Reviewerly/exHarmony huggingface dataset:
Biographical predictors of individual psychological characteristics included in the ‘Big Five’ of personal qualities

The article analyzes the role of biographical predictors of individual psychological characteristics that are included in the ‘Big Five’ of personal qualities. The analysis of the relationship of answers to psychobiographical questions included in the questionnaire of management potential (Y.V. Sinyagin, 2014) with the results of the ‘Big Five’ model is presented. The necessity of studying personality traits conducive to successful activities is substantiated. On the basis of empirical research, predictors of managerial success are characterized. Arguments are given for including data containing biographical information in methods of psychological analysis of a person. The authors show that the obtained empirical data can find their practical application in several areas. First of all, it is their direct use during interviews and interviews with leaders.
Examining the linkage among market orientation, learning orientation and innovation performance: The mediation role of knowledge management

Small and medium enterprises (SMEs) exports contribute as a driver of the global economy and a source of economic growth. In the global era, to main a competitive advantage, SMEs need to produce behavior to ensure the feasibility of innovation performance. The purpose of this study is to investigate the relationship between market orientation, learning orientation, and knowledge management on innovation performance. The research method uses a research design cross-section. The study population includes all export-oriented manufacturing industries. SMEs and is registered in the Bali Provincial Industry and Trade Office database (2019). A total of 94 senior managers are SME interested in participating in the survey. Seven hypotheses are presented related to market orientation, learning orientation, knowledge management, and innovation performance, and the mediating effect of knowledge management. Structural equation model based Partial least square (SEM-PLS) is used to test the hypothesis. These findings reveal that learning orientation in manufacturing SME operation contributes to knowledge management. Knowledge management plays a mediating role in the relationship between market orientation and learning orientation on innovation performance. These results contribute additional insights to managers and policymakers about the concepts of market orientation, learning orientation, and knowledge management to achieve innovation performance.
Analysis of University Students' Views and Usage Levels of Web 2.0 Tools in Terms of Various Variables

The aim of this study is to determine the university students' views on Web 2.0 tools and their level of use of Web 2.0 tools according to the variables of gender, Foreign Language Level, Computer usage level and frequency of access to the internet. The study group consists of 427 students who continue their studies at the Ahmet Kele?o?lu Faculty of Education of Necmettin Erbakan University in the academic year 2019-2020. The data in the study was taken with a three-part tool. In the first section, demographic characteristics of the students who participated in the research were determined. In the second section, the frequencies of use of Web 2.0 technologies were asked. In the third section, students were asked about their skills in using Web 2.0 tools. Data were analyzed by statistical package programs. T-test and variance analysis were used to determine whether the students' views on Web 2.0 tools differ according to gender, foreign language level, computer usage level and frequency of access to the internet. The Scheffe test was used to determine which group the differentiation originated from. In terms of the frequency of use of Web 2.0, men are significantly different from women, those who have computers and those who are connected to the internet for 7 hours and more than others. In terms of Web 2.0 skill level, men are significantly different from women, those who have computers, those who have 7 hours and more internet connections, compared to others.
Virtual Learning and Training Needs in a Peripheral Region of Colombia

This research seeks to identify necessary elements that allow students to improve self-regulation of learning and self-efficacy in their results in order to achieve a thorough education based on the experience of undergraduate users with their virtual platform. A quantitative method was used, involving a survey questionnaire which would subtract the objective aspects of data collection. The sample was determined according to simple random sampling. As part of the research, the perceptions of students and professors was analyzed, inquiring them on their levels of selfregulation and self-efficacy. Considering the results, the appreciation of the platform was positive while opinions were divided as for the levels of selfregulation and their conception of self-efficiency.
A Symbolic Algorithm for the Case-Split Rule in String Constraint Solving

Case split is a core proof rule in current decision procedures for the theory of string constraints. Its use is the primary cause of the state space explosion in string constraint solving, since it is the only rule that creates branches in the proof tree. Moreover, explicit handling of the case split rule may cause recomputation of the same tasks in multiple branches of the proof tree. In this paper, we propose a symbolic algorithm that significantly reduces such a redundancy. In particular, we encode a string constraint as a regular language and proof rules as rational transducers. This allows to perform similar steps in the proof tree only once, alleviating the state space explosion. In our preliminary experimental results, we validated that our technique (implemented in a Python prototype) works in many practical cases where other state-of-the-art solvers, such as CVC4 or Z3, fail to provide an answer.
METHOD FOR SEARCHING SEQUENTIAL PATTERNS OF USER&amp;#x27;S BEHAVIOR ON THE INTERNET

One of the important tasks of data mining is to isolate patterns and detect related events insequential data based on the analysis of sequential patterns. The article examines the possibility ofusing sequential patterns to analyze the events of search and cognitive activity of users when interactingwith Internet resources of an open information and educational environment. Searchingfor sequential patterns is a complex computational task whose goal is to retrieve all frequent sequencesrepresenting potential relationships within elements from a transactional database ofsequences of search activity events with a given minimum support. To solve it, the article proposesa method for searching for patterns in sequences of events to detect hidden patterns that indicatepossible levels of vulnerability when performing information search tasks in the Internet space.A mathematical model of user behavior in a search session based on the theory of sequential patternsis described. To improve the computational efficiency of the method, a modified algorithmfor generating sequential patterns has been developed, at the first stage of which AprioriAll isperformed, which forms frequent candidate sequences of all possible lengths, and at the secondstage, a genetic algorithm for optimizing the input parameters of the feature space of the generatedset to search for maximum patterns. A series of computational experiments were carried out ontest data from the MSNBC corpus, the SPMF open source data mining library. The comparativeanalysis was carried out with the VMSP and GSP algorithms. The research results confirmed theefficiency of the search for maximum sequential patterns by the proposed algorithm in terms of theexecution time and the number of extracted patterns. The results of the experimental studies of themethod showed that to increase the stability and accuracy of the work, the sample size obtained asa result of the GA operation will reduce the required number of scans of the pattern database,providing acceptable computational costs comparable to the VMSP algorithm and the GSP algorithmthat exceeds the search time for sequential patterns. an average of more than 150 %.
Determinants of information and communication technology (ICT) adoption in developing countries

Background/Objective: The adoption of Information and communication technology (ICT) in developing countries is increasing during last two decades. This study explores the determinants of ICT adoption in 67 selected developing countries. Methods/Statistical analysis: Panel data was collected from World Bank and International telecommunication websites for the period of 2000 to 2018. This study explores the impact of access to electricity, ICT good imports, financial development index, GDP per capita, urban population, control of corruption and government effectiveness on ICT adoption. Selected developing countries are divided into four panels such as low income, lower middle, upper middle and high income countries. Pesaran CSD, Friedman CSD and Frees CSD tests are used to check the presence of cross-sectional dependency in the panel data. The results confirmed the presence of crosssectional dependency in the variables and hence CIPS second generation unit root test is used for stationarity. Kao test is used to check the long run cointegration among the variables. FMOLS is used for regression analysis. Findings: The regression results show the mixed findings in different panels. The results indicate that access to electricity is an important determinant of ICT adoption in low and lower middle income developing countries. ICT imports and Government effectiveness are among the significant determinants of ICT adoption in low, upper middle and high income developing countries. GDP per capita is an important variable for each panel. Urban population is found to enhance ICT adoption in lower middle and high income developing countries. It is recommended that Government should focus on these important determinants to increase the ICT adoption in selected developing countries. Novelty/Application: ICT development index is used as a dependent variable instead of components of ICT such as internet, mobile phone and computer penetration. New econometrics techniques and variables are used in analysis.
THE INTEGRATION OF AI AND VIRTUAL LEARNING IN TEACHING EFL UNDER COVID-19

Appears in: ICERI2020 Proceedings Publication year: 2020Pages: 7866-7872ISBN: 978-84-09-24232-0ISSN: 2340-1095doi: 10.21125/iceri.2020.1740Conference name: 13th annual International Conference of Education, Research and InnovationDates: 9-10 November, 2020Location: Online Conference
The Use of Manipulative Tactics in Hate Speech

The aim of the present article is to study some ways of verbal expression of adversary social relationships. It is a study based on the linguistic material of one online article, which shows clearly that the language resources used by the Azerbaijani propagandists manipulate the public, create an exaggeratedly negative image of Armenia as an aggressor state and impose anti-Armenian opinion on the readers. The need for a linguistic study of hate speech is quite actual since at the modern Information Age or New Media Age the confrontation between the conflicting sides is often escalated via verbal duelling, accusation and repudiation that spreads rapidly far and wide with the help of online resources. The spread of fake, misleading and falsified information that discredits the opposing side, inciting hatred and animosity against a group of people is growing dangerously. Worst of all, we cannot but admit the fact that, unfortunately, malice and antagonism are becoming part of modern civilization. The linguistic analysis carried out in the present article demonstrates how certain verbal manipulative tactical tools that are deliberately used by the author, create abusive hate speech against Armenia and its policy.
Research and Analysis of Electromagnetic Trojan Detection Based on Deep Learning

The electromagnetic Trojan attack can break through the physical isolation to attack, and the leaked channel does not use the system network resources, which makes the traditional firewall and other intrusion detection devices unable to effectively prevent. Based on the existing research results, this paper proposes an electromagnetic Trojan detection method based on deep learning, which makes the work of electromagnetic Trojan analysis more intelligent. First, the electromagnetic wave signal is captured using software-defined radio technology, and then the signal is initially filtered in combination with a white list, a demodulated signal, and a rate of change in intensity. Secondly, the signal in the frequency domain is divided into blocks in a time-window mode, and the electromagnetic signals are represented by features such as time, information amount, and energy. Finally, the serialized signal feature vector is further extracted using the LSTM algorithm to identify the electromagnetic Trojan. This experiment uses the electromagnetic Trojan data published by Gurion University to test. And it can effectively defend electromagnetic Trojans, improve the participation of computers in electromagnetic Trojan detection, and reduce the cost of manual testing.
Multidimensional Information Fusion in Active Sonar via the Generalized Labeled Multi-Bernoulli Filter

Under the conditions of low detection probabilities, high clutter rates, low data-sampling rates, large measurement errors, and unknown prior information of the target position, multi-object tracking is difficult.This paper proposes a multidimensional information fusion method in active sonar via the generalized labeled multi-Bernoulli (GLMB) filter.After modeling the position measurement, radial velocity measurement and amplitude measurement of the target and clutter, new target births are adaptively generated by the measurement-driven model, predictions are made by the target motion model and updates are performed via multidimensional measurements with the generalized likelihood in the GLMB filter, which enables the measurement information of different dimensions to be elegantly applied to information fusion and significantly improves the filter performance.The contribution of specific dimension measurement to fusion can be evaluated by the Kullback-Leibler (KL) divergence.In the efficient implementation, we propose flat Gibbs sampling to realize multiple hypothesis optimization.Moreover, the filtering recursion is derived from Gaussian mixtures.Simulations are presented to verify the proposed method.
Gender Recognition by Using Acoustic Features of Sound with Deep Learning and Data Mining Methods

Human voice is a physical phenomenon that contains some information on it. This information has been examined in studies on sound for years. Studies such as recognition of speech, age and gender determination, and emotional analysis were carried out and successful results were obtained. In this study, gender was determined according to the acoustic characteristics of the speeches compiled from seven different languages by using the acoustic features of the voice. In addition to data mining techniques, deep learning was also used in the classification process. Our dataset consisting of 4739 different speech samples belonging to approximately 800 people. 95.59% and %92.15 best success rate was achieved respectively with gradient boosting and random forest in classification process. Deep learning method with Multilayer Perceptron (MLP) was achieved 96.22% correct classification rate in our analysis.
Patch-based Painting Style Transfer

Approaches based on convolutional neural network framework have proven to be effective in various of style transfer scenario. Most approaches train the network to process the photo as a whole, and gradually transform it into a user specified painting style. In contrast to that, this paper proposed a brush-based concept, which inherits the spirit of the stroke-based rendering. The input photo was first partitioned into small patches. A modified version of the generative adversarial network has been adopted to transform each patch into a brushstroke of a given style. After merging all the generated brushstrokes, it produces the desired style transfer result. The proposed style transfer approach preserves the geometrical structure of the input image; only color and texture are to be altered during the process.
Keynote Speech: Blockchain for Cyber Physical Systems

Cyber Physical Systems (CPS) are transforming the way we interact with the physical world around us. However, centralised approaches for CPS systems are not capable of addressing the unique challenges of CPS due to the complexity, constraints, and dynamic nature of the interactions. To realize the true potential of CPS, a decentralized approach that takes into account these unique features is required. Recently, blockchain-based solutions have been proposed to address CPS challenges. Yet, applying blockchain for diverse CPS domains is not straightforward and has its own challenges. In this talk, we share our experiences in applying blockchain technology for CPS with an aim to provide insights and highlight the challenges and future opportunities.
Automatically Evolving Texture Image Descriptors Using the Multitree Representation in Genetic Programming Using Few Instances

The performance of image classification is highly dependent on the quality of the extracted features that are used to build a model. Designing such features usually requires prior knowledge of the domain and is often undertaken by a domain expert who, if available, is very costly to employ. Automating the process of designing such features can largely reduce the cost and efforts associated with this task. Image descriptors, such as local binary patterns, have emerged in computer vision, and aim at detecting keypoints, for example, corners, line-segments, and shapes, in an image and extracting features from those keypoints. In this article, genetic programming (GP) is used to automatically evolve an image descriptor using only two instances per class by utilising a multitree program representation. The automatically evolved descriptor operates directly on the raw pixel values of an image and generates the corresponding feature vector. Seven well-known datasets were adapted to the few-shot setting and used to assess the performance of the proposed method and compared against six handcrafted and one evolutionary computation-based image descriptor as well as three convolutional neural network (CNN) based methods. The experimental results show that the new method has significantly outperformed the competitor image descriptors and CNN-based methods. Furthermore, different patterns have been identified from analysing the evolved programs.
E-Government in a New Technological Ecosystem

The growing communication needs demanded by a constantly changing society significantly stimulate the current information and communications technology industry.Consequently, we can see an exponential development of ICT which, corroborated in the IoT context with the new paradigms in the field of software development (cloud computing, fog computing, blockchain, etc.), and in the nanotechnology industry draws the boundaries of a new technological ecosystem.As any ecosystem presupposes the existence of a food chain, and in the case of the new technological ecosystem we discover the producers and decomposers.They can be assimilated to technological tools and primary resources on the one hand (IoT, internet, water, air, electricity, etc.), respectively to individuals and communities on the other hand.This is the overall image or background over which aggressive digital solutions are being developed, in various fields, which respond to increasingly specialized, more concrete needs : education, culture, socialization, sports, transport, health, ..., administration, government.In this paper we will present the main aspects of governance in the new technological ecosystem, emphasizing the aspects of e-government and democracy, voting and e-legislation contained in what we generically call e-government.
The Current Situation and New Ideas of Vocal Music Education in Universities

Vocal music teaching is one of the compulsory subjects for music majors in colleges and universities. Nowadays, the school attaches great importance to the cultivation of students' comprehensive quality. Therefore, in the teaching work of college vocal teachers, we must combine the actual situation and continuously strengthen the innovative ideas of vocal education in colleges and universities. This article elaborated on the current situation of vocal teaching in colleges and universities, and deeply analyzed the reform of vocal music in colleges and universities, hoping to provide some substantive references for vocal education in colleges and universities so that music majors can better improve their comprehensive quality.
HSNR: A Network Representation Learning Algorithm Using Hierarchical Structure Embedding

Vertices in the same group tend to connect densely, and usually share common attributes. Groups of different sizes reflect the relations of vertices in different ranges, and also reflect the features of different orders of the network. In this work, we propose a novel network representation learning algorithm by introducing group features of vertices of different orders to learn more discriminative network representations, named as network representation learning algorithm using Hierarchical structure embedding (HSNR). HSNR algorithm firstly constructs hierarchical relations of network structures of different orders based on greedy algorithm and modularity. In order to introduce hierarchical features into the network representation learning model, HSNR algorithm then introduces the idea of multi-relational modeling from knowledge representation, and converts the hierarchical relations into the triplet form between vertices. Finally, HSNR proposes a joint learning model embedding vertex triplets into the network representations. The experimental results show that the HSRN algorithm presented has an excellent performance in network vertex classification task on three real-world datasets.
Self-efficacy analysis of EFL student with digital game experiences

The English lessons in the school curricula are designed to be a benchmark of student foreign language competency measurement. To succeed in every single aspect of the lesson, students need to enhance their will and motivation so they can fulfill all the needs and complete it. Self-efficacy is one of the most critical factors which can sustain student academic intention amongst all. Thus, the role of experience is one of the factors which can keep a prominent way of developing functional self-efficacy. As a form of experience, playing a video game (also known as gaming behavior) is the one who considered valuable in hoisting the effectiveness of student's English learning, especially in developing the existence of student's self-efficacy. Some students in different stages of formal education schools like elementary, and senior high scholars with different specifications toward the experience and the characteristics of self-efficacy will be the object of this research. Using a descriptive qualitative approach, six students from a different grade and school are analyzed and proved in having self-efficacy toward English lesson. This thing implies that students that have some experiences in playing digital games are provided with self-efficacy and appears more dominant than the students without such experiences. http://dx.doi.org/10.23960/aksara/v21i1.pp17-32