Reviewerly/exHarmony huggingface dataset:
Managing the integration of teaching resources for college physical education using intelligent edge-cloud computing

Abstract These days, colleges and universities have accumulated many resources in teaching and scientific research due to the acceleration of education information in China. However, many teaching resources are in short supply due to a lack of standardized resource construction and the closeness of management methods. Physical education significant teaching resources in Chinese colleges and universities must be utilized. If not integrated, it would seriously restrict the development of physical education in China. However, the traditional management of physical education teaching resources tends to worsen the data management, which is easy to cause the loss of physical education teaching resources data. With the development of the Internet of Things (IoT), cloud computing, and other technologies, intelligent edge cloud computing can ensure the integrity of physical education teaching resources and improve utilization. In this paper, cloud computing is used to manage physical education teaching resources in colleges and universities, and virtualization technology is used to research physical education teaching resources. Moreover, a resource scheduling method is proposed to ensure equal load distribution across various edge resources. The proposed strategy also provides increased utilization levels for computing resources. The comparison between the sports teaching resources under cloud computing and traditional sports teaching resources found that cloud resource management's sharing degree of teaching equipment has increased by 20.6% compared with conventional resource management. The sharing degree of courses has increased by 16.5%, and the utilization rate of sports venues has increased by 27.1% compared with traditional resource management. The utilization rate of sports film and television materials increased by 30.7%. Teachers and students benefit significantly from college and university teaching resources in the context of cloud computing. It demonstrates how cloud computing may assist colleges and universities in managing and integrating their teaching resources more effectively. The management and integration of college sports resources through cloud computing can promote the mutual exchange of college resources and have practical significance for the development of college education.
Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review

BackgroundSignificant advancements in the field of information technology have influenced the creation of trustworthy explainable artificial intelligence (XAI) in healthcare. Despite improved performance of XAI, XAI techniques have not yet been integrated into real-time patient care.ObjectiveThe aim of this systematic review is to understand the trends and gaps in research on XAI through an assessment of the essential properties of XAI and an evaluation of explanation effectiveness in the healthcare field.MethodsA search of PubMed and Embase databases for relevant peer-reviewed articles on development of an XAI model using clinical data and evaluating explanation effectiveness published between January 1, 2011, and April 30, 2022, was conducted. All retrieved papers were screened independently by the two authors. Relevant papers were also reviewed for identification of the essential properties of XAI (e.g., stakeholders and objectives of XAI, quality of personalized explanations) and the measures of explanation effectiveness (e.g., mental model, user satisfaction, trust assessment, task performance, and correctability).ResultsSix out of 882 articles met the criteria for eligibility. Artificial Intelligence (AI) users were the most frequently described stakeholders. XAI served various purposes, including evaluation, justification, improvement, and learning from AI. Evaluation of the quality of personalized explanations was based on fidelity, explanatory power, interpretability, and plausibility. User satisfaction was the most frequently used measure of explanation effectiveness, followed by trust assessment, correctability, and task performance. The methods of assessing these measures also varied.ConclusionXAI research should address the lack of a comprehensive and agreed-upon framework for explaining XAI and standardized approaches for evaluating the effectiveness of the explanation that XAI provides to diverse AI stakeholders.
Possibilities and limitations of social media in education processes during the pandemic: The teachers perspective

The health emergency derived from the spread of COVID-19 led to the declaration of confinement for the protection of the population. During this temporary period, the educational centers of the Spanish state were forced to suspend face-to-face classes at all educational levels. To safeguard the teaching processes, educational centers and teachers relied on social media to continue their work. The objective of this study is to understand the possibilities and limitations of social media as the only means of communication in the educational and socialization processes of students from the perspective of teachers. The methodology used is based on the collection of data through a questionnaire distributed in the secondary education centers of the autonomous regions of the Basque Country and Navarra. The questionnaire was distributed electronically, respecting the anonymity of the teaching staff and the center in which they practice. The results reveal that the digital media the possibilities and limitations of these media in the teaching processes, showing that some of these are surmountable and others are not.
Derivative Based Nonbacktracking Real-World Regex Matching with Backtracking Semantics

We develop a new derivative based theory and algorithm for nonbacktracking regex matching that supports anchors and counting, preserves backtracking semantics, and can be extended with lookarounds. The algorithm has been implemented as a new regex backend in .NET and was extensively tested as part of the formal release process of .NET7. We present a formal proof of the correctness of the algorithm, which we believe to be the first of its kind concerning industrial implementations of regex matchers. The paper describes the complete foundation, the matching algorithm, and key aspects of the implementation involving a regex rewrite system, as well as a comprehensive evaluation over industrial case studies and other regex engines.
Training and Synthesis Based Vehicle Color Conver sion Technique Using GAN

While recent Convolutional Neural Networks (CNNs) for object detection have been substantially improved, they require a large amount of annotated data to further improve their accuracy to the level of human. Such annotated data is scarce. Manual annotation of object labels is a time consuming and expensive process. Recently, generative models are being employed to automate the manual annotation and produce diverse training data. This leads to an increase in the accuracy of the target model. This paper presents a method to train a Generative Adversarial Network (GAN) network to translate the vehicle colors in the given dataset. It generates augmented image data by translating the selected vehicle objects to 7 color domains with the least compromise in the quality of generated images. We demonstrate the result of training the proposed GAN model and performance of vehicle color translation using Comprehensive Cars (CompCars) dataset.
BotBuster: Multi-Platform Bot Detection Using a Mixture of Experts

Despite rapid development, current bot detection models still face challenges in dealing with incomplete data and cross-platform applications. In this paper, we propose BotBuster, a social bot detector built with the concept of a mixture of experts approach. Each expert is trained to analyze a portion of account information, e.g. username, and are combined to estimate the probability that the account is a bot. Experiments on 10 Twitter datasets show that BotBuster outperforms popular bot-detection baselines (avg F1=73.54 vs avg F1=45.12). This is accompanied with F1=60.04 on a Reddit dataset and F1=60.92 on an external evaluation set. Further analysis shows that only 36 posts is required for a stable bot classification. Investigation shows that bot post features have changed across the years and can be difficult to differentiate from human features, making bot detection a difficult and ongoing problem.
Literasi Numerasi pada Pembelajaran Tematik Terpadu di Kelas Rendah Sekolah Dasar

This study aims to describe numeracy literacy in integrated thematic learning in the lower grades of elementary school. The participants in this study were low grade students, that is grades 1,2 and 3 of Elementary School. This study uses a descriptive type of qualitative method. The type of data collected is the result of problem solving done by the participants. Data collection techniques in this study using tests. The test instrument consists of several literacy questions and numeracy questions. Based on the results of the research, students can work on literacy questions well even though the accuracy in working on the questions is still lacking, while in the matter of numeracy which is numeracy literacy errors are still found so that students' numeracy literacy skills still need to be improved.
Assessment of Bitcoin carbon footprint

Bitcoin is a breakthrough financial technology but a volatile asset in financial markets with a complex fundamental consensus algorithm (Proof-of-Work) limiting its large-scale adoption due to environmental-related issues. Hitherto, the role of its technical and infrastructural composition that drives carbon footprint from an ecological perspective is rarely discussed in the literature. Here, we use machine learning and econometric techniques to analyze the past, present, and future changes in Bitcoin's carbon footprint with daily data spanning July 18, 2010 to December 04, 2021. We document technical drivers, decomposition effects, causal nexus, and implications of the Bitcoin blockchain's increasing energy and carbon footprint. We show that Bitcoin's technical drivers could have potential impacts on Bitcoin's carbon footprint, and subsequently, global climate change. For example, the network's hashrate increases mining difficulty––thereby increasing Bitcoin's energy consumption and subsequently, carbon footprint. We observed a direct association between the marginal effect of block size and transaction count––implying that a higher block size improves transaction efficiency and then reduces Bitcoin's energy and carbon footprint. Besides, low mining difficulty increases market capitalization whereas increasing mining difficulty reduces bitcoin mining profit in the long run. This infers the reward for mining Bitcoin has a diminishing return in the long term. Thus, the adoption of advanced hardware for Bitcoin mining will spur energy and carbon intensity, yet will have a low return on investment. We highlight environmental regulations and regulatory changes that could limit Bitcoin's carbon footprint.
Copper Isotopes Used in Mineral Exploration

Abstract The use of copper isotopes related to ore deposit location and genesis has greatly expanded over the past twenty years. The isotope values in ores, rocks, soils, and water range greater than 10‰ and provide ample isotopic variation to identify and interpret complex geological process. From an exploration standpoint, the copper isotope values in waters, sulfides and weathered rocks vector to mineralization at depth. Ground and surface waters display the greatest potential for both green and brownfields exploration, whereas Fe-oxides and other related ore minerals isotope compositions for exploration are nascent. From an ore genesis perspective, the copper isotopes serve as a redox proxy to aid in unraveling magmatic and hydrothermal processes related to metal sulfide precipitation. In summary, the use of copper isotopic approaches by the mining industry are ideal as they point to processes directly related to the metal of economic interest and should be employed in all stages of the mine life from exploration to extraction, and to environmental monitoring post-mining activities.
LLMs' Capabilities at the High School Level in Chemistry: Cases of ChatGPT and Microsoft Bing Chat

This study evaluates the potential and challenges of large langue models (LLMs) for education in chemistry. Specifically, we analyze the performance of two state -of-the art of LLMs, ChatGPT and Microsoft Bing AI Chat, on a quiz dataset consisting of 200 multiple-choice questions in chemistry at the high school level. The results show that ChatGPT and Microsoft Bing AI Chat have limitations in answering questions at the application and high application levels. We also compare the scores of the LLMs models with Vietnamese students, indicating that their performance is still lower than the ability of Vietnamese students. The findings suggest that LLMs have great potential in assisting learning and teaching, but further development is needed to improve their ability to solve complex questions at the high application level.
Improved EfficientDet Algorithm for Basketball Players’ Upper Limb Movement Trajectory Recognition

Whether a basketball player’s movements are standard or not is a key indicator to evaluate a basketball player and a key point for basketball player training. In order to improve the training effect of basketball players, effective and fast accurate recognition of basketball players’ action trajectories is crucial. In order to solve the problems of inadequate extraction of limb action features and loss of detail information due to multiple scales of limb action features caused by traditional recognition algorithms, an action trajectory recognition algorithm based on the improved EfficientDet-D0 is proposed. The algorithm firstly adds a spatial attention mechanism in the backbone network of EfficientDet-D0, where can locate the limb action trajectory features in the image more accurately. Secondly, in the feature fusion network, in order to describe the high-frequency detail information lost by downsampling, the idea of Laplace pyramid is used to fuse the detail feature maps in the top-down fusion path, and cross-level connections are added to make full use of the feature information of different resolutions, so that the acquired high-level feature map information is richer. Finally, the whole network is trained using the migration learning technique and Adam optimizer. The experimental results show that the model achieves a PCP value (percentage correct parts) of 95.6% on the upper limb pose dataset and takes only 5.34 s for 1000 tests. Compared with the traditional algorithm, it has a higher accuracy rate and stronger robustness, with strong practical applicability, realizing the effective and accurate recognition of basketball players’ upper limb action trajectories in real time.
Threshold optimization and random undersampling for imbalanced credit card data

Abstract Output thresholding is well-suited for addressing class imbalance, since the technique does not increase dataset size, run the risk of discarding important instances, or modify an existing learner. Through the use of the Credit Card Fraud Detection Dataset, this study proposes a threshold optimization approach that factors in the constraint True Positive Rate (TPR) ≥ True Negative Rate (TNR). Our findings indicate that an increase of the Area Under the Precision–Recall Curve (AUPRC) score is associated with an improvement in threshold-based classification scores, while an increase of positive class prior probability causes optimal thresholds to increase. In addition, we discovered that best overall results for the selection of an optimal threshold are obtained without the use of Random Undersampling (RUS). Furthermore, with the exception of AUPRC, we established that the default threshold yields good performance scores at a balanced class ratio. Our evaluation of four threshold optimization techniques, eight threshold-dependent metrics, and two threshold-agnostic metrics defines the uniqueness of this research.
New possibilities for medical support systems utilizing artificial intelligence (AI) and data platforms

In Japan, there is a growing initiative to construct centralized databases and platforms that can aggregate and manage a vast range of medical, health, and caregiving data for research and analysis. Recent advancements in artificial intelligence (AI), particularly in general-purpose models like the Segment Anything model and Chat GPT, promise significant progress towards utilizing such data-rich platforms effectively for healthcare. Traditionally, AI has displayed superior performance by learning specific images or languages, but now it is advancing towards creating models capable of learning universal traits from images and languages by training on extensive datasets. The challenge lies in the fact that these general-purpose models are trained on data that does not sufficiently incorporate medical information, making their direct application to healthcare difficult. However, the introduction of data platforms can potentially solve this problem. This would lead to the development of universally applicable models to process medical images and AI assistants that can support both doctors and patients. These medical AI assistants can function as a "sub-doctor" with extensive knowledge, assisting in comprehensive analysis of symptoms, early detection of rare diseases, and more. They can also serve as an intermediary between the doctor and the patient, helping to simplify consultations and enhance patient understanding of medical conditions and treatments. By bridging this gap, the AI assistant can help to reduce doctors' workload, improve the quality of healthcare, and facilitate early detection and prevention in the elderly population.
Federated Generative Model on Multi-Source Heterogeneous Data in IoT

The study of generative models is a promising branch of deep learning techniques, which has been successfully applied to different scenarios, such as Artificial Intelligence and the Internet of Things. While in most of the existing works, the generative models are realized as a centralized structure, raising the threats of security and privacy and the overburden of communication costs. Rare efforts have been committed to investigating distributed generative models, especially when the training data comes from multiple heterogeneous sources under realistic IoT settings. In this paper, to handle this challenging problem, we design a federated generative model framework that can learn a powerful generator for the hierarchical IoT systems. Particularly, our generative model framework can solve the problem of distributed data generation on multi-source heterogeneous data in two scenarios, i.e., feature related scenario and label related scenario. In addition, in our federated generative models, we develop a synchronous and an asynchronous updating methods to satisfy different application requirements. Extensive experiments on a simulated dataset and multiple real datasets are conducted to evaluate the data generation performance of our proposed generative models through comparison with the state-of-the-arts.
Quantum Multi-Agent Meta Reinforcement Learning

Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing. Motivated by this, in this article we re-design multi-agent reinforcement learning (MARL) based on the unique characteristics of quantum neural networks (QNNs) having two separate dimensions of trainable parameters: angle parameters affecting the output qubit states, and pole parameters associated with the output measurement basis. Exploiting this dyadic trainability as meta-learning capability, we propose quantum meta MARL (QM2ARL) that first applies angle training for meta-QNN learning, followed by pole training for few-shot or local-QNN training. To avoid overfitting, we develop an angle-to-pole regularization technique injecting noise into the pole domain during angle training. Furthermore, by exploiting the pole as the memory address of each trained QNN, we introduce the concept of pole memory allowing one to save and load trained QNNs using only two-parameter pole values. We theoretically prove the convergence of angle training under the angle-to-pole regularization, and by simulation corroborate the effectiveness of QM2ARL in achieving high reward and fast convergence, as well as of the pole memory in fast adaptation to a time-varying environment.
Multi-Scale Control Signal-Aware Transformer for Motion Synthesis without Phase

Synthesizing controllable motion for a character using deep learning has been a promising approach due to its potential to learn a compact model without laborious feature engineering. To produce dynamic motion from weak control signals such as desired paths, existing methods often require auxiliary information such as phases for alleviating motion ambiguity, which limits their generalisation capability. As past poses often contain useful auxiliary hints, in this paper, we propose a task-agnostic deep learning method, namely Multi-scale Control Signal-aware Transformer (MCS-T), with an attention based encoder-decoder architecture to discover the auxiliary information implicitly for synthesizing controllable motion without explicitly requiring auxiliary information such as phase. Specifically, an encoder is devised to adaptively formulate the motion patterns of a character's past poses with multi-scale skeletons, and a decoder driven by control signals to further synthesize and predict the character's state by paying context-specialised attention to the encoded past motion patterns. As a result, it helps alleviate the issues of low responsiveness and slow transition which often happen in conventional methods not using auxiliary information. Both qualitative and quantitative experimental results on an existing biped locomotion dataset, which involves diverse types of motion transitions, demonstrate the effectiveness of our method. In particular, MCS-T is able to successfully generate motions comparable to those generated by the methods using auxiliary information.
Repair Is Nearly Generation: Multilingual Program Repair with LLMs

Most programmers make mistakes when writing code. Some of these mistakes are small and require few edits to the original program – a class of errors recently termed last mile mistakes. These errors break the flow for experienced developers and can stump novice programmers. Existing automated repair techniques targeting this class of errors are language-specific and do not easily carry over to new languages. Transferring symbolic approaches requires substantial engineering and neural approaches require data and retraining. We introduce RING, a multilingual repair engine powered by a large language model trained on code (LLMC) such as Codex. Such a multilingual engine enables a flipped model for programming assistance, one where the programmer writes code and the AI assistance suggests fixes, compared to traditional code suggestion technology. Taking inspiration from the way programmers manually fix bugs, we show that a prompt-based strategy that conceptualizes repair as localization, transformation, and candidate ranking, can successfully repair programs in multiple languages with minimal effort. We present the first results for such a multilingual repair engine by evaluating on 6 different languages and comparing performance to language-specific repair engines. We show that RING can outperform language-specific repair engines for three of these languages.