the stated goals, showing its potential for the support of early profiling of students. Real data from engineering degrees of EU Higher Education institutions show the potential of the tool for managing high education and validate its applicability on real scenarios.
A Spatial Flow Clustering Method Based on the Constraint of Origin-Destination Points’ Location

With the development of mobile positioning technology, a large number of Origin-Destination (OD) flow data with spatial and temporal details have been produced. These OD flow could give us a great opportunity to research geographical phenomena such as spatial interaction and mobility patterns. The OD flow clustering approach is an effective way to explore the main mobility patterns of the objects. At the same time, similarity measurement plays a key role in OD flow clustering. However, most of the previous OD flow similarity measurement methods failed to make full use of the spatial information of the flow including spatial proximity and geometric similarity. In this paper, we considered both position information and geometric properties of OD flow and propose a new method to measure the spatial similarity between OD flows. Specifically, the proposed method sets the neighbor threshold with the length of OD flows and the parameter $\alpha $ dynamically. Based on the constraint of the OD points’ location, the directions of the flows are implicitly restricted. The sole-parameter $\alpha $ has a practical value as it determines the maximum length difference and the maximum directional difference that can be tolerated between similar flows. The proposed method passed a simulation experiment with synthetic flows and a case study with 283,008 taxi trips in Beijing in one day. The results show that the proposed method can discover the dominant mobility pattern from a large number of flow data effectively. In the case study, the dominant flow clusters reveal the taxi mobility patterns of residents at different distances in Beijing.
Evaluation of Asset Performance Using Integral Data Mining Scheme

The asset evaluation is an indispensable process in asset management and asset disposal. Seldom studies were found in exploring asset-usage performance of laboratory equipment in universities. The influential parameters, such as source, usage, and benefit that influence the performance of asset management in universities should be considered more structurally and deeply. In this study, a structural and complete index system and an integral data mining method are proposed to investigate the effect of related index parameters on the asset-usage performance of laboratory equipment in universities. The proposed integral method includes two phases. The first phase is to find out the relative weight of each influential parameter using the integral Analytic Hierarchy Process (AHP) scheme which extensively contains the index-weight and time-weight models based on Orness measure in conjunction with the method of relative closeness degree. The second phase is to investigate the global correlation between second-level indexes and the final performance using Grey System Theory (GST). The results of case study indicate that the proposed integral AHP-and-GST method can not only be used to globally and locally understand the effect of influential parameters on fixed asset of laboratory equipment, but also provide a decision-making basis for asset allocation.
Application of Linear Programming in Optimizing the Allocation of Agents in a 3-shift 24x7 eCustomer Service Operation

This paper highlights the Application of Linear Programming in determining the optimized allocation. The Focus of this study is maximizing the efficiency of the eCustomer Care Service for the year 2019 by determining the number of agents to be allocated on every shift and every channel of transaction to improve the resolution rate. The research employed Operations Research techniques in constructing Linear Programming Model. As a result of the study, the agents were allocated based on the number of receive cases and the resolve cases in each shift and improved the company productivity rate.
Computational Efficiency Analysis of SiC MOSFET Models in SPICE: Static Behavior

Transient simulation of complex converter topologies is a challenging problem, especially in detailed analysis tools like SPICE. Much of the recent literature on SPICE transistor modeling ignores the requirements of application designers and instead emphasizes detail, physical accuracy, and complexity. While these advancements greatly improve model accuracy, they also serve to increase computational complexity, making the resulting models less attractive to application designers. While some authors depart from this trend and present models which emphasize simulation speed, their results and analysis are limited to qualitative observation. This research develops a methodology to quantify the computational cost of model features and competitively benchmark models against each other. Additionally, it reviews recently published SiC mosfet models and presents a trade study on several candidate models likely to fare well in complex application simulations. Finally, this study also identifies key considerations which should be carried forward into future model design.
Maritime Target Detection using Frequency Estimation

Coherent detection schemes have been shown to offer performance benefits over non-coherent techniques in the maritime radar context. To further improve performance, the use of sparse representations with judiciously selected dictionaries has been proposed as a method to separate target returns from surrounding sea clutter. This paper exploits a robust frequency estimation scheme to effectively separate the target from clutter and noise. The approach offers two advantages: (1) it does not require a dictionary to be designed to support the scheme and (2) the scheme is capable of estimating multiple frequencies, thus suitable for targets with multiple Doppler components. In this work, we use both real and simulated sea clutter with synthetic targets for evaluation. It is found that the proposed technique can improve performance over traditional coherent detection for both point targets and more complex targets with multiple frequency components.
Accelerating Image Processing on FPGAs using HLS and PYNQ

Image processing is a highly computationally intensive process in which heterogeneous system architectures are required for high-resolution images or real-time applications. Zynq SoC is integrated in a processor and FPGA so that it can be an answer for such problems. The HLS tools reduced the FPGA implantation time while the PYNQ framework enhanced productivity. We implemented the Sobel edge detection application as an example of image processing in a Zynq MPSoC device. We presented how FPGAs can accelerate image processing and how HLS tools and the PYNQ framework enable rapid implementation.
Carbonate removal by acid fumigation for measuring the δ13C of soil organic carbon

Ramnarine, R. , Voroney, R. P., Wagner-Riddle, C. and Dunfield. K. E. 2011. Carbonate removal by acid fumigation for measuring the d 13 C of soil organic carbon. Can. J. Soil Sci. 91: 247-250. Complete removal of carbonates from calcareous soil samples is critical for accurate measurement of the quantity and isotopic signature (d13C) of soil organic carbon (SOC). Carbonates confound SOC and d13C measurements because they have d13C values ranging from -10‰ to +2‰, whereas those of soil organic carbon range from -27‰ to -13‰, depending on the source of plant residues. Commonly used methods for removing carbonates involve treatment with acid followed by repeated water washings; however, these methods are time consuming, labour-intensive and lead to losses of acid- and water-soluble organic carbon. Fumigation of soil samples with HCl was evaluated as an alternative method, and the time required for complete carbonate removal was determined in this study. Moistened soil samples, taken from 0- to 10-cm and 30- to 50-cm depths, were exposed to HCl vapours for periods of 0, 6, 12, 24, 48, 72, and 96 h, followed by measurements of total C and d13C using coupled elemental analyzer-isotope ratio mass spectrometry. The minimum time required to remove all carbonates was ca. 30 h and 56 h for surface and subsurface soils containing 0.80 and 1.94% inorganic C, respectively. Therefore, the fumigation period required is dependent on the total carbonate content of the sample and the nature of the carbonate (pedogenic vs lithogenic). In our study, the rate of removal of inorganic carbon was 0.08-0.10 mg h-1 for soil samples sizes with 2.4 to 5.8 mg of carbonate-C, a rate similar to previous studies on acid fumigation. A “correction factor” was used to account for a change in sample mass due to fumigation and is necessary for accurate determination of SOC concentration using our proposed methodology.
Attendance Monitoring With GPS Tracking on HR Management

During the new normal era in the Covid-19 era, several companies experienced problems in the form of queues when using the attendance list scan tool every morning and evening. The human resource management department needs to take strategic steps in the form of the latest innovations. That is what underlies us to design an Android-based attendance application using GPS online. The GPS on the smartphone will monitor the user's position so that it can be used to find out the position of attendance. The mobile phone number and Mac address of the employee's cellphone will be recorded in the system and become the employee ID. The use of a smartphone is better than the use of the RFID or microchip on the ID card because very often the ID card is left in the office, while the smartphone is always carried anywhere. We chose extreme programming using an object-oriented approach that includes planning, designing, coding, and testing. Besides, we also added features for submitting work leave, late permits, and absences from work by attaching evidence. The result obtained is the attendance application which makes it easier for employees to do attendance and this innovation is very helpful for human resource management in the Covid-19 era. In testing the application with 30 samples, it was found that 27 users could display the right position, while the other 3 positions were not suitable.
Improving Low Power Listening (LPL) Mechanism to Save Energy Consumption in WSN

As stated in the literature, Low Power Listening (LPL) duty cycle is one of the most common energy conservation solution for WSN. By using channel check mechanism, the purpose of LPL solutions is to reduce the energy consumption of the listening phase. In this paper, we propose to study the performances and limitations of this kind of solutions. Therefore, we deploy a ContikiMAC LPL on both real and simulated WSN platform to demonstrate the impact of LPL on the energy consumptions of the node radio and microcontroller but also on the application Quality of Service. Based on the obtained results, shortcomings of LPL solutions are highlighted and potential improvements are discussed such as the use of multi-parameter dynamic duty cycle.
An Unscented Kalman Filter-based Visual Pose Estimation Method for Underwater Vehicles

Pose estimation is a critical technique for underwater vehicles to perform observation and manipulation tasks. Acoustic positioning systems are traditionally used for underwater vehicles, but visual systems have the advantages of low cost, high resolution and high data rates, which have unique advantages in underwater vehicle positioning system. In this paper, an unscented Kalman filter-based visual pose estimation is designed based on the models of underwater vehicles and visual systems. Select the image information of feature points as measurement information, perform the 6DOF pose estimation based on the kinematics model of underwater vehicle. The proposed algorithm is validated by comparative tests with a conventional Perspective-N-Points algorithm, based on the visual data of visual targets collected by a real underwater vehicle.
Voltage and Reactive Power Control in Islanded Microgrids Without Communication Link

Microgrids can be operated in on-grid mode, but also in off-grid mode (island operation). In off-grid mode, grid forming units have to ensure the grid’s voltage and frequency stability. For more than one grid forming unit, the active and reactive power sharing has to be handled. This paper presents a method for voltage and reactive power control for systems without a superordinated control system or a communication link between the grid forming units. A failsafe concept is included, that means that a stable operation is given also in case that one grid forming unit is disconnected.
Evaluation of Measurement Information During Tests of the Heart Resuscitation Device

An adaptive algorithm for evaluation of measurement information obtained during tests of the heart resuscitation device based on the minimization of the Kullback divergence criterion, measuring the difference between two distributions of random variables provided. In the paper the rate of convergence of estimates of measurement information to true values, as well as their stability to errors in the description of the basis function are studied. The paper discusses algorithm working under condition of deficient information concerning the knowledge of the distribution of the error terms of the system described in state space form.
Deep Learning and Information Geometry for Drone Micro-Doppler Radar Classification

In this work, we build dedicated learning models for micro-Doppler radar time series classification. We develop both deep temporal architectures based on time-frequency representations, and also directly study the signal's underlying statistical Gaussian process using Information Geometry on Riemannian manifolds by developing and improving symmetric positive definite (SPD) neural networks. We also propose the aggregation of all proposed models in a single, highly performing classification pipeline.
Towards Efficient and Robust LiDAR-based 3D Mapping in Urban Environments

For autonomous driving, 3D mapping is an important task for accurate and robust navigation. This paper presents an efficient method for 3D mapping in urban scenarios. To improve the mapping robustness, a degeneracy-aware factor graph is constructed, which considers the degradation of scan matching constraints and prior observations. To alleviate the side effects caused by dynamic vehicles, an efficient likelihood-field-model-based multi-object detection and tracking algorithm is applied to filter dynamic objects. Extensive tests in real-world datasets show that our approach works well in dynamic urban environments.
IoT Aided Smart Light Sensing Automation using Passive Infrared Sensors

Internet of Things home automation industry is opening the flood gates to a whole new world of sensors and microcontrollers that reduce human effort and simplify how usual things function around us. This project aims to explore ESP8266 and its capabilities along with PIR sensors to make a home automation prototype that focusses on "Conservation of Energy". By establishing a Web Server which would record the data of the number of persons entering/exiting a given room, we explore the ability of this module to use wifi protocols, being a microcontroller. The setup process for the apparatus requires feasibility, perseverance and precision. Once accomplished we move on to the equally difficult process of setting up an Integrated Development Environment (IDE) and pushing our program for the server onto the moduleKeeping in mind the potential of the project, the future scope including applications using the project has been discussed in this project.
Green Cloud Communication System for Big Data Management

This paper makes an effort to survey and study the open challenges in the field of energy-efficient and green Cloud infrastructures. The useful software that offers the possibility to implement and evaluate Cloud environments is CloudSim, which we also use in order to demonstrate and propose our idea. Moreover, we consider the CloudSim’s simulator architecture in order to achieve our proposal. Consequently, we investigate and propose a system framework for better use of Big Data management, based on Cloud federated network. Additionally, we propose an algorithm for achieving an energy-efficient resource allocation technique for Big Data management on Green Cloud environment. The experimental results demonstrate that our proposed model has immense potential as it offers significant performance gains regarding the cost saving and the better data management under large workload scenarios.
A Container-based IoT-oriented Programmable Logical Controller

The programmable-logical-controller (PLC) has been the key building block of industrial control systems throughout the whole automation revolution, where its role has been mainly to command low-level regulatory feedback control loops. Modernized versions of PLCs aim at facilitating its integration with cloud-based solutions, in the context of the Industrial Internet of Things (IIoT) paradigm. This work is a step forward in this direction and presents a container-based formulation of an IoT-oriented programmable controller, named IoT-PLC, in which each functionality works within a separate container. This IoT-PLC device has regulatory control capabilities, fog-computing functionalities as filtering and field data storage, and multiple wireless interfaces managed independently by individual containers. A virtual device model is used as an abstraction method to represent real entities, and OPC-UA is available for straightforward compatibility with the upper control layers.
Facile synthesis of multiporous CuCo2O4 microspheres as efficient electrocatalysts for rechargeable Li-O2 batteries

Multiporous CuCo2O4 microspheres have been synthesized via a facile solvothermal method and used as bifunctional electrocatalysts for rechargeable Li-O2 batteries. The Li-O2 battery utilizing CuCo2O4 shows a higher specific capacity of 5607 mAh g-1 than that with pure Ketjen black (KB). Moreover, the Cu-Co2O4-based electrode enables much enhanced cyclability than that of the carbon-only cathode. Such excellent catalytic performance of CuCo2O4 could be associated with its inherent catalytic activity and multi-porous microspheres structure.
Action Recognition Using Frame Average Feature Map with 2D Convolutional Neural Network for Real-Time Video Analysis.

A typical video action recognition system has a high computational cost and is not suitable for real-time applications. To solve this problem, we propose an action recognition method using a two-dimensional convolutional neural network (2D CNN), which has a significantly lower computational cost than a 3D CNN. In addition, the proposed method uses a small number of frames from the video for an accurate result. The proposed method consists of: i) pretrained VGG16,which is a2D CNN, to train the action of the video andii) a test with an average of ten frames per dataset. The proposed method improved the recognition performance with a reduce computational cost by using the average of several frames instead of directly analyzing all the frames for real-time video analysis environments.
Recognition and Control of the Athlete's Movements Using a Wearable Electronics System

The paper presents a wearable electronics system and an algorithm for recognizing and controlling the athlete's movements accuracy. The proposed system is composed of a sensor network, which comprises a microcontroller, accelerometers, myosensors and a heart rate module. The presented motion recognition algorithm consists of signal acquisition, signal preprocessing, feature extraction and normalization, dynamic human motion (exercise) detection and recognition, and the exercise accuracy control. The proposed algorithm is based on the use of modern machine learning methods, in particular, on the basis of recurrent neural networks. As an experiment, basic exercises were used: push-ups, squats and the deadlift. Experimental results showed that our approach could reach a 97½ recognition level as well as make it possible to detect errors during the exercising.
Low-Leakage and Variable VHOLD Power Clamp for Wide Stress Time Range From ESD to Surge Test

In recent years, on-chip clamp designs for enhancing destructive immunity of ICs mounted on systems against residual currents of system level immunity tests have been proposed. To cope with a long duration pulse (several tens ${\mu }\text{s}$ ) such as a surge immunity test is one of the issues of the system level on-chip clamp. A clamp combined RC-trigger and static trigger is one approach to achieve both a low voltage clamping against component level ESD events and system level surge protection. However, the combined clamp using the conventional static trigger is difficult to achieve both a high current capability against the long pulse event and a low standby leakage current. This work presents a combined clamp that overcome the trade-off between the long pulse current capability and the standby leakage current using a variable holding voltage technique. The proposed clamp achieves the higher current capability against the long pulse event without increasing the standby leakage by holding voltage lowering while keeping a higher trigger voltage. It can contribute to increase PCB design freedom and to protect ICs efficiently from wider time range immunity tests.