Reviewerly/exHarmony huggingface dataset:
DIGITAL TOOLS AS A MEANS FOR INCREASING MOTIVATION OF FUTURE TEACHERS’ PROFESSIONAL ACTIVITY

The day-to-day realities require the search for new approaches to stimulating and motivating students in terms of their professional training. Thus, we have analyzed the scientific, pedagogical and publicistic literature on the research topic to determine the most effective means to increase student motivation for successful professional activity. Since Ukraine is rapidly entering the era of digital technologies, they have become the object of our study. Nowadays many digital tools have been created to use them for educational purposes. We have identified the most effective, easy-to-use and the most functional tools: Kahoot, Quizizz, Formative, Online Test Pad, Screencast-O-Matic, Camtasia, Windows Live Movie Maker, Avidemux, Crello, Storyjumper, Bookemon, Kindlebookmaker, Trello, Strides, Goal Tracker: Making Habits, Coach.me, Bliss Gratitude Journal, Habitica, YouTube, MindMeister, MindMup, Mindomo, Coggle, Draw.io, Blogger, Tilda, Google Site. The use of digital tools provides an opportunity to update the educational process, diversify the methods of presentation and build trajectories of students’ individual development. In order to increase students’ motivation for professional activity, tasks were developed using these digital tools. Their content is aimed at combining motivation for personal development and improving professional growth. To determine the effectiveness of the proposed tasks, a pedagogical experiment was conducted. It was determined at the ascertaining stage of the study that 78% of the students (first-year students) had a low level of motivation for self-realization in professional activities, 22% – the average level, and there were no students with a high level of motivation. After the formative stage of the experiment, an assessment of the effectiveness of the work was made and it was found that the students in the experimental group showed much higher performance than those in the control one. The control stage of the experiment showed an increase in the motivation of first–year students for further professional activity. The obtained results have proved that it is necessary to exert a constant impact on the motivational sphere of student personality using various digital tools.
Research on Athlete Training Effect Evaluation Based on Machine Learning Algorithm

In order to achieve a quantitative analysis of the training effects of athletes, this paper combines machine learning algorithms to evaluate and analyze athletes’ training effects and analyzes the evaluation algorithms based on machine learning interpretable models. Moreover, after analyzing a variety of algorithms, this paper selects an intelligent evaluation method suitable for this model and builds an intelligent evaluation system based on the current athletes’ training needs. In addition, this paper verifies the effect of the system with the support of intelligent algorithms and experiments. The experimental research results show that the athlete training effect evaluation system based on the machine learning algorithm proposed in this paper has good results, and it can be applied to subsequent athletes’ sports training evaluation.
Research on HPLC Experimental Platform Component Scheme

Abstract Power line communication is a kind of communication mode which uses power line as transmission medium and carries on data transmission and information exchange through carrier. Power line has wide distribution, high economy, high transmission rate and good reliability. However, as a communication mode without special transmission media, there is still a lack of power line communication experimental platform in the industrial field. The communication experiment platform is composed of power line carrier field test platform, protocol consistency test platform, interconnection test platform and courts simulation platform. In this paper, the design scheme of the experimental platform is proposed from the aspects of hardware and software respectively. In terms of technology, the requirements of reliability, efficiency, enforceability and practicability are fully considered.
Nairobi’s Rise as a Digital Platform Hub

The Kenyan capital of Nairobi has become a host of thriving industries and innovations based on the production, consumption, and domestication of digital payments platforms such as M-Pesa. Adapting these mobile phone–based applications to its informal economies and urban culture, Nairobi has developed into a seedbed for information technology advances and constellations of new services. These platforms have played an especially prominent role in filling infrastructure gaps in the provision of water and electricity. The author argues that these processes should provoke us to extend our outlooks and dialogues toward such modes of smart urbanism and trajectories of technological development that may exceed the modernity of Western models.
Clustering-Based Semi-Supervised Cross-Modal Retrieval Using Scene Graph

In this paper, we propose a clustering-based semi-supervised cross-modal retrieval method to relieve the problem of insufficient annotation in cross-modal datasets. First, we reconstruct cross-modal data as scene graph structure to filter meaningless information. Second, we extract embedding representation features of images and texts to put them into a common space. Finally, we propose a clustering-based classification method with modality-independent constraint to discriminate samples. According to our experimental results, significant improvement on performance shows the accuracy of our method in terms of three widely used cross-modal datasets compared with the state-of-the-art methods.
Blockchain and Corporate Banking

Blockchain technology has grown in prominence after the introduction of cryptocurrencies. The appeal of cryptocurrencies is undeniably based on the legitimacy of the same, which is ensured by blockchain technology. Blockchain technology is extremely common due to three main characteristics. The first is decentralization, which means that there is no central authority or single point of failure that can be used to bring the system down so each node is self-contained. The second feature is immutability, which prevents false entries. Third, since any entry must be checked by all nodes in the scheme, it allows for openness. If no consensus is reached, the submission is denied. The use of blockchain technologies has expanded across a wide range of sectors. Banking is one sector that is going to be greatly affected by blockchain technologies. It facilitates the transfer of large sums of money while still posing significant security risks. As a result, blockchain technology makes it simple to fix these two issues. Blockchain technologies can be used to automate several operations in the banking industry. This paper aims to describe such processes and demonstrate how blockchain technology can be used in various areas of the banking industry.
Looking for archetypes: Applying game data mining to hearthstone decks

Digital Collectible Cards Games such as Hearthstone have become a very prolific test-bed for Artificial Intelligence algorithms. The main researches have focused on the implementation of autonomous agents (bots) able to effectively play the game. However, this environment is also very attractive for the use of Data Mining (DM) and Machine Learning (ML) techniques, for analysing and extracting useful knowledge from game data. The objective of this work is to apply existing Game Mining techniques in order to study more than 600,000 real decks (groups of cards) created by players with many different skill levels. Data visualisation and analysis tools have been applied, namely, Graph representations and Clustering techniques. Then, an expert player has conducted a deep analysis of the results yielded by these methods, aiming to identify the use of standard - and well-known - archetypes defined by the play methods will also make it possible for the expert to discover hidden relationships between cards that could lead to finding better combinations of them, enhancing players' decks or, otherwise, identify unbalanced cards that could lead to a disappointing game experience. Moreover, although this work is mostly focused on data analysis and visualization, the obtained results can be applied to improve Hearthstone Bots' behaviour, e.g. predicting opponent's actions after identifying a specific archetype in his/her deck.
ERSA: enhanced RSA cryptography algorithm to guarantee high security level for data in cloud environment

Data security and user security still cause major trepidation in the cloud environment. To tackle these security problems, this paper presents a novel enhanced Rivest Shamir Adleman (ERSA) algorithm to ensure high security level in the cloud environment. Security enhancement is realised by the following phases: 1) authentication phase in which ERSA-based digital signature is utilised; 2) evaluation phase exploits fuzzy inference system (FIS) for ensuring required security level; 3) encryption phase involves the ERSA algorithm for encrypting data to be stored in the cloud. ERSA algorithm improves security in two ways. One is by generating prime numbers according to required security level using Sieve of Atkins (SoA) algorithm. Another one is by enhancing the security of RSA key with the help of non-prime factor. Thus, the RSA algorithm is improved through simple computations in ERSA algorithm. Extensive simulation results ensure better performance in encryption time, decryption time, key generation time, and security level.
Identification of Relevant Quantities in Arithmetic Word Problems Using Siamese Neural Network

Presence of irrelevant sentences containing irrelevant quantities in Math Word Problems (MWPs) throws an immense challenge in formulating the final equation(s). Failure in identification of the relevant quantities with respect to the question asked in an MWP reduces the overall system performance. This paper demonstrates a novel deep learning-based approach using Siamese neural network to classify the relevant and irrelevant sentences towards identification of the relevant quantities in arithmetic MWPs. The proposed relevance classifier produced an accuracy of 91.0% on a dataset variant (RQDV) prepared from a Combined dataset of two standard datasets consisting of arithmetic word problems. Our relevance classifier may benefit any existing arithmetic MWP solvers in improving their system performance, and we test this by performing a case study on a prototype system in which the integration of our relevance classifier improved the accuracy of the MWP solver from 68.0 to 81.0% on the Combined dataset.
Long-Range Feature Dependencies Capturing for Low-Resolution Image Classification

Recognition of images with low-resolution is extremely challenging, due to the feature smoothness caused by the loss of structural details. Specifically, after losing the structural details, low-resolution image patches with different structural properties tend to have a uniform distribution in the specific channels of deep representation space, which will introduce ambiguity for image recognition. To address this problem, this paper proposes a novel Feature Enhancement Module (FE-Module). The module first extracts similar features as the pre-trained classification networks. Then it captures features across different depths to make use of all the hierarchical features. Finally, the module explores the patches with similar structures to remedy local feature smoothness for accurate low-resolution image classification. Extensive experiment results demonstrate that the proposed method can effectively enhance the feature discrimination ability and improve recognition performance.
Commercializing Blockchain in a Smart City: Autonomous Vehicles, Cryptocurrency, and Contract Law

This paper aims to provide a direction to the manifestation of a smart city, with blockchain-based implementations for economic, legal, and vehicular domains. The paper sees the three domains as three legs of the city, relevant to individuals and corporations, personal usages and businesses alike. Properties of blockchain and the consequences of its potential applications are kept as the focus of the paper. Since all systems in a city have to be commercialized to be viable for large-scale use, this paper presumes that innovations for such systems are required to be commercialized. This consideration shapes the approach with which such innovations are discussed, since scalability, security, and law-abidingness become essential. By analyzing the suitability of these technologies in their widespread adoption and their commercialization, this work intends to present direction for overcoming challenges in existing work in blockchain in economic, legal, and vehicular sectors.
Facial expression recognition in video sequence based on LBP feature and GRU

Human facial expression can convey rich information, let the computer understand the human intention, and make correct responses through facial expression recognition, which is a hot spot in artificial intelligence research at present. Compared with a single frame image, a video sequence image contains the information of expression changing along the time axis, which can provide more help for expression recognition. Therefore, this paper proposes an expression recognition method based on video sequence images. Firstly, the facial landmark points are extracted from the sequence images. Secondly, four key regions that contribute greatly to expression recognition are calculated through the landmark points. Then the LBP features of these four regions are calculated and fused to form the expression features of a single frame image. Finally, these expression features are sequentially sent to the GRU network for training to obtain the face expression classification model. The experimental results show that the proposed algorithm has high recognition accuracy.
Cybercompliance: A Legal but also Ethical Concept that Allows to Reduce the Current High Risks of Corporations

The increase in cyber attacks, as well as the mass usage of technology, such as the Internet of Things and M2MMachine To Machine (M2M) connection, together with the millions and millions of personal and organisational data stored in the cloud, put government bodies, organisations and people themselves at constant risk. That is why companies and individuals, given the enormous proliferation of rules, as well as the inclusion of legal entity liabilityLegal Entity Liability regulationsRegulations with the intention of reducing risk by forcing the imposition of, in many cases, costly measures, must comply with said rules especially in cyberspaceCyberspace. We call this concept CYBERCOMPLIANCECybercompliance, and its ultimate goal is to reduce devastating consequences and, in the best-case scenario, eliminate them.
The Effect of Transformational Leadership on Service Recovery Performance in Public Hospitals of Ahwaz: A Mediating Role of Professional Ethics and Emotional Work

The Effect of Transformational Leadership on Service Recovery Performance in Public Hospitals of Ahwaz: A Mediating Role of Professional Ethics and Emotional Work
Optimisation of computer virtual image reconstruction method based on feature point matching

Traditional computer virtual image reconstruction methods have the problems of slow reconstruction speed and insufficient image clarity. To solve this problem, an optimisation method of computer virtual image reconstruction based on feature point matching is proposed. SURF operator is used to extract image feature points. After obtaining feature points, image feature points are matched according to TZNCC constraints. The virtual image is reconstructed by sparse method, and the high resolution depth image in virtual vision is represented by dictionary sparse linear combination. The sparse coefficient of the image is solved by alternating direction multiplier algorithm, and the problem of virtual image reconstruction is transformed into a problem of solving sparse signal, so that a better reconstruction effect can be obtained. The experimental results show that the proposed method has high speed and clarity of image reconstruction.
Getting Started with PyQt

Hello! Welcome to Beginning PyQt: A Hands-on Approach to GUI Programming. You are probably here because you would like to learn how to build applications and need some help getting started. Maybe you have a personal program you need to build, or perhaps you would like to build a custom software application for others to use. Whatever your reason may be, you’ll need to figure out where is the best place to start.
Research on the Application of Internet of Things Based on Artificial Intelligence Technology

Rapidly developed artificial intelligence technology has become one of the main areas of high-tech future competition. The advancement of communication technology has entered the agenda for the application of the Internet of Things, and has entered the implementation phase. Artificial intelligence has been used as an important network. This paper introduces the concept of artificial intelligence and the Internet of Things, discusses the technical support and combination behind them, based on related applications on the Internet and artificial intelligence. Provide some reference value for the development of the Internet of Things.
Iris

Iris is a wearable robot. As an experiment designed to bring onscreen animation closer to physical, real-life animation, Iris is given independent perception and temperament by using facial expression recognition and emotion synthesis. Similar to cartoon animation, this temperament is expressed through movement but generated in real-time. By connecting sensors that measure physiological signals of the wearer's body, the emotion of the Iris is affected by the wearer's emotions. It can be perceived as an independent individual and also a part of the wearer's body. This ability to perceive and emote reflects both a certain connection and difference with the wearer, as if showing a split personality. Giving different responses to others, affects interpersonal interactions and relationships. These effects are heightened during Covid-19, when our faces are covered by masks, the Iris performs a special way to augment humans' expression.
IMPLEMENTASI DAN PENGGUNAAN SISTEM INFORMASI ADMINISTRASI SURAT MENYURAT BERBASIS WEB

The Upper Silat Sub-District Head Office is precisely at Jl. Jend. Sudirman No. 08, is an organization of the Kapuas Hulu Regency Government. Which organization is run by the Government apparatus. The sub-district is the sub-district head's working area as a regional apparatus headed by the sub-district head who is under and responsible to the regent who has the main task of assisting the regent in the administration of the government, development and community life development fields. Where at the moment the procedure applied to archiving incoming and outgoing mails in the Kalinyamat, Sumatra Utara District starts from reception, manufacture, storage, documentation, to letter verification, all done conventionally which results in the letters in Kalinyamat, Sumatra Utara District not being managed with well, it is very difficult when looking for old documents because they have to look one by one. Objectives to accelerate the process of filing incoming and outgoing mail in the Kalinyamat, Sumatra Utara District starting from receiving, making, storing, documenting, and verifying letters. For research methods used are: Data collection, system analysis, system design and system implementation. The results of this study are expected to accelerate the process of archiving correspondence in the Kalinyamat, Sumatra Utara District
Style-Based Interactive Eyewear Recommendations

In this demonstration, we introduce STYLE PTTRNS, a style-based tool for eyewear recommendations. The tool first analyses the facial characteristics of a customer and matches those to eyewear characteristics in a style-harmonious manner. Consequently, the customer navigates through the eyewear collection while interacting with high-level expression attributes such as delicateness and strongness. This article presents the customer journey, our pragmatic approach to the recommendation system using styling expert input to bypass a cold-start problem and implementation details.
Deep-Skip Net: Research on UAVs Image Semantic Segmentation Technology

UAVs rely on scene understanding and autonomous movement to perform tasks. The common method of scene understanding is to use GPS + map. We hope to create offline maps through semantic segmentation to get rid of the constraints of GPS. But the UAV image scale changes too much. Related algorithms, for example, Multi-Scale-Dilation Net extracts multi-resolution features for fusion, and U-Net fuses features of various scales in the convolution process. The larger the scale of these two algorithms, the greater the weight of the target. This paper hopes to strengthen the characteristics of small-scale targets and proposes the algorithm Deep-Skip Net, which itself is also an encoder-decoder network similar to U-Net, but does not perform full-scale Skip connections. And Skip connection to the smallest two scales to ensure network accuracy. For the original scale, deep-level skip connections are used to retain the effective characteristics of small-scale objects. Deep-Skip Net conducted a comparative experiment on the UAV data set UAVid, and evaluated the results by averaging the intersection and comparison. The results of the comparative experiment show that, compared with the Multi-Scale-Dilation Net of the author of the data set, the overall performance of Deep-Skip Net is increased by 8%, and the segmentation effect for small-scale humans is increased by 20.4%. Both large-scale objects and small-scale objects have more balanced prediction capabilities.
Research on human-computer interaction portability evaluation model in complex environment

Human-computer interaction is a technology to study the relationship between users and systems. Good user experience can greatly enhance user stickiness. With the development of Internet technology, users begin to face a variety of systems, but the interaction methods involved are different. Learning new interaction means an increase in user learning costs. As a part of product design, the design of interaction also needs to cover special user groups as much as possible, such as blind people. Thus, it is necessary to study whether different ways of interaction can migrate to each other. Traditional research on user experience mainly focuses on qualitative aspects, such as questionnaire survey. In this paper, we propose a quantitative model to evaluate the portability of interaction. For software interaction design, the macro concept of user experience is quantified by different dimensions, and a unified index model and calculation method are output to guide and evaluate the portability of different software interactions.
Machnine Learning for Sentiment Analysis Using Algerian Dialect

Text analysis has a great importance, especially in areas such as politics, productions, and services... etc. Currently, the social networks full of the texts in which, the Internet-users express in different subjects, the interest of their opinions is considerable, where the comprehension of the content conveyed by these texts is an essential element. We can say that the good manager who listens well to the opinions of citizens. In this sense, the Sentiment Analysis is very important to meet the needs of citizens. In this work, we will implement predefined algorithms that analyze and classify a set of publications derived from social networks. The classes that we have defined are: positive, negative or neutral. Our work is among the first one that uses and compares several comment classification algorithms on Facebook, using Algerian dialect.