Reviewerly/exHarmony huggingface dataset:
The I-TEC Design Framework for Kinesthetic Transmission in Online Spaces

Disseminating dance in online spaces provides an opportunity for kinesthetic knowledge to reach broader audiences. However, the transmission of dance in online spaces also primarily relies on visual and aural modalities that cannot fully capture the nuanced physical sensations of a kinesthetic experience. In recent years there has been an influx of interactive online dance resources; yet there is little analysis of how these works effectively translate kinesthetic knowledge to online audiences. We bring together research in dance film and interaction design practices to explore kinesthetic transmission in online spaces and conduct analyses on interactive digital dance resources. Based on our literature review and analyses of these dance resources we propose the I-TEC design framework for kinesthetic transmission. In this framework, Instructional, Translational, Exploratory, and Contextual interactions are brought together to expose the multiple embodiments, perspectives, and translations of kinesthesia.
An Assisted Teaching Method of College English Translation Using Generative Adversarial Network

College English translation instruction is an important part of developing students’ English application skills. The generation network in GAN (generative adversarial network) is combined with reinforcement learning technology in this paper to create a basic text generation model that solves the problem that the original GAN model cannot handle discrete data. The correctness of students’ English translation ability is analyzed using a neural network model trained by PSO (particle swarm optimization), which can help teachers estimate students’ translation ability and provide a reference for the next teaching. The results show that the proposed model’s accuracy rate is clearly higher than the comparison model’s, with a maximum accuracy rate of over 85%. The findings indicate that this research model has the potential to improve the quality of English translation instruction.
The Presence of Television Media in Disaster Reporting to Increase the Community’s Disaster Literacy Skills

Television media, on the one side, has an influence on the audience, but in reporting disasters, it is not uncommon to exploit disaster victims. On the other side, television media has the potential to make a positive contribution to disaster reporting. The purpose of this study is to determine the presence of television media in news to improve the disaster literacy skills of the community. In this study, the author uses a type of library research with descriptive analysis. Descriptive analysis method provides clear, objective, systematic, analytical and critical descriptions and information regarding the presence of television media in disaster reporting to improve the community’s disaster literacy skills. This research found that the role of television media in reporting disasters can improve people's disaster literacy skills. The presence of television media has been going well enough in reporting disasters, but it still needs to change the concept or packaging of news to avoid the impression of "dramatization" to build community empathy so that they are willing to be actively involved to reduce the risk of disaster in an applicative manner. A good and deep understanding will be obtained by the public from the media. Thus, the public will not only get information from communicators (resource persons) about disasters at a cognitive level but also will increase the affective and conative aspects so that they have their own awareness in managing disaster risks as well as possible. This paper explained there are three stages of disaster literacy that can be built by television such as cognitive aspect, affective aspect and conative aspect in disaster reporting that can reduce the risk of disaster. The stages are: (1) when the news is carried out by television media before a disaster occurs, (2) stage of reporting during disasters and (3) stage of television media coverage after disasters.
Stage Together: Remote Rehearsal of Theater Blocking

The COVID-19 pandemic has forced many theater productions to shift to remote rehearsals and performances using online video conference platforms. Video conference software makes it possible to share individual performances, but loses the sense of presence together and provides no way to visualize blocking movements on the shared space of the stage. Rehearsal by video conference can cause productions to either omit blocking altogether or have only a few on stage rehearsals, sometimes just days before opening. This paper presents a Web-based system that enables productions to remotely rehearse blocking at any stage of the production. Remote performers can propose their own blocking, rehearse scripted cues and visualize movements in a shared virtual stage. This paper describes the design, implementation, and focus group evaluations of Stage Together with a college theater production of Medea by Euripides.
Corrigendum: Task Roadmaps: Speeding Up Task Replanning

Missing ListingIn the original article, Listing 1 and 2 were not included in the typesetting process. The missing listings appear below. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
A Feature Representation Method Based on Dual Segment and Entropy Evaluation for Aeroengine Gas Path Anomaly Detection

Traditional methods for gas path anomaly detection cannot fully extract remarkable shape features that can represent the gas path anomaly mode. Therefore, a feature representation method based on dual segment and entropy evaluation for aeroengine gas path anomaly detection is proposed in this paper. Taking the temporal and spatial correlations of the multivariate time series into consideration, the expression rule of the anomaly mode in the multivariate gas path parameter deviation time series is analyzed, on this basis, time series subsequence segment method is determined. To obtain the features that best fit the anomaly expression rule, a dual segment method based on piecewise optimal fitting is proposed. The entropy evaluation method is introduced to comprehensively evaluate and optimize the primary features while calculating the common shape features of subsequence, and then the remarkable shape feature matrix for anomaly detection is determined. Finally, the early warning for the gas path anomaly is realized by mining the potential anomaly mode of the gas path state using isolation forest model. The experimental results show that this method can improve the accuracy of aeroengine gas path anomaly detection.
A New Network Pruning Framework Based on Rewind

Model pruning is one of the main methods of deep neural network model compression. However, the existing model pruning methods are inefficient, there is still a lot of redundancy in the network, and the pruning has a great impact on the accuracy. In addition, the traditional pruning process usually needs to fine-tune the network to restore accuracy, and fine-tuning has a limited effect on accuracy recovery, so it is difficult to achieve a high level of accuracy. In this paper, we propose a new neural network pruning framework: the channel sparsity is realized by introducing a scale factor, and the sparse network is pruned by setting a global threshold, which greatly improves the efficiency of pruning. After pruning, this paper proposes a rewind method to restore the accuracy, that is, save the weight after training, and then reload it on the network for training to restore the accuracy. In addition, we also study the best rewind point of the three networks. The experimental results show that our method significantly reduces the number of parameters and FLOPs without affecting or even improving the accuracy, and the rewind method proposed by us achieves a better accuracy recovery effect than fine-tuning. At the same time, we find that the epoch with the highest accuracy is the best rewind point, and the accuracy is the highest after saving its corresponding weight and retraining the model.
A Survey on the Perception of Beauty Specialized High School Teachers Following the Introduction of the Vocational High School Credit System

This study provides the professional characteristics and expertise in the professional characteristics that provide expertise in this field. A total of 38 questionnaires were prepared for teachers of high school specializing in beauty, and responses from a total of 81 people were used for the study during August 1, 2021 through online and offline methods. The questionnaire measured the major concepts of the credit system, curriculum, evaluation, graduation requirements, and introduction effect on a 5-point Likert scale, The collected data were analyzed with SPSS Statistics 21.0. As a result of the analysis, there was a statistically significant difference in the work area for each implementation type (p&lt;.05), and there was no significant difference in the recognition of the credit system according to the implementation type and work area (p&gt;.05). There was no significant difference in the perception of the credit system according to the type of implementation and work area (p&gt;.05). Therefore, in order to improve the level of recognition and satisfaction of teachers, students, and parents of the credit system in the situation ahead of implementation, the continued interest and support of the Ministry of Education and provincial offices of education should be accompanied. Specific explanations and examples of the contents should be provided when composing training for the credit system and production of educational materials. It is expected to settle successfully through the organic application of the NCS-based curriculum and credit system.
Decision Tree Technique for Arabic Sentences Classification with Preprocessing of NLP by Using of Words Features

The Arabic language suffers from a lack of programs that analyze and deal with it, especially those that are particularly interested in it, because automatic understanding of the meaning of Arabic words relies on multiple processors, including lexical processing, morphological processing, syntactic processing, and the meaning of words in a language. Despite our best efforts, we were unable to find a system or program that categorizes Arabic sentences into nominal or dative sentences, maybe due to a lack of interest or a conviction among people working in the field of natural language processing that the subject is boring. Despite common belief, this system is critical for language comprehension and may be included into natural language processing. A decision tree was employed to categorize Arabic phrases in our current study, including text segmentation and filtering by key processors. A collection of papers is arranged in such a way that each document represents its own piece of text. The research was evaluated using three evaluation approaches (accuracy, recall, and F-measurement), and when the system was built and applied on the gathered documents, the success and efficacy of the system was observed to a considerable extent, with results ranging from 79 to 100%.
DISRUPTIVE TECHNOLOGY ON THE CYBERSPACE: THE CONTESTATION BETWEEN CRIMINAL JUSTICE SYSTEM AND CYBERCRIMINALS

The paper examines the contestation between criminal justice system and cybercriminals. Qualitative methodology was adopted for collecting data. The study sample was drawn from Nigeria Police Crime prevention and Investigative Unit, Cyber forensics experts, officials from the Economic &amp; Financial Crimes Commission (EFCC), Cyber security experts, acadamics in Criminology and Security Studies, judges and prosecutors. Theses were interviewed to obtain information on the running battle between the authourites and the criminal elements. Findings revealed that cyber crime increased exponentially despite the promulgation of cybercrime prevention and prohibition Act of 2015. It was reported that Nigeria youth used the lack of empowernment as their excuse to engage in cybercrime. Findings also revealed varied corps of victims, people and local businesses in Nigeria. The paper recommends the need to campaign for moral regeneration, educating internet users on how to stay protected online, appeals for atitutude change among the youths, and those involved in cybercrime, specialised trainng for criminal justice officials to enable them tackle cyber related crimes.above all, the criminal justice system must be swift in apprehension, prosecution and setencing offenders.
Research on Passive Location of Maneuvering Target Based on IMM-SRCKF Algorithm

To address the problem of three-dimensional non-cooperative maneuvering target tracking in the air, a multi-sensor cooperative passive positioning solution based on the Kalman filter framework is proposed. Firstly, the constant velocity (CV) model and the Singer model are involved to approximate the unknown kinetic characteristic of the non-cooperative target. Secondly, the network positioning is carried out by multiple passive sensors deployed in different areas on the ground, and a centralized observation equation is established. Then, the registration error of each sensor is extended to the target state vector considering its influence on the positioning accuracy, and a unified state space model is built up. Finally, the interacting multiple model (IMM) algorithm based on the square root cubature Kalman filter (SRCKF) filter is devised to realize the estimation of the target state as well as the registration error. Numerical simulations indicate that the presented algorithm can achieve the joint estimation of the target state and the sensor registration error in real time with high accuracy.
Distributed and Privacy-Preserving Data Dissemination at the Network Edge via Attribute-Based Searchable Encryption

Multi-Access Edge computing represents one of the most important enabling technologies for the Industrial Internet of Things. It allows advanced data processing and customized service provisioning, very close to the end-users. In the presence of many Multi-Access Edge computing applications, however, it is fundamental to ensure effective and privacy-preserving data dissemination at the network edge. From the security perspective, Attribute-based Encryption and Searchable Encryption techniques can be jointly used to achieve data confidentiality, flexible protection against unauthorized access, and privacy-preserving data dissemination. Available solutions, however, generally focus the attention on cloud-based approaches, use edge computing to implement some of the cryptographic tasks, and limit the investigation to single cryptographic operations. Indeed, no works investigate the adoption of these techniques in scenarios with multiple data producers and end-users, and fully operating at the network edge. To bridge this gap, this work proposes a novel methodology supporting fast and privacy-oriented data dissemination directly at the network edge. In the considered distributed network infrastructure, Multi-Access Edge computing applications express the interest to receive specific data by sending Trapdoors to Edge Servers. Data sources protect their contents through Attribute-based Encryption and deliver them to Edge Servers. In turn, Edge Servers implement Attribute-based Searchable Encryption functionalities to properly disseminate received contents towards Multi-Access Edge nodes hosting the applications that generated valid Trapdoors. The performance of the conceived approach has been evaluated through computer simulations. Obtained results highlight the benefits achieved against baseline (i.e., cloud-based) solutions.
Suggestion of AR Presentation Tool for PC Operating Handicapped Users

Some young and elderly people cannot operate PC applications currently. In this paper, the AR (Augmented Reality) presentation tool in Microsoft PowerPoint using NyARToolKit for Unity, is suggested to the person who cannot use a PC (PowerPoint application). It can display the 3D model from the AR figure block and output it to a PowerPoint file. After that, we investigate the effect of the AR presentation tool to compare the PC use and AR figure block operation. In this result, the AR presentation tool is easy, quick, intuitive to use.
OL-JCMSR: A Joint Coding Monitoring Strategy Recommendation Model Based on Operation Log

A surveillance system with more than hundreds of cameras and much fewer monitors strongly relies on manual scheduling and inspections from monitoring personnel. A monitoring method which improves the surveillance performance by analyzing and learning from a large amount of manual operation logs is proposed in this paper. Compared to fixed rules or existing computer-vision methods, the proposed method can more effectively learn from the operators’ behaviors and incorporate their intentions into the monitoring strategy. To the best of our knowledge, this method is the first to apply a monitoring-strategy recommendation model containing a global encoder and a local encoder in monitoring systems. The local encoder can adaptively select important items in the operating sequence to capture the main purpose of the operator, while the global encoder is used to summarize the behavior of the entire sequence. Two experiments are conducted on two data sets. Compared with att-RNN and att-GRU, the joint coding model in experiment 1 improves the Recall@20 by 9.4% and 4.6%, respectively, and improves the MRR@20 by 5.49% and 3.86%, respectively. In experiment 2, compared with att-RNN and att-GRU, the joint coding model improves by 11.8% and 6.2% on Recall@20, and improves by 7.02% and 5.16% on MRR@20, respectively. The results illustrate the effectiveness of the our model in monitoring systems.
Machine Art: Exploring Abstract Human Animation Through Machine Learning Methods

Visual media and performance art have a symbiotic relationship. They support one another and engage the audience by providing an experience or telling a story. This comparative study explores the accuracy, efficiency, and cost factors of using machine learning based motion capture methods in performance art. There is extensive research in the field of machine learning methods for human pose estimation, but the outputs of such work are rarely used as inputs for performance art. In this paper we present a practice-based research project that involves producing animations that match a performer's movements using machine learning based motion capture methods. We use human poses derived from low-cost video capture as an input into high-resolution abstract forms that accompany and synchronise with dance performances. A single-camera approach is examined and compared to existing methods. We find that compared with existing motion capture methods the machine learning based methods require less setup time, and less equipment is required resulting in considerably lower cost. This research suggests that machine learning has considerable potential to improve the quality of human pose estimation in performance art, visual effects and motion capture, and make it more accessible for arts companies with limited resources.
Inappropriate Visual Content Detection Based on the Joint Training Strategy

In the information age, massive Internet data brings convenience to us. But there is some inappropriate visual content (pornography, violence, politics, terrorism, etc.), among which the dissemination of pornographic content has an adverse influence, especially for children and minors. Therefore, we present an inappropriate visual content detection method based on the joint training strategy in an end-to-end manner, which realizes the identification and location of inappropriate visual content while retaining the base class (80 categories in the COCO dataset) detection. To solve the difficulty of sample labeling, in this paper we propose a combined training strategy of detection and classification. And the Focal loss is used to improve the sample imbalance in the network sharing training. The algorithm can achieve multi-label output and has good recognition accuracy. Finally, a more challenging dataset INVC of inappropriate visual content is proposed, which includes three types of sample data in complex backgrounds at different scales, such as indoor, beach, street, etc.
Voice Prescription using Natural Language Understanding

Misinterpretation of hand-written drug prescriptions may lead to improper medication given by the chemist, unavailability of proper records of drug prescription results in numerous issues. Research is hindered in the concerned domain due to missing data. Most of the issues can be countered if a systematic and standard prescription is available. In this paper, we intend to provide an interface for the practitioners to prescribe orally and prepare drug prescriptions in the form of a well-arranged table in compliance to the eprescription norms using prescribers dictation as input to remove the need for hand-written drug prescriptions and minimize any error related to misinterpretation of the drug prescription. This paper is focused on the task of extraction of meaningful information from the prescriber’s utterance through the slot filling approach of Natural Language Understanding. We use data augmentation to deal with class imbalance and inadequacy of data and experiment on both real data as well as the data with artificially generated prescriptions. We use the prescriber’s utterance as input, perform speech-to-text conversion and then apply state-of-the-art Natural Language Understanding techniques on it to extract meaningful information from the sentence to generate the drug prescription.
Implementation of Low Complexity Finite Field GF (2<sup>m</sup>) Multiplier Using Irreducible Primitive Polynomials

Advancements in wireless technology led to the evolution of many modern wireless networks and wireless communication applications. Internet of Things (IoT) is a state of art communication technology and Edge devices are fundamental in this kind of communication. Number of edge devices required mainly depends on the complexity of applications. In IoT edge devices security is a major concern, asymmetric algorithms like Elliptic curve cryptography(ECC) can be used to address this issue which depend significantly on finite field GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) arithmetic operations, where GF denotes Galois Field. In particular, Galois Field multiplication is a complex but important operation as it can also be utilized in important functions like division and inversion. In our work, realization of a low computational complexity finite field GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) multiplier employing primitive polynomials as field irreducible polynomials is done. Primitive polynomials are employed in an available multiplication algorithm and this modified multiplier is realized in MATLAB. It is observed that computational steps for realizing the multiplication are reduced, which in turn can lead to improvement in time efficiency. A Verilog model is developed and simulated to use as a golden reference for comparing the MATLAB results.