Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Cloud Edge Fusion Computing Platform for the Smart Grid

With the development of the smart grid, the number of access equipment capacity reached more than 100 million, resulting in long delays in the transmission of a large amount of data obtained from the terminal side to the cloud computing center. The emergence of powerful edge computing can greatly reduce the propagation delay, improve the management efficiency of IoT equipment in the smart grid, and reduce the communication delay between devices. However, due to decentralization, classical edge computing platforms face management and security challenges. In this paper, we propose a blockchain-based cloud edge fusion computing (BCEC) platform for controlling the smart grid, which integrates blockchain techniques to ensure the safety of the smart grid equipment. Specifically, we propose a three-level five-dimension credit scoring algorithm to ensure the security of the system. Through this algorithm, cloud computing and edge computing are combined, and the credit score of terminal equipment is allocated, taking into account the participation and security. Finally, we propose a time greedy allocation algorithm based on the Gini coefficient as a scheduling algorithm to ensure the fairness of system allocation and minimize the allocation delay. We deploy BCEC in the simulation environment for experimentation and the results show that BCEC can provide calculation services with low latency and high fairness for the smart grid system.
Tennis Motion Precision Video Monitoring Simulation Based on Artificial Intelligence Algorithm

With the continuous development of computer algorithms, artificial intelligence algorithms are also developing vigorously. With the development of supercomputer technology, algorithms applied in various fields are also in vogue. Tennis motion precision video monitoring simulation based on artificial intelligence algorithm is also constantly improved on the basis of the development of the algorithm, this progress is based on the progress of scientific theory, but also the result of intelligent people continue to strengthen their academic level, specialized in technical algorithm. Tennis motion precision video monitoring simulation based on artificial intelligence algorithm is an advanced video detection technology, its effect lies in the fine processing of sports items, sports items from a smaller dimension will be detected in a high precision way for video processing. This paper studies the relevant knowledge theory of tennis motion accuracy video monitoring simulation, related content of artificial intelligence algorithm, and introduces the related knowledge field of tennis motion accuracy video detection. Tennis movement accuracy based on artificial intelligence algorithm simulation video monitoring, this paper use the data to illustrate the tennis movement precision video detection of a variety of advantages, from many aspects confirmed the tennis movement precision video detection based on artificial intelligence algorithm simulation in many aspects of high efficiency, entered data test, proved that the precision in image, image resolution, Fluency and fidelity of 85.98%, 90.14%, 93.06% and 98.25% efficiency.
Reproduction of human capital for efficient digital development (“smart city”)

This research aims to identify professions necessary for the formation of smart cities, as well as risks associated with the emergence of such entities. We applied a market (comparative) approach for the analysis of digital technologies and professions suitable for Industry 4.0. It was found that a “smart city” has several interrelated dimensions: “smart economy”, “smart environment”, “smart people” and “smart technologies”. In order to reduce risks when building such a complex system, significant changes to the education system are required. Due to these changes, many professions and industries involved in the field of digitalization and “smart cities” will be created. These professions may range from specialists in bioinformatics and operators, who form and apply computational methods, to those working in the field of cyber pharmacy with big data. Planning of “smart cities” requires improved living standards, including energy efficient utilities, educational performance, public safety, etc. To implement the concept of “smart city” in a comprehensive way, it is important to provide highquality training and retraining of specialists, introduce new specialties in universities, generate employment mainly in smart working and establish an ongoing environment, including the business one, for boosting innovations.
Fine-Tuning a Pre-trained Transformer-Based Encoder-Decoder Model with User-Generated Question-Answer Pairs to Realize Character-Like Chatbots

In order to realize character-like chatbots, it is necessary to collect dialogue data of particular characters. However, collecting such data is not an easy task. To solve this problem, we have previously proposed a method called “Role-play-based question answering” in which many users play the role of a particular character to answer questions, resulting in a large number of question-answer (QA) pairs associated with that character. In this study, we investigated how character-like dialogue could be realized by fine-tuning a pre-trained Transformer-based encoder-decoder model, which has shown its effectiveness in dialogue modelling, with the QA pairs collected via role-play-based question answering. The results of automatic and manual evaluations show that, with the fine-tuned model, it is possible to significantly outperform a retrieval-based baseline and that, with 44 K QA pairs, it is possible to achieve high naturalness and characterness scores.
Explicit Sparse Attention Network for Multimodal Named Entity Recognition

Multi-modal named entity recognition (MNER) is a multi-modal task aim to discover named entities in text with visual information. Existing MNER approaches model dense interactions between visual objects and textual words by designing co-attention mechanisms to achieve better accuracy. However, mapping interactions between each semantic unit (visual object and textual word) will force the model to calculate irrelevant information, which results in the model’s attention to be distracted. In this paper, to tackle the problem, we propose a novel model which concentrates the model’s attention by explicitly selecting the most relevant segments to predict entities. This method based on top-k selection can reduce the interference caused by irrelevant information and ultimately help the model to achieve better performance. Experimental results on benchmark dataset demonstrate the effectiveness of our MNER model.
ReMac

Distributed matrix computation solutions support query interfaces of linear algebra expressions, which often contain redundancy, i.e., common and loop-constant subexpressions. However, existing solutions fail to find all redundant subexpressions. Moreover, eliminating the found redundancy leads to new execution order of operators, which may have side effect. To exploit the benefits of redundancy elimination, we propose a new system called ReMac , which performs automatic and adaptive elimination. In particular, automatic elimination adopts a block-wise search that exploits the properties of matrix computation for speed-up. Adaptive elimination employs a cost model and a dynamic programming-based method to generate efficient plans with redundancy elimination. In this demonstration, attendees will have an opportunity to experience the effect that automatic and adaptive elimination have on distributed matrix computation.
Application Analysis of Virtual Reality Integration Environment Based on VR Technology in Physical Education Teaching

It is a major trend for the development of physical education to apply the virtual and real environment to physical education. This research adopts the literature research method and the experimental research method. Taking the fixed-point shooting teaching in basketball teaching as an example, the 3DMAX modeling software is used to construct the virtual situation, realize the integration of the virtual teacher model, animation and scene, and use the “extreme key” function in Adobe Premiere software in the virtual reality fusion design to integrate the teacher and the virtual teaching situation, and create a real-time, three-dimensional and interactive teaching video, Carried out physical education teaching design and experimental analysis based on VR technology, and fully verified the effectiveness of the virtual reality fusion environment in physical education teaching.
Machine learning‐based methods for joint detection‐channel estimation in <scp>OFDM</scp> systems

In this work, two machine learning (ML)‐based structures for joint detection‐channel estimation in OFDM systems are proposed and extensively characterized. Both ML architectures, namely Deep Neural Network (DNN) and Extreme Learning Machine (ELM) are developed for provide improved data detection performance and compared with the conventional matched filter (MF) detector equipped with the minimum mean square error (MMSE) and least square (LS) channel estimators. The bit‐error‐rate (BER) performance vs computational complexity trade‐off is analyzed, demonstrating the superiority of the proposed DNN‐OFDM and ELM‐OFDM detectors methodologies.
A Multi-Task Multi-Stage Transitional Training Framework for Neural Chat Translation

Neural chat translation (NCT) aims to translate a cross-lingual chat between speakers of different languages. Existing context-aware NMT models cannot achieve satisfactory performances due to the following inherent problems: 1) limited resources of annotated bilingual dialogues; 2) the neglect of modelling conversational properties; 3) training discrepancy between different stages. To address these issues, in this paper, we propose a multi-task multi-stage transitional (MMT) training framework, where an NCT model is trained using the bilingual chat translation dataset and additional monolingual dialogues. We elaborately design two auxiliary tasks, namely utterance discrimination and speaker discrimination, to introduce the modelling of dialogue coherence and speaker characteristic into the NCT model. The training process consists of three stages: 1) sentence-level pre-training on large-scale parallel corpus; 2) intermediate training with auxiliary tasks using additional monolingual dialogues; 3) context-aware fine-tuning with gradual transition. Particularly, the second stage serves as an intermediate phase that alleviates the training discrepancy between the pre-training and fine-tuning stages. Moreover, to make the stage transition smoother, we train the NCT model using a gradual transition strategy, i.e., gradually transiting from using monolingual to bilingual dialogues. Extensive experiments on two language pairs demonstrate the effectiveness and superiority of our proposed training framework.
Feedback on the Result of Online Learning of University Students of Health Sciences

Faced with the challenge of online teaching-learning, university teachers continued with the responsibility of developing their learning sessions, innovating teaching material and methodology during this process, changing the way of generating learning in health sciences students, through the application of videos, summary readings and practices carried out with family members who acted as patients, in order to achieve the planned competition. The importance of letting students know their achievements in relation to what is evaluated, helps them to understand their way of learning, assess their learning result and self-regulate. This is how feedback motivates the student to rethink their learning strategies. The purpose of this study was to determine the effect of feedback on the online learning outcome of health sciences university students, in a non-experimental research, descriptive-correlational level, with a sample of 294 students. The results obtained showed that feedback in university students of Health Sciences in virtual environments is effective when applied in a timely manner and can be planned, based on the evidence of the learning outcome. To achieve this, they must be previously trained, from the first semesters of study, in feedback literacy, making it part of the self-regulation of their learning.
System design and error correction of simultaneous phase-shifting point-diffraction interferometer for dynamic wavefront detection with 400 mm aperture

In this paper, a simultaneous phase-shifting point-diffraction interferometer (SPS-PDI) at 632.8 nm is designed with the assistance of an off-axis parabolic mirror (OAPM), through which the dynamic wavefront with 400 mm aperture can be detected. In the system, a polarization point-diffraction plate (P-PDP) is developed to modulate the polarization states of the reference light and the test light through a simultaneous phase-shifting system based on a chessboard phase grating and a retarder array, and four phase-shifting interferograms can be acquired to realize dynamic detection. Furthermore, the circular carrier squeezing interferometry (CCSI) is proposed to suppress the phase errors generated by position mismatch, intensity distortion, and phase-shift error. The detection result of the SPS-PDI is consistent with the 4D PhaseCam6000 dynamic interferometer. The difference of the peak-to-valley (PV) and root-mean-square (RMS) values are only 0.04λ and 0.008λ. Additionally, the capacity to detect dynamic wavefront is good.
An adaptive spatiotemporal correlation filtering visual tracking method

Discriminative correlation filter (DCF) tracking algorithms are commonly used for visual tracking. However, we observed that different spatio-temporal targets exhibit varied visual appearances, and most DCF-based trackers neglect to exploit this spatio-temporal information during the tracking process. To address the above-mentioned issues, we propose a three-way adaptive spatio-temporal correlation filtering tracker, named ASCF, that makes fuller use of the spatio-temporal information during tracking. To be specific, we extract rich local and global visual features based on the Conformer network, establish three correlation filters at different spatio-temporal locations during the tracking process, and the three correlation filters independently track the target. Then, to adaptively select the correlation filter to achieve target tracking, we employ the average peak-to-correlation energy (APCE) and the peak-to-sidelobe ratio (PSR) to measure the reliability of the tracking results. In addition, we propose an adaptive model update strategy that adjusts the update frequency of the three correlation filters in different ways to avoid model drift due to the introduction of similar objects or background noise. Extensive experimental results on five benchmarks demonstrate that our algorithm achieves excellent performance compared to state-of-the-art trackers.
Lessons learned from independent external validation of an AI tool to detect breast cancer using a representative UK data set

Objective: To pilot a process for the independent external validation of an artificial intelligence (AI) tool to detect breast cancer using data from the NHS breast screening programme (NHSBSP). Methods: A representative data set of mammography images from 26,000 women attending 2 NHS screening centres, and an enriched data set of 2054 positive cases were used from the OPTIMAM image database. The use case of the AI tool was the replacement of the first or second human reader. The performance of the AI tool was compared to that of human readers in the NHSBSP. Results: Recommendations for future external validations of AI tools to detect breast cancer are provided. The tool recalled different breast cancers to the human readers. This study showed the importance of testing AI tools on all types of cases (including non-standard) and the clarity of any warning messages. The acceptable difference in sensitivity and specificity between the AI tool and human readers should be determined. Any information vital for the clinical application should be a required output for the AI tool. It is recommended that the interaction of radiologists with the AI tool, and the effect of the AI tool on arbitration be investigated prior to clinical use. Conclusion: This pilot demonstrated several lessons for future independent external validation of AI tools for breast cancer detection. Advances in knowledge Knowledge has been gained towards best practice procedures for performing independent external validations of AI tools for the detection of breast cancer using data from the NHS Breast Screening Programme.
Monocular Supervised Metric Distance Estimation for Autonomous Driving Applications

The task of monocular distance estimation is a major area of research in the computer vision field. Especially relevant this task is to the autonomous driving applications, where robustness and accuracy of the distance estimation significantly affect driving safety. In this paper we propose a simple, fast and efficient deep learning model capable of extracting distance information for a detected object from monocular images. The model is trained and tested on the KITTI benchmark and compared to the Monodepth2 model. The conducted experiments show that the proposed convolutional neural network architecture outperforms Monodepth2 by 11% on average according to the weighted average mean absolute error.
An Exploration on a Framework of Discussion-based Instruction Complying with the Traits of Introverted Learners

The purpose of this study is to investigate the characteristics of introverted students represented in discussion-based instruction and to suggest a conformable model for discussion-based instruction, making it possible for all the learners to be reciprocal. Based on the extensive literature review for the purpose, this study describes specifically that the introversion is not an recessive trait, but an estimable personality contrary to the extroversion in classroom discussion. Furthermore, it ascertains that introverted learners possess comparative advantages in terms of intelligences, capabilities, thinking modalities, and learning behaviors than the extroverted. In accordance with the introverted personality and competencies manifested in discussion-based instruction, a framework of the instructional strategies is prescribed in detail as follows; 1) diversification of the expression, 2) individualization of the participation, 3) activation of online discussion, and 4) adaptive evaluation. In addition, it proposes the actionable methods for each phase of the instructional strategies. The researcher cordially expects that the result of this study could become a fruitful contribution for proper understanding of the introverted students and designing the commensurate instruction strategies which would facilitate the potentials of the introverted and confer a benefit on the extroverted concurrently in discussion-based instruction. Finally, limitations of the research are identified. According to the constraints, it brings up the necessities of follow-up research.
Hackathon as a technology of forming a socially successful personality of a pupil

The purpose of the article is to substantiate the instrumental and technological potential of the hackathon as a means of developing quality project products by stakeholder teams and a platform for solving various problems of the modern education system; to highlight the results of the application of educational hackathon technology in the practice of constructing a safe educational environment as a space for the formation of socially successful personality of a pupil. The system-structural analysis of foreign and domestic psychological and pedagogical sources concerning the problem of preparation and implementation of hackathon technology in the educational practice of general secondary education was chosen as theoretical methods, as well as theoretical generalization, comparison and reinterpretation of data to formulate conclusions of conducted research. Empirical methods were event analysis of educational practice and content analysis of normative-legal acts and activity reports of institutions of general secondary education, which direct and regulate the interaction of administration, teachers, pupils and their parents, social partners of educational institutions concerning pupil development. Analysis of the content of modern scientific sources dedicated to understanding the potential of hackathon technology in the formation of socially successful personality of the student, allowed to obtain the following results. The spread of hackathon technology in various fields – from business to education – demonstrates its exceptional effectiveness as a means of solving problems in a dynamic environment. A reinterpretation of previous research findings and the results of our research proves that the hackathon is in fact a platform for potentially successful startups that brings together interested talented and active people, as it results in a ready-to-run minimum viable product, its first version is tested by a jury, during demofest (demonstration of an idea) and pitching (short verbal presentation) of projects. The toolkit of educational hackathons works effectively in solving various pedagogical tasks, in particular, in building dialogue between participants in the educational process, adjusting teamwork in forming a socially successful personality, involving a wide range of social partners in this process. The multimedia, integration and versatility of this technology contribute to the successful implementation of the hackathon. The introduction of the hackathon in the educational process activates the key competencies of its participants, including social, civic, information and communication, etc., and promotes analytical, critical and creative thinking, social partnership skills, social activity and social mobility – those powerful components of socially successful individuals who are able to ensure their own quality of life and the realization of a growing personality.