Reviewerly/exHarmony huggingface dataset:
Gender Identification from Arabic Speech Using Machine Learning

Speech recognition is becoming increasingly used in real-world applications. One of the interesting applications is automatic gender recognition which aims to recognize male and female voices from short speech samples. This can be useful in applications such as automatic dialogue systems, system verification, prediction of demographic attributes (e.g., age, location) and estimating person’s emotional state. This paper focuses on gender identification from the publicly available dataset Arabic Natural Audio Dataset (ANAD) using an ensemble-classifier based approach. More specifically, initially we extended the original ANAD to include a gender label information through a manual annotation task. Next, in order to optimize the feature engineering process, a three stage machine learning approach is devised. In the first phase, re restricted to features to the two widely used ones; namely, MFCC and fundamental frequency coefficients. In the second phase, six distinct acoustic features were employed. Finally, in the third phase, the features were selected according to their associated weights in Random Forest Classifier, and the best features are thereby selected. The latter approach enabled us to achieve a classification rate of 96.02% on the test set generated with linear SVM classifier.
A Survey on Shortest Unique Substring Queries

The shortest unique substring (SUS) problem is an active line of research in the field of string algorithms and has several applications in bioinformatics and information retrieval. The initial version of the problem was proposed by Pei et al. [ICDE’13]. Over the years, many variants and extensions have been pursued, which include positional-SUS, interval-SUS, approximate-SUS, palindromic-SUS, range-SUS, etc. In this article, we highlight some of the key results and summarize the recent developments in this area.
A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture

In this paper, a fusion method based on multiple features and hidden Markov model (HMM) is proposed for recognizing dynamic hand gestures corresponding to an operator’s instructions in robot teleoperation. In the first place, a valid dynamic hand gesture from continuously obtained data according to the velocity of the moving hand needs to be separated. Secondly, a feature set is introduced for dynamic hand gesture expression, which includes four sorts of features: palm posture, bending angle, the opening angle of the fingers, and gesture trajectory. Finally, HMM classifiers based on these features are built, and a weighted calculation model fusing the probabilities of four sorts of features is presented. The proposed method is evaluated by recognizing dynamic hand gestures acquired by leap motion (LM), and it reaches recognition rates of about 90.63% for LM-Gesture3D dataset created by the paper and 93.3% for Letter-gesture dataset, respectively.
Statistical analysis of teachers' digital competence levels

Purpose of the study . The study of the qualitative parameters of the human resources potential of the economy is necessary to describe the conditions in which digitalization processes develop, to identify the problems of training specialists. The professional education system is considered in the article as an institution that provides human resources to the digital economy of the region. Innovative processes increase the requirements not only for the system of training specialists, but also for the skills of the lecturer, individual level of mastering information and communication technologies. The purpose of this study is to diagnose the levels of digital competencies of lecturers of professional educational institutions and to identify, on the basis of statistical analysis, the impact on the level of digital competencies of a lecturer of various parameters, such as the lecturer’s age; the disciplines he/her teaches; the data of continuing education in the field of information and communication technologies. Materials and methods . This paper provides an overview of approaches to solving the problems of staffing education in the context of informatization. Taking into account the proposed model of digital competencies of lecturers, the testing complex was approved. Methods of statistical data analysis were applied: descriptive statistics, correlation coefficients were calculated, a range diagram and scatter diagrams of lecturers' testing results were constructed. For the information processing, the qualitative indicators were converted into quantitative ones and the statistical analysis software packages were used: Microsoft Excel and STATISTICA 10.0. Results . As a result of the lecturers’ testing of professional educational institutions and statistical analysis, data were obtained on the level of human resources in terms of the formation of knowledge and skills in the field of information and computer technologies. The relationship between the level of digital competence and various factors has been identified. The factors that were analyzed in the course of the study: age, subjects taught, the period of limitation of the advanced training in the field of information and communication technologies. Conclusion . The introduction of the proposed model of competencies and diagnostic methods will allow diagnosing lecturers' skills in working in a digital environment and will ensure the adoption of informed managerial decisions in the development of the human resources of the vocational education system both at the level of the educational organization and at the level of public administration bodies. This model can be used to obtain information about the formation of digital competencies of different groups: employees of organizations and enterprises, state and municipal employees.
Succinct suffix sorting in external memory

Given a size-N input string X, a number of algorithms have been proposed to sort the suffixes of X into the output suffix array using the inducing methods. While the existing algorithms eSAIS, DSAIS, and fSAIS presented remarkable time and space results for suffix sorting in external memory, there are still potentials for further improvements. We propose here a new algorithm called nSAIS by reinventing the core inducing procedure in DSAIS with a new set of data structures for running faster and using less space. The suffix array is computed recursively and the inducing procedure on each recursion level is performed block by block to facilitate sequential I/Os. If X has a byte-alphabet and N=O(M2/B), where M and B are the sizes of internal memory and I/O block, respectively, nSAIS guarantees a workspace less than N bytes besides input and output while keeping the linear I/O volume O(N) which is the best known so far for external-memory inducing methods. Our experiments on typical settings show that, our program for nSAIS with 40-bit integers not only runs faster than the existing representative external memory algorithms when N keeps growing, but also always uses the least disk space around 6.1 bytes on average. The techniques proposed by this study can be utilized to develop fast and succinct suffix sorters in external memory.
The Role and Challenges of Using Digital Tools for COVID-19 Contact Tracing

This aim of this study is to understand the role of digital tools used for COVID-19 Contact tracing and the challenges faced during the implementation of such tools. Contact tracing represents a concept adopted by governments around the world to control the spread of COVID-19. Using digital tools in contact tracing is far more effective than mass quarantine, as it addresses challenges tracers face during conventional contact tracing. Using literature review as the research methodology, related literature was retrieved from different databases such as Google Scholar and ProQuest, shortlisted according keywords and phrases and studied for information focused on answering the research questions. The findings revealed that despite the challenges, digital tools remain significant in controlling the spread of COVID-19. Some of the challenges include the inability to reach children and people who do not own smartphones, or lack access to mobile networks and required technology in locations they live in. Another challenge is surveillance creep brought on by the fear of the breach of privacy through the use of such digital tools.
Towards Integration of Heterogeneous Controllers in an IOT-based Automation System

Heterogeneous controllers (such as: various brands of PLCs and microcontrollers), are existed in an automated system. This research propose a framework to integrate them by using NodeRed, an open source project to gather various devices and services in an IOT setting. The research framework was applied successfully in three cases: a smart home prototype for elderly, a smart door using face recognition method, and an automated system for multiple houses in separated locations.
Virtual reality as a tech tool for students studying Russian in China

The article analyses the experience of implementing virtual reality (VR) in foreign language teaching, particularly teaching the Russian language in Chinese universities. Basing on different approaches and the results of applying VR in practical classes at the Russian Language Faculty of Sichuan University, the research on the effectiveness of VR technologies has been done in terms of stimulating Russian language learners motivation, enhancing their knowledge, strengthening their language capability, developing basic oral Russian, as well as improving the quality of teaching Russian in a non-native environment. The research subject is determined by the transformation of the educational process among students with a relatively good language capability. The novelty and actuality of the topic are due to the following factors. Firstly, it is modelling situations with real-world samples when the organizer of educational process makes it immersive by using computer and recordings. Secondly, auto-reading module can be used. Thirdly, the system can check the consistency between the original text and the one recognized by voice recognition module. The author reviews indicators of the experiment, aimed at assessing teaching results and summarizing the changes in knowledge acquiring with using VR technologies at the Russian Language Faculty of Sichuan University. According to the results of the survey, the author studies the pros and cons of the changes brought by VR and concludes the opinions of the tested students and teachers participating in the experiment. The results of the experiment illustrate changing roles of teachers and students. To be specific, the teacher is no longer a simple organizer, but a supervisor, assistant and consultant, playing a supporting role. Students gradually master the functions of training systems. It stimulates them to work independently under VR. This has been proved by the students results in the semester exam and the state accreditation test of translators. Besides, the statistical data are presented on the differences between the traditional model of language teaching and the one under VR.
Deep quantised portrait matting

Portrait matting is of vital importance for many applications such as portrait editing, background replacement, ecommerce demonstration, and augmented reality. The portrait matt can be accessed by predicting the α value of the original picture. Previous deep matting methods usually adopt a segmentation network to tackle portrait matting tasks. However, these traditional methods will introduce unpleasant blemishes in the matting results sometimes. The authors find that the key factor behind this phenomenon is how they model the matting problem. On the one hand, α value predicting can be modelled as a regression task. On the other hand, it can be viewed as a classification task of predicting background or foreground. To solve this problem, they explore different methods to model the nature of the α matting problem and propose a novel quantisation‐based adaption. Their method comes up with an α quantisation loss to achieve multi‐threshold filtering. Furthermore, they apply an α merging block to improve conventional regression methods. With their method, the gradient loss is reduced by 7.53% relatively, with mean square error and sum of absolute difference decreased by 14.7% relatively, leading to a more visually pleasant α matt in several segmentation backbones.
The relationship of thinking styles and academic performance of students

Introduction . In the conditions of modern Russian education, the emphasis is shifted to the ability of students to carry out non-standard professional duties and to work independently. All this leads to an increase in the number of studies to identify thinking styles in the student age. The aim of the current publication is to present the results of an empirical study focusing on identification of the relationship of thinking styles and academic performance among the students of Astrakhan State University. Methodology and research methods . In order to achieve the aim, the authors used the following research methods: Methods of Thinking Style (by A. K. Belousova), Learning Styles Questionnaire (by P. Honey, A. Mumford), Implicit Theories and Learning Goals Questionnaire (by C. Dweck), Scale of Academic Motivation (by T. O. Gordeeva, O. A. Sychev, E. N. Osin). To process the data, mathematical-statistical methods were employed: the One-Sample Kolmogorov-Smirnov Test, the Independent Samples t Test (Student Test), the Mann-Whitney U Test, the Pearson’s Correlation Coefficient, the Spearman’s Rank Correlation Coefficient. All calculations were performed using the IBM SPSS Statistics 21 computer program. Results . In the course of empirical study, 82 students of five faculties of Astrakhan State University (40 boys and 42 girls aged 20 to 23 years old) were involved. As a result of empirical research, it was revealed that an initiative style of thinking with the variable “enriched personality” has a positive correlation, and critical thinking has a positive correlation with the variable “introjected motivation” and “external motivation”. Managerial and practical thinking styles positively correlate with the variables “achievement motivation”, “growing in telligence”, “enriched personality”, “cognitive motivation”, “adoption of learning goals”. Scientific novelty . The tendency to a reliable connection of thinking styles and academic performance was revealed. A positive correlation between managerial, practical thinking styles and cognitive motivation, achievement motivation is found out. Students with proactive, practical, managerial thinking styles have an above-average academic performance. Practical significance . The materials of the publication are of practical importance for teachers and students, as the study of the stylistic peculiarities of the individuality of students directly contributes to the effectiveness of the process of skills and knowledge development.
Microcontroller-based Random Number Generator Implementation by Using Discrete Chaotic Maps

In recent decades, chaos theory has been used in different engineering applications of different disciplines. Discrete chaotic maps can be used in encryption applications for digital applications. In this study, firstly, Lozi, Tinkerbell and Barnsley Fern discrete chaotic maps are implemented based on microcontroller. Then, microcontroller based random number generator is implemented by using the three different two-dimensional discrete chaotic maps. The designed random number generator outputs are applied to NIST (National Institute of Standards and Technology) 800-22 and FIPS (Federal Information Processing Standard) tests for randomness validity. The random numbers are successful in all tests.
Improved analysis of spectral algorithm for clustering

Spectral algorithms are graph partitioning algorithms that partition a node set of a graph into groups by using a spectral embedding map. Clustering techniques based on the algorithms are referred to as spectral clustering and are widely used in data analysis. To gain a better understanding of why spectral clustering is successful, Peng et al. (2015) and Kolev and Mehlhorn (2016) studied the behavior of a certain type of spectral algorithm for a class of graphs, called well-clustered graphs. Specifically, they put an assumption on graphs and showed the performance guarantee of the spectral algorithm under it. The algorithm they studied used the spectral embedding map developed by Shi and Malic (2000). In this paper, we improve on their results, giving a better performance guarantee under a weaker assumption. We also evaluate the performance of the spectral algorithm with the spectral embedding map developed by Ng et al. (2001).
A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination

COVID-19 is a global pandemic that is affecting more than 200 countries worldwide. Efficient diagnosis and treatment are crucial to combat the disease. Computer-interpretable guidelines (CIGs) can aid the broad global adoption of evidence-based diagnosis and treatment knowledge. However, currently, no internationally shareable CIG exists.The aim of this study was to establish a rapid CIG development and dissemination approach and apply it to develop a shareable CIG for COVID-19.A 6-step rapid CIG development and dissemination approach was designed and applied. Processes, roles, and deliverable artifacts were specified in this approach to eliminate ambiguities during development of the CIG. The Guideline Definition Language (GDL) was used to capture the clinical rules. A CIG for COVID-19 was developed by translating, interpreting, annotating, extracting, and formalizing the Chinese COVID-19 diagnosis and treatment guideline. A prototype application was implemented to validate the CIG.We used 27 archetypes for the COVID-19 guideline. We developed 18 GDL rules to cover the diagnosis and treatment suggestion algorithms in the narrative guideline. The CIG was further translated to object data model and Drools rules to facilitate its use by people who do not employ the non-openEHR archetype. The prototype application validated the correctness of the CIG with a public data set. Both the GDL rules and Drools rules have been disseminated on GitHub.Our rapid CIG development and dissemination approach accelerated the pace of COVID-19 CIG development. A validated COVID-19 CIG is now available to the public.
Dual attention based feature pyramid network

Object detection could be recognized as an essential part of the research to scenarios such as automatic driving and pedestrian detection, etc. Among multiple types of target objects, the identification of small-scale objects faces significant challenges. We would introduce a new feature pyramid framework called Dual Attention based Feature Pyramid Network (DAFPN), which is designed to avoid predicament about multi-scale object recognition. In DAFPN, the attention mechanism is introduced by calculating the top-down pathway and lateral pathway, where the spatial attention, as well as channel attention, would participate, respectively, such that the pyramidal feature maps can be generated with enhanced spatial and channel interdependencies, which bring more semantical information for the feature pyramid. Using the COCO data set, which consists of a considerable quantity of small-scale objects, the experiments are implemented. The analysis results verify the optimized performance of DAFPN compared with the original Feature Pyramid Network (FPN) specifically for the identification on a small scale. The proposed DAFPN is promising for object detection in an era full of intelligent machines that need to detect multi-scale objects.
Syntax-based Transformer for Neural Machine Translation

The Transformer (Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, and Polosukhin 2017), which purely depends on attention mechanism, has achieved state-of-the-art performance on machine translation (MT). However, syntactic information, which has improved many previous MT models, has not been utilized explicitly by Transformer. We propose a syntax-based Transformer for MT, which incorporates source-side syntax structures generated by the parser into the self-attention and positional encoding of the encoder. Our method is general in that it is applicable to both constituent trees and packed forests. Evaluations on two language pairs show that our syntax-based Transformer outperforms the conventional (non-syntactic) Transformer. The improvements of BLEUs on English-Japanese, English-Chinese and English-German translation tasks are up to 2.32, 2.91 and 1.03, respectively. Furthermore, our ablation study and qualitative analysis demonstrate that the syntax-based self-attention does well in learning local structural information, while the syntax-based positional encoding does well in learning global structural information.
A Principled Approach to Data Valuation for Federated Learning

Federated learning (FL) is a popular technique to train machine learning (ML) models on decentralized data sources. In order to sustain long-term participation of data owners, it is important to fairly appraise each data source and compensate data owners for their contribution to the training process. The Shapley value (SV) defines a unique payoff scheme that satisfies many desiderata for a data value notion. It has been increasingly used for valuing training data in centralized learning. However, computing the SV requires exhaustively evaluating the model performance on every subset of data sources, which incurs prohibitive communication cost in the federated setting. Besides, the canonical SV ignores the order of data sources during training, which conflicts with the sequential nature of FL. This paper proposes a variant of the SV amenable to FL, which we call the federated Shapley value. The federated SV preserves the desirable properties of the canonical SV while it can be calculated without incurring extra communication cost and is also able to capture the effect of participation order on data value. We conduct a thorough empirical study of the federated SV on a range of tasks, including noisy label detection, adversarial participant detection, and data summarization on different benchmark datasets, and demonstrate that it can reflect the real utility of data sources for FL and has the potential to enhance system robustness, security, and efficiency. We also report and analyze "failure cases" and hope to stimulate future research.
Quantized ternary pattern and singular value decomposition for the efficient mining of sequences in SRSI images

The growth and development of particular region over time can be witnessed by remote sensing images. Although such raw images have less possibility to derive the insights, Serial Remote Sensing Images (SRSI) has the large potential to discover the patterns. The evolution of spatial patterns in various areas including urban development, expansion of vegetation cover and agriculture is the evidence for the utilization of SRSI accumulation. The application of conventional sequential pattern-mining algorithms on the SRSI images results in high computational complexity. This issue can be resolved by grouping the pixels and mining sequence patterns. A one-pass framework is introduced to compress and hide the data in the marked stream without any loss. In this paper, we proposed a Quantized ternary pattern based pixel grouping and Singular Value Decomposition—Run Length Coding based pattern mining. The algorithms are experimented using a dataset, namely, the Cropland data layer dataset. The proposed algorithm is efficient in terms of mining time and sequence pattern generation.