Reviewerly/exHarmony huggingface dataset:
COMPETENCE-BASED APPROACH IN THE SYSTEM OF FORMATION OF OCCUPATIONAL MOBILITY OF FUTURE PHYSICAL EDUCATION TEACHERS IN THE UNITED STATES OF AMERICA

The system of higher education of USA is characterized by a liberal system of university management and based on the absolute priority of institutional autonomy and minimal state intervention. The specialists’ training in physical education and sports field is no exception. Examining of the practice of a realization of the competency approach in different states of America has made it possible to build a nine-level flowchart that presents the professional skills, knowledge and abilities needed to be successful in a specific sphere of physical education or sports.
The Right to physical and mental immunity in the light of the new constitution of the Republic of Armenia and modern challenges in this field in Armenia

The Constitution of the Republic of Armenia (RA) for the first time envisaged the right to physical and mental immunity (in the former edition of personal immunity) in 2015. According to Article 25(1) of the RA Constitution, everyone shall have the right to physical and mental integrity. That right may be restricted only by law, for the purpose of state security, preventing or disclosing crimes, protecting public order, health and morals or the basic rights and freedoms of others. The bodily rights of the person, including the right to physical and mental immunity, are recognized in professional literature as somatic rights that have become a subject of vigorous debates as a new generation of human rights. In general, human somatic (bodily) rights constitute a wholeness, which includes: the right to life; the right to die or the right to choose a way of giving up one’s life (right to suicide, right to use euthanasia), etc. Current report in the light of the RA Constitution report discuss some of important issues in this field: eugenic experiments, medical intervention, organs and tissues transplantation, etc.
The Past and Future of Visualization for Computing in Science and Engineering

The CiSE Visualization Corner (VizCorner) department reflects the long term and fruitful overlap between computer-based visualization and computational science and engineering. Looking into what the VizCorner has covered in the last decade, we identify current trends in visualization research, and possible future developments for visualization in science and engineering.
LTKW: An Improvement of Extractive Automatic Text Abstract model on Long Chinese Text

Natural language processing is an important direction of artificial intelligence. Automatic text abstract is an information compression technique that automatically converts a collection of text or text into a short summary according to a certain type of application. However, text summaries have not received much attention in the Chinese language field compared to the hot areas as automatic question and answer, knowledge mapping, sentiment analysis and other natural language processing tasks.
An Approach to Temporal Phase Classification on Videos of the Volleyball's Basic Reception Technique

In this paper we provide an approach on sports analysis using Deep learning techniques. As part of a current project, the volleyball's basic reception technique has been divided into temporal phases. We performed an evaluation over our own labelled dataset consisting in 14814 frames from 69 videos depicting the desired reception technique. A model based on the YOLO algorithm was trained to locate the player region and trim the frames. Two time fusion methods over the frames wereproposed and evaluated with CNN models which were created based on the ResNet models and a transfer learning approach was used to train them. The results show that these models were able of classifying the frames with their corresponding phase with an accuracy of 92.21% in our best model. Also it can be seen that the RGB merging method shown in this paper helps to slightly improve the performance of the models. Furthermore, the models were capable of learning the temporality of the phases as the mistakes done by the models occurred between consecutive phases.
A New Method for Removing Image Noise

A new denoising method is proposed to solve the problem that the noise of three-dimensional target images collected by photoelectric theodolites is varied and randomly distributed. Firstly, based on the universal law of gravity, the cross-correlation degree is calculated by taking the image pixels as the particles. Secondly, the Otsu algorithm is used to calculate the threshold to determine whether the pixel is noise. Finally, the results of the denoising algorithm are compared with those of Wiener filter, mean filter, and median filter, and the four algorithms are compared and evaluated from both subjective and objective data [1–3]. The results show that when the salt and pepper noise density is less than 20% [4], the visual effect of the algorithm is better than the other four algorithms, and the peak signal-to-noise ratio (psnr) is between 30 and 45 dB. The PSNR is increased to more than 10 dB by using this algorithm instead of the optimal algorithm of the other three algorithms. The visual effect is consistent with the data. The pixel correlation filtering algorithm is feasible and meets the needs of practical engineering.
Fog Computıng – A Rasperry Pı Decentralızed Network

Ever since the concept of parallel processing and remote computation became feasible, Cloud computing is at its highest peak in its popularity. Although cloud computing is effective and feasible in its usage, using the cloud for frequent operations may not be the be the most optimal solution. Hence the concept of FOG proves to be more optimal and efficient. In this paper, we propose a solution by improving the FOG computing concept of decentralization by implementing a secure distributed files system utilizing the IPFS and the Ethereum Blockchain technology. Our proposed system has proved to be efficient by successfully distributing the data in a Raspberry Pi network. The outcome of this work will assist FOG architects in implementing this system in their infrastructure and also prove to be effective for IoT developers in implementing a Raspberry Pi decentralized network while providing more security to the data.
Cognitive Robotics and Control

Robotics and control are both research and application domains that have been frequently engineered with the use of interdisciplinary approaches like cybernetics [...]
Improving the Visibility of Nighttime Images for Pedestrian Recognition using In-Vehicle Camera

There is a need for methods to recognize night pedestrian to reduce pedestrian traffic accidents at night. In this paper, we proposed the method that converts images using continuous nighttime images from in-vehicle camera. The proposed method performs feature extraction that doesn't depend on own vehicle speed because inputting continuous nighttime images, and the function of convolution layer that performs dimensionality reduction. In order to confirm the effectiveness of the proposed method, we prepared the images of simulation and camera. The nighttime images were conversed with the proposed method. After conversion, we calculated the recognition performance by applying object detection which is an object recognition method. We showed the proposed method is more robust against the own vehicle speed change.
Effect of Combining Teaching Learning-Based Optimization (TLBO) with Different Search Techniques

This paper investigates the effect to ensemble teaching learning-based optimization (TLBO) with other meta-heuristics methods like artificial bee colony (ABC), biogeography-based optimization (BBO), differential evolution (DE) and genetic algorithm (GA). Three different schemes to generate sub-population from the main population are proposed, and the effect of migration of solutions from one sub-population to the other is also explored. The experiments are performed on different unconstrained and constrained benchmark optimization problems. The results are investigated using the statistical test like Friedman rank test and Holm-Sidak post hoc test. The results reveal that the ensemble of different optimization methods is effective than the basic algorithms.
Linear Complexity of &lt;i&gt;n&lt;/i&gt;-Periodic Cyclotomic Sequences over 𝔽&lt;sub&gt;&lt;i&gt;p&lt;/i&gt;&lt;/sub&gt;

Periodic sequences, used as keys in cryptosystems, plays an important role in cryptography. Such periodic sequences should possess high linear complexity to resist B-M algorithm. Sequences constructed by cyclotomic cosets have been widely studied in the past few years. In this paper, the linear complexity of n-periodic cyclotomic sequences of order 2 and 4 over 𝔽p has been calculated, where n and p are two distinct odd primes. The conclusions reveal that the presented sequences have high linear complexity in many cases, which indicates that the sequences can resist the linear attack.
Template attacks on nano-scale CMOS devices

Abstract Profiled attacks are widely considered to be the most powerful form of side-channel analysis attacks. A common form is known as Gaussian template attacks which fit a Gaussian distribution to better model the behavior of the target device. Since profiled attacks build the model based on a device identical to the target device, manufacturing variances are an important factor for the success of such attacks. With shrinking the feature size, the influence of manufacturing variation on the power consumption of integrated circuits increases. It has been warned that this issue might render template attacks less effective. We evaluate this assumption on an ASIC design manufactured in 40 nm technology. We characterize the introduced variation and show that these can be easily mitigated. By performing attacks on multiple samples of the same ASIC, we show that template attacks on small technology sizes are still successful.
WITHDRAWN: A Dynamic Energy-efficient Resource Management Scheme and Performance Optimization in Cloud Data Centers

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal
Social Attentive Network for Live Stream Recommendation

Live streaming platforms not only provide live videos but also allow social interactions between viewers via real-time chatting. However, none of existing research has studied the social impact for recommending live streams. In this work, we formulate a new personalized recommendation problem by factoring in both video and social contents (chats). Accordingly, we 1) design a new attention network ANSWER to identify viewers’ attention on video and social contents, and 2) rank the channels based on the attentive features. We collect a real dataset from Twitch for evaluation. The experimental results manifest that ANSWER outperforms baselines by at least 26.6% in terms of [email protected]
Multi-aspects Intelligent Requirements Prioritization Technique for Value Based Software Systems

Requirements engineering (RE) is an important phase of software engineering. During this phase, an important set of activities are carried out to manage requirements elicitation, verification, prioritization and validation. Dimension and dynamics of software development are changing with the passage of time. Economic growth in different sectors is increasing the demand for software development. This enhancement has introduced the concept of Value Base Software (VBS) development. Requirements prioritization is playing a vital role in ordering requirements to support the release planning of the software. A prioritization process is considered as highly complex process and depends on the nature and size of requirements. VBS systems are entirely different from typical software development, and prioritization process for VBS is also very challenging. A need arises from the provision of prioritization techniques to support the technical and business aspects-based prioritization. Existing techniques are not qualified to meet the expectation of the industry for VBS development. Therefore, this research contribution is an effort made, based on an intelligent decision support system for requirements prioritization in the domain of VBS system. Aspects based requirements prioritization is applied to many requirements and results are produced in two clusters. Results are claimed as a prioritized list of requirements for traditional as well as value-based system.
Speaker Specific Formant Dynamics of Vowels

Automatic Speech and Speaker Recognition technology has growing demands in variety of voice operated devices. Although the input for all such systems is speech signal, the features useful for each application/task are different. Of the different speech sounds, vowel sounds spectrally well-defined and well represented by formants. Formants which represent resonances of vocal tract are the result of physiology of individual’s speech production mechanism as well as nature of speech (words) being spoken. In this way formants are features of speech as well as of speaker. In this paper significance of formants for speech and speaker recognition is explored through experimental analysis. Formant tracking and estimation is done using adaptive formant filter bank and single pole formant based filter. Twelve vowel sounds represented in ARPABET (Advanced Research Project Agency bet) form are used to estimate the first four formants. The analysis based on extracting and emphasizing speaker specific clues indicates that higher formants carry more speaker specific information than first (lower) formant.
Interactive image segmentation using region-based fully connected CRF models with graph cut based fine tuning

Interactive image segmentation has gained much popularity since it can enhance the segmentation performance with human interactions. In this paper, we propose a novel framework that can effectively estimate the binary labels based on Conditional Random Field (CRF) models combining region-level information with pixels-level information. The user scribbles are treated as soft constraints. We use pixel-layer with local CRF and region-layer with fully connected CRF to model the graph. Then we combine the pixel information and region information together to calculate the pixels' likelihood term. In contrast to using relations between the pixel-layer and the region-layer during the probability computation, we calculate the probabilities of pixels and regions separately to reduce computational complexity. After the likelihood computation, a graph cut based model is used to refine the pixel-level label predictions of the image in order to avoid label inconsistency in regions and capture the boundary details. Due to the less relationship between layers and utilizing the refinement, the proposed model can provide high accuracy and user inputs insensitive results. Our experiments show that the proposed approach can efficiently and accurately segment the images compared with other state of art methods with only a few scribbles. Graph cut based fine tuning improves our segmentation accuracy and our method needs less computational cost than traditional multi-layer-based methods.
Peri-literary communication games of high school students

Seven interviews with high school students were conducted in order to establish the relationship between their use of Internet resources and the level of their interpretation, communication and social skills. Respondents, who used the Internet as a source of information, comprehend literature factographically and its adaptations as distortions of the original texts. Their communication skills were poorly developed. They were passive in interpersonal contact. They made only superficial contact with the interviewer, in order to hide the way they really used Internet resources. They perceived the interview as a hierarchical relation, and were subordinated to the interviewer.&#x0D; Students who posted texts on the Internet were able to get the interviewer engaged in the topic they were discussing. They created their own image and influenced the environment. They assumed role of a node in the network, limiting the influence of social hierarchy.&#x0D; A small sample does not allow for generalization of findings. Nonetheless, it was found that students who posted texts on the Internet presented highly developed interpretation, communication and social skills.
Evaluation of a sensor array for navigation in a dynamic environment in an eight degrees-of-freedom omnidirectional vehicle

Robot navigation trough dynamics environments is a complex task that is both time consuming and highly computational intense. In this paper, it is studied the usage of low power and low complexity sensors mounted in an 8 degrees of freedom mini autonomous vehicle applied to the navigation trough a dynamic scenery. Using a TSP-based modification to the classical A* navigation algorithm for the path planning stage, the 3 proposed systems initially work alone where they are evaluated, and further on, implemented as an integrated system. In this respect, the aim of this experiment, made in conjunction of MATLAB and V-REP, is to compare different sensors arrays in many case studies, discuss the behavior and the performance to further selection of them.
A Study on the Graphical Programming Heterogeneous Data Conversion Technique based on Pattern Matching

Intuitive with a low error rate and high reliability, graphical programming has become the development trend of embedded software development. However, the heterogeneity of the graphical programming system makes it impossible for programs, components, and projects to interact across different systems. XML has advantages such as self-description and scalability, thereby facilitating the expression of various types of data. With XML being the intermediary language for heterogeneous systems, the paper proposes an XML automatic conversion method based on pattern matching. This method achieves automatic conversion of graphical programming heterogeneous data by converting the heterogeneous source data into the specific XML descriptive format and applying it in the PLC embedded graphical programming in the field of industrial control.
Market basket model of ocean energy system

Frequent item sets play an essential role in many data mining tasks where one tries to find interesting patterns from databases, such as association rules, correlations, and sequences. This chapter includes ocean energy assessment through a market basket model, the development of frequent data set mining of ocean energy systems, as well as fuzzy logic and the Apriori algorithm for the frequent item-set mining of tidal and wave energy systems. Finally, Park Chen Yu and SON algorithms are used to examine data mining of wave and tidal power system parameters.
An Aggregate Signature Scheme with Forward Security and Non-repudiation

Based on bilinear pairing, elliptic curve and strong RSA assumption proposed a new aggregate signature scheme with forward security. The traits of the scheme is as follows: It have the non-repudiation, which realized the authentication between the secret key generation center and users; Have the forward security, based on the strong RSA assumption implementing the security of signature information, and to further guarantee the system's security.
Blockchain Architecture Stack to Smart Education

Blockchain is an emerging technology that serves as an immutable ledger and publicly available infrastructure for building decentralized applications. Blockchain-based applications ensure transparency and trust between all parties involved in the interaction. Nowadays, educational organizations focus on online education and propose to create a system based on educational smart contracts in a public ledger. This public ledger shall be shared between major online and offline educational institutes around the world. From a software architecture perspective, blockchain enables new forms of distributed software architectures across a large network of untrusted participants. The objective of this chapter is to propose a blockchain architecture stack to smart education. The proposed architecture exploits the benefits of the blockchain and global ecosystem simplification to create a globally trusted higher education credit system.
Nonlinear Image Enhancement Based on Non-sub-sampled Shearlet Transform and Phase Stretch Transform

In this paper, non-sub-sampled shearlet transform (NSST) multi-scale analysis is combined with phase stretch transform (PST) to nonlinearly enhance images. The components of different scales after NSST multi-scale decomposition are processed by nonlinear models with different thresholds, and the noise is well suppressed while enhancing the detail features. The thresholds of the enhanced model are determined by the local standard deviation of PST feature map. Experiments on Matlab platform show that the proposed algorithm has improved image distortion, cleared details, and enhanced image contrast.
Rough Waypoint Extraction Algorithm Based on Mean Shift Clustering Saliency Analysis

This paper studies the problem of rough extraction of salient waypoints for UAV scene matching navigation, and a saliency analysis algorithm based on mean shift clustering is researched. The input image is preprocessed by color space conversion and Gaussian low-pass filtering. Mean-shift clustering is used to divide the image into sub-regions as the basic unit for candidate waypoint extraction. The Euclidean distance between the pixel color value and the average color value of the full image is used to represent the saliency. The sub-regions with high saliency value in the image are extracted as candidate waypoints.
Computer Communication Technology and Electronic Information in Artificial Intelligence Application

Abstract In the context of stable social and economic development, the core position of mankind has gradually emerged. People’s behavior is controlled by their consciousness, which in turn creates a good living environment. With the development of science and technology, manpower has been largely liberated. Social productivity is constantly growing, and socio-economic order is constantly being reformed. To take full advantage of technological innovation, artificial intelligence is the most advanced technology. Domestic big data technology is rapidly innovating, and computer communication technology is increasingly used in the field of artificial intelligence. Communication and electronic information technology are important supports for the spread of artificial intelligence and can process various data in a timely manner. Process the complex data information and upload the processing results to the artificial intelligence platform. Build different data matrix models, analyze data parameters, and finally apply them to the field of artificial intelligence. The organic integration of computer communication technology and electronic information technology to successfully complete various tasks. It fully demonstrates the close connection between information technology and artificial intelligence, and provides theoretical support for the subsequent work. This article focuses on the application of computer communication technology and electronic information in artificial intelligence, and puts forward comments and suggestions based on actual conditions.