Reviewerly/exHarmony huggingface dataset:
Fall Detection, Location and Identification for Elderly Institution

This paper focused on the development of a system that could detect and locate falls, and identify the victims in a short period of time. The system used triaxial-accelerometer in detecting a fall, signal strengths from access points in locating the position, and media access control (MAC) addresses in identifying the name of the victim. Wemos D1 was used as the microcontroller in measuring and averaging signal strengths, computing the resultant acceleration coming from the MPU6050 triaxial-accelerometer and sending the values together with the MAC address to the database. The software developed accesses the database, computes for the location, and displays the outputs to the user while sounding an alarm. To test its functionality, different categories of testing were conducted. The fall function was tested and produced a recall of 100% and a precision of 97.5%. The response time was measured by how much time it took from the event of the fall to the software displaying the location and sounding the alarm. The computed average response time was 1.1128 seconds and was considered low and fast enough. The displaying of the location was tested while considering the size of the area of the testing. The area considered had the size of 10 m by 6 m and the test produced accuracies of 82.8% and 90% on x and y axes respectively. This means that the margin of error for the x-axis was 1.72 m and 0.6 m on the y-axis. In the end, the fall detection system was able to perform its function and provide reliable output that could help elderly institution, as well as elderly people, to lessen the risks and consequences of a fall.
Answering Arabic Complex Questions

Factoid questions and definitional questions are commonly used in recent works in Question Answering (QA). These questions can be answered by a Named Entity (NE) or a short sentence. However, a more challenging and less common question to deal with is the complex question, or why-question. This issue motivated us to develop a new question-answer system for answering Arabic complex questions in the medical domain. Our system uses Natural Language Processing (NLP) and Information Retrieval (IR) techniques. Using the NooJ platform, we construct dictionaries and transducers to analyze complex questions and to extract causal sentences which represent the answers of why-questions. Experimentations of our Arabic medical question answering system show interesting results.
AAM: An Advanced Attention Module in Convolutional Neural Networks

With the development of convolutional neural networks, attention has been successfully applied to object detection, object classification, and other fields. Meanwhile, it has significantly improved the performance in these scenes. To fully find the attention's ability out, this paper uses the FaceBoxes as a baseline and studies some famous attention modules'effect for face detection, like SE, CBAM, and ECA. According to the results, an advanced attention module (AAM) based on CBAM is proposed and tested on the validation dataset of the WIDER FACE. The performance is better than the above attention modules. Compared with the baseline, it is increased by 2.0%, 3.0%, and 2.8%, respectively, and compared with the CBAM by 0.1%, 0.9%, and 1.1%, which fully proves the effectiveness of AAM, and it does not increase model size.
Active Hidden Naive Bayes

Over the years, many learners that take advantage of the Bayesian theory have been developed and proved to be both efficient and performant in terms of classification predictiveness. Hidden Naive Bayes is no exception since its polynomial complexity makes it a desired base classifier to conduct under Weakly Supervised Learning that, unlikely the Supervised Learning, takes advantage of both Labeled and Unlabeled instances in order to create accurate learning models. In this work, we exploit Hidden Naive Bayes under Active Learning scheme, where human interaction is needed for resolving the more disambiguous cases and integrating its knowledge into the learning loop. We compare the proposed Active Learner against 4 state-of-the-art classifiers under the same learning strategy over 14 binary and multiclass datasets.
Open Research Data in the Global South: Issues and Anomalies in the Indian Context

Globally, open science movement is gaining momentum ever since the launching of the preprint repository Arxiv.org in 1991 and the signing of the global declaration Budapest Open Access Initiative in 2002. Free online scholarships have been getting support from the research funding agencies while they are committed to disseminating public-funded research studies to the common citizens. Following extensive global consultation, UNESCO submitted a draft Recommendation on Open Science to its 193 Member States in September 2020. The Global South is taking an interest in the global open science movement. Notably, the BRICS nations are leading the developing nations in producing open-access scholarship. In India, many universities and research institutions have established their respective open knowledge and research data repositories. At the same time, scholarly periodicals published by many of the public institutions have also been made accessible online freely. In the open science ecosystem, open research data is also an essential element. Globally, FAIR Data Principles have been adopted by the scientific community to make research data Findable, Accessible, Interoperable, and Reusable (FAIR). The FAIR Data Principles were published in 2016, while the term was coined in 2014. The present chapter discusses the salient features of FAIR Data Principles. The chapter also highlights the issues and challenges in India's open research data ecosystem as a member of the developing nations in the Global South. The chapter also highlights the availability of open research data in India vis-à-vis in the Global South, highlighting a few success stories.
Whale Optimization Algorithm Based on Nonlinear Weights and Single Point Crossove

Focusing on the problems of slow convergence speed and low search accuracy in the whale optimization algorithm, a whale optimization algorithm with nonlinear weights and single point crossover is proposed. Firstly, the algorithm introduces a non-linear weight factor in the stage of whales surrounding prey and bubble net attack to speed up the algorithm convergence; secondly, the algorithm selects several individuals randomly in the whale population for single-point crossover to increase communication between populations and to improve the algorithm to jump out the local maximum. Finally, through 12 test functions, the improved algorithm is compared with the whale optimization algorithm, particle swarm algorithm, gray wolf optimization algorithm and the whale optimization algorithm that only uses nonlinear weights and only uses single-point crossover. The experimental results show that the improved algorithm are improved significantly both in the convergence speed and the optimization accuracy.
Applying an integrated approach to analyse item features for curriculum-based assessments

This study explored how to apply the item feature analysis for ensuring the validity of student assessments. The item feature analysis provides evidences of ‘how and what to be assessed’. We considered item features for curriculum-based assessments, focusing on Korean and Math in middle schools. The results of implementing item feature analysis in Korean/Math tests from 2019 NAEA indicated that both Korean and Math tests consisted of valid items based on the national curriculum. Students’ strength and weakness were observed from analyzing the relationships between students’ achievement and features of Korean and Math test items. Item features based on the curriculum and test mode have been proved as critical applications for ensuring the validity of curriculum-based assessment as well as building a tool for student assessment. Follow-up study for more broadened use of the results of study was proposed to imply item feature analysis in schools and to widen the application across the feature categories for each subject. The results of this study would contribute to extend researches on the validity of student assessment and provide the theoretical basis for selecting item features for computer-based assessments.
Public Key Cryptosystem Based on Optimized Chaos-Based Image Encryption

Now is the era of online data and transaction, all this happens on an unsecured channel. With this huge data transfer, comes the need of protecting this data. Thus, to achieve security during transmission, several symmetric key encryption algorithms have been proposed. Inspired from researchers, we propose an asymmetric key image security algorithm based on chaotic tent map integrated with Optimized Salp Swarm Algorithm (SSA) for key generation and encryption for gray scale images. Diffusion and confusion are carried out in each round to mix plain text and key to it more secure. Experimental analysis shown by SSA are encouraging and is secure enough to resist brute force, differential cryptoanalysis and key sensitivity analysis attack and is suitable for practical application.
Detection and Learning of Color Objects Using Kernelized Support Correlation Filter

Moving Object detection and tracking are important for many computer vision applications. The support vector machines (SVMs) are used to reduce the cost of computation and to increase the efficiency of computation. Kernelized correlation filters are used for efficient object tracking. In this method, the patches of translated images are used through dense sampling to form the circulant matrix. Support correlation filtering is an approach which is composed using combination features of the support vector machine and circulant matrix. We use a discrete Fourier transform to pose the tracking problem. We further use kernel function with support correlation filters to improve the performance of the object tracking algorithm. Kernelized support correlation filter is a good alternative to improve the accuracy and speed of the object tracking process.
Side Channel Attack Resistance of the Elliptic Curve Point Multiplication using Eisenstein Integers

Asymmetric cryptography empowers secure key exchange and digital signatures for message authentication. Nevertheless, consumer electronics and embedded systems often rely on symmetric cryptosystems because asymmetric cryptosystems are computationally intensive. Besides, implementations of cryptosystems are prone to side-channel attacks (SCA). Consequently, the secure and efficient implementation of asymmetric cryptography on resource-constrained systems is demanding. In this work, elliptic curve cryptography is considered. A new concept for an SCA resistant calculation of the elliptic curve point multiplication over Eisenstein integers is presented and an efficient arithmetic over Eisenstein integers is proposed. Representing the key by Eisenstein integer expansions is beneficial to reduce the computational complexity and the memory requirements of an SCA protected implementation.
Predicting Kinship Using GANs : Review

In past few years, face synthesis has become a topic of central interest to the research community. It involves several aspects like kinship verification age regression and age estimation. In this paper, we have briefly summarized recently used approaches for the facial aging and kinship models. The authors have reviewed the different datasets that were beneficial in the previous research works. This survey provides direction to future research possibilities and helps the reader analyze different models adopted up to date. Further study in this area can yield better techniques that can withstand real-world limitations.
Metallograms as an Objective and Illustrative Basis for Metallogenic Analysis

Abstract The paper presents results of huge generalization of geological and metallogenic materials on paleosoids of Kazakhstan which allowed for the first time composing the metallogenograms for seventy eight (78) structural-formational zones (SFZ). Their analysis showed that all SFZs of Kazakhstan’s paleosoids contain ores and led to development of scientifically substantiated recommendations for planning the prospecting works within the newly identified promising SFZ areas with favourable metallogenic geodynamic conditions and ore-bearing stratigraphic levels.
A Local Feature Oriented Ensemble Method for Caricature Recognition

Caricature is a popular art form. Caricature artists usually exaggerate facial features to highlight the characteristics of people. Although it is not difficult for people to recognize caricature faces even if the face structures are far away from real faces, traditional face recognition methods do not perform well in matching caricature faces with real faces. Based on the Webcaricature database and Residual Neural Network (ResNet), an ensemble method that emphasizes local features of faces is proposed in this paper. Local features are trained separately to form models that evaluate the distances of different features. Then all local feature models are weighted and combined with a full-face model. In order to stabilize the average performance, the weight of each local feature must be controlled within an appropriate range. Through many experiments, the ensemble model that integrates eyes, mouth, and full-face model with adapted weights demonstrated better performance, and the overall accuracy rate of recognition indicated considerable improvement. This paper proposed a promising ensemble method to deal with the current difficulties in caricature recognition.
Fire frequency analysis and prediction based on back propagation neural network model and Long-Short Term Memory model

Abstract It is reported that there have been many fires in many places this year. This article wants to explore the factors affecting the occurrence of fires and make predictions to prevent the occurrence of forest fires. The correlation between forest indexes and fire frequency in Fujian, Guangxi, Heilongjiang, Hunan, Sichuan, Yunnan and Zhejiang provinces was analyzed based on forest indexes and fire frequency prediction in seven provinces from 1998 to 2018. According to the six relevant indexes of seven provinces in 20 years, the Pearson correlation coefficient between them and the number of fires was analyzed to find the correlation between the variables. The Grey system theory Model (GM (1, 1)) was established to predict the six related index values. The BP neural network model was established based on the historical and the predicted values were substituted into the Back Propagation (BP) neural network model to predict the number of fires. Then, a prediction model based on Long-Short Term Memory (LSTM) was constructed through deep learning library Keras, and the predicted value was substituted into the model to predict the number of fires. Zhejiang Province and Hunan Province were taken as examples to compare the accuracy of the two models’ predictive values. It was found that the LSTM model had better fitting effect, and the LSTM model was finally used to predict the number of future fires. The forest ecological risk identification and prediction system based on Graphical User Interface system (GUI) was developed, the phase space reconstruction of one-dimensional data with time series characteristics was carried out, and the high-dimensional data were trained by using fuzzy neural network to realize data prediction and evaluation. This prediction can be applied to the forest prevention and control departments, and the annual fire situation can be predicted based on historical data, so as to prevent and reduce losses in advance.
A Weighted Multi-scale Fusion Technique for Single Image Haze Removal

We present a fusion-based image dehazing methodology employing two different input images. The original hazy image was preprocessed by Gamma correction. The Gamma corrected image was contrast enhanced to derive the first input and white balanced to derive the second input. Saliency, chromaticity, visibility, and haze density were extracted as haze relevant features from the two input images, which served as weight maps. The weights and the input images were then fused in a multi-scale framework for obtaining the de-hazed image.
Development of Soft Skills and Resilience in the Integral Formation of University Students based on Goldstein's Analysis

The purpose of the study is to prove if the development of soft skills improves the resilience of undergraduates, the present study is quantitative, non-experimental, descriptive, comparative and cross-sectional correlation al, the sample was 66 students between 18 and 20 years old, the sampling used was non-probabilistic, the population was students from the Faculty of Electrical and Electronic Engineering of the University National of Callao. A survey was the technique used and the instruments were applied to the students of I and II term of electrical engineering, the instrument for data collection of Social Skills was the Arnold Goldstein checklist and evaluation of Arnold Goldstein and for Resilience the Walning and Young questionnaire, the questionnaire was conducted virtually due to the coronavirus pandemic. According to the results obtained between two groups of I and II term, it is concluded by saying that the teaching of social skills improves resilience in undergrates.
Mathematical Model for a Calibration of Multiple-View Thermal Camera

This paper presents a new method for the non-overlap laceration camera calibration. The equipment used in the experiment is multi-thermal camera equipment, and three cameras are looking in different directions at a certain angle. Also, there is no tipping point between the cameras and therefore cannot use the existing camera-calibration method. The method presented in this paper computes the angle mathematically in the equipment, through which the relationship between each camera is identified and the pre-processing process is completed to proceed with the calibration.
The New Theory of Learning in the Era of Educational Information 2.0—Connected Constructivism

Constructivism and Connectivism are two important schools of learning theory. The development of these two theories reflects the characteristics of the educational information era. This paper reviews the origin, development background and main viewpoints of constructivism and connectivism, and summarizes the characteristics of the two theories. In response to the changes in learning in the education information 2.0 era, on the basis of the original two learning theories, a new theory that is more suitable for learning in the Internet era is proposed: connected constructivism, and discusses the main points of the new theory.
The Study of Model for Two-Handed Pointing Tasks in Pen + Touch Interfaces

In pen + touch interfaces, two-handed pointing tasks are quite basic. We focus on two-handed pointing tasks on a stationary touchable tablet under asynchronous conditions, in which another target will not appear until one target is touched and dwelled. Fitts' law cannot accurately model such pointing tasks based on the results of our pilot study. This paper seeks to establish an ergonomics model for bimanual pointing tasks under asynchronous interaction. We designed and implemented three experiments with bimanual pointing tasks varying the target widths and distances. A series of bimanual pointing tasks with ten different IDs (Index of Difficulty) were carried out by 18 participants. The movement time (MT) and errors were collected and analyzed. Through experiments, we found that the index of difficulty of pointing tasks has a significant impact on MT (F9,170 = 6.276, p <; 0.001). Our proposed model achieves a fit of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.973, while the Fitts' law model only achieves a fit of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.623. The experimental results show that our model has a strong effectiveness and applicability for bimanual pointing tasks in pen + touch interfaces.
Classification of critical thinking in mathematics using particle swarm optimization based neural network algorithms

Abstract Critical thinking in mathematics can be defined as the processes and abilities used to understand concepts, apply, synthesize and evaluate the information generated. Critical thinking in mathematics is a skill for higher order thinking. It is understood that logical thought plays a part in spiritual growth, social progress, behavioral growth, cognitive development and science progress. This study aims to classify critical thinking skills. The method used to determine the classification of critical thinking skills is to use the Neural Network algorithm. A method which has the potential to classify structured data is the neural network. In this study, a neural network algorithm model was developed. A technology that has the potential to identify structured data is the neural network. In this study, a neural network algorithm model was created. With this neural network model, it can be seen the classification of critical thinking skills. The amount of data used as data in this analysis was 150 in the form of school data and as many as 40 measures were measured in the form of math scores. On the basis of the research results, it was found that the neural network model based on Particle Swarm Optimization achieved an accuracy value of up to 93.33 percent with a 2 percent variance, tested using the k-cross-validation method.
Improved MapReduce Load Balancing through Distribution-Dependent Hash Function Optimization

Load balancing of skewed data in MapReduce systems like Hadoop is a well-studied problem. Many heuristics already exist to improve the load balance of the reducers thereby reducing the overall execution time. In this paper, we propose a lightweight optimization approach for MapReduce systems to minimize the makespan for repetitive tasks involving a typical frequency distribution. Our idea is to analyze the observed frequency distribution for the given task so as to identify an optimal offset parameter c to add in the hash function to minimize makespan. For two different bucketing methods - modulo labeling and consecutive binning - we present efficient algorithms for finding the optimal value of c. Finally, we present simulation results for both bucketing methods. The results vary with the data distribution and the number of reducers, but generally reduce makespan by 20% on average for power-law distributions, Results are confirmed with experiments on well-known real-world data sets.
Blockchain-based open infrastructure for URL filtering in an Internet browser

This research is dedicated to the development of a prototype of open infrastructure for users' internet traffic filtering on a browser level. We described the advantages of a distributed approach in comparison with current centralized solutions. Besides, we suggested a solution to define the optimum size for a URL storage block in Ethereum network. This solution may be used for the development of infrastructure of DApps applications on Ethereum network in future. The efficiency of the suggested approach is supported by several experiments.
Text Categorization: A Lazy Learning-Based Approach

The instantaneous magnification of digital texts has made text categorization a prime technique for managing and sorting text data. The goal of this study is to present a lazy learning-based text categorization system using text representation features. Experiments were performed on 14,373 texts from nine different categories procured from various online news corpus and magazines. The experimental results show an encouraging outcome of 96.24% accuracy using the K* algorithm. The performance of the system was also tested with other existing approaches and datasets and our system produced a better result.