Reviewerly/exHarmony huggingface dataset:
Diversified Teaching Method in Basic Piano Course Instruction for Higher Education Teachers

Diversified teaching plays an important role in the sustainable development of China’s basic piano course instruction for higher education teachers. A diversified curriculum system should be designed to promote the development of each student, and efforts should be made to cultivate high-quality talents who can adapt to the various needs of the society as well as to promote the reform and development of basic piano course instruction for higher education teachers. The aim of this study was to explore the teaching of basic piano course for higher education teachers based on the diversified teaching method. The teaching methods use for basic piano course were analyzed in this study, and two research methods were used to carry out the present study so as to effectively improve the operation of the diversified teaching method. Through a survey of the current situation of piano instruction in two higher education faculties, we can see that the teaching of basic piano course in higher education is still dominated by the traditional teaching method although the majority of students preferred the diversified teaching method.
Emotional Intelligence of Schoolchildren in the Educational Process

This article is based on a questionnaire of a relatively limited number of Ukrainian teachers-practitioners. We proved that emotional intelligence (hereafter – EI) in an educational institution can be sufficiently diagnosed by observation, and without using psychometric methods. We also classified and presented the main components of emotional intelligence, their essence, correlation with other qualities of psyche; gave markers for the definition of EI and markers of their dynamics over time. The article has theoretical nature, which determines the analytical and generalizing approach to its writing. While it has been writing, we had used a sociological survey (to collect data) and a number of theoretical methods: analysis, generalization, statistics (frequency selection, ranking) etc. The main data were obtained by analyzing the thematic literature and questionnaires of primary school teachers with sufficient experience (120 persons). The questionnaire was filled out mainly in electronic form, and sporadically in-person. The result was the systematization, correlation and labeling of the most common qualities of EI. Also, the hypothesis put forward in the article found theoretical confirmation (it is confirmed by the analysis of the experience of primary school teachers). The article is based on an investigation of only one, central, region of Ukraine, so it is representative and needs to expand the scope of research. This fact is a limitation of the study.
Methodological Foundations of Psychological and Pedagogical Counseling

The article analyzes the theoretical and methodological foundations of psychological and pedagogical counseling in the sphere of today's education in the interdisciplinary aspects, in particular, neuropsychology and neuropedagogy. An attempt to substantiate psychological and pedagogical counseling as a service of providing psychological assistance to the client through training and forming a new experience of adaptive behavior in accordance with the requirements of the educational environment is made. Theoretical and methodological foundations of psychological and pedagogical counseling are analyzed on the basis of the works on the current issues of psychological counseling by foreign and domestic researchers. The importance of psycho-consultative interaction in the conditions of inclusive education and teaching special children, taking into account the competencies of neuropedagogy, neuropsychology and neurophysiology was substantiated. The personal approach in the methodology of today's psychological and pedagogical counseling is defined as the main one, which is integrated with psychological approaches to counseling. The methodological foundations of psychological and pedagogical counseling are defined by theoretical and methodological principles, psychological mechanisms for organizing and conducting counseling work and ethical principles and norms for manifestation of professional competence of a counselor. It was found that psychological approaches such as person-centered, problem-oriented, and professional-competence approaches are most often applied to psychological and pedagogical counseling.
PENGEMBANGAN MEDIA WEB DALAM PROMOSI PRODUK PEMBERDAYAAN TENUN SONGKET DI DESA MUARA PENIMBUNG ULU

This development research aims to produce web media in the promotion of songket weaving empowerment products as a valid and practical promotional media using the ADDIE development model (analysis, design, development, implementation and evaluation) which is carried out at the Mardiah Songket Gallery in Muara Penimbung Ulu village. The data collection techniques used in this study were interviews and questionnaires. The result of this study is that web media in the promotion of songket weaving products that have a media validity level get a total value of 38 with a percentage of 95%, then the material gets a total value of 32 with a percentage of 80%, and the language gets a total score of 34 with a percentage of 94.4%. This songket weaving product empowerment promotional web has a practicality level of 95% which is very feasible. Based on the results of validation and implementation, it can be concluded that the development of web media in the promotion of songket weaving empowerment products is categorized as very valid or very feasible and practical.Keywords: web, promotion, empowerment
Multi-Terminal Platform for Blended Training of English Micro-Lectures Under the Trend of Smart Guiding Data and Online Integration

This paper proposes a software development system and method for multi-terminal embedded platform, which effectively integrates the development environment of each terminal embedded platform into a unified platform. The emergence of a large number of micro-lectures has provided a large number of high-quality resources for the classroom teaching practice of the blended teaching mode. Taking the teaching of industrial English courses as an example, this paper conducts practical research on the blended teaching mode based on micro-course resources. Users can easily develop applications based on various platforms without installing various cumbersome development environments and configuring terminal platforms on the local computer. A hybrid teaching mode framework is designed, and an evaluation method is proposed. Teaching practice shows that the use of blended teaching mode enhances the teaching effect, and it is worth popularizing in the basic computer courses of colleges and universities.
Quantum Multi-Solution Bernoulli Search with Applications to Bitcoin&amp;apos;s Post-Quantum Security

A proof of work (PoW) is an important cryptographic construct enabling a party to convince others that they invested some effort in solving a computational task. Arguably, its main impact has been in the setting of cryptocurrencies such as Bitcoin and its underlying blockchain protocol, which received significant attention in recent years due to its potential for various applications as well as for solving fundamental distributed computing questions in novel threat models. PoWs enable the linking of blocks in the blockchain data structure and thus the problem of interest is the feasibility of obtaining a sequence (chain) of such proofs. In this work, we examine the hardness of finding such chain of PoWs against quantum strategies. We prove that the chain of PoWs problem reduces to a problem we call multi-solution Bernoulli search, for which we establish its quantum query complexity. Effectively, this is an extension of a threshold direct product theorem to an average-case unstructured search problem. Our proof, adding to active recent efforts, simplifies and generalizes the recording technique of Zhandry (Crypto'19). As an application, we revisit the formal treatment of security of the core of the Bitcoin consensus protocol, the Bitcoin backbone (Eurocrypt'15), against quantum adversaries, while honest parties are classical and show that protocol's security holds under a quantum analogue of the classical “honest majority'' assumption. Our analysis indicates that the security of Bitcoin backbone is guaranteed provided the number of adversarial quantum queries is bounded so that each quantum query is worth <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mi>p</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>&amp;#x2212;</mml:mo><mml:mn>1</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:math> classical ones, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>p</mml:mi></mml:math> is the success probability of a single classical query to the protocol's underlying hash function. Somewhat surprisingly, the wait time for safe settlement in the case of quantum adversaries matches the safe settlement time in the classical case.
Forensic study on the effect of atypical surfaces and writing instruments on handwriting characteristics

Handwriting examination is becoming a captivating field nowadays, as there are numerous cases where handwriting is not written on a usual surface – i.e., paper, with a conventional writing instrument – i.e., pen. However, while it is interesting to analyze these types of handwriting samples, most forensic document examiners find it difficult due to the effect atypical surface and writing instrument have on handwriting characteristics. Thus, a systematized study has been carried out on documents prepared on unconventional surfaces such as cardboard, cloth piece, and blackboard, with uncommon writing instruments such as lipstick, kajal, and chalk. Correspondingly, studies were carried out on writing samples provided by 40 individuals using the conventional pen/paper as well as unconventional cardboard/kajal, cardboard/pen, cloth/pen, cloth/lipstick, cloth/kajal, and board/chalk combinations. Various handwriting features were examined and compared among aforementioned combinations, and changes in handwriting characteristics were reported. Those could be due to the atypical writing instruments and surfaces which conjointly impact the overall identity of the handwriting and expert’s conclusions thereon.
ANITA: Analytic Tableau Proof Assistant

This work presents the system ANITA (Analytic Tableau Proof Assistant) developed for teaching analytic tableaux to computer science students.The tool is written in Python and can be used as a desktop application, or in a web platform.This paper describes the logical system of the tool, explains how the tool is used and compares it to several similar tools.ANITA has already been used in logic courses and an evaluation of the tool is presented.
PELATIHAN PERAWATAN DAN PERBAIKAN PERALATAN LISTRIK RUMAH TANGGA BAGI MASYARAKAT RUKUN WARGA 09 KELURAHAN BUNULREJO – KOTA MALANG

Maintenance and repair of household electrical appliances for the Rukun community Residents 09 Bunulrejo Village / Kec. Blimbing - Malang City, gives additional the skills and motivation for the Rukun Community of 09 Bunulrejo Village for practice maintenance and repair of household electrical appliances for the Rukun community Residents 09 Bunulrejo Village. Besides that it can also improve skills addition to the community of Rukun Warga 09 Kelurahan Bunulrejo in the field of care and repair of household electrical appliances for the 09 Rukun Warga community in the Village Bunulrejo that applies in this era
Two-layered logics for paraconsistent probabilities

We discuss two two-layered logics formalising reasoning with paraconsistent probabilities that combine the Lukasiewicz $[0,1]$-valued logic with Baaz $\triangle$ operator and the Belnap--Dunn logic.
Scene Matters: Model-based Deep Video Compression

Video compression has always been a popular research area, where many traditional and deep video compression methods have been proposed. These methods typically rely on signal prediction theory to enhance compression performance by designing high efficient intra and inter prediction strategies and compressing video frames one by one. In this paper, we propose a novel model-based video compression (MVC) framework that regards scenes as the fundamental units for video sequences. Our proposed MVC directly models the intensity variation of the entire video sequence in one scene, seeking non-redundant representations instead of reducing redundancy through spatio-temporal predictions. To achieve this, we employ implicit neural representation as our basic modeling architecture. To improve the efficiency of video modeling, we first propose context-related spatial positional embedding and frequency domain supervision in spatial context enhancement. For temporal correlation capturing, we design the scene flow constrain mechanism and temporal contrastive loss. Extensive experimental results demonstrate that our method achieves up to a 20\% bitrate reduction compared to the latest video coding standard H.266 and is more efficient in decoding than existing video coding strategies.
On Card guessing with two types of cards

We consider a card guessing strategy for a stack of cards with two different types of cards, say $m_1$ cards of type red (heart or diamond) and $m_2$ cards of type black (clubs or spades). Given a deck of $M=m_1+m_2$ cards, we propose a refined counting of the number of correct color guesses, when the guesser is provided with complete information, in other words, when the numbers $m_1$ and $m_2$ and the color of each drawn card are known. We decompose the correct guessed cards into three different types by taking into account the probability of making a correct guess, and provide joint distributional results for the underlying random variables as well as joint limit laws.
Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

In recent years, the problem of fuzzy clustering has been widely concerned. The membership iteration of existing methods is mostly considered globally, which has considerable problems in noisy environments, and iterative calculations for clusters with a large number of different sample sizes are not accurate and efficient. In this paper, starting from the strategy of large-scale priority, the data is fuzzy iterated using granular-balls, and the membership degree of data only considers the two granular-balls where it is located, thus improving the efficiency of iteration. The formed fuzzy granular-balls set can use more processing methods in the face of different data scenarios, which enhances the practicability of fuzzy clustering calculations.
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks

Indiscriminate data poisoning attacks aim to decrease a model's test accuracy by injecting a small amount of corrupted training data. Despite significant interest, existing attacks remain relatively ineffective against modern machine learning (ML) architectures. In this work, we introduce the notion of model poisoning reachability as a technical tool to explore the intrinsic limits of data poisoning attacks towards target parameters (i.e., model-targeted attacks). We derive an easily computable threshold to establish and quantify a surprising phase transition phenomenon among popular ML models: data poisoning attacks can achieve certain target parameters only when the poisoning ratio exceeds our threshold. Building on existing parameter corruption attacks and refining the Gradient Canceling attack, we perform extensive experiments to confirm our theoretical findings, test the predictability of our transition threshold, and significantly improve existing indiscriminate data poisoning baselines over a range of datasets and models. Our work highlights the critical role played by the poisoning ratio, and sheds new insights on existing empirical results, attacks and mitigation strategies in data poisoning.
Gradient-Free Structured Pruning with Unlabeled Data

Large Language Models (LLMs) have achieved great success in solving difficult tasks across many domains, but such success comes with a high computation cost, and inference latency. As developers and third parties customize these models, the need to provide efficient inference has increased. Many efforts have attempted to reduce inference cost through model compression techniques such as pruning and distillation. However, these techniques either require labeled data, or are time-consuming as they require the compressed model to be retrained to regain accuracy. In this paper, we propose a gradient-free structured pruning framework that uses only unlabeled data. An evaluation on the GLUE and SQuAD benchmarks using BERT$_{BASE}$ and DistilBERT illustrates the effectiveness of the proposed approach. By only using the weights of the pre-trained model and unlabeled data, in a matter of a few minutes on a single GPU, up to 40% of the original FLOP count can be reduced with less than a 4% accuracy loss across all tasks considered.
Do the Test Smells Assertion Roulette and Eager Test Impact Students' Troubleshooting and Debugging Capabilities?

To ensure the quality of a software system, developers perform an activity known as unit testing, where they write code (known as test cases) that verifies the individual software units that make up the system. Like production code, test cases are subject to bad programming practices, known as test smells, that hurt maintenance activities. An essential part of most maintenance activities is program comprehension which involves developers reading the code to understand its behavior to fix issues or update features. In this study, we conduct a controlled experiment with 96 undergraduate computer science students to investigate the impact of two common types of test smells, namely Assertion Roulette and Eager Test, on a student's ability to debug and troubleshoot test case failures. Our findings show that students take longer to correct errors in production code when smells are present in their associated test cases, especially Assertion Roulette. We envision our findings supporting academia in better equipping students with the knowledge and resources in writing and maintaining high-quality test cases. Our experimental materials are available online: https://wajdialjedaani.github.io/testsmellstd/
PACMan2: Next Steps in Proposal Review Management

With the start of a new Great Observatories era, there is renewed concern that the demand for these forefront facilities, through proposal pressure, will exceed conventional peer-review management's capacity for ensuring an unbiased and efficient selection. There is need for new methods, strategies, and tools to facilitate those reviews. Here, we describe PACMan2, an updated tool for proposal review management that utilizes machine learning models and techniques to topically categorize proposals and reviewers, to match proposals to reviewers, and to facilitate proposal assignments, mitigating some conflicts of interest. We find that the classifier has cross-validation accuracy of $80.0\pm2.2\%$ on proposals for time on the Hubble Space Telescope and the James Webb Space Telescope.
Gradient-Guided Knowledge Distillation for Object Detectors

Deep learning models have demonstrated remarkable success in object detection, yet their complexity and computational intensity pose a barrier to deploying them in real-world applications (e.g., self-driving perception). Knowledge Distillation (KD) is an effective way to derive efficient models. However, only a small number of KD methods tackle object detection. Also, most of them focus on mimicking the plain features of the teacher model but rarely consider how the features contribute to the final detection. In this paper, we propose a novel approach for knowledge distillation in object detection, named Gradient-guided Knowledge Distillation (GKD). Our GKD uses gradient information to identify and assign more weights to features that significantly impact the detection loss, allowing the student to learn the most relevant features from the teacher. Furthermore, we present bounding-box-aware multi-grained feature imitation (BMFI) to further improve the KD performance. Experiments on the KITTI and COCO-Traffic datasets demonstrate our method's efficacy in knowledge distillation for object detection. On one-stage and two-stage detectors, our GKD-BMFI leads to an average of 5.1% and 3.8% mAP improvement, respectively, beating various state-of-the-art KD methods.
Vulnerability Mimicking Mutants

With the increasing release of powerful language models trained on large code corpus (e.g. CodeBERT was trained on 6.4 million programs), a new family of mutation testing tools has arisen with the promise to generate more "natural" mutants in the sense that the mutated code aims at following the implicit rules and coding conventions typically produced by programmers. In this paper, we study to what extent the mutants produced by language models can semantically mimic the observable behavior of security-related vulnerabilities (a.k.a. Vulnerability-mimicking Mutants), so that designing test cases that are failed by these mutants will help in tackling mimicked vulnerabilities. Since analyzing and running mutants is computationally expensive, it is important to prioritize those mutants that are more likely to be vulnerability mimicking prior to any analysis or test execution. Taking this into account, we introduce VMMS, a machine learning based approach that automatically extracts the features from mutants and predicts the ones that mimic vulnerabilities. We conducted our experiments on a dataset of 45 vulnerabilities and found that 16.6% of the mutants fail one or more tests that are failed by 88.9% of the respective vulnerabilities. More precisely, 3.9% of the mutants from the entire mutant set are vulnerability-mimicking mutants that mimic 55.6% of the vulnerabilities. Despite the scarcity, VMMS predicts vulnerability-mimicking mutants with 0.63 MCC, 0.80 Precision, and 0.51 Recall, demonstrating that the features of vulnerability-mimicking mutants can be automatically learned by machine learning models to statically predict these without the need of investing effort in defining such features.
SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowds

With the increasing availability and affordability of personal robots, they will no longer be confined to large corporate warehouses or factories but will instead be expected to operate in less controlled environments alongside larger groups of people. In addition to ensuring safety and efficiency, it is crucial to minimize any negative psychological impact robots may have on humans and follow unwritten social norms in these situations. Our research aims to develop a model that can predict the movements of pedestrians and perceptually-social groups in crowded environments. We introduce a new Social Group Long Short-term Memory (SG-LSTM) model that models human groups and interactions in dense environments using a socially-aware LSTM to produce more accurate trajectory predictions. Our approach enables navigation algorithms to calculate collision-free paths faster and more accurately in crowded environments. Additionally, we also release a large video dataset with labeled pedestrian groups for the broader social navigation community. We show comparisons with different metrics on different datasets (ETH, Hotel, MOT15) and different prediction approaches (LIN, LSTM, O-LSTM, S-LSTM) as well as runtime performance.