Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceived social support and school engagement in secondary students Apoyo social percibido e implicación escolar del alumnado de educación secundaria

Secondary education is a stage in which many changes occur, including evolution in students’ relationships with teachers, family, and peers. Despite adolescents’ need for independence from adults, these processes continue to affect students’ adjustment. Therefore, perceived social support can be an important contextual variable in school engagement to prevent high levels of educational failure and school dropout. Consequently, the main aims of this study are: to examine the relationship between perceived social support (from teachers, family, and peers) and the dimensions of school engagement (behavioral engagement, cognitive engagement, and emotional engagement); to analyse differences in perceived social support and school engagement by sex and age; and to test the predictive capacity of perceived social support on school engagement. A total of 1468 Basque secondary school students (51 % female; 49 % male), aged between 12 and 17, participated in the study (M = 14.03; SD = 1.36). The Social and Family Support Scale (AFA), the Teacher and Classmate Support Scale (TCMS), and the School Engagement Measure (SEM) were administered. Student’s t-test, correlations and regression analyses were performed. The results indicate positive relationships between Oihane FERNÁNDEZ-LASARTE, Eider GOÑI, Igor CAMINO and Estibaliz RAMOS-DÍAZ Revision accepted: 2018-05-20. This is the English version of an article originally printed in Spanish in issue 272 of the revista española de pedagogía. For this reason, the abbreviation EV has been added to the page numbers. Please, cite this article as follows: Fernández-Lasarte, O. , Goñi, E. , Camino, I. , & Ramos-Díaz, E. (2019). Apoyo social percibido e implicación escolar del alumnado de educación secundaria | Perceived social support and school engagement in secondary students. Revista Española de Pedagogía, 77 (272), 123-141. doi: https://doi.org/10.22550/REP77-1-2019-06 https://revistadepedagogia.org/ ISSN: 0034-9461 (Print), 2174-0909 (Online) Oihane FERNÁNDEZ-LASARTE, Eider GOÑI, Igor CAMINO and Estibaliz RAMOS-DÍAZ 124 EV re vi st a es p añ ol a d e p ed ag og ía ye ar 7 7 , n . 2 7 2 , Ja n u ar yA p ri l 2 0 1 9 , 1 2 3 -1 4 1 perceived social support and school engagement. Secondly, significant differences in perceived social support and school engagement by sex and age are also observed. Thirdly, perceived social support has explanatory power for school engagement, particularly emotional engagement. Finally, the results are discussed and directions for future research are pro-
Refining Word Reesprentations by Manifold Learning

Pre-trained distributed word representations have been proven useful in various natural language processing (NLP) tasks. * However, the effect of words’ geometric structure on word representations has not been carefully studied yet. The existing word representations methods underestimate the words whose distances are close in the Euclidean space, while overestimating words with a much greater distance. In this paper, we propose a word vector refinement model to correct the pre-trained word embedding, which brings the similarity of words in Euclidean space closer to word semantics by using manifold learning. This approach is theoretically founded in the metric recovery paradigm. Our word representations have been evaluated on a variety of lexical-level intrinsic tasks (semantic relatedness, semantic similarity) and the experimental results show that the proposed model outperforms several popular word representations approaches.
CEN: Classifier Ensemble Networks based on Joint Optimization of Hyperplanes

Recent studies have demonstrated that the classification accuracy of single convolutional networks can be improved by incorporating model ensemble methods, which combine results from independently learned single networks. In this study, we propose a Classifier Ensemble Network (CEN), which substitutes model ensemble methods by embedding the information of a full hierarchy of classes into a single network. CEN’s multiple classifiers, which are defined at various category levels, share feature layers and are simultaneously learned. The classifiers generate different semantic hyperplanes, and the joint optimization of hyperplanes alleviates overfitting on target classes. We evaluate our proposed architecture using the ImageNet dataset with a clear label hierarchy. We performed extensive experiments, specifically for various category levels, to evaluate the effectiveness of CEN architecture when compared to a baseline model and its ensemble models. The CEN obtains meaningful improvements to the top-1 accuracy. The proposed ensemble concept can be applied to most existing convolutional networks.
Comparative analysis of enzymatic activity of tannase in non-conventional yeasts to produce ellagic acid

Ellagic acid is a polyphenolic compound obtained by the degradation of ellagitannins present in various fruits and plants, which has a lot of applications in various areas such as nutraceutical, pharmaceutical, cosmetic and food industry (Vázquez-Flores et al., 2012). A large amount of research has been carried out to produce ellagic acid where three main factors have been considered, degrading enzymes of ellagitannins such as tannase (EC 3.1.1.20) and β-glucosidase (EC 3.2.1.21), filamentous fungi of the genus Aspergillus that are the principal producing organisms of these enzymes, and different vegetal sources rich in ellagitannins. The use of fungi entails an increase in the amount of substrate required for their growth and a longer time for fermentation, which in turn has an impact on the energy costs. For these disadvantages, the investigation is developing alternatives to improve said process, using non-conventional yeasts as microorganisms that produce the enzymes of interest, reducing fermentation time, costs of maintenance and adaptation of microorganisms.
Personal meaning and motivation when learning mathematics: A theoretical approach

Personal meaning, understood as the personal relevance of an object or an action (Vollstedt, 2011), seems to be closely related to motivation. However, the structural relationships between personal meaning and motivation are unexplored yet. Two motivation theories, self-determination theory (Deci & Ryan, 2002) and expectancy-value theory (Wigfield & Eccles, 2000), are used to work out these relations. The focus of this paper lies on theoretical considerations.
Substrate-Embedded Low-Resistance Solenoid Inductors for Integrated Voltage Regulators

Integrated power conversion and delivery are the key to achieve high-efficiency and high-performance data processing. Power inductors with high power density and low dc resistance are the key enabler to realize miniaturized voltage regulators (VRs) with high efficiency. With the integrated voltage regulators (IVRs), interconnection length from the battery to the loads can be dramatically reduced, resulting in lower conduction loss and higher power efficiency. This article demonstrates substrates with embedded high-power-density solenoid inductors with an ultralow dc resistance for the IVRs. By incorporating advanced metal–polymer magnetic composites as the cores, the inductors show a high inductance density of 7 nH/mm<sup>2</sup> with an ultralow dc resistance of 10 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega $ </tex-math></inline-formula>. With a low thickness of <inline-formula> <tex-math notation="LaTeX">$< \! \! 500~\mu \text{m}$ </tex-math></inline-formula>, the power inductors can be embedded into substrates and positioned close to the loads to realize the IVRs. A substrate-compatible panel-level process was developed to embed the magnetic-core inductors with high throughput and low cost. Compared with the air-core inductors, the magnetic core achieved four times improvement in inductance for the same dc resistance, indicating substantial footprint reduction and improvement in efficiency.
Social Data Platform, D-Ocean

Social Data Platform, D-Ocean is data oriented Social Network Service for helping people to search or share data with others worldwide [1]. D-Ocean isn't just a collection of data sets, but also focuses on people who have contributed to the data economy. Users, such as data scientists, data engineers and etc., can be assessed by others equally. This paper describes how D-Ocean provides capability of Data Marketplace as well as Social Network Service. We introduce use cases where users find new insights that can only be discovered by interchanging own data with ones from other users and also how D-Ocean can help those users using unique features as online platform.
Robust, Fast and Accurate Lane Departure Warning System using Deep Learning and Mobilenets

Every year, millions of people die from fatalities on the road. This paper develops a lane departure warning system that will alert the driver when the driver may be veering off the road. Recent advances in Deep learning and Artificial Intelligence have shown that Convolutional Neural Networks can be excellent at extracting and identifying features in an image. However, Convolutional Neural Networks are often run on Expensive GPU's with colossal memory and typically run millions of operations in a second. This is a challenging problem for embedded characterized by limited memory or processing power and a real-time capability. In this paper, a lightweight, robust and low memory architecture is explored to enable its incorporation as an embedded system. The proposed final architecture utilizes a novel semantic regression technique that integrates the accuracy of semantic segregation and the speed of regression. An end-to-end Deep learning system is used which takes images as an inputs and outputs the found lane in one shot. The developed system achieves 91.83% accuracy on Malaysian roads.
An Improved ACO Algorithm Optimized Fuzzy PID Controller for Load Frequency Control in Multi Area Interconnected Power Systems

In this paper, an Improved Ant Colony Optimization (IACO) algorithm optimized fuzzy PID (FPID) controller is proposed for Load Frequency Control of multi area systems. The nonlinear incremental evaporation rate and improvement of pheromone increment updating are proposed in the IACO algorithm to improve the quality of solution. And, a modified objective function using integral time multiply absolute error (ITAE), overshoot, undershoot and settling time with appropriate weight coefficients is proposed to improve the performance of the controller. Initially, a two-area non-reheat thermal system is applied and the FPID controller parameters are optimized by the IACO algorithm with five different objective functions. The modified objective function has better performances than four conventional objective functions. To demonstrate the robustness of the proposed control method, sensitivity analysis is implemented under wide variation of operating conditions and system parameters. Further, the proposed approach is also extended to two-area four-sources hydro thermal power system with/without High Voltage Direct Current (HVDC) link. The superiority of the proposed approach is shown by comparing the results with ZN, GA and hPSO-PS algorithms. The robustness of the proposed method is verified under random load disturbances. Finally, the proposed approach is extended to a two-area power system with governor dead band nonlinearity and results show that the proposed approach can cope with nonlinearity well. The results obtained from all simulations show that the proposed algorithm and modified objective function achieves better performances, such as minimum objective values (ITAE = 0.0255, ITSE = 9.30e-5, ISE = 1.91e-4 and IAE = 0.0273) obtained for the two-area non-reheat thermal system.
Learning Traffic as Images for Incident Detection Using Convolutional Neural Networks

The timely and accurate detection of traffic incidents is beneficial to reduce associated economic losses and avoid secondary crashes. Inspired by the impressive success of the image classification algorithms, especially convolutional neural networks (CNNs), this study proposes a novel framework to detect highway traffic incidents by learning the traffic state as images. In such a framework, the probe vehicles equipped with the global positioning system equipment are used to obtain data. The Gramian Angular Difference Fields and Piecewise Aggregation Approximation algorithms are used to convert the link speed time series data into images. CNNs can extract the traffic features based on these images and consider an incident detection problem as a binary classification task. Further, the effectiveness of the proposed framework is evaluated by applying it to detect the traffic in a real-world environment, i.e., the Guangzhou Airport Expressway. The results illustrate that the proposed model outperforms several other algorithms with respect to almost all the performance indexes, including the detection rate with different false alarm rates and the area under the receiver operating characteristic curve.
Estimation of Target Failure Measures for E/E/PE Safety-Related Software

We discuss SIL (safety integrity level) assessment for software of E/E/PE (Electrical/Electronic/Programmable Electronic) safety-related systems. The SIL is known as a graded measure representing the safety integrity of E/E/PE safety-related systems, and has been defined in IEC 61508, which is an international standard issued totally in 2000 (the second version has been issued in 2010) for E/E/PE safety-related systems. We try to develop mathematical models for estimating the approximations of the PFD (time-averaged probability of failure on demand) and PFH (time-averaged frequency of dangerous failure per hour), which are the target failure measures of SIL, for the E/E/PE safety-related software by applying the notion of software reliability modeling and assessment methods.
A Robust Vehicle Trajectory Prediction System Deep learning based approach with a novel fusion technique

the task of driving, seemingly simple for humans, can, in some situations, puzzle even the most advanced AI systems in existence today. As extensive research is ongoing in this direction within the scientific community, this paper introduces a deep-learning based system than can accurately and robustly predict a vehicle’s near future trajectory. It does so by using a specifically built convolutional neural network that identifies the road’s shape based on disparity images, which are fused in an original way with lane marking information extracted from grayscale images. The prediction system outputs the trajectory in the form of predicted car movements which are then transformed into a road-augmented projection and simple steering wheel / brake commands, for easy visualization and understanding. The system’s performance is proven by a dedicated metric, as well as a visual analysis.
RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid

In the application of wireless sensor networks (WSNs) to smart grid, real-time and accurate wireless link quality prediction (LQP) is important to determine which link is reliable enough to undertake the communication task. However, the existing LQP methods are neither suitable to describe the dynamic stochastic features of link quality nor to ensure the validity of prediction results. In this paper, a random-vector-functional-link-based LQP (RVFL-LQP) algorithm is proposed. The algorithm selects the signal-to-noise ratio (SNR) as the link quality metric and decomposes the raw SNR sequence into the time-varying sequence and the stochastic sequence according to the analysis of wireless link characteristics. Then, the RVFL network is used to establish the prediction model of the time-varying sequence and the variance of the stochastic sequence. Lastly, the probability-guaranteed interval boundary of SNR is predicted, and the validity and practicability of prediction results are evaluated by comparative experiments and real-world application, respectively.
The simplest and efficient form of Spiking Neuron

Neuromorphic computing is an emerging field that mimics the functionality of human brain to overcome the paradigm of conventional computing. This uses artificial (silicon) neuron as a basic building block in their architectures. There are some neuron models which exactly resembles the biological nature, but complex to design, consumes more hardware to implement, inefficient in computation and some models are very simple and computationally efficient. This paper proposes and discusses the simplest and efficient neuron model for generation of various spiking patterns like tonic spiking, class one excitable and integrator. The proposed model is a reduction model from the Integrate and Fire (IAF) neuron. Izhekivich, IAF and the proposed models are implemented using MATLAB and Simulink. It is observed that the proposed model and IAF model give the same spiking patterns. In addition, the proposed model gives more spike count when compared with the IAF in a given time period. Hence, the proposed one is much faster and simple to implement as it is a first order differential equation with constant slope, and with reduced number of variables. The analog version of the proposed neuron is implemented using GPDK library at 180 nm technology node, for generating tonic spiking, phasic spiking and bursting.
Deep Learning Based Neural Network Controller for Quad Copter: Application to Hovering Mode

In the past few years, new advances in Deep Neural Networks (DNN) and Deep Learning (DL) has made it possible to control Rotary Unmanned Aerial Vehicles (RUAVs) with a variety of robust and intelligent techniques. In this work, a feedforward-based deep neural network is utilized to control the altitude, hovering mode, of an RUAV system. An automated search routine was developed to determine the optimum architecture of the neural network for the controller. This network was trained using the supervised learning technique, and the controller performance was compared for three different DL/DNN training paradigms; the standard feedforward method, the greedy layer-wise method, and the Long Short-Term Memory (LSTM) method in which the response of each controller was presented, where it was found that the greedy layer-wise method gives the most optimal result.
Effective Geo-Social Group Detection in Location-Based Social Networks

In this paper, we formulate and investigate the problem of geo-social group detection over location-based social networks. We introduce a model, called geo-social group, to enforce both structural cohesiveness and spatial cohesiveness. We propose effective optimization techniques to efficiently enumerate all communities in a network. Extensive empirical studies on real graphs demonstrate the effectiveness of our model and the efficiency of our techniques.
A Comprehensive Review on Malware Detection Approaches

According to the recent studies, malicious software (malware) is increasing at an alarming rate, and some malware can hide in the system by using different obfuscation techniques. In order to protect computer systems and the Internet from the malware, the malware needs to be detected before it affects a large number of systems. Recently, there have been made several studies on malware detection approaches. However, the detection of malware still remains problematic. Signature-based and heuristic-based detection approaches are fast and efficient to detect known malware, but especially signature-based detection approach has failed to detect unknown malware. On the other hand, behavior-based, model checking-based, and cloud-based approaches perform well for unknown and complicated malware; and deep learning-based, mobile devices-based, and IoT-based approaches also emerge to detect some portion of known and unknown malware. However, no approach can detect all malware in the wild. This shows that to build an effective method to detect malware is a very challenging task, and there is a huge gap for new studies and methods. This paper presents a detailed review on malware detection approaches and recent detection methods which use these approaches. Paper goal is to help researchers to have a general idea of the malware detection approaches, pros and cons of each detection approach, and methods that are used in these approaches.
Detecting Non-Redundant Collective Activity of Neurons

Brain activity is characterized by the activity of huge number of neurons that are densely connected in complex networks. Detecting how neural activity is coordinated across time and space over brain circuits is a very challenging task. In particular, ensembles of neurons may co-activate in specific patterns whose expression correlates to various stimuli, cognitive states, or behavioral outcomes. These patterns are expressed on a variety of timescales, from milliseconds to hundreds of milliseconds or seconds. Here, we extend a recently-introduced method for extracting stereotypical firing patterns by applying several post-processing steps that enable the precise estimation of pattern wavefronts irrespective of the integration timescale used to detect patterns. Using electrophysiology data recorded in mouse visual cortex, we show that this approach enables a more precise estimation of the relation between firing patterns and meso-scale dynamics, reflected in the local-field potential. The method removes the redundancy and blurring generated by the convolution of spike-trains with exponentially-decaying kernels and offers a sharper representation of the expression of firing patterns in brain data.
Integrated Multichannel Electrochemical–Quartz Crystal Microbalance Sensors for Liquid Sensing

This paper highlights the design, simulation and fabrication of an array of twelve integrated electrochemical – quartz crystal microbalance (IEQCM) sensors on a single substrate for liquid sensing. Integration of both measurement techniques is made possible by combining the three electrode electrochemical device with the top and bottom electrodes for the microbalance. Important design parameters such as the working electrode radius and gap spacing, were studied using both theoretical calculations and COMSOL Multiphysics® finite element simulations. The sensor’s working electrode radius affects the magnitude of the frequency response while the gap affects the capacitance and current density which are important for electrochemical measurements. It was found that the best values for the working electrode radius was 2 mm and gap spacing was 0.5 mm. The sensors were fabricated using microfabrication techniques for the gold electrode and screen printing techniques for the reference electrode. Water contact angle, atomic force microscopy, and scanning electron microscope were utilized to study the surface roughness of the IEQCM sensor. IEQCM has a low contact angle of 53.0 ± 1° and low surface roughness of 1.92nm. For liquid sensing, an array of circular chambers were fabricated using polydimethylsiloxane (PDMS) and placed on top of the quartz substrate for liquid testing. Electrochemical measurements and cyclic voltammetry were performed using the sensor in ferri-ferrocyanide and phosphate buffered saline solution to study the function of scan rates on the peak current with respect to the potential difference. For mass sensing measurements, liquid water droplets of 1uL – 10 uL were placed onto the sensing surface and the change in resonance frequencies of the sensors were measured. These resonance frequency changes can be converted in mass change/area in accordance to the advanced Sauerbrey equation. The multichannel IEQCM sensor shows good potential as a parallel sensor for both biosensing and environmental applications.