Reviewerly/exHarmony huggingface dataset:
Parallel Genetic Algorithm for Optimizing Compiler Sequences Ordering

Recent compilers provided many of optimization passes, thus even for expert programmers it is really hard to know which compiler optimization among several optimizations passes can improve program performance. Moreover, the search space for optimization sequences is very huge. Therefore, finding an optimal sequence for even a simple code is not an easy task. The goal of this paper is to find a set of a good optimization sequences using parallel genetic algorithm. The method firstly classifies the programs into three clusters then applying three versions of genetic algorithms each one to cluster in parallel. In order to enhance the result, the migration strategy between these three algorithms is applied. Three optimal sequences at the same time are obtained from this method. However, the proposed method improved the execution time on average by 87% compared with the O2 optimization flag. This method also outperforms the sequential version of genetic algorithm on average of the execution time by 74.8% in case of using Tournament selection and 72.5% in case of multi-selection method. LLVM framework is used to validate and execute the proposed method. In addition, Polybench, Standerford, Shootout benchmarks are used as case study to verify the effectiveness of the proposed method.
Young People Leisure Activities Transformation during Quarantine Self-Isolation: Characteristics and Regulation Problem

The objective of the research is to study the characteristics of leisure activities of young people in self-isolated conditions during the coronavirus pandemic. As a research method, the authors use an online survey that allowed them to more effectively visualize the features of leisure activities and the peculiarities of their regulation among self-isolated students. The article also considers the leisure strategies of young people in conditions of self-isolation and social fortune. The novelty of the research lies in the fact that the hierarchy of leisure preferences of young people was studied for the first time during the period of self-isolation. It is concluded that the leisure practices of young people are identified by the regular use of the Internet, instead of the use of sports or any other practice. The priority areas of the Internet application are: study and communication, reading books and consulting news. In addition, there is the ability to communicate on social networks, search for video and audio recordings and games on the network. Preferred game genres are shown to be multiplatform simulators and strategies.
Regularized graph-embedded covariance discriminative learning for image set classification

Riemannian manifold has attracted an increasing amount of attention for visual classification tasks, especially for video or image set classification. Covariance matrices are the natural second-order statistics of image sets. However, nonsingular covariance matrices, known as symmetric positive defined (SPD) matrices, lie on the non-Euclidean Riemannian manifold (SPD manifold). Covariance discriminative learning (CDL) is an effective discriminative learning method that employs the Riemannian manifold in the SPD kernel space. However, in practice, the discriminative learning of CDL often suffers from the problems of poor generalization and overfitting caused by a finite number of training samples and noise corruption. Hence, we propose to address these problems by importing eigenspectrum regularization and graph-embedded frameworks. Discriminative learning with SPD manifold is generalized by the graph-embedded framework, which combines with eigenspectrum regularization in the SPD kernel space. Three local Laplacian graphs of graph-embedded framework and two eigenspectrum regularized models are incorporated to the proposed method. Comprehensive mathematical deduction of the proposed method is depicted with the “kernel tricks.” Experimental results on set-based face recognition and object categorization tasks reveal the effectiveness of the proposed method.
The Formation of Key Competencies in Geometry Lessons

The article discusses the tasks of developing students’ creative abilities in geometry lessons. The systematic work of a teacher in instilling students’ skills in finding various ways to solve contributes to the development of logical search methods, which, in turn, develops students’ research abilities. If it is required to apply several methods at the same time, schoolchildren try to find the most original, beautiful, economical solution. For this, students perceive many creative facts, methods and techniques, analyze them from the point of view of applicability to a given situation in a task, and gain experience in applying the same knowledge to various issues. All this activates the educational activities of students, instills interest in the subject.
Comparative Analysis of Signal Denoising Methods for Object Selection in Gaze-Based Interactive Applications

Extracted data from an eye tracker generally contain inaccurate and noisy signals of spatial gaze position. The noise can affect performance of object selection in gaze-based interactive applications that implement smooth pursuit eye movement to control digital contents. Some signal denoising methods—such as Moving Average Filter, Kalman Filter, and Particle Filter—have been used in a smooth-pursuit-based interactive application to denoise the signal. There is another signal denoising method named Naive Segmented Linear Regression (NSLR) that is able to denoise gaze position signal by segmenting the signal and performing linear regression. Unfortunately, the use of NSLR in a smooth-pursuit-based interactive application is yet to be considered. To fill this research gap, we apply the NSLR signal denoising method and compare its performance on gaze-based object selection against the above-mentioned filters. Experimental results show that gaze-based object selection with Euclidean Distance and Moving Average Filter obtains the best accuracy of 92.37±5.63%, whereas, object selection with Pearson Product Moment Coefficient and Kalman Filter yields the best accuracy of 23.57±2.08%. In future, our results can be used as guidance in the development of smooth-pursuit-based interactive applications.
Artificial Neural Networks and Fault Injection Attacks

This chapter is on the security assessment of artificial intelligence (AI) and neural network (NN) accelerators in the face of fault injection attacks. More specifically, it discusses the assets on these platforms and compares them with those known and well-studied in the field of cryptography. This is a crucial step that must be taken in order to define the threat models precisely. With respect to that, fault attacks mounted on NNs and AI accelerators are explored.
An Autonomous Fallers Monitoring Kit: Release 0.0

Fall is one of the main causes of the reduction of the Quality of Live in Elderly, since it has a high impact in mortality rate as well as in the probability of different disabilities like Fractures, head injuries, etc, and mainly a loss of confidence. Thus, a monitoring instrument to follow elderly activity is required and the most common non-invasive and easy-to-use are based on wearable devices. This work presents our first prototype of an autonomous, low-cost and easy-to-use elderly activity monitoring kit suitable to be deployed in nursery houses. The prototype is composed of: i) our own data analytics web application deployed in a low cost server located in our laboratory (SERVER) and ii) a transparent plastic case with a set of 10 commercial smartbands (WD), their respective charging docks, one NUC (Next Unit of Computing) as data gateway, one Wi-Fi router and one LTE router. Concerning the case, we can state that each WD has built-in a 3DACC (3DA), a Gyroscope (GY) and a wrist Heart Rate sensor (HR). Besides each WD is running our own software instead the pre-built commercial one in order to achieve two issues: recording high frequency data (1 Hz for HR and 10 Hz for 3DA and GY) and optimize the battery life (18 h with 10 Hz continuous sensors recording). Finally, a first trial of the presented kit is being carried out in a Nursery House in Burgos (Spain), and the participants enrolled criteria have been designed by a gerontologist from the Diputación de Burgos (Spain).
The dynamics of communication in global virtual software development teams : A case study in the agile context during the Covid-19 pandemic

The increase of globalization of business led to the creation of global virtual software development teams in which communication plays an important role. The lack of recent studies raises the need ...
Symbolic Similarity Relations for Tuning Fully Integrated Fuzzy Logic Programs

Inspired by our previous experiences in the design of fuzzy logic languages not dealing yet with similarity relations, in this work we introduce a symbolic extension of FASILL (acronym of “Fuzzy Aggregators and Similarity Into a Logic Language”). Since one of the most difficult tasks when specifying a fuzzy logic program is determining the right weights/connectives used in the rules and the similarity relation of FASILL programs, our technique is able to symbolically execute them with unknown parameters, so that the user can guess the impact of their possible values in further developments. Then, it is possible to automatically tune such programs by appropriately substituting (with the concrete values that best satisfy the user’s preferences) the symbolic constants appearing in their program rules and similarity relations.
A Comparison of Thai Sentence Boundary Detection Approaches Using Online Product Review Data

In Natural Language Processing (NLP), the goal of sentence boundary detection (SBD) is to identify sentence boundaries in a phrase, paragraph, or document, which can be used in current NLP applications, including sentimental analysis, contextual chatbot, and machine translation, etc. Previous studies and existing NLP libraries often provide a straightforward approach to the task; for instance, they assume that a sentence always ends with certain punctuation symbols such as a period, a semicolon, a exclamation mark, or a question mark. The mentioned approach is impractical for other languages, such as Thai, where there is no symbol to designate where a sentence ends. With regard to developing an effective sentimental analysis or machine translation for the Thai language, a solid effort in detecting sentence boundary is needed. There is also as a need validating the SBD model against a real-world dataset, by involving the use of an online textual corpus. This paper attempts to compare Condition Random Fields (CRF) and Bidirectional Long-Short Term Memory with CRF layer (BiLSTM-CRF) on the online textual dataset. We scraped our own corpus from the top Thai web forums through the use of a Scrapy web-crawling framework. In the paper, 2,496 comments related to beauty product reviews were manually segmented by a Thai linguistic expert. Our experimental results revealed that the CRF based on the word-based labelling approach with widow size outperformed the BiLSTM-CRF.
HEAM: Heterogeneous Network Embedding with Automatic Meta-path Construction

Heterogeneous information network (HIN) embedding is widely used in many real-world applications. Meta-path used in HINs can effectively extract semantic information among objects. However, the meta-path faces challenges on the construction and selection. Most of the current works construct dataset-specific meta-paths manually, which rely on the prior knowledge from domain experts. In addition, existing approaches select a few explicit meta-paths, which lack of much subtle semantic information among objects. To tackle the problems, we propose a model with automatic meta-path construction. We develop a hierarchical aggregation to learn effective heterogeneous embeddings with meta-path based proximity. We employ a multi-layer network framework to mine long meta-paths based information implicitly. To demonstrate the effectiveness of our model, we apply it to two real-world datasets and show the performance improvements over state-of-the-art methods.
Trusted Records Scheme in Scene Investigation

Crime scene investigation is an important police task of which aim is to preserve the integrity of physical evidence, assist investigators to reach an ultimate conclusion, and provide legally valid evidences and reports to the court. The scene investigation recording is not only a hard task but also the most important step in the crime scene investigation work. The digitized scene survey records have been widely used in police work, but many problems such as untimely production, non-objectivity and illegality appear in actual operation. Based on the Chinese cryptographic standards, this paper proposes a set of trusted processing schemes for data records, which guarantees the credibility of the records by collecting the time, the space and the people related to the data. It ensures the objective of the production time, location, and operator.
Development of A Training Model for Working with Young Children in The Conditions of a Master’s Program and Additional Education in A Pedagogical University

The paper presents and characterizes an integrative model of teacher training for working with young children. The purpose of the study is to develop a universal model that ensures the fulfillment of the society’s demand for the training of teachers in course of master’s programs and continuing pedagogical education, to help eliminate the shortage of professional staff in preschools. The model has been developed under the competence paradigm of education and taking into account the requirements of federal state educational standards of higher education and the professional standards for teachers in the conditions of coherence between the work-related activities (functions) and competencies. The universality of the model is determined by a unified educational outcome, content, forms, and technologies, as well as indicators and criteria for the effective preparation of teachers for working with young children both in the framework of their master’s program and in the continuing education system.
Quantum Algorithms and Experiment Implementations Based on IBM Q

With the rapid development of quantum theory and technology in recent years, especially the emergence of some quantum cloud computing platforms, more and more researchers are not satisfied with the theoretical derivation ... | Find, read and cite all the research you need on Tech Science Press
Comparing the Effectiveness of Different Classifiers of Data Mining for Signature Recognition System

Handwritten signatures are commonly used for the signification, authentication, and validation of people's important transactions and documents. However, this security measure could also be a threat at the same time. This study aims to develop a model to detect and recognize a signature. This study also tries to extract, investigate and compare the use of static and dynamic features using Five well-known (5) classifiers such as AutoMLP, Naïve Bayes, Neural Network, Gradient Boosted Trees and Generalized Linear Model. The classifier that shows the most acceptable model is Neural Network which shows an accuracy rate of 92.88%.
Research on Massive News Events Evolution Prediction Based on Improved PrefixSpan Algorithm

Abstract The Internet reports massive news event every day. Sequential pattern mining is adopted to study the timing relationship between news event, which can provide reference for the development or prediction of news events. With its advantages in performance and efficiency, PrefixSpan often becomes preferred algorithm in the field of sequence pattern mining. But unfortunately, because of the large amount of news and the long time span between some news events, the event sequences is long and dense, which results many subsequences in the frequent pattern and reduces the algorithm performance. In this paper, we propose an improved PrefixSpan algorithm by integrate subsequences of the supersequences in the frequent pattern and introduce news event weight in the pruning step of the PrefixSpan algorithm. Experiment results show that without affecting expression ability of frequent patterns, the improved PrefixSpan algorithm increases the efficiency by about 20 times as before and eliminates about 10% of the redundant subsequences to get a more concise frequent patterns set.
A Remote Sensing Image Matching Algorithm Based on Anisotropic Scale Space

Aiming at the influence of nonlinear brightness difference and noise on remote sensing image matching algorithm, a remote sensing image matching algorithm based on anisotropic scale space is proposed. Firstly, we use the additiveoperator splitting (AOS) algorithm to build the anisotropics cale space; Then, we extract feature points in the nonlinear scale space by Harris algorithm, and use the improved SIFT descriptor to describe the features; Finally, the nearest neighbor distance ratio is used to filter the matching point pairs, and the fast sample consensus (FSC) algorithm is used to delete the mismatch to improve the matching accuracy. This method can maintain the matching accuracy while increase the number of points. Extensive experiments results on different sensors that our method can improve the matching accuracy.
Support Vector Regressive Dragonfly Optimized Shift Invariant Deep Neural Learning Based Handover for Seamless Data Delivery in Heterogeneous Network

In a Wireless Sensor Network (WSN), seamless mobility management can change the current mobile node's location point to another network devoid of any link failure during communication.The seamless mobility system is very useful to detect the nearest base station over the wireless network without any distinct interference.In this paper, a novel technique called Support Vector Regressive Dragonfly Optimization based Shift Invariant Deep Neural Learning (SVRDO-SIDNL) is introduced for improving the seamless data transmission with minimum delay.The Shift Invariant Deep Neural Learning comprises of many layers to learn the series of input.For each layer, the different processes are carried out to accomplish the traffic optimized seamless data delivery.The input layer of the deep neural learning receives mobile nodes with coverage region and then is sent to the hidden layer.The mobile nodes' signal strength is analyzed by applying the support vector regression at the hidden layer.Then, the node with weak signal strength is identified and performs the handover.Through oppositional learned multi-objective dragonfly optimization technique, recognition of nearby attachment points with greater bandwidth availability is performed for the handover process.Then, the mobile node connection is altered from the existing attachment point to a new attachment point without losing connectivity.The simulation results reveal that the SVRDO-SIDNL technique offers a greater delivery rate, throughput with lesser packet loss at less delay.
Private Two-Terminal Hypothesis Testing

We study private two-terminal hypothesis testing with simple hypotheses where the privacy goal is to ensure that participating in the testing protocol reveals little additional information about the other user's observation when a user is told what the correct hypothesis is. We show that, in general, meaningful correctness and privacy cannot be achieved if the users do not have access to correlated (but, not common) randomness. We characterize the optimal correctness and privacy error exponents when the users have access to non-trivial correlated randomness (those that permit secure multiparty computation).
[Human activity recognition based on the inertial information and convolutional neural network].

With the rapid improvement of the perception and computing capacity of mobile devices such as smart phones, human activity recognition using mobile devices as the carrier has been a new research hot-spot. The inertial information collected by the acceleration sensor in the smart mobile device is used for human activity recognition. Compared with the common computer vision recognition, it has the following advantages: convenience, low cost, and better reflection of the essence of human motion. Based on the WISDM data set collected by smart phones, the inertial navigation information and the deep learning algorithm-convolutional neural network (CNN) were adopted to build a human activity recognition model in this paper. The K nearest neighbor algorithm (KNN) and the random forest algorithm were compared with the CNN network in the recognition accuracy to evaluate the performance of the CNN network. The classification accuracy of CNN model reached 92.73%, which was much higher than KNN and random forest. Experimental results show that the CNN algorithm model can achieve more accurate human activity recognition and has broad application prospects in predicting and promoting human health.随着智能手机等移动设备感知、计算能力的飞速提升，以移动设备作为载体的人体活动识别成为新的研究热点。利用智能移动设备中的加速度传感器等采集到的惯导信息进行人体活动识别，相比于常用的计算机视觉识别，具有应用方便、成本低且更能反映人体运动本质等优势。本文采用智能手机采集到的 WISDM 数据集，构建了基于加速度计惯导信息和卷积神经网络（CNN) 的人体活动识别模型，并同时引入 K 最近邻算法（KNN）和随机森林算法来对 CNN 网络进行评估。CNN 模型的分类正确率达到了 92.73%，相较于 KNN 和随机森林都有很大提高。实验结果表明，与 KNN、随机森林算法相比，CNN 算法模型可以实现更精确的人体活动识别，在预测和促进人体健康水平方面具有广阔的应用前景。.
Deep Neural Networks: Incremental Learning

In this research paper, the problem of Incremental Learning is addressed. Based on the idea of extracting features incrementally using Auto-Encoders, CNNs, Deep Learning architectures are proposed. We implemented proposed architectures on Raw dataset (containing collection of main classes and dummy classes) and compared the results with CIFAR10 dataset. Experimental investigations are reported.
Automated Transcription for Pre-modern Japanese Kuzushiji Documents by Random Lines Erasure and Curriculum Training

Recognizing the full-page of Japanese historical documents is a challenging problem due to the complex layout/background and difficulty of writing styles, such as cursive and connected characters. Most of the previous methods divided the recognition process into character segmentation and recognition. However, those methods just provide character bounding boxes and classes without text transcription. In this paper, we enlarge our previous human-inspired recognition system from multiple lines to the full-page of Kuzushiji documents. The human-inspired recognition system simulates the human eye movement during the reading process. For the lack of training data, we propose a random text line erasure approach that randomly erases text lines and distorts documents. For the convergence problem of the recognition system for full-page documents, we employ curriculum training that trains the recognition system step by step from the easy level (several text lines of documents) to the difficult level (full-page documents). We tested the step training approach and random text line erasure approach on the dataset of the Kuzushiji recognition competition on Kaggle. The results of the experiments demonstrate the effectiveness of our proposed approaches. These results are competitive with other participants of the Kuzushiji recognition competition.