Reviewerly/exHarmony huggingface dataset:
Research on predictive control of students’ performance in PE classes based on the mathematical model of multiple linear regression equation

Abstract Aiming to solve the problems in the traditional multiple regression analysis model for predicting college sports performance based on the principles of econometrics, a predictive model that combines genetic algorithm (GA), college sports performance evaluation and regression analysis is proposed. GA is used to conduct dynamic and supervised optimisation evaluation of college sports performance; on this basis, combined with regression analysis and GA's global optimisation capabilities, a complex nonlinear relationship between student sports performance and influencing factors is established; the student's performance is calculated based on the college sports performance. The results show that the method has high prediction accuracy and good stability.
Hate speech targets in COVID-19 related comments on Ukrainian news websites

The research focuses on hate speech in the comments section of Ukrainian news websites. Restricted to solely COVID-19 related comments, it seeks to analyze the development of hate speech rates throughout the pandemic. Using a semi-automated machine-learning-aided approach, the paper identifies hate speech in the comments and defines its main targets. The research shows that a crisis like the COVID-19 pandemic can strengthen existing negative stereotypes and gives rise to new forms of stigmatization against social and ethnic groups.
An Unsupervised Convolution Neural Network for Deformable Registration of Mono/Multi-Modality Medical Images

Image registration is a fundamental and crucial step in medical image analysis. However, due to the differences between mono-mode and multi-mode registration tasks and the complexity of the corresponding relationship between multimode image intensity, the existing unsupervised methods based on deep learning can hardly achieve the two registration tasks simultaneously. In this paper, we proposed a novel approach to register both mono- and multi-mode images $\color{blue}{\text{in a differentiable }}$. By approximately calculating the mutual information in a $\color{blue}{\text{differentiable}}$ form and combining it with CNN, the deformation field can be predicted quickly and accurately without any prior information about the image intensity relationship. The registration process is implemented in an unsupervised manner, avoiding the need for the ground truth of the deformation field. We utilize two public datasets to evaluate the performance of the algorithm for mono-mode and multi-mode image registration, which confirms the effectiveness and feasibility of our method. In addition, the experiments on patient data also demonstrate the practicability and robustness of the proposed method.
Application of Big Data and Artificial Intelligence in Architectural Design

With the progress and development of the times, science and technology have gradually developed towards specialization and integration, and the quantitative degree has gradually become an important symbol to measure the degree of discipline development, including architectural design. Under the background of big data, architectural design is facing severe challenges of informationization, technology and sustainable development. In architectural design, the design that used to rely on the personal experience and intuition of architects has been gradually eliminated, and the traditional architectural design means can no longer meet the needs of modern architecture. Under this background, we can introduce the outstanding achievements of disciplines into the direction of architectural design, provide a brand-new “methodology” for architectural design, and build a brand-new architectural design theory and method system. With the continuous development of artificial intelligence, new methods have appeared in the field of architectural design. Firstly, the design conditions include many fields, such as humanistic features, geographic information, construction technology and entity functional space, which not only include quantitative engineering data, but also include qualitative analysis of users' psychological feelings and evaluation information. In architectural design, we should not only pay attention to raster data, but also pay attention to vector data, so people can't make clear the actual value of architectural design data without auxiliary tools. Therefore, it is necessary to actively pay attention to the application of data in architectural design, and consider architectural design as a complex system in order to build a systematic and perfect architectural design system.
Virtual Therapeutics – Requirements to deliver value with virtual reality and biofeedback applications for alcohol addiction therapy

Abstract The application of virtual reality (VR) as a supportive tool in psychotherapy has gained great popularity in recent years. Especially for addiction therapy, a combination of virtual exposure and learning or training coping skills by using biofeedback has a high potential to improve conventional therapy. To add value, the new therapy system has to meet the needs of patients and practitioners likewise. Added values consistently named by experts included, but were not limited to, new possibilities of creating individual exposition or coping scenarios, enhanced psychoeducation, a shorter duration of treatment, telemedical aspects, the possibility of measuring and predicting craving and finally an improvement in abstinence. Besides literature research, we evaluated existing technical solutions in the field of virtual addiction treatment, surveyed experts and evolved a concept that led to a first prototype. The prototype consists of a wireless VR headset and a wireless multi-sensor system for measuring the physiological reaction to stimuli or the effectiveness of coping strategies by means of biofeedback. For further studies we developed both, a virtual exposure and a coping scenario and tested the hardware and software in a pilot study in order to elaborate factors that could negatively affect the therapy adherence, the effectiveness of exposition (immersion) and possible hurdles in practical use. Cybersickness and the lack of haptic feedback turned out to be the main limiting factors. Concepts for the next iteration of the therapy system will reflect these points for upcoming clinical studies. In our proof of concept, we demonstrated that virtual therapy can be implemented with a reasonable effort of time and costs. The combination of software and hardware, that supplements the traditional therapeutic approach, lays the foundation for upcoming clinical use and trials to prove the better outcome of VR enhanced addiction therapy.
Be Consistent, Work the Program, Be Present Every Day

Recovery from substance abuse disorders (SUDs) is a lifelong process of change. Self-tracking technologies have been proposed by the recovery community as a beneficial design space to support people adopting positive lifestyles and behaviors in their recovery. To explore the potential of this design space, we designed and deployed a technology probe consisting of a mobile app, wearable visualization, and ambient display to enable people to track and reflect on the activities they adopted in their recovery process. With this probe we conducted a four-week exploratory field study with 17 adults in early recovery to investigate 1) what activities people in recovery desire to track, 2) how people perceive self-tracking tools in relation to their recovery process, and 3) what digital resources self-tracking tools can provide to aid the recovery process. Our findings illustrate the array of activities that people track in their recovery, along with usage scenarios, preferences and design tensions that arose. We discuss implications for holistic self-tracking technologies and opportunities for future work in behavior change support for this context.
Noise-immune image blur detection via sequency spectrum truncation

Abstract Blur detection is aimed to differentiate the blurry and sharp regions from a given image. This task has attracted much attention in recent years due to its importance in computer vision with the integration of image processing and artificial intelligence. However, blur detection still suffers from problems such as the oversensitivity to image noise and the difficulty in cost–benefit balance. To deal with these issues, we propose an accurate and efficient blur detection method, which is concise in architecture and robust against noise. First, we develop a sequency spectrum-based blur metric to estimate the blurriness of each pixel by integrating a re-blur scheme and the Walsh transform. Meanwhile, to eliminate the noise interference, we propose an adaptive sequency spectrum truncation strategy by which we can obtain an accurate blur map even in noise-polluted cases. Finally, a multi-scale fusion segmentation framework is designed to extract the blur region based on the clustering-guided region growth. Experimental results on benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance and the best balance between cost and benefit. It offers an average F 1 score of 0.887, MAE of 0.101, detecting time of 0.7 s, and training time of 0.5 s. Especially for noise-polluted blurry images, the proposed method achieves the F 1 score of 0.887 and MAE of 0.101, which significantly surpasses other competitive approaches. Our method yields a cost–benefit advantage and noise immunity that has great application prospect in complex sensing environment.
Big Data Workflows: Locality-Aware Orchestration Using Software Containers

The emergence of the edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing big data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric big data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.
Attitudes toward Applying Facial Recognition Technology for Red-Light Running by E-Bikers: A Case Study in Fuzhou, China

The application of facial recognition technology (FRT) can effectively reduce the red-light running behavior of e-bikers. However, the privacy issues involved in FRT have also attracted widespread attention from society. This research aims to explore the public and traffic police’s attitudes toward FRT to optimize the use and implementation of FRT. A structured questionnaire survey of 270 people and 94 traffic police in Fuzhou, China, was used. In the research, we use several methods to analyze the investigation data, including Mann–Whitney U test, Kruskal–Wallis test, and multiple correspondence analysis. The survey results indicate that the application of FRT has a significant effect on reducing red-light running behavior. The public’s educational level and driving license status are the most influential factors related to their attitudes to FRT (p &lt; 0.001). Public members with these attributes show more supportive attitudes to FRT and more concerns about privacy invasion. There are significant differences between the public and traffic police in attitudes toward FRT (p &lt; 0.001). Compared with the public, traffic police officers showed more supportive attitudes to FRT. This research contributes to promoting the application of FRT legitimately and alleviating people’s concerns about the technology.
Blockchain-based carbon trading mechanism to elevate governance and smartness

Climate change has negative effects on life on earth as well as the environment and economy. Harmful greenhouse gases, especially CO2, emitted from industrial processes executed by developed countries were identified as the primary reason behind climate change. The world recognized the risk of excessive CO2 emissions, therefore, the United Nations established the Kyoto protocol which aims at reducing the release of CO2 by monetizing its emissions. To achieve its goal, the Kyoto protocol established a carbon emission trading scheme to distribute and trade CO2 allowances among and between emitting participants. However, the carbon emission trading scheme fell short of its goal due to lack of integrity, governance, high intermediary cost and other security and technical issues revealing the need for a solution to elevate the scheme's operations. Meanwhile, blockchian emerged as a revolutionary way to operate business models due to its decentralization, security, traceability, immutability, and privacy. This study proposes an efficient and smart carbon emission trading system utilizing blockchain and smart contract. Our paper proposes a carbon emission trading system using Ethereum blockchain smart contract to automate trading Kyoto units between entities and reduce operations cost. The proposed system performs buyers registration and Kyoto units trading using Ether cryptocurrency for payment without intermediary but rather through smart contract and blockchain. The result is a robust and smart system that inherits blockchain features of trust, immutability, distribution, governance, and transparency.
Cross-Domain Attribute-Based Access Control Encryption

Logic access control enforces who can read and write data; the enforcement is typically performed by a fully trusted entity. At TCC 2016, Damgård et al. proposed Access Control Encryption (ACE) schemes where a predicate function decides whether or not users can read (decrypt) and write (encrypt) data, while the message secrecy and the users' anonymity are preserved against malicious parties. Subsequently, several ACE constructions with an arbitrary identity-based access policy have been proposed, but they have huge ciphertext and key sizes and/or rely on indistinguishability obfuscation. At IEEE S&P 2021, Wang and Chow proposed a Cross-Domain ACE scheme with constant-size ciphertext and arbitrary identity-based policy; the key generators are separated into two distinct parties, called Sender Authority and Receiver Authority. In this paper, we improve over their work with a novel construction that provides a more expressive access control policy based on attributes rather than on identities, the security of which relies on standard assumptions. Our generic construction combines Structure-Preserving Signatures, Non-Interactive Zero-Knowledge proofs, and Re-randomizable Ciphertext-Policy Attribute-Based Encryption schemes. Moreover, we propose an efficient scheme in which the sizes of ciphertexts and encryption and decryption keys are constant and thus independent of the number of receivers and their attributes. Our experiments demonstrate that not only is our system more flexible, but it also is more efficient and results in shorter decryption keys (reduced from about 100 to 47 bytes) and ciphertexts (reduced from about 1400 to 1047).
INTEGRATION OF DIGITAL TECHNOLOGIES IN THE EDUCATIONAL ENVIRONMENT OF PRESCHOOL EDUCATION INSTITUTIONS

The article reveals the relevance of updating the professional skills of a preschool teacher, in particular the ways of interaction and communication under the condition of development of digital society, education system and human life in general. The main preconditions for the use of digital technologies in the educational process of preschool education institutions have been studied, including: the ban on parents attending preschool education institutions; giving parents the advantage of communicating with teachers using modern digital tools and technologies and early involvement of children in media content. This has accelerated the use of digital technologies by teachers not only to develop individual plans for teaching and educating children, but also to enhance emotionality of lessons, children’s mental development, children’s creativity support. The approaches to digital technologies integration into the educational process of preschool education are systematized and the methodical approaches to the use of digital technologies in the work of educators, speech therapists, music teachers and physical education instructors of children are characterized. The methodical aspects of creating tasks for modern preschool children of generation α with the use of available online resources are characterized and their influence on the development of a preschool child is singled out. The results of the survey of educators of preschool institutions of Zhytomyr region obtained using empirical methods: observations, surveys, questionnaires, interviews are summarized. In general, the integration of digital technologies into the educational process of preschool education is outlined, which allows to present information from different educational fields, forms of activities in the process of implementing the requirements of the Basic component of preschool education holistically and interesting for modern children of generation α.
Encoding the Meaning Triangle (Object, Entity, and Concept) as the Semantic Foundation for Entity Alignment

Entity alignment intends to find equivalence relations between entities that exist in different knowledge graphs but semantically represent the same real-world object. Despite great progress has been made in entity alignment, concept - an indispensable piece in the meaning triangle (real-world object, symbolic entity, and concept) is inadequately addressed by the current approaches. In this study, we adopt the three-way distinction (so called meaning triangle []) in knowledge representation as the semantic foundation for entity alignment, and present a framework called C4EA (Concept for Entity Alignment) to integrate and unify concept and entity into entity alignment. C4EA uses a graph neural network and knowledge representation model to encode entity and entity relationship in a low-dimensional vector, and uses Box Embedding to represent concept as a hyper-rectangle in the vector space. We treat entity as a hyper-rectangle with a range of zero (a point in space), so that entities which are challenging to align through finite equivalent entity links can be drawn closer to each other in the representation space. Our experimental results on three public multilingual entity alignment datasets demonstrate the effectiveness of the proposed solution.
Layer-wise Model Pruning based on Mutual Information

Inspired by mutual information (MI) based feature selection in SVMs and logistic regression, in this paper, we propose MI-based layer-wise pruning: for each layer of a multi-layer neural network, neurons with higher values of MI with respect to preserved neurons in the upper layer are preserved.Starting from the top softmax layer, layer-wise pruning proceeds in a top-down fashion until reaching the bottom word embedding layer.The proposed pruning strategy offers merits over weight-based pruning techniques: (1) it avoids irregular memory access since representations and matrices can be squeezed into their smaller but dense counterparts, leading to greater speedup; (2) in a manner of top-down pruning, the proposed method operates from a more global perspective based on training signals in the top layer, and prunes each layer by propagating the effect of global signals through layers, leading to better performances at the same sparsity level.Extensive experiments show that at the same sparsity level, the proposed strategy offers both greater speedup and higher performances than weight-based pruning methods (e.g., magnitude pruning, movement pruning).
Federated learning‐powered visual object detection for safety monitoring

Abstract Visual object detection is an important artificial intelligence (AI) technique for safety monitoring applications. Current approaches for building visual object detection models require large and well‐labeled dataset stored by a centralized entity. This not only poses privacy concerns under the General Data Protection Regulation (GDPR), but also incurs large transmission and storage overhead. Federated learning (FL) is a promising machine learning paradigm to address these challenges. In this paper, we report on FedVision —a machine learning engineering platform to support the development of federated learning powered computer vision applications—to bridge this important gap. The platform has been deployed through collaboration between WeBank and Extreme Vision to help customers develop computer vision‐based safety monitoring solutions in smart city applications. Through actual usage, it has demonstrated significant efficiency improvement and cost reduction while fulfilling privacy‐preservation requirements (e.g., reducing communication overhead for one company by 50 fold and saving close to 40,000RMB of network cost per annum). To the best of our knowledge, this is the first practical application of FL in computer vision‐based tasks.
Face Recognition Combined with Gabor Wavelet and Lightweight Convolutional Neural Network

The existing deep convolutional neural network model has achieved high accuracy in face recognition, but it has a large amount of calculation, high resource consumption, and loss of local features of the faces. Therefore, this paper proposes a method of combining Gabor wavelet and lightweight convolutional neural network, using the GMNet network which consists of convolutional layer (Gabor layer) that implements the function of Gabor filter on the basic elements of convolutional neural network and lightweight convolutional neural network to extract more discriminative face features. It can not only reduce the number of parameters and computational complexity, but also extract more distinguished face features to improve the accuracy of face recognition. The experimental results on the LFW, CFP-FP, and AgeDB-30 datasets show that the improved model can enhance the performance of face recognition and maintain good results when the illumination, posture, age and other change.