Reviewerly/exHarmony huggingface dataset:
Model of formation of professional competences of future software engineers under the conditions of the shortened cycle of professional training at universities

The author's model of formation of professional competences of future software engineers under the conditions of the shortened cycle of professional training at universities is characterized in the article. The relevance of such a study is due to the need of the labour market in the quality of training of software engineers in a short time, and hence the need to create appropriate conditions and use effective forms, methods and tools of training. In order to pre-evaluate the effectiveness of the proposed organizational and pedagogical conditions for the formation of professional competencies of future software engineers under the conditions of the shortened cycle of professional training at universities and forecasting the results of planned impacts, we have developed a structural and functional model of this process. The model contains the following blocks: block of organizational and pedagogical conditions, target block (purpose and tasks), methodological block (scientific approaches, pedagogical principles), content and procedural block (content, forms, methods and means of forming professional competencies of future software engineers), evaluation and reflective block (components of professional competencies of future software engineers, criteria, levels and indicators of their formation, assessment tools and diagnostic techniques), result block (expected result of the research process).
Classifying Quality of Web Services Using Machine Learning Classification and Cross Validation Techniques

The growing amount of online services provided through the Internet is continually increasing. As a result, consumers are finding it more difficult to choose the proper service among a huge number of functionally comparable candidate services. It is unrealistic to inspect every web service for its quality value since it consumes a lot of resources. As a result, the subject of Web quality of service prediction has gotten a lot of attention in recent years. Using machine learning techniques, the present work suggests a model for the classification of the quality of web services by using cross validation techniques. Four algorithms of classification machine learning are applied in this work: Logistic Regression, Random Forest (DF), Support Vector Machine (SVM) and Neural Network (NN). When comparing the results, it was discovered that the Random Forest had the best accuracy. The cross validation, person correlation and normalization techniques are used in this work to compare them with the result of the algorithms. After choosing the best algorithm, a web service is created for the forecast of quality using Azure Machine Learning studio.
Multiple Weather Scene Detection Utilizing the EfficientNet Family

Weather is a critical factor in all facets of human life. Not only can extreme weather conditions endanger human life, but they can also wreak havoc on the transportation system, resulting in a national disaster. Thus, weather scene detection has become a primary focus of human interest to mitigate the destruction caused by weather hazards. There are numerous traditional approaches to weather detection that are ineffective. Noise, missing values, and similarity between different weather images all contribute to the difficulty of detecting weather scenes. As a result, deep neural networks perform exceptionally well in this case when it comes to accurately detecting the weather. In this paper, experimentation is conducted with various state-of-the-art methodologies such as ResNet-18, ResNet-50, ResNext50, EfficientNet family. This research work finds that even having a lower number of parameters for EfficientNet versions b0-b3, these versions outperform ResNet-18, ResNet-50, and ResNext-50 for accuracy, precision, f1-score, recall, and loss. This study also finds that EffecientNet b7 outperforms all the other methodologies by having an accuracy of 92.5%, f1-score of 90%, precision of 92.5%, and 91.2% recall score.
Ranking Mental Illness among Social Media Users

Currently we are in the era of the internet. We can express our thoughts through social media i.e. Facebook, Twitter, Instagram etc. Now we are in the most challenging stage of this century as well as the rest of the world due to COVID-19 pandemic and the after effects of the virus infection. Some of the survivors are experiencing trauma and other mental health issues. To determine the mental health condition of someone, we performed sentiment analysis on their social media posts. This paper focuses mainly on sentiment analysis of twitter data written in Bengali language and tries to find out whether the user is mentally depressed or not. The expressions of those users are either negative or positive, sometimes neutral. Using different contemporary Machine learning algorithms like Naive Bayes, Support Vector Machines (SVM), we can determine the mental health condition of any user. We also built a model by applying the Random Forest classifier on social media posts and examined them whether they were in a mental condition or not.
Binary Classification using K-Nearest Neighbor Algorithm on FPGA

The K-Nearest Neighbor (K-NN) algorithm is one of the most widely used algorithms for machine learning applications. Using K-NN algorithm on massively large data requires high computation power. This high computation demand can not be met by traditional software-based approach using CPUs. Field Programmable Gate Array (FPGA) can be used to implement K-NN algorithm to achieve hardware acceleration. In this paper, we implemented K-NN on Xilinx FPGA using Verilog Hardware Description Language (HDL) by exploiting the raw hardware power. The result shows that our FPGA-based solution is on average 30 thousand times faster than traditional software-based solution on CPU.
A Framework for Tracing the Real Identity of a Bitcoin Scammer

Bitcoin stores its transaction details in an open distributed public ledger. In cryptocurrencies, the real identity of a user is hidden, and the only information about a user publicly available is his public address, which is not linkable to his real identity, and the transactions are sent to the public address. Among all the cryptocurrencies, bitcoin is renowned for providing the highest anonymity. This feature is exploited by the scammers and illegal users to perform their illegal transactions anonymously. To rein in such illegal activities, it is essential to expose the real identity of bitcoin users. In this paper, we propose a technique to trace the real identity of a bitcoin user. In this technique, the blockchain maintains a public ledger where every transaction of that chain is recorded and anyone within the blockchain can monitor that. The proposed platform will have a tracker for tracking a bitcoin user. A victim submits a complain to the platform giving the pubic address of the scammer. The platform continues to track the scammer. The proposed technique exposes the user identity exploiting the bitcoin and real-world currency conversion scenarios. While the existing transaction tracing techniques require the IP address and/or absence of the mixing services, the proposed technique is free of such kinds of requirements.
Finger movements tracking of the human hand using the smart camera to control the Allegro Hand robot

Gestures are one of the richest ways of communication that humans have. Therefore, automated hand detection and finger behavior are some of the most compelling challenges in robot vision. Recently, many ideas and technologies have been used in programming to use cameras to recognize the fingers of human hands and apply them to robots. In this paper; the innovative algorithm is based on intelligent ideas and mainly relies on image analysis after camera shooting. In order to identify the fingers of the hand by a camera, the idea is to assign each finger to a percentage in pixels of the binary image, which represents the area of the finger. Each ratio compares to the live image to determine which fingers the user has chosen to control the Allegro Hand robot. Also in this work, the hand can move freely in front of the camera, relying on a new idea of measuring the distance between the hand and the camera. We use for this a small ticket in geometric form square and color different to black and white. This ticket is affixed to the back of the user's hand and through which we can know the size of the hand, regardless of the distance on the Z-axis of the image. This is important in determining the proportions of the fingers according to the distance from the camera. The camera is here combined with a hand human and Allegro Hand to ensure more interactive communication, harmony and easier. The result of the smart camera is tested with multiple experimental trials with a robotic hand. Many finger positions were identified correctly in front of the camera.
Methodological features of the organization of design project activities in teaching physics at school

The purpose of the study is to determine the methodological features of the organization of design engineering activities when teaching physics at school. The article focuses on the formation of design and research skills of students, which is a mandatory requirement of the Federal State Educational Standard of the General Education. From our point of view, one of the stages of the implementation of project activities should be design, so the formation of design skills is also reflected in the FSES BGE. The scientific novelty of the research lies in the definition of the concept of "project design activity", the definition of the methodology of the organization of project design activity in teaching physics at school. As a result, the requirements for the content and organization of design engineering activities in teaching physics at school are identified, a model of the methodology of its organization is proposed; the program of the circle is developed and an example of one of the implemented projects is given.
Performance Evaluation of Blockchain Consensus Algorithms for Electronic Health Record Sharing

In any blockchain network, a consensus algorithm is considered a major factor in determining the performance of the network. This study aims to investigate consensus algorithms in healthcare blockchains for electronic health record (EHR) sharing by analyzing the scalability performance of these algorithms. Several consensus algorithms have been proposed in the literature and have been used for various types of blockchain networks. Herein, we analyze the performance of proof of work, practical Byzantine fault tolerance, and proof of authority (PoA) algorithms in the healthcare domain and propose an improved real-time PoA consensus algorithm for EHR sharing for faster consensus and block generation. Results of performed simulations show that the proposed algorithm outperforms previous consensus algorithms in terms of consensus time, number of blocks generated, network throughput, and scalability.
An Automated Approach to Domain-specific Term Extraction for Programming Language

Automatic Term Extraction is a common upstream task of both natural language and programming language processing tasks. However, most existing domain-specific term extraction methods are designed for natural language, and do not satisfy demand for programming language. In this paper we proposed an automated cross-language method for extracting domain-specific terms from source code. To test the domain-specific terms extraction method on real software projects, we choose open source repositories in Github Collections. Then the extraction results of our method and TF-IDF were checked by crowdsourcing, with 12 experienced software developers. Compared with TF-IDF, our method shows better performance on multiple measures. Especially, the AUC of our method is 0.7050, while TF-IDF is 0.4212.
Universal Interactive Verification Framework for Federated Learning Protocol

Federated learning protocol provides an applicable solution for large-scale deep learning in distributed scenario. However, existing federated learning system is vulnerable to many attacks and threats. This paper proposes a universal verification framework for federated learning protocol, aiming to analyze the security and privacy risks of interactions between clients and servers during the training and inference process. Based on reinforcement learning technology, our verification framework is capable to adopt various conditions. Furthermore, it presents a set of interactive verification metrics for comprehensive evaluation, including data confidentiality, privacy threat, model availability, model robustness and system vulnerability.
Co-attention Based Deep Model with Domain-Adversarial Training for Spam Review Detection

Douban has become one of the most popular Chinese film review platforms and has been attacked by various spammers. In our research based on real data from Douban, we found that many spammers spontaneously form different groups, which have different targets and have different effects on movies. In this paper, by dividing reviews into four categories, namely, true positive reviews, spam positive reviews, true negative reviews, and spam negative reviews, we design a co-attention based neural network to fuse multiple features to classify reviews. In order to improve the robustness of the detection model, we adopt the idea of domain-adversarial training. In the domain-adversarial training method, we use real data and noisy data for model training, and the model is indiscriminative for whether the data source is real data or noisy data. The experimental results show that our proposed domain-adversarial training method can improve both the best classification performance and robustness.
The use of WEB-oriented technologies in the process of WEB-programming teaching for technical universities students

The realities of the XXI century - informatization, digital transformation, high technologies, WEB-technologies in particular - put Ukraine state in front of global challenges of creating a digital state. It is the implementation of a number of digital projects which form the digital space of the state, in particular those coordinated by the Ministry of Digital Transformation of Ukraine. The development of a significant number of applications requires the state to constantly update them, maintain them working, which requires qualified programmers. The task of technical universities is to train competitive specialists in the IT field. This study analyzes the results of a survey of students of NTUU "Kyiv Polytechnic Institute. Igor Sikorsky ”, the purpose of which was to find out what integrated development environments and programming languages ​​senior students independently choose when developing programs within the educational and professional programs of the faculty, and which correlate with software used by programmers in the implementation of large IT-enterprises, in particular WEB-programming languages. The results of the study showed as well that the rating of programming languages and the use of integrated development environments meet modern requirements for software development. 76.7% of graduates, while being students, are already employed in IT companies as full-time employees. The high percentage of employment suggests that graduate students of the Faculty of Informatics and Computer Engineering of Igor Sikorsky NTUU KPI are competitive in the modern IT labor market.
Adaptive learning environment design in the system of future maritime specialits’ training

In an environment of education reformation aimed at transition of higher education to competence based and individual approaches, we face the need to construct individual learning path for every future maritime professional. In this respect technology of adaptive learning based on modern ICT becomes of high importance. At the same time COVID-19 pandemic has changed system of education at all its levels, but the issue of quality and efficiency is still to be considered and studied by scientists and practitioners. Under these conditions the issue of adaptive information environment creation becomes relevant for training modern and competitive specialists. This environment should be based on implementation of adaptive technologies for education and training of maritime students, therefore, article provides investigation of pedagogical problem of future navigators’ professional culture building in training system of adaptive information environment of maritime educational establishment. Feasibility of adaptive learning technologies implementation is grounded as a tool for future navigators’ professional culture building in the process of their fundamental education and training. Example of higher mathematics adaptive learning implementation for future navigators at Kherson State Maritime Academy is considered. Higher mathematics adaptive learning was introduced through: adaptive feeding of educational content of the course; problems solving support based on examples and pre-created typical algorythms; adaptive testing; analysis of test tasks answers; system teacher support; constant support conditions for individual tasks completion; adaptive course navigation, etc. As the result of experiment there was found out that higher mathematics adaptive learning for future navigators presupposes: individual learning path designing; possibility to timely provide advising and objective control as well as evaluation; enhancement of learning activity and motivation of through improved degree of autonomy; promotion of students’ research skills development; creation of cooperation, partnership and maritime brotherhood atmosphere.
Trends of Using Blockchain Technology in the Smart Grid

Recently, the traditional power grid has been evolving into a new type of intelligent system known as smart grid. The new smart grid uses information and communication technologies to automate and optimize the power generation and distribution process. Despite improvements, there are still challenges that cannot be solved using existing technologies. Blockchain is an emerging technology with unique features to solve some of remaining challenges in smart grids. In this paper, we review the recent developments in this area. We describe the existing smart grid challenges, explore blockchain features suitable for smart grids, and highlight notable existing successful implementations. The review shows several applications such as peer to peer energy trading and autonomous asset management that have been enabled by blockchain technology. It also highlights challenges such as performance and regulations that might hinder the future use of blockchains in smart grids.
Combination of Hough Transform and Neural Network on recognizing mathematical symbols

Offline printed mathematical symbol recognition is a particularly difficult task. Recognizing mathematical symbols is one stage within the overall system for recognition of mathematical documents. We describe many experiments using MultiLayer Perceptron (MLP), Hough Transform (HT), k Nearest Neighbors (kNN) and structural Freeman chain code, to enhance symbol recognition of printed mathematics. First, we investigate the use of a MLP based method. Second, we compare the performance of a proposal neural structural method, named HT-MLPs, on symbols that initial MLP usually confuses. The inclusion of HT in MLP reduces symbol confusion rate by 21&#x0025; and improves recognition rates from 72&#x0025; to 93&#x0025;. To improve the efficiency of the proposed method, we compare it to KNN then to Freeman code based methods, commonly used in pattern recognition. While analyzing results, we show that HT-MLP always gives a lower mean confusion and rejection and higher success rates than the others solutions.
Intra-linguistic and extra-linguistic annotation tool for the “Jumla Dataset” in Qatari sign language

The present paper describes an ongoing project on designing and creating the First Qatari Sign Language dataset called &#x201C;Jumla Dataset: The Jumla Qatari Sign Language Corpus&#x201D; with intra-linguistic and extra-linguistic levels of the written Arabic text. The annotation of videos in Qatari Sign Language (QSL) takes input from signers to identify the Arabic glosses components toward representing the QSL in a written way with high accuracy, furthermore to the use of the annotation output in the development of computational Sign Language tools. The QSL annotation is based on an input of 4 videos recorded by deaf persons or Sign Language interpreters from different angles (front, left side, right side, and facial view). The output is a JSON file containing all the interpreted sentences given as an entry record. The glosses are annotated for each period and aligned with the Arabic content. Moreover, the presented tool, available as an open-source, provides a management system to classify all records from cameras, motion capture systems, and edited files in addition to the possibility to create components for each gloss annotation terminology depending on the target Sign Language.
3DYOLO: Real-time 3D Object Detection in 3D Point Clouds for Autonomous Driving

In the recent era, a lot of interest is attracted by the autonomous vehicles which can sense surroundings and navigate without human intervention. Object detection and recognition form a major part of autonomous driving systems. Lidar sensors can be used to capture point clouds of driving environment. Detecting multiple 3D objects in point clouds in real time and defining their boundaries with the help of 3D bounding boxes are critical in motion planning by self driving systems. This paper proposes a LiDAR-based 3D object detection system that operates in real-time, with emphasis on autonomous driving scenarios. A state-of-the-art 2D standard object detector for RGB images, YOLOv4, is used as the base for object detection. The multi-class 3D bounding boxes are generated using a complex regression approach. An Euler-Region-Proposal Network (E-RPN) is used to predict the pose of the object. The proposed model receives point cloud data as input and outputs 3D bounding boxes with classes in real-time. The experiments done on the KITTI benchmark dataset proves that the proposed system outperforms existing methods in terms of accuracy and performance.
A Path Planning Algorithm Based on Improved RRT* for Unmanned Aerial Vehicle

Rapidly-exploring Random Tree star (RRT*) is an extension of Rapidly-exploring Random Tree (RRT) algorithm which uses random sampling to discover a collision-free path in a given environment. Although it can guarantee the global search's completeness and asymptotic optimality, there are some problems such as a slow convergence rate, a huge dense sampling space and unsmooth search paths in a complex environment. This paper proposes an improved RRT* algorithm, CG-RRT*(Changeable Bounds based Gaussian Distribution Sampling RRT*) to solve these problems. This algorithm generates more promising nodes by sampling in a flexible and variable connectivity region to find the initial path faster. Then the path is smoothed by eliminating unnecessary nodes through path optimization. Finally, Gaussian distribution sampling is used to accelerate the algorithm's convergence. Simulation and experimental results under various obstacle environments show that the CG-RRT* algorithm can find the initial path faster, converge to a shorter path with a shorter number of iterations and be smoother. The proposed approach can improve search efficiency, accelerate convergence, and reduce memory and processing time.