Reviewerly/exHarmony huggingface dataset:
Analysis on Training Strategies of Students’ Self-exercise Consciousness in Physical Education Teaching in Higher Vocational Colleges

Physical education, as an important part of higher vocational education system, is an important way for higher vocational students to achieve all-round development of morality, intelligence and physique. In physical education teaching in higher vocational colleges, it is necessary to take the cultivation of students’ awareness of independent exercise as an important teaching goal, guide students’ enthusiasm to participate in physical exercise, and let students form a good habit of lifelong exercise in participating in physical education learning, which is also of great significance to the improvement of students’ physical fitness and overall quality. Therefore, in higher vocational physical education teaching, it is vital to innovate and optimize the physical education teaching mode, and take the cultivation of students' autonomous exercise consciousness as an important content, so that students can fully understand the importance of autonomous exercise and consciously participate in various physical exercise projects. This thesis discusses the cultivation strategies of students’ autonomous exercise consciousness in higher vocational physical education teaching.
Exploration on the Development of “Audio English” School-based Curriculum in Junior High School

With the development of globalization and the increasing demand for English application skills, English education in junior high school is facing new challenges. The purpose of this paper is to discuss the development and implementation of “Audio English” school-based curriculum. Through literature research and practical experience, this paper presents the importance of developing such a school-based curriculum and analyses the key elements to be considered in the development process. Meanwhile, through the presentation of implementation strategies, it demonstrates the effectiveness of the “Audio English” school-based curriculum in improving students’ listening and speaking skills. The paper summarizes the feedback from the development of “Audio English” school-based curriculum in junior high school, and puts forward the future development direction and feasible suggestions. Finally, the paper emphasizes the importance of curriculum evaluation to ensure the continuous innovation and improvement of the school-based curriculum.
An Introduction to Intercultural Communication Teaching of English in Colleges and Universities in the Era of Big Data

This paper focuses on the teaching of English intercultural communication in colleges and universities in the era of big data, and analyses the challenges faced by the imbalance of educational resources, the training needs of teachers, and the acceptance level of students. In order to solve these problems, countermeasures such as establishing a platform for sharing educational resources, carrying out professional training programmes and designing interesting teaching contents are proposed. The aim is to promote the intercultural communication teaching of English in colleges and universities to adapt to the requirements of the big data era and to cultivate international talents with intercultural thinking skills.
Quantitative Analysis of Factors Influencing Secondary School Students' Satisfaction with the "Double Reduction" Policy: A Perspective on EGRA

Since the implementation of the "Double Reduction" policy, the academic burden of students has been greatly reduced. However, limited body of literatures exist that empirically investigates the efficacy of implementing the "Double Reduction" policy. To address this gap, a survey was conducted among 441 secondary school students in Chengdu, Sichuan Province. The study employed Entropy-weighted Gray Relational Analysis (EGRA) to prioritize the factors associated with satisfaction with policy implementation. Subsequently, Importance-Performance Analysis (IPA) quadrant diagram was utilized to categorize and analyze the factors into four quadrants for comprehensive summary. The research indicated that: (1) Homework duration, examination frequency and examination form are the three most important factors. (2) Personalized homework, stratified homework, practical homework, examination form and homework duration are the five key factors in the IPA improving region, which can significantly enhance student satisfaction and boost their learning efficiency. (3) Examination frequency and Intelligent homework and other 10 factors in the advantage and maintaining region, students' satisfaction is higher, and the policy reform effect is more obvious. The results are crucial for policymakers to further promote and implement the "Double Reduction" policy effectively.
Discussion on the optimization path of physical education teaching environment in colleges

Entering the new era of education, China's physical education has been widely concerned by the society from all walks of life, among which the teaching environment occupies an important position in the teaching system and directly affects the quality and teaching efficiency of college physical education. In the current college physical education, the setting of teaching environment is influenced by various factors such as the level of economic development, social and cultural construction, government policies and educational conditions Teaching environment plays a decisive role in the quality of physical education in colleges and universities. Therefore, we should attach great importance to the construction of teaching environment and optimize the setting of teaching environment to create a positive teaching atmosphere for students to stimulate their interest in physical education and promote their development.
Design of information integrity scheme in smartphone forensics

The vulnerability of electronic data itself makes the originality of digital forensic information become the key to the process of computer evidence identification. Based on smartphone forensics, using the ECDSA signature mechanism, this paper puts forward an anti-repudiation scheme to prove the integrity of electronic evidence. The scheme can transfer the obtained forensics information in real-time and safely during the operation of the smartphone system, and fix the evidence to ensure its integrity. On the premise of some cryptographic assumptions, the security of the scheme is proved.
Evaluation Model of Study Style Based on Neural Network and TOPSIS

Through the establishment of neural network model and weighted TOPSIS comprehensive evaluation model, this paper analyzes the course learning quality of university process assessment. First of all, a descriptive analysis is carried out, the main influencing factors of students' learning attitude are found, and a neural network model reflecting students' attitude is established and verified. Then the study style and class style of colleges and classes are evaluated reasonably by using TOPSIS comprehensive evaluation model, and the weights are calculated and optimized for the second time, and the optimized weights and corresponding scores are obtained. It is found that the three colleges with the best style of study are [308833, 308861, 308882], and the five classes with the best style of study are [16111009, 16111047, 16110895, 16110975, 16110938,16110894].
Hand Action Recognition from RGB-D Egocentric Videos in Substations Operations and Maintenance

This paper proposes a novel multimodal fusion network (MRDFNet) for egocentric hand action recognition from RGB-D videos. First, we utilize three separate streams to extract individual spatio-temporal features for different modalities, which include RGB frames, optical flow stacks, and depth frames. Particularly, for RGB and depth streams, an Attention-based Bidirectional Long Short-Term Memory network (Bi-LSTA) is used to identify regions of interest both spatially and temporally. Then, the extracted features are fed into a fusion module to obtain the integrated feature, which is finally used for egocentric hand action recognition. The fusion module is capable of learning complementary information from multiple modalities, i.e., preserving the distinctive property for each modality and meanwhile exploring the shareable property across modalities. Experimental results on both self-collected RGB-D Egocentric Manual Operation Dataset in Electrical Substations (REMOD-ES) and the THU-READ containing daily-life actions show the superiority of the proposed approach over state-of-the-art methods.
“FORWARD WITH DEMENTIA” CO-DESIGN OF AN ONLINE GUIDE FOR DEMENTIA PATIENTS AND FAMILIES

Objective: Through a co-design process we set out to improve post diagnostic dementia support as part of a wider international programme. A partnership approach was taken with people living with dementia, carers and healthcare professionals. Methods: A scoping review and thematic analysis of existing national and international dementia policies guidelines and campaigns were undertaken to identify gaps and to create a core evidence base. We established co-design local working groups in each of the five partner countries. A series of 20 objective led iterative workshops, four in each country over a period of six months were held. In this way a prototype website was designed, built, populated, and refined. Evidence based English language content was generated and edited collaboratively, core themes were derived from operationalising national Dementia Clinical Guidelines, utilising current evidence, responding to co-designers’ requests and formative research findings. The website was then adapted regionally through translation, links to local information, and including culturally appropriate images, stories and news items. These adapted sites were then user tested and further refined ahead of awareness raising campaigns. Results: We co-designed a dementia friendly accessible resource to support people living with dementia, carers and healthcare professionals for the first 12 months following diagnosis. The workshops determined that the resource should be available online, current, practical, and relevant at a regional and individual level. The need for a practical, empathetic and individually tailored resource was identified. Language, tone, and online accessibility were essential, particularly for people living with dementia. It was important that the content be written and presented specifically and discretely for people recently diagnosed with dementia, for care partners and for healthcare professionals. Conclusion: We set out to improve the dialogue around dementia diagnosis. To empower patients and practitioners through providing information, practical tools, improving awareness of and access to available local supports. Through a person-centred approach with target audience groups together we have developed an online actionable guide Forward with dementia (https://www.forwardwithdementia.org) which supports decision making and to help people find their way forward from a diagnosis of dementia.
Meta-Reinforcement Learning for UAV-Assisted Mobile Edge Computing of Virtual Reality Services

Virtual reality (VR) services face significant challenges, such as large-volume data transmission, high computing demands, and extreme latency sensitivity, particularly on wireless VR devices with limited resources. Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) emerges as a promising solution to ease the computational burden on user devices and enable efficient VR service delivery. This paper proposes a UAV-assisted collaborative MEC framework, involving one access point (AP), one UAV, and VR users, catering to various VR scenarios. VR videos are cached in 2D field of view (FoV) format at the AP, while users request 3D FoV rendering. The 3D FoV model is derived via edge computing at the AP, UAV, or VR users. We formulate an optimization problem with the objective of maximizing VR users’ average service completion rate (ASCR) while minimizing UAV energy consumption, subject to UAV movement, energy, and transmit power constraints. Then, we model the problem as a Markov decision process (MDP) and employ meta-reinforcement learning (Meta-RL) for an optimal strategy. Extensive simulations confirm the efficacy of our approach in enhancing the user experience, efficiently leveraging UAV capacities for edge computing and communications. The robust generalization ability of our Meta-RL algorithm ensures suitability for diverse real-world scenarios.
Pedagogical foundations for improving the network communication culture of future teachers

The problem of formation a teacher's network communicative culture in the context of digital transformation of the educational process is considered as the most important direction, which is currently very relevant in education. In addition, the pedagogical basics of improving the network communicative culture of future teachers are considered. The article notes thatthe network communication culture is also undergoing changes: the language and style of communication are changing, new forms and channels of communication are emerging, corresponding to the fast pace of modern life, which allows for a lot of exchange. Get information, get quick feedback, and build productive dialogue and cooperation. Thus, the developing network communi-cation culture requires an understanding of transformational processes, the development of existing models and criteria for assessing the level of its development in the new conditions of digital transformation of socie-ty. At present, the development of the network communicative culture of future teachers can be realized not only under the influence of a system of external factors, but also through methods of self-development. In pedagogy, the degree of productivity of education directly depends on the level of activity of educational and cognitive activity. This research was funded by the Science Committee of the Ministry of Education and Sci-ence of the Republic of Kazakhstan (NoAP13067801)
Advances In Intelligent Systems: From Artificial Intelligence To Machine Learning And Beyond

A documentary review was carried out on the production and publication of research papers related to the study of the variables INTELLIGENT SYSTEMS, ARTIFICIAL INTELLIGENCE and AUTOMATIC LEARNING. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period 2017-2022 by Latin American institutions, achieving the identification of 196 publications. The information provided by this platform was organized through graphs and figures categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors towards the proposed theme is referenced through a qualitative analysis. Among the main findings made through this research, it is found that Brazil with 104 publications was the Latin American country with the highest scientific production registered in the name of authors affiliated with institutions of that nation. The Area of Knowledge that made the greatest contribution to the construction of bibliographic material referring to the study of Intelligent Systems, Artificial Intelligence and Machine Learning was Computer Science with 151 published documents, and the Type of Publication most used during the period indicated above were Conference Articles with 55% of the total scientific production.
Hyperparameter Optimization and Neuroevolution with Binary Quadratic Meta-heuristics and Evolution Strategies

This study extends previous work on quantum-enhanced selection operators [Von Dollen et al.(2022)], where selection operators of evolutionary algorithms for classical black-box optimization are framed as quadratic binary optimization (QUBO) models, applied to black-box objective functions. We test our algorithms using both elitist and non-elitist strategies. First, we benchmark all heuristics over black-box, multi-modal functions. We find that for 94.4% of test cases, our method shows performance improvements with respect to fitness. Then, we similarly benchmark over the problem of hyperparameter selection for machine learning models, also a black-box optimization problem, and compare it to random search. We find that our method achieves some improvement with an average validation accuracy of 98.492% and highest ranking of 4.529412 (out of 5) when averaged across all test cases. We also apply our method towards neuroevolution, where we evolve weight spaces for small feedforward neural networks for learning tasks. In these settings, our method shows improved average reward in the reinforcement learning case and increased velocity in learning the XOR function.
Contextual Target-Specific Stance Detection on Twitter: Dataset and Method

To understand different aspects of online human behaviors, e.g., the public stances toward various social and political issues, contextual target-specific stance detection has become one of the most important studies on social media. Considering the lack of appropriate data for the studies of contextual target-specific stance detection on Twitter, which is one of the most popular online social platforms worldwide, we introduce CTSDT, a new dataset that consists of a large number of annotated target-specific conversations collected from Twitter. Furthermore, we propose a new contextual target-specific stance detection model called ConMulAttn, which is the first method that can learn both the contents of the posts and the concrete relationships between the posts in a conversation. We conduct extensive evaluation using CTSDT as well as another two popular datasets, CreateDebate and ConvinceMe, for contextual target-specific stance detection. The evaluation results validate the necessity of introducing our dataset CTSDT. Besides, according to the evaluation results, our proposed model ConMulAttn can outperform the state-of-the-art contextual target-specific stance detection method by up to 25% in F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score, indicating the effectiveness and superiority of our solution. Our study has the potential to assist policymakers in utilizing conversation data from online social platforms to efficiently gain real-time insights into public stances on target topics, such as vaccination.
Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization

Uplift modeling has shown very promising results in online marketing. However, most existing works are prone to the robustness challenge in some practical applications. In this paper, we first present a possible explanation for the above phenomenon. We verify that there is a feature sensitivity problem in online marketing using different real-world datasets, where the perturbation of some key features will seriously affect the performance of the uplift model and even cause the opposite trend. To solve the above problem, we propose a novel robustness-enhanced uplift modeling framework with adversarial feature desensitization (RUAD). Specifically, our RUAD can more effectively alleviate the feature sensitivity of the uplift model through two customized modules, including a feature selection module with joint multi-label modeling to identify a key subset from the input features and an adversarial feature desensitization module using adversarial training and soft interpolation operations to enhance the robustness of the model against this selected subset of features. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of our RUAD in online marketing. In addition, we also demonstrate the robustness of our RUAD to the feature sensitivity, as well as the compatibility with different uplift models.
Identifying Sick Cells From High-Resolution Solid-State Micropore Data

Abstract Early detection of diseases such as cancer can drastically improve prognosis and treatment. To this end, solid-state micropores can measure distinct mechanical properties of diseased cells from their translocation behavior — detected as pulses in the temporal data stream of ionic current — and help diagnose diseases at early stages. However, the obstacle in such approaches is that the accuracy of the sensor is affected by noise, making the pulse detection task too subjective. This is inefficient especially when the disease-relevant data is only a fraction of the total acquired data. Thus, it is important to intelligently automate the detection process to eliminate the noise and to identify useful patterns towards error-free decision-making in real-time. This work describes a pattern detection approach based on moving-average filtering, which mitigates the impact of noise. Moreover, a detection threshold is computed from the mean and standard deviation of the data. The threshold is then used to detect different types of pulses stemming from the healthy and diseased human cells when these translocate through micropores. Extent of smoothing is an important factor for the data: greater smoothing suppresses the noise but deteriorates the pulse shape and vice versa. Additionally, the design approach computes useful features of the detected data and delivers the results for real-time analysis. This can help physicians and scientists to change their strategies of diagnosis by providing a validation of manual reviews.
Desain UI/UX Aplikasi SEWA Menggunakan Metode Design Thinking

Stunting in toddlers is a child problem that is still a concern for the government. The government has implemented various policies in an effort to reduce cases of stunting under five in Indonesia. In line with increasingly advanced and rapid technological developments, this study aims to design the UI/UX of an early stunting detection application for toddlers to help posyandu cadres measure and detect early cases of stunting and malnutrition in toddlers. The research method used in UI/UX development is the Design Thinking method. While the application testing method uses the SEQ method. The results of usability testing use the SEQ method with a total of 6 respondents consisting of officers and parents, obtaining a SEQ value of 6.26. The test results above 5.5 mean that this application is easy to use and can provide solutions to existing problems. The Design thinking method is proven to be able to produce a good interface according to user needs.Keywords: UI/UX; User Interface; User Experience; Design Thinking; SEQ.
Named Entity Recognition for Code Review Comments

This paper addresses the problem of named entities recognition from source code reviews. The paper provides a comparative analysis of existing approaches and proposes its own methods to improve the quality of problem solving. Proposed and implemented improvements include: methods to deal with data imbalances, improved tokenization of input data, the use of large arrays of unlabeled data, and the use of additional binary classifiers. To assess quality, a new set of 3,000 user code reviews was collected and manually labeled. It is shown that the proposed improvements can significantly increase the performance measured by quality metrics, calculated both at the token level (+22%) and at the entire entity level (+13%).
A proposal to increase data utility on Global Differential Privacy data based on data use predictions

This paper presents ongoing research focused on improving the utility of data protected by Global Differential Privacy(DP) in the scenario of summary statistics. Our approach is based on predictions on how an analyst will use statistics released under DP protection, so that a developer can optimise data utility on further usage of the data in the privacy budget allocation. This novel approach can potentially improve the utility of data without compromising privacy constraints. We also propose a metric that can be used by the developer to optimise the budget allocation process.