Reviewerly/exHarmony huggingface dataset:
Neural Network for Intelligent and Efficient Volleyball Passing Training

Passing is a relatively basic technique in volleyball. In volleyball passing teaching, training the correct passing technique plays a very important role. The correct pass can not only accurately grasp the direction of the ball point and the drop point but also effectively connect the defense and the offense. In order to improve the efficiency and quality of volleyball passing training, improve the precise extraction of sport targets, reduce redundant feature information, and improve the generalization performance and nonlinear fitting capabilities of the algorithm, this paper studies volleyball based on the nested convolutional neural network model and passing training wrong movement detection method. The structure of the convolutional neural network is improved by nesting mlpconv layers, and the Gaussian mixture model is used to effectively and accurately extract the foreground objects in the video. The nested multilayer mlpconv layer automatically learns the deep-level features of the foreground target, and the generated feature map is vectorized and input to the Softmax classifier connected to the fully connected layer for passing wrong behavior detection in volleyball training. Based on the detection of nearly 1,000 athletes’ action datasets, the simulation experiment results show that the algorithm reduces the acquisition of redundant information and shortens the calculation time and learning time of the algorithm, and the improved convolutional neural network has generalization performance and nonlinearity. The fitting ability has been improved, and the detection of abnormal volleyball passing behaviors has achieved a higher accuracy rate.
Multicriteria interpretability driven deep learning

Abstract Deep Learning methods are well-known for their abilities, but their interpretability keeps them out of high-stakes situations. This difficulty is addressed by recent model-agnostic methods that provide explanations after the training process. As a result, the current guidelines’ requirement for “interpretability from the start” is not met. As a result, such methods are only useful as a sanity check after the model has been trained. In an abstract scenario, “interpretability from the start” implies imposing a set of soft constraints on the model’s behavior by infusing knowledge and eliminating any biases. By inserting knowledge into the objective function, we present a Multicriteria technique that allows us to control the feature effects on the model’s output. To accommodate for more complex effects and local lack of information, we enhance the method by integrating particular knowledge functions. As a result, a Deep Learning training process that is both interpretable and compliant with modern legislation has been developed. Our technique develops performant yet robust models capable of overcoming biases resulting from data scarcity, according to a practical empirical example based on credit risk.
Teacher Training for Educational Change: The View of International Experts

Digital competence has become a new type of literacy that significantly impacts on people's lives, as it is paramount to their social and occupational integration. In educational institutions, teachers play a major role in developing the digital skills of their students. This study aims to determine the key elements of teacher training in the use of innovative technologies and methodologies with a view to providing alternatives to improve the educational process. The objective is to overcome deficiencies in the training of teachers in new technologies and innovative methodologies. A qualitative study based on interview data is used in two phases. For the first phase research, semi-structured interviews were conducted with eight international education experts from various fields on how best to train teachers. The second phase research involved a text mining analysis of the interview transcripts based on sentiment analysis and word counts using the advanced R statistical programming language. The second phase research was conducted using the R statistical programming language. The interview transcripts were analysed using sentiment analysis and a word cloud. The results reveal that teacher education should be practice-focused and supervised by senior teachers with the aid of students and heads of schools. Moreover, standards should be set to monitor the digital competence of teachers, who should be trained on an ongoing basis to keep them up to date in the use of digital technologies.
Adiabatic Quantum Computing for Solving the Multi-Target Data Association Problem

Quantum computing promises significant improvements of computation capabilities in various fields such as machine learning and complex optimization problems. Recent technological advancements suggest that the adiabatic quantum computing ansatz may soon see practical applications. In this work, we adopt this computation paradigm to develop a quantum computation based solver of the well-known multi-target data association (MTDA) problem, a complex nonlinear integer programming optimization task. The feasibility of the presented model is demonstrated by numerical simulation of the adiabatic evolution of a system of quantum bits towards the optimal solution encoded in the model Hamiltonian.
Anchor Points Based Accurate Fault Locating in Large-Scale Photovoltaic Plants via Aerial Infrared Videos

For large-scale photovoltaic plants, accurate fault locating provides strong support for efficient maintenance. Based on aerial infrared videos, a progressive locating method is proposed to locate faults in the photovoltaic plants. The photovoltaic string is first located, and the faults are located on the modules of the corresponding photovoltaic string. For the former, the image extracted from the infrared video is binarized in HSV color space, and then the target pixels are horizontally and vertically projected to realize photovoltaic string segmentation and locating. For the latter, the faults are first located in the local coordinate system of each frames and then located in the overall coordinate system of the video by conversion. The conversion is realized by the matching of the anchor points in adjacent frames. The anchor points are detected by AKAZE algorithm and the matching is achieved by Lucas–Kanade sparse optical flow algorithm. The overall coordinate expression of the fault is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(R, C, (x, y))$</tex-math></inline-formula> that consists of the row index number <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> of the photovoltaic string in the photovoltaic plant, the column index number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C$</tex-math></inline-formula> of the photovoltaic string in the current row <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R$</tex-math></inline-formula> , and the faulty module location <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(x, y)$</tex-math></inline-formula> on the photovoltaic string. Infrared videos of a 30-MW photovoltaic plant are tested, and the average accuracy of locating was 97.5%. Compared with nonprogressive locating method, the average accuracy of locating is higher by 17.5%. It can be seen that the proposed progressive locating method can accurately locate faults in large-scale photovoltaic plants.
Optimizing combinational logic circuits using Grammatical Evolution

This paper applies Grammatical Evolution (GE) to the optimization of combinational logic circuits on gate-level logic. We demonstrate the ability of GE to evolve complex combinational circuits using gate-level combinational logic and show that GE can similarly provide optimized solutions for different digital circuit problems at the gate level. Our methodology is applied to the Advanced Encryption standard (AES) S-box building blocks and the results compared to other evolutionary algorithms. Our results show comparable results with traditional Genetic Algorithm (GA) and Cartesian Genetic Programming (CGP).
T2L2: A Tiny Three Linear Layers Model for Service Mashup Creation

Mashup creation is a classic problem in service computing and can be solved using service recommendation approaches. There are many service recommendation studies and have achieved remarkable results. However, there is a growing tendency for these studies to use multiplex data and more complicated models to improve the performance of recommendations, especially after the emergence of deep learning. This trend has led to a heavy reliance on computational resources and an increased cost of data acquisition, which limits the practical use of these methods, but the performance gains are still very limited. In this paper, we improve recommendation performance by rethinking the characteristics of the data in the mashup creation scenario, i.e. representation heterogeneity between services and mashup, rather than the use of multiplex data and more complicated models. To achieve this, we propose a Tiny Three Linear Layers (T2L2) model. T2L2 is a tiny model with three linear layers requiring only requires functional descriptions of services and mashups as input. The first two linear layers are used to align the representation space of services and mashups. The last linear layer is used to calculate the matching scores of services and mashups. Extensive experiments conducted on a real-world dataset from ProgrammableWeb show that T2L2 outperforms existing state-of-the-art methods in commonly-used evaluation metrics with a significant reduction in model complexity and required data.
A Study on the Differences of Online and Offline Classes in College Beauty Education Caused by COVID-19

This paper study the difference of online and offline classes in college beauty education caused by COVID-19 to respond the post-Corona era, and to use it as an opportunity for educational innovation. For this study, the questionnaire was approved by the Dongnam Health University Institutional Bioethics Committee on February 19, 2021 (1044371 202102-HR-001-01), and a survey was conducted using the Naver office form to 448 students and graduates of the beauty-related departments of colleges in the metropolitan area who experienced online and offline classes due to COVID-19 from April 1st to April 18th, 2021. For statistical methods, frequency analysis, paired t-test, reliability analysis, and χ&lt;sup&gt;2&lt;/sup&gt;-test were used. The result are as follows; First, the difference in class satisfaction was significantly higher in online class at theoretical subjects, but in offline class at practical subjects. Second, the difference in immersion was significantly higher in offline classes for both theoretical and practical subjects. Third, the interaction between professor-students and student-student was significantly greater in offline classes in both theory and practice subjects. In conclusion, it is better to use online classes appropriately for theoretical subjects and offline classes for practical subjects.
Towards the development of an effective image watermarking system

Abstract Digital data communication or sharing has increased day by data because of excessive usage of the Internet. It is necessary to protect digital data from unauthorized access. However, it is very difficult to ensure the authenticity of the host data. Digital image watermarking is a significant technology for ensuring this authentication. Existing hybrid domain algorithms are not effective enough to overcome the potential challenges of watermarking techniques. Hence, the current image watermarking technology is devoted toward developing more effective methods with optimization and machine‐learning algorithms for ensuring a better trade‐off among the basic design requirements like imperceptibility, robustness, and capacity simultaneously in addition to security that can be confirmed with lightweight image encryption algorithms. In this research, a framework of standard image watermarking system for ensuring the basic design requirements is given, the existing literature is studied, and the recent trends of image watermarking techniques and their applications are also described to investigate the performance of the state‐of‐the‐art methods along with their limitations. Finally, we have concluded our study by pointing to new research directions.
A Hybrid Salp Swarm Algorithm with Artificial Neural Network Model for Predicting the Team Size Required for Software Testing Phase

The software project director has to keep estimating the required resources and planning the schedule for deliverables. Unfortunately, such estimation and planning are not accurate unless careful monitoring and control plan is maintained because software development is risky. In this paper, an investigation was carried out by integrating a promising metaheuristic algorithm with an artificial neural network to optimize the network parameters to address predicting the size of a software test team. The target of this integration was to enhance the accuracy of network prediction. The proposed method has been evaluated on two datasets. These datasets have different characteristics and have been extracted from the industry repository. The comparative results proved the superiority of the proposed method over the other methods.
Blockchain-Based Authentication and Registration Mechanism for SIP-Based VoIP Systems

The Session Initiation Protocol (SIP) is the principal signalling protocol in Voice over IP (VoIP) systems, responsible for initialising, terminating, and maintaining sessions amongst call parties. However, the problem with the SIP protocol is that it was not designed to be secure by nature as the HTTP digest authentication used in SIP is insecure, making it vulnerable to a variety of attacks. The current solutions rely on several standardised encryption protocols, such as TLS and IPsec, to protect SIP registration messages. However, the current centralised solutions do not scale well and cause algorithm overload when encoding and decoding SIP messages. In trying to rectify this issue, we propose in this paper a blockchain-based lightweight authentication mechanism, which involves a decentralised identity model to authenticate the SIP client to the SIP server. Our mechanism uses a smart contract on the Ethereum blockchain to ensure trust, accountability and preserves user privacy. We provided a proof-of-concept implementation to demonstrate our work. Further analysis of this approach&#x0027;s usability, mainly CPU and memory usage, was conducted comparing to IPsec and TLS. Then we discussed our system&#x0027;s security and presented a security analysis. Our analysis proves that our approach satisfies the SIP protocol security requirements.
REGINA—Reasoning Graph Convolutional Networks in Human Action Recognition

It is known that the kinematics of the human body skeleton reveals valuable information in action recognition. Recently, modeling skeletons as spatio-temporal graphs with Graph Convolutional Networks (GCNs) has been reported to solidly advance the state-of-the-art performance. However, GCN-based approaches exclusively learn from raw skeleton data, and are expected to extract the inherent structural information on their own. This paper describes REGINA, introducing a novel way to REasoning Graph convolutional networks IN Human Action recognition. The rationale is to provide to the GCNs additional knowledge about the skeleton data, obtained by handcrafted features, in order to facilitate the learning process, while guaranteeing that it remains fully trainable in an end-to-end manner. The challenge is to capture complementary information over the dynamics between consecutive frames, which is the key information extracted by state-of-the-art GCN techniques. Moreover, the proposed strategy can be easily integrated in the existing GCN-based methods, which we also regard positively. Our experiments were carried out in well known action recognition datasets and enabled to conclude that REGINA contributes for solid improvements in performance when incorporated to other GCN-based approaches, without any other adjustment regarding the original method. For reproducibility, the REGINA code and all the experiments carried out will be publicly available at https://github.com/DegardinBruno.
Towards cheat-preventing in block-based progressive visual cryptography for general access structures

Existing block-based progressive visual cryptography schemes (BPVCS) that prevent cheating are limited to ( 2 , n ) and ( k , n ) thresholds. They are unsuitable for implementing complex sharing techniques in the actual world. A cheating immune BPVCS for general access structure (GAS) is proposed in this paper. To find the parameters that make up the proposed scheme, a simplified multiple assignment procedure based on integer linear programming is introduced. The secret parts can be decrypted by the members of a qualified set. Participants in a forbidden set, on the other hand, are not allowed to know about the secret. The proposed scheme includes cheat-prevention and progressive recovery capabilities. In addition, to generate meaningful shadows for BPVCS, a modified extended VCS (EVCS) is proposed. Our modified EVCS can solve the cover-interference problem in conventional BPVCS. The effectiveness and benefits of the proposed BPVCS are supported through the experimental findings and comparisons.
Photographic application of the Coral Health Chart in turbid environments: The efficiency of image enhancement and restoration methods

Monitoring bleaching provides an indication of coral reef degradation and is thus a reliable tool for coral reef health assessment. Underwater photography of coral colonies integrated with the use of the CoralWatch Coral Health Chart is a non-destructive method for monitoring bleaching events although it has shortcomings in turbid waters due to the low quality of the resulting images. In this study, various image enhancement and restoration methods were assessed to choose the most precise method that could improve the Coral Health Chart readings in the turbid reefs of Chabahar Bay, a monsoonal area with constant sediment and nutrient input. In doing so, the print edition of the Coral Health Chart was fixed on randomly chosen merulinid corals and then photographed. Images were processed by different approaches (such as pseudo coloring, Retinex, contrast limited adaptive histogram equalization [CLAHE], and LAND & SEA SCUBA software) after which some image quality indices were assessed and compared between images. To test the reliability of each method in Coral Health Chart readings, volunteers were asked to complete a questionnaire and assign the color of selected areas of corals to a category of the chart. According to the results, the Retinex-enhanced images had the highest quality and reliability in the Coral Health Chart readings. The good performance of the Retinex method was found in the enhancement of sharpness and contrast, which corrected the haze, reduced color distortion, and retrieved lost details. Therefore, we propose Retinex-enhanced images to be used for bleaching monitoring using the Coral Health Chart in turbid environments.
Leveraging Intermediate Artifacts to Improve Automated Trace Link Retrieval

Software traceability establishes a network of connections between diverse artifacts such as requirements, design, and code. However, given the cost and effort of creating and maintaining trace links manually, researchers have proposed automated approaches using information retrieval techniques. Current approaches focus almost entirely upon generating links between pairs of artifacts and have not leveraged the broader network of interconnected artifacts. In this paper we investigate the use of intermediate artifacts to enhance the accuracy of the generated trace links - focusing on paths consisting of source, target, and intermediate artifacts. We propose and evaluate combinations of techniques for computing semantic similarity, scaling scores across multiple paths, and aggregating results from multiple paths. We report results from five projects, including one large industrial project. We find that leveraging intermediate artifacts improves the accuracy of end-to-end trace retrieval across all datasets and accuracy metrics. After further analysis, we discover that leveraging intermediate artifacts is only helpful when a project's artifacts share a common vocabulary, which tends to occur in refinement and decomposition hierarchies of artifacts. Given our hybrid approach that integrates both direct and transitive links, we observed little to no loss of accuracy when intermediate artifacts lacked a shared vocabulary with source or target artifacts.
Load data recovery method based on SOM-LSTM neural network

In the collection and transmission of power big data, the problem of data missing exists. In response to this problem, this paper proposes a power data detection and repair method based on SOM-LSTM. Firstly, a large amount of collected power data is analyzed and the type of missing data is determined. Then, the SOM is used to classify the power data. The LSTM is trained according to the characteristic values of different users to complete the detection and repair of different types of missing power data. Finally, the analysis is based on actual data in some regional loads of China. Experimental results show that, compared with the extreme learning machine (ELM) and LSTM, the proposed SOM-LSTM model reduces the mean absolute error (MAE) by 0.2498 and 0.3425, and the root mean square error (RMSE) by 0.1048 and 0.1469, respectively.
Refining User Stories via Example Mapping: An Empirical Investigation

New techniques for managing, specifying, and analyzing requirements in software engineering projects are frequently presented by consultants and agile trainers. However, the effectiveness of these techniques is not evaluated in a rigorous manner, leaving practitioners with the question “Will it work in our company?” In this paper, we investigate the performance of a user story refinement technique named Example Mapping (EM). This is a time-boxed workshop in which people from different disciplines work collaboratively in order to refine, or clarify, a user story with the use of examples. The creation of such examples is intended not only to obtain a more precise specification, but also and mostly to achieve shared understanding on the user story to develop among the team members. We investigate the performance of EM via two longitudinal case studies. To enable a rigorous validation of EM, we first define the Refinement Evaluation Tool (RET), a survey-based measurement instrument that extends the Method Evaluation Model with questions that cover the shared understanding dimension. The results from our case studies show that EM contributes to the shared understanding within a team; certain conditions are necessary: the user stories should not be too small-sized. We also investigated the learning effect for EM; our data indicates that two sessions are generally necessary for the team members to use the technique effectively.