Reviewerly/exHarmony huggingface dataset:
ASSESSMENT OF THE QUALITY OF EDUCATION THROUGH THE PARTICIPATION OF STUDENTS IN PROFILE STUDENT OLYMPIADS AND SCIENTIFIC COMPETITIONS

The article substantiates the role of student specialized Olympiads and scientific competitions as elements of an independent assessment of the quality of education of university students. On the example of a specific group, an assessment of the quality of education of students-economists of ANTU was given
Enhancing human parsing with region‐level learning

Abstract Human parsing is very important in a diverse range of industrial applications. Despite the considerable progress that has been achieved, the performance of existing methods is still less than satisfactory, since these methods learn the shared features of various parsing labels at the image level. This limits the representativeness of the learnt features, especially when the distribution of parsing labels is imbalanced or the scale of different labels is substantially different. To address this limitation, a Region‐level Parsing Refiner (RPR) is proposed to enhance parsing performance by the introduction of region‐level parsing learning. Region‐level parsing focuses specifically on small regions of the body, for example, the head. The proposed RPR is an adaptive module that can be integrated with different existing human parsing models to improve their performance. Extensive experiments are conducted on two benchmark datasets, and the results demonstrated the effectiveness of our RPR model in terms of improving the overall parsing performance as well as parsing rare labels. This method was successfully applied to a commercial application for the extraction of human body measurements and has been used in various online shopping platforms for clothing size recommendations. The code and dataset are released at this link https://github.com/applezhouyp/PRP .
Towards the applicability of voice quality in forensic phonetics

Voice quality derived from long-term laryngeal settings stands out as a potentially individualizing trait of speakers. This places it in an advantageous situation with respect to other phonetic parameters used in forensic linguistics. However, anyone confronted with its analysis will immediately run into a methodological difficulty stemming from its inherently multidimensional nature. In this lies its main disadvantage and the fundamental reason why its analysis is not always considered in the traditional approach used in the comparison of speakers for identification purposes. Based on an experimental inquiry on voice disguised by means of falsetto, this study shows that it is possible to work with a reduced set of laryngeal features responsible for voice quality and facilitate its interpretation and explanation, which is a critical issue for forensic practice.
BIBLIOMETRIC ANALYSIS OF SCIENTIFIC PUBLICATIONS IN THE FIELD OF ONLINE EDUCATION

Online learning has become a necessity for students in Ukraine since the beginning of the pandemic and then the full-scale invasion of Ukraine by the Russian Federation. The development of online education and the creation of effective tools for its implementation are becoming relevant. A significant number of teachers and students have gone abroad for evacuation, and therefore the distance format is the most acceptable for them. In order to avoid significant losses in knowledge acquisition due to the war, it is necessary to develop online education and create appropriate initial content. The article presents the results of a bibliometric analysis of a number of scientific publications on online education that were published in the "pre- and post-conflict" periods (2014-2018 and 2019-2023, respectively). The purpose of the study is to examine the conceptual apparatus of the term "Online Education" in the context of 5 years before and after the COVID-19 pandemic. To conduct the analysis, the authors used such bibliometric tools as VOSviewer and Bibexcel. Based on the results of the bibliometric analysis, the authors created a graphical visualization and cluster distribution of the conceptual apparatus (including interrelationships). The LinLog/modularity normalization method was used to build the network visualization. The most cited publications of certain time periods were analyzed. Thus, there has been a sharp increase in the publication activity of scientists on the topic of online learning and online education since 2019. Conclusions have been drawn about current trends in online education related to the global pandemic. When building the visualization clusters, four patterns were identified: the cluster components reflect the concepts of classical educational elements that have moved to the online environment; the main advantages of online education and tools that make it accessible to users; elements of the online learning process and their features; the main methodology of online teaching, which is based on the use of massive open online courses and training videos on various platforms. Scientific research during the COVID-19 pandemic and developments in the field of distance learning technologies will become the basis for maintaining the educational process during the war in Ukraine. Studies in different countries show that the number of platforms and open primary courses that can be used by teachers and professors in teaching students is growing.
Strata Fee Management in Condominiums via Smart Contracts

Condominiums and similar properties use a stratum to manage daily operations, and owners fund it through strata fees. While existing strata fee management systems may be able to handle such funds, such systems could be more inherently transparent. It is possible to leverage the digital ledger from blockchain networks and smart contracts to build a fully transparent strata fee management system. This paper proposes designing a strata fee management system based on a smart contract in the Ethereum network. Both strata corporations and homeowners can interact with the smart contract to execute common procedures such as paying strata fees and handling expenses. Using smart contracts for strata fee management, it is believed that the chance of fraud by strata corporations is lowered compared to other systems.
Efficient Fingerprinting Attack on Web Applications: An Adaptive Symbolization Approach

Website fingerprinting is valuable for many security solutions as it provides insights into applications that are active on the network. Unfortunately, the existing techniques primarily focus on fingerprinting individual webpages instead of webpage transitions. However, it is a common scenario for users to follow hyperlinks to carry out their actions. In this paper, an adaptive symbolization method based on packet distribution information is proposed to represent network traffic. The Profile Hidden Markov Model (PHMM exploits positional information contained in network traffic sequences and is sensitive to webpage transitional information) is used to construct users’ action patterns. We also construct user role models to represent different kinds of users and apply them to our web application identification framework to uncover more information. The experimental results demonstrate that compared to the equal interval and K-means symbolization algorithms, the adaptive symbolization method retains the maximum amount of information and is less time-consuming. The PHMM-based user action identification method has higher accuracy than the existing traditional classifiers do.
An evaluation on large language model outputs: Discourse and memorization

We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs). Our analysis is done with off-the-shelf, readily-available tools. We find a correlation between percentage of memorized text, percentage of unique text, and overall output quality, when measured with respect to output pathologies such as counterfactual and logically-flawed statements, and general failures like not staying on topic. Overall, 80.0% of the outputs evaluated contained memorized data, but outputs containing the most memorized content were also more likely to be considered of high quality. We discuss and evaluate mitigation strategies, showing that, in the models evaluated, the rate of memorized text being output is reduced.
Data Fusion for Cross-Domain Real-Time Object Detection on the Edge

We investigate an edge-computing scenario for robot control, where two similar neural networks are running on one computational node. We test the feasibility of using a single object-detection model (YOLOv5) with the benefit of reduced computational resources against the potentially more accurate independent and specialized models. Our results show that using one single convolutional neural network (for object detection and hand-gesture classification) instead of two separate ones can reduce resource usage by almost 50%. For many classes, we observed an increase in accuracy when using the model trained with more labels. For small datasets (a few hundred instances per label), we found that it is advisable to add labels with many instances from another dataset to increase detection accuracy.
Problems and Tasks of Higher Pedagogical Education at the Present Stage of Country’s Development

The analysis of the reasons why numerous attempts to update the Russian education system do not lead to the desired result and cause increasing criticism is carried out. One of the main reasons is seen in the violation of the systematic approach in solving the issues of the development of education in modern conditions. It is pointed out that the educational sphere, in fact, is always based on solving three problems: “what to teach”, “how to teach”, “why to teach”. The key question, that illuminates everything else, is the main question – in what social, economic and technological structure of society a person who receives education at any level, from preschool to university, has to live and work. Violation of the sequence of solving these problems, their inconsistency and the lack of a scientific justification ahead of the decisions made, inevitably lead to internal contradictions in the system being built and reduce its effectiveness. The analysis of why the Bologna system, which is widespread in a large number of countries with different levels of socio-economic status, turned out to be ineffective in Russia was made. It is shown that this system in its origin is a system of general higher non-professional education and an attempt to adapt it for receiving professional education in our country cannot be successful by its definition. It is said about the need of building a domestic multi-level system of professional higher education in modern conditions.
Implementation of Quantum Optimization Algorithm

Quantum computing is a quickly growing research field. This article introduces the basic concepts of quantum computing, recent developments in quantum searching Quantum computers are computing devices that can theoretically have computed power that is many orders of magnitude greater than that of conventional computers. The quantum bit, or qubit, which is the quantum state of electrons in an atom, is the fundamental data unit in a quantum computer. Qubits have the potential to exist in several superposed states at once, making them capable of carrying much more data than do standard two-state bits. The mathematical basis of the proportionality of qubit states is like that of the input weights of neural networks. The technology of quantum computers has made some progress, but much more work needs to be done in terms of research and development before it can be used in everyday life. There are also views that suggest this outcome is impossible. A recent field of study known as quantum computing (QC) combines components of computing, physics, and mathematics. Interest in quantum computing is rising among academics, technologists, and businesspeople. For the past ten years, it has given researchers in the scientific, technological, and industrial disciplines a place to conduct their work. The fundamentals of QC have been developed using notions from quantum physics. The parallel processing functionality in QC has simplified the currently employed algorithms. Numerous optimization-related challenges and issues were solved with the aid of this function. Intelligent computational techniques that were inspired by quantum mechanics have been applied in a variety of fields. A qubit's ability to be in superposition is one of its characteristics that distinguishes it from a conventional bit. Superposition is one of the fundamental principles. Entanglement is another one of quantum physics' counterintuitive occurrences. When each particle's quantum state cannot be characterized separately from the quantum state of the other particle(s), a pair or group of particles are said to be entangled
An Interleaving Semantics of the Timed Concurrent Language for Argumentation to Model Debates and Dialogue Games

Time is a crucial factor in modelling dynamic behaviours of intelligent agents: activities have a determined temporal duration in a real-world environment, and previous actions influence agents' behaviour. In this paper, we propose a language for modelling concurrent interaction between agents that also allows the specification of temporal intervals in which particular actions occur. Such a language exploits a timed version of Abstract Argumentation Frameworks to realise a shared memory used by the agents to communicate and reason on the acceptability of their beliefs with respect to a given time interval. An interleaving model on a single processor is used for basic computation steps, with maximum parallelism for time elapsing. Following this approach, only one of the enabled agents is executed at each moment. To demonstrate the capabilities of language, we also show how it can be used to model interactions such as debates and dialogue games taking place between intelligent agents. Lastly, we present an implementation of the language that can be accessed via a web interface. Under consideration in Theory and Practice of Logic Programming (TPLP).
A Break Of Barrier To Classical Differential Fault Attack On The Nonce-Based Authenticated Encryption Algorithm

Abstract It had always been believed that there was an inherent barrier to Differential Fault Attack (DFA) on the nonce-based authenticated encryption algorithm. At CHES 2016, Saha et al. proposed an Internal Differential Fault Attack on a parallelizable counter-mode algorithm. They induce the attack to classical DFA at the expense of one more fault injection in every encryption process. In this paper, we propose the DFA on HYENA, which is a nonce-based authenticated encryption mode for GIFT-128. Our work is the first pure classical DFA on a nonce-based authenticated encryption algorithm with only one fault injected in every decryption process. Firstly, we give the DFA on GIFT-128 with a fault injected into the 39th-round input. Based on this work, we inject a fault in the underlying GIFT-128 of a HYENA decryption process and make this decryption process still generate the correct tag and output plaintext. This makes the necessary conditions of DFA satisfied. Experiments show that at most 56 key bits of HYENA can be recovered with only a few faulty ciphertexts. In addition, our fault injection is easier to achieve than most other work about fault attack, because the injection location is relatively random and the fault type can be arbitrary. It should be noted that the left 72 key bits cannot be recovered in this way.
Extended reality as a health education strategy of adolescents at school: protocol for systematic review and meta-analysis

Extended reality (XR) is the ensemble of interactive experiences based on a computer-simulated environment that encompasses virtual reality and augmented reality and has been proven to be potentially innovative in the field of health education with adolescents. The objective of this study is to present a systematic review and meta-analysis protocol that seeks to evaluate the main effects of interventions that use XR on health parameters (food intake, sound quality and physical activity) of adolescent students.The literature search will be performed in the following databases: MEDLINE, Embase, Scopus, ERIC, ScienceDirect, Web of Science, Cochrane, LILACS, APA and ADOLEC. Intervention studies (clinical trials-randomised or non-randomised) and quasi-experimental studies will be included. The risk of bias will be assessed using the Risk of Bias in Non-randomized Studies of Interventions tool for randomised controlled trials (RCTs), non-RCTs and quasi-experimental trials. Two independent researchers will conduct all the assessments, and any disagreements will be consulted with a third reviewer. Data analysis and synthesis will be performed using RevMan V.5.4.1 software. The study will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guideline.Ethical approval and human consent were not required, as this is a protocol for a systematic review and only secondary data will be used. The findings will be published in a journal and presented at conferences. In case of any changes to this protocol, it will be updated in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses website, and the modifications will be explained in the final report of this review.CRD42022373876.
The Relation between Sleep Problems and Depression of College Students: Testing Mediating Effects of Cognitive Reappraisal and Expressive Suppression

본 연구의 주목적은 대학생의 수면문제와 우울 간 관계에서 인지적 재평가와 표현 억제의 매개효과를 검증하는 것이었다. 전국 5개 지역의 대학교에 재학 중인 대학생 375명을 대상으로 자기보고식 온라인 설문조사를 실시였다. 기술통계, 독립표본 t 검증, 상관관계분석, Process Macro 4.1의 병렬다중매개모형을 이용해 자료를 분석하였다. 그 결과, 첫째, 대학생의 수면문제, 인지적 재평가 및 표현 억제와 우울 수준에서 성별에 따른 차이가 유의하지 않았다. 둘째, 대학생들이 수면문제를 자주 경험할수록 인지적 재평가 전략을 더 적게 사용하였고, 우울이 높게 나타났다. 또한 인지적 재평가 전략을 더 많이 사용할수록 우울이 낮게 나타났다. 반면, 표현 억제 전략의 사용은 수면문제나 우울과 별다른 관련성을 보이지 않았다. 셋째, 병렬다중매개모형을 검증한 결과, 대학생의 수면문제는 우울에 직접적으로 영향을 미칠 뿐 아니라 인지적 재평가를 통해 간접적으로도 영향을 미쳤다. 즉 대학생들이 수면문제를 많이 경험할수록 우울이 심했을 뿐 아니라 수면문제를 자주 경험할수록 인지적 재평가 전략을 덜 사용하였으며 이는 우울을 악화시키는 기제로 작용하였다. 이러한 결과는 대학생의 우울 예방 및 중재에 있어 수면의 질 관리 및 적절한 정서조절전략의 사용이 필요함을 시사한다.The purpose of this study is to test the mediating effects of cognitive reappraisal and expressive suppression in the relation between sleep problems and depression in college students. Participants were 375 undergraduate students recruited from nationwide universities. Sleep problems, cognitive reappraisal, expressive suppression, and depression were assessed by a self-report using web-based questionnaires. The data were analyzed using descriptive statistics, t-tests, Pearson’s correlations, and regression analyses in PROCESS macro version 4.1. The major findings are as follows. First, the average levels of sleep problems, cognitive reappraisal, expressive suppression, and depression were not significantly different as a function of gender. Second, greater sleep problems were associated with less use of cognitive reappraisal and higher depressive symptoms while the use of expressive suppression was not associated with sleep problems or depressive symptoms. Third, sleep problems were linked to greater depressive symptoms directly and indirectly by way of less use of cognitive reappraisal. The findings suggest that sleep problems and emotion regulation strategies may play important roles in preventing depression in college students.
Policy generation network for zero‐shot policy learning

Abstract Lifelong reinforcement learning is able to continually accumulate shared knowledge by estimating the inter‐task relationships based on training data for the learned tasks in order to accelerate learning for new tasks by knowledge reuse. The existing methods employ a linear model to represent the inter‐task relationships by incorporating task features in order to accomplish a new task without any learning. But these methods may be ineffective for general scenarios, where linear models build inter‐task relationships from low‐dimensional task features to high‐dimensional policy parameters space. Also, the deficiency of calculating errors from objective function may arise in the lifelong reinforcement learning process when some errors of policy parameters restrain others due to inter‐parameter correlation. In this paper, we develop a policy generation network that nonlinearly models the inter‐task relationships by mapping low‐dimensional task features to the high‐dimensional policy parameters, in order to represent the shared knowledge more effectively. At the same time, we propose a novel objective function of lifelong reinforcement learning to relieve the deficiency of calculating errors by adding weight constraints for errors. We empirically demonstrate that our method improves the zero‐shot policy performance across a variety of dynamical systems.
An Efficient Solver for ASP(Q)

Abstract Answer Set Programming with Quantifiers ASP(Q) extends Answer Set Programming (ASP) to allow for declarative and modular modeling of problems from the entire polynomial hierarchy. The first implementation of ASP(Q), called QASP, was based on a translation to Quantified Boolean Formulae (QBF) with the aim of exploiting the well-developed and mature QBF-solving technology. However, the implementation of the QBF encoding employed in qasp is very general and might produce formulas that are hard to evaluate for existing QBF solvers because of the large number of symbols and subclauses. In this paper, we present a new implementation that builds on the ideas of QASP and features both a more efficient encoding procedure and new optimized encodings of ASP(Q) programs in QBF. The new encodings produce smaller formulas (in terms of the number of quantifiers, variables, and clauses) and result in a more efficient evaluation process. An algorithm selection strategy automatically combines several QBF-solving back-ends to further increase performance. An experimental analysis, conducted on known benchmarks, shows that the new system outperforms QASP.
Improving Steerability Detection via an Aggregate Class Distribution Neural Network

In this paper, we establish an aggregate class distribution neural network (AGGNN) structure to determine whether an arbitrary two-qubit quantum state is steerable. Compared to the classification results obtained using a support vector machine (SVM) and a backpropagation neural network (BPNN), we obtain higher-accuracy quantum-steering classification models via the AGGNN, as well as steerability bounds of generalized Werner states, which are more similar to the theoretical bounds. In particular, when we only know partial information about the quantum states, higher-performance quantum-steering classifiers are obtained compared to those via SVM and BPNN.