Reviewerly/exHarmony huggingface dataset:
Few-shot sheep counting based on density map regression

Abstract With the continuous expansion of breeding scale, the automatic counting of sheep has attracted a lot of research attention in recent years, which aims to count the number of sheep in real time to rationally use grassland resources and design appropriate agricultural production policies. The traditional method of counting sheep mainly relies on manual work, which is time-consuming and labor-intensive. Thanks to the rapid development of image acquisition equipment and pattern recognition technology, the recognition and counting of sheep based on deep learning has become the current mainstream counting framework. However, for dense flocks of sheep, such as occlusions between sheep, the accuracy of existing methods cannot meet the needs of practical applications. In this paper, we suggest FamNet, a few-shot sheep counting method based on UAV images and density map regression. Specifically, we first collected 190 sheep images with different resolutions, aspect ratios, backgrounds, and sheep numbers; secondly, by selecting count samples to extract objects in the images, and using density map regression to predict the number of sheep, this enables sheep counting in small numbers of samples. A large number of experimental results have verified the effectiveness of the method in this paper, which can achieve an average counting accuracy of 91.02%. In addition, we discuss the impact of sample selection, backbone selection, and feature scaling on model accuracy. All the results show that the FamNet flock counting method proposed in this paper can be applied to flock counting, has good adaptability to flock counting under different background conditions and can count the number of flocks in a timely and effective manner.
Performance analysis of web applications created in the Spring and Laravel frameworks

The article compares the performance of two test applications created using the Spring Boot and Laravel application frameworks. The aim of the study is to find an answer to the research question: which framework offers better time performance. Twelve test scenarios were created for the analysis, and they were used to conduct performance tests using the Apache JMeter tool. Additionally, application metrics and community support for both frameworks were compared. The research confirmed that the application built on the Spring Boot framework has better performance.
Non-Intrusive Balance Tomography Using Reinforcement Learning in the Lightning Network

The Lightning Network (LN) is a second layer system for solving the scalability problem of Bitcoin transactions. In the current implementation of LN, channel capacity (i.e., the sum of individual balances held in the channel) is public information, while individual balances are kept secret for privacy concerns. Attackers may discover a particular balance of a channel by sending multiple fake payments through the channel. Such an attack, however, can hardly threaten the security of the LN system due to its high cost and noticeable intrusions. In this work, we present a novel non-intrusive balance tomography attack, which infers channel balances silently by performing legal transactions between two pre-created LN nodes. To minimize the cost of the attack, we propose an algorithm to compute the optimal payment amount for each transaction and design a path construction method using reinforcement learning to explore the most informative path to conduct the transactions. Finally, we propose two approaches (NIBT-RL and NIBT-RL- β ) to accurately and efficiently infer all individual balances using the results of these transactions. Experiments using simulated account balances over actual LN topology show that our method can accurately infer \(90\%\sim 94\% \) of all balances in LN with around 12 USD.
Actual problems of teacher training for the system of additional education

The article analyzes the current problems associated with the training of teaching staff for the system of additional education. The need to develop new teaching methods is emphasized, especially in the field of digital educational space, which can help teachers work in rapidly changing realities.
Formation of meta-subject educational results of students - the main line of primary education programs

The requirements of the federal state educational standard for meta-subject results of mastering primary education programs are discussed. The genesis of the content of the concept of universal educational actions of schoolchildren is investigated. It is proved that the formation of universal educational activities is a priority direction (main line) and specificity of the initial level of general education as a prerequisite for the success of all subsequent education. The results of monitoring the work of teachers on the formation of meta-subject educational results of junior schoolchildren are discussed. A new discipline of pedagogical education “Methodology for the formation of meta-subject educational results of students in primary school” is presented.
Research of educational potential of the project "film education of youth" for sighted and blind students

The article describes the results of the study of psychological qualities and attitudes to the moral categories of sighted and blind students studying filmmaking. The psychological characteristics of students belonging to different types of creative activity – "asterisks", "problematic" and recommendations for them are described. The educational effects of the project are revealed – changes in the motivational sphere, attitude to moral categories, the creation of their own creative works, awareness of life meanings and priorities, the ability to formulate and justify their own point of view, creative tasks and solutions in the process of interaction with masters and teamwork.
One of the ways to teach robotics using the Mobile Quantorium

The article argues for the importance of additional education for adolescents in the field of technical creativity. The emphasis is on Continuing Education Centers, in particular on Children's Technology Parks “Quantoriums”. The development of an additional general educational program “LEGO Mindstorms EV3 Robotics in the Mobile Technopark” is given, which is implemented for 16 hours in the State Educational Institution “RCDOD”, Saransk. The purpose and objectives of this program, thematic planning are described, the content that students must master is revealed, and tasks for the entrance (survey), current (laboratory work), intermediate (test, project work) and final control (project) are given. The results of testing the program in various regions of the Republic of Mordovia are presented. It is concluded that the developed educational materials can help teenagers master the basics of robotics, encourage them to choose a robotics profession, and show the importance of robotics.
Value attitude to pedagogical knowledge as a condition for overcoming cognitive difficulties of students in the learning process

The article deals with the ways of formation of valuable attitude to pedagogical knowledge among the undergraduate students of pedagogical university. The relationship between this process and the mechanism of overcoming cognitive difficulties of motivational, cognitive, organizational and reflexive nature is analyzed. The specificity of organizing discussions in the form of a class, aimed at forming students' understanding of the importance of pedagogical activity is described. The experience of the Institute of Innovative Educational Practices in organizing research activities of undergraduate students within the framework of the conference "Teacher: Mastery and Inspiration" is considered.
Face Recognition using Deep Learning and TensorFlow framework

Detecting human faces and recognizing faces and facial expressions have always been an area of interest for different applications such as games, utilities and even security. With the advancement of machine learning, the techniques of detection and recognition have become more accurate and precise than ever before. However, machine learning remains a relatively complex field that could feel intimidating or inaccessible to many of us. Luckily, in the last couple of years, several organizations and open-source communities have been developing tools and libraries that help abstract the complex mathematical algorithms in order to encourage developers to easily create learning models and train them using any programming languages.&#x0D; As part of this project, we will create a Face Detection framework in Python built on top of the work of several open-source projects and models with the hope to reduce the entry barrier for developers and to encourage them to focus more on developing innovative applications that make use of face detection and recognition.
A study of the user experience while working with mobile applications cooperating with sports bands

The article analyses user experience while using two mobile applications, FitPro and Zepp Life that work with sports bands designed to monitor physical activity. As part of the study, the users were asked to perform tasks during which they used each app’s features. During the same research scenario, first on the first application and then on the second application, the user’s eye activity was recorded by means of the eyetracker. Then, each participant took part in a survey. Task completion time, eye-tracking indicators (the number of fixations, fixation duration, the number of blinks), users’ evaluation and the level of diagnosed errors were used for comparative analysis. All the results confirmed that the FitPro application, despite containing fewer features, was more intuitive, easier to use, more efficient, had a clear interface and, as a result, was better rated by the users.
Formation of the basis of ecological culture of the personality of students in the educational space of primary school

The article deals with the problem of the formation of the foundations of ecological culture of primary school students in the process of education's environmentalization; the emphasis is placed on the importance of strengthening the educational aspect of environmental education, socialization of the consciousness of students, environmentally oriented practical educational and social activities; the importance of the resource approach is substantiated; the tools and conditions for efficient design of the process of education's environmentalization are named.
A comparative analysis of the Flutter and React Native frameworks

The article describes a comparative analysis of the resource usage performance of mobile applications developed using Flutter and React Native frameworks. The purpose of this study was to see which technology is more efficient. The study consisted in creating two mobile apps with the same functionalities and then comparing usage of the following resources: Virtual Memory (VIRT), Shared Memory (SHR), Central Processing Unit (CPU), Resident Set Size (RES) and Memory (MEM). The functionality of the application consisted of actions on the GUI. The test was conducted on a Huawei P20 Lite smartphone, using the Android Debug Bridge (ADB) tool and custom scripts in the Bash shell language. The results do not clearly indicate which technology is more efficient.
CSAIL2019 Crypto-Puzzle Solver Architecture

The CSAIL2019 time-lock puzzle is an unsolved cryptographic challenge introduced by Ron Rivest in 2019, replacing the solved LCS35 puzzle. Solving these types of puzzles requires large amounts of intrinsically sequential computations, with each iteration performing a very large (3072-bit for CSAIL2019) modular multiplication operation. The complexity of each iteration is several times greater than known FPGA implementations, and the number of iterations has been increased by about 1000x compared to LCS35. Because of the high complexity of this new puzzle, a number of intermediate, or milestone versions of the puzzle have been specified. In this article, we present several FPGA architectures for the CSAIL2019 solver, which we implement on a medium-sized Intel Agilex device. We develop a new multi-cycle modular multiplication method, which is flexible and can fit on a wide variety of sizes of current FPGAs. We introduce a class of multi-cycle squarer-based architectures that allow for better resource and area trade-offs. We also demonstrate a new approach for improving the fitting and timing closure of large, chip-filling arithmetic designs. We used the solver to compute the first 22 out of the 28 milestone solutions of the puzzle, which are the first reported results for this problem.
VdaBSC: A Novel Vulnerability Detection Approach for Blockchain Smart Contract by Dynamic Analysis

Smart contracts have gained immense popularity in recent years as self-executing programs that operate on a blockchain. However, they are not immune to security flaws, which can result in significant financial losses. These flaws can be detected using dynamic analysis methods that extract various aspects from smart contract bytecode. Methods currently used for identifying vulnerabilities in smart contracts mostly rely on static analysis methods that search for predefined vulnerability patterns. However, these patterns often fail to capture complex vulnerabilities, leading to a high rate of false negatives. To overcome this limitation, researchers have explored machine learning-based methods. However, the accurate interpretation of complex logic and structural information in smart contract code remains a challenge. In this study, we present a technique that combines real-time runtime batch normalization and data augmentation for data preprocessing, along with n-grams and one-hot encoding for feature extraction of opcode sequence information from the bytecode. We then combined bidirectional long short-term memory (BiLSTM), convolutional neural network, and the attention mechanism for vulnerability detection and classification. Additionally, our model includes a gated recurrent units memory module that enhances efficiency using historical execution data from the contract. Our results demonstrate that our proposed model effectively identifies smart contract vulnerabilities.
Analyzing the Relationship Between Inventory Policies and CSR Practices: Case of Jordanian Companies

Abstract The present research aims to assess the impact of inventory policies on the Corporate Social Responsibility practices of companies listed on the Amman Stock Exchange. The companies included in the research belonged to manufacturing or related sectors in which holding inventory is inevitable. The sample size chosen for the research was 45 companies, mainly because of the availability of financial data from the period 2012 to 2022. The data for the research was extracted from the Amman Stock Exchange, and we utilized a regression-based ANOVA model for our analysis, containing the dummy variable to enhance the model’s validation and facilitate the statistical analysis, hence enabling a deeper understanding. We utilized. We also used the GMM model to validate our analysis. The financial variables of the research were based on the reported financial statements of the companies. The study explores the impact of the relationship between inventory policy and Corporate Social Responsibility on the different stakeholders, such as customers, employees, and environmental groups. We found that the CSR disclosure requirements also tend to have an impact on the inventory policies of these companies. Our results revealed that inventory levels are positively associated with satisfied customers. We found that the company’s total CSR and sales ratio do not have a straight-line relationship. It led us to conclude that inventory levels are positively associated with satisfied customers.
Students' Perception on the Implementation of Online Learning in English Speaking Skill

Due to the Covid-19 pandemic, the government stipulated the PPKM agenda that limit the social interaction in public place, including school. Since then, the teaching and learning process was conducted through online learning. this research aims to know the students' perception toward the use of online learning in students’ speaking skills at Junior High School Al-Fattah Buduran Sidoarjo. In this research, the researcher focuses on the development of speaking skills in students through online learning during the pandemic. This research is a descriptive qualitative method by emphasizes a variety of approaches and takes an interpretive and naturalistic approach to its subject. Based on the research result, it was found that the implementation of online media for English speaking skill is considered less efficient for students since they are unable to practice their speaking ability with their teachers or their classmates. In addition, the most frequent obstacles faced by students in online learning is the poor signal and connection in their home or the teachers’ home that caused students experience the difficulties in understanding the lesson.
The Causes and Measures of Online Deviance

The Internet as a virtual space facilitates the phenomenon of people’s deviant behavior. Due to its enormous scale and versatility, it plays an indispensable role in modern society. It is important to maintain online environment and cybersecurity is now a major challenge to society and traditional law enforcement. How is the Internet fueled deviant behavior? What’s the difficulties of dealing with online deviance? How to implement intervention without damaging the benefits from the network? This study explains the causes of virtual deviance and gives measures, through the nature of the Internet to answer these questions. Online deviance is influenced by factors like online disinhibition, including benign and toxic disinhibition, which can lead to aggressive behavior due to the absence of immediate feedback. The Internet's characteristics, such as dissociative anonymity and minimized status and authority, worsen online disinhibition, resulting in behaviors like online harassment. Low costs and lenient enforcement contribute to the proliferation of online deviance. Accessible tools and tutorials for cyber deviance, along with online anonymity, further encourage such behavior, creating a self-perpetuating cycle. Defining online deviance is challenging due to its global nature and constantly evolving forms. Laws and definitions vary among countries, and the anonymous nature of online activities hampers identification and classification. Gathering evidence for cyber deviance cases is also complex, as electronic evidence can be tampered with or deleted. It suggests that law enforcement should pay more attention to deal with the reports from Internet users, by implementing targeted warnings and cautions in real world to stop potential criminals, as well as help potential victims be more effective in defending their rights. It also mentions an intervention from family regulatory layer can protect adolescents against deviant behavior from online community. The ideal control effect may be expected to differ, depends on local agencies’ capacity and different online policies. This study aims to elucidate the phenomenon of virtual deviance and provide feasible methods to improve online environment. The problems in online society need to be taken seriously.
The Impact of social media on Deviance and Crime

The popularity of social media platforms in the digital age has revolutionized how people engage with one another, share information, and interact with their environment. With the framework of social learning theory and routine activity theory, this article aims to summarize and analyze the impact of social media on deviance and crime. As a result, it is now clear how social media has an impact on a variety of societal issues, such as crime and deviance. Using information from a wide range of academic sources, this paper explores the complex connection between social media and crime or deviance. Through the lenses of routine activity theory, social learning theories, and cyber lifestyle-routine activities theory, this study examines the intricate interplay between online behaviors and offline behaviors in an effort to better understand the complex effects of social media on deviance and crime. Meanwhile, people can harness the benefits of social media while reducing its negative effects on crime and deviance by developing rules that address these issues and encouraging a culture of responsible online activity.
An Exploration of Augmented Reality User Interface Design for the Stanley &amp; Audrey Burton Gallery Mobile Application

The main aim of this research is to develop a mobile application that features augmented reality (AR) functions in order to enhance the visitor experience at the Stanley &amp; Audrey Burton Gallery at the University of Leeds. Data for this study were collected using different questionnaire surveys, applying quantitative research methods to the survey results. This study is unable to encompass the entire range of theories concerned with interaction design. The reader should bear in mind that the study is grounded in user-centric principles. It is found that young audiences account for the majority of users who demonstrated positive reactions to augmented reality app technology. This demographic of users pay more attention to the entertainment value offered in interactive interfaces as well as the aesthetics of design. Although such users have not used the gallery’s AR app, they have high expectations for this project. Many substantive suggestions are put forward throughout the iterative design process. This research contributes to a deeper understanding of the field of AR as well as human-computer interactions more widely. The findings offer further important contributions to the field of interactive experience.
Digital Forensics and Computer Crimes: The Case of North Macedonia

Abstract The subject of the research refers to the aspect of digital forensics and computer crime and the latter is one of the reasons for the evolution of crime in general. Based on the growing trend of technology development and the increase in the number of digital crimes, a special emphasis is given to the statistical aspect of computer crime as well as measures to reduce the impact of computer crime, including the ethical and legal aspects in the Republic of North Macedonia (RNM). To conduct this research, statistical data on computer crime in RNM from 2011 to 2020 were used, based on historical data of analysis, as well as comparison, correlation, and the ANOVA-test. The highest number of crimes were registered in 2020 and a special feature is that a positive linear trend can be observed during the last decade. “Damage and unauthorized access to a computer system” took the first place in computer crimes according to the Criminal Procedure Law.