Reviewerly/exHarmony huggingface dataset:
Effects of Virtual Reality Pilates Training on Duration of Posture Maintenance and Flow in Young, Healthy Individuals: Randomized Crossover Trial

This study explored the use of virtual reality (VR) technology to enhance the effectiveness and duration of low-intensity movements and postures in Pilates-derived exercises. We postulate that by leveraging the flow state in VR, individuals can engage in these exercises for longer periods while maintaining a high level of flow.The purpose of this study was to compare differences in posture maintenance and flow between VR Pilates training and conventional Pilates training, and the correlation between the 2 factors.The 18 participants in each group received either VR training or conventional training and were switched to the other training type after a 2-day wash-out period. Each group performed Pilates movements in a VR environment and a conventional environment, divided into 4 types. After training sessions, participants were evaluated for flow using a self-report questionnaire. In addition, a sports video analysis program was used to measure the duration of posture maintenance in 2 video-recorded sessions. Repeated-measures ANOVA and correlation analysis were performed on the measured duration of posture maintenance and flow scores. In all cases, the statistical significance level was set at P<.05.Results for the duration of posture maintenance verification by type showed that simple behavior (F1,16=17.631; P<.001), upper body-arm coordination behavior (F1,16=6.083; P=.04), upper body-leg coordination behavior (F1,16=8.359; P<.001), and whole-body coordination behavior (F1,16=8.426; P<.001) all showed an interaction effect at P<.05. Flow (F1,16=15.250; P<.001) also showed an interaction effect. In addition, significant correlations were determined between duration of all types of posture maintenance and flow in the VR training group at P<.05.Our results indicate that VR Pilates training may be more useful than conventional Pilates training in improving the duration of posture maintenance and that it promotes a significantly higher degree of flow when compared with conventional Pilates training.
Developing a System Based on Block Chain Technology for e-Voting Mechanism

Next-generation electronic voting systems have been made possible by the maturation of blockchain-based systems. Blockchain technology, the basis for electronic voting, might be used to improve online voting security. Nevertheless, due to its resource intensive nature, one of Blockchain’s key principles, Work, cannot be applied in the E-voting founded blockchain. As a consequence, the information included in a transaction block may be changed and its hash easily recalculated. In totalling, the Proof-of-Work is necessary to verify the block’s legitimacy. Hence, the digital signature may provide a means to address these concerns. Thus, the data is encrypted using an enhanced version of the Blowfish technique, which improves both security and efficiency. We also assess the security of the hybrid blockchain we propose and compare it to the security of the traditional blockchain via the discussion and analysis of attack execution. We learned new things about the safety, speed, of blockchain-based electronic voting schemes through our tests.
Anomaly detection speed-up by quantum restricted Boltzmann machines

Abstract Quantum machine learning promises to revolutionize traditional machine learning by efficiently addressing hard tasks for classical computation. While claims of quantum speed-up have been announced for gate-based quantum computers and photon-based boson samplers, demonstration of an advantage by adiabatic quantum annealers (AQAs) is open. Here we quantify the computational cost and the performance of restricted Boltzmann machines (RBMs), a widely investigated machine learning model, by classical and quantum annealing. Despite the lower computational complexity of the quantum RBM being lost due to physical implementation overheads, a quantum speed-up may arise as a reduction by orders of magnitude of the computational time. By employing real-world cybersecurity datasets, we observe that the negative phase on sufficiently challenging tasks is computed up to 64 times faster by AQAs during the exploitation phase. Therefore, although a quantum speed-up highly depends on the problem’s characteristics, it emerges in existing hardware on real-world data.
Research on multimedia intelligent algorithm based on recognition and detection of table tennis ball striking action

Abstract There are many different types of batting motions in table tennis, and the accurate identification of these motion patterns is of great importance for the analysis of batting motions. In this paper, firstly, we explore the efficient representation of the batting motion in the time domain by reasonably placing the sensors and processing the collected data with respect to the characteristics of the batting motion. Secondly, based on the SVM algorithm, in real-world applications, the issue of creating complicated hypersurfaces is resolved using the kernel function technique., so that the simple hypersurfaces in the original space can achieve satisfactory classification results. Finally, a table tennis batting action recognition algorithm based on the SVM algorithm is calculated. The algorithm can efficiently extract the features of batting actions in temporal order and finally accomplishes an average recognition correctness of 93.58%, which is 4.155% more accurate than decision trees, 5.255% more accurate than random forests, and 3.305% more accurate than integrated classifiers. The best performance among the four classifiers indicates that the model has the best performance. This study demonstrates that the SVM algorithm model can quickly and effectively classify and identify action signals for the batting action during table tennis training.
REAL-TIME TRAFFIC SIGN DETECTION AND MAPPING FOR DRIVER ASSISTANCE USING CNN AND OPEN-SOURCE LIBRARIES

The rising number of accidents caused by failure to obey traffic laws and road signs can be addressed through a traffic sign detection system and its mapping for road sign navigation. This paper addresses a solution that utilizes generated training data made from photos and location of road traffic signs. Our system detects and notifies drivers of upcoming traffic signs during navigation, even if they are obscured by weather or other factors. The system assures fewer accidents and allows drivers to focus on driving rather than reading every single traffic sign on road and achieves the accuracy up to 99.27%.
The Role of Marketing Knowledge Sharing in Building Organizational Immunity

This study aimed to assess the impact of sharing marketing knowledge on attaining organizational immunity in Jordanian industrial corporations using a quantitative methodology. Marketing managers from Jordan's 53 industrial corporations made up the study's population. 40 questionnaires were found to be valid for analysis. The following outcomes have been obtained: The sharing of marketing knowledge influences building organizational immunity in Jordanian industrial corporations. The benefit of marketing knowledge sharing on obtaining organizational immunity in Jordanian industrial corporations is linked to marketing managers' academic qualifications. The following are the researcher's recommendations: Creating unique procedures for assessing economic data relating to the company's operations to foresee any future marketing changes. In addition to focusing on educating marketing department employees in the art of gathering information about the external environment regularly, to confront and face any marketing changes that may arise in the surrounding environment.
Interpreting vision and language generative models with semantic visual priors

When applied to Image-to-text models, explainability methods have two challenges. First, they often provide token-by-token explanations namely, they compute a visual explanation for each token of the generated sequence. This makes explanations expensive to compute and unable to comprehensively explain the model's output. Second, for models with visual inputs, explainability methods such as SHAP typically consider superpixels as features. Since superpixels do not correspond to semantically meaningful regions of an image, this makes explanations harder to interpret. We develop a framework based on SHAP, that allows for generating comprehensive, meaningful explanations leveraging the meaning representation of the output sequence as a whole. Moreover, by exploiting semantic priors in the visual backbone, we extract an arbitrary number of features that allows the efficient computation of Shapley values on large-scale models, generating at the same time highly meaningful visual explanations. We demonstrate that our method generates semantically more expressive explanations than traditional methods at a lower compute cost and that it can be generalized to a large family of vision-language models.
Exploring perception and support strategies for Generative AI Literacy: Focusing on pre-service secondary teachers

Objectives The study was conducted to explore the future directions for the implementation of a generative AI&#x0D; literacy education program for pre-service secondary teachers.&#x0D; Methods A survey was conducted among 94 pre-service secondary teachers from A University. The purpose of&#x0D; the survey was to assess their level of interest in understanding and utilizing generative artificial intelligence, their&#x0D; requirement for relevant education, and the assistance plans they might need. Additionally, in-depth interviews&#x0D; were conducted with 6 pre-service secondary teachers.&#x0D; Results 79.8% of pre-service secondary teachers reported having experience with AI programs, and 46.8% indicated&#x0D; that their knowledge of AI-related topics was at an introductory level. To support education in AI literacy, a practical&#x0D; space was provided where technologies related to AI could be used and AI literacy education should encompass&#x0D; both specialized majors and general liberal arts education. In-depth interviews revealed that the intention to enroll&#x0D; in AI literacy courses stemmed from the desire to enhance the capabilities of prospective secondary teachers, a&#x0D; personal interest in AI literacy, and curiosity about the courses themselves. Furthermore, the importance of&#x0D; adapting to societal changes and integrating AI-related subjects into the curriculum was emphasized.&#x0D; Conclusions It was suggested that the AI subject for pre-service secondary teachers should be taught as a&#x0D; practical-oriented regular course, focusing on enhancing the ability to critically analyze and embrace the information&#x0D; provided by artificial intelligence, rather than focusing on coding. It was also recommended to provide disciplinespecific&#x0D; education tailored to each subject area.
Case Study of Auditory Training Program for an Presbycusis on Virtual Reality

Objectives In this study, a virtual reality (VR) auditory training program, which was created to provide experiences similar to reality through computer software and hardware with the development of technology according to the 4th industry, was conducted with auditory training for 6 weeks (1 session per week) for two elderly people with hearing loss, and the change in word recognition and sentence comprehension ability was investigated.&#x0D; Methods For this purpose, 6 sessions of auditory training programs using virtual reality contents (eating out, go on a trip, making phone calls, watching radio, talking in public transportation (subway), using public institutions (hospital)) were provided for 6 weeks (1 session per week). The effects of the auditory training program, word recognition and sentence comprehension tests were conducted before-and-after implementing the program.&#x0D; Results The results of the virtual reality-based auditory training program for two elderly people with hearing loss are as follows: First, presbycusis A and B showed improved scores in word recognition as auditory training sessions were repeated, and training was effective even under difficult hearing conditions with noise. Second, when the pre-test and post-test values were compared, there was no significant difference in the change in word and sentence recognition of the two presbycusis.&#x0D; Conclusions This study focused on improving listening ability by conducting a daily life-oriented virtual reality auditory training program for the elderly with hearing loss. In addition, it is meaningful in that it provides motivation for hearing ability through auditory training to the elderly who have worn hearing aids for a long time and the necessity of systematic auditory training.
Learning Japanese Using Sail App at A British University

Objective: This study investigates if the Sail App Japanese speaking at least once a week for 25 minutes with native speakers enhance their speaking and listening ability in three months. Methods: Fourteen undergraduate students (Ab Initio, Intermediate and Advanced Japanese level) who studied Japanese language at University of Sussex in the 2020/21 used Sail App to practice speaking Japanese with native Japanese speakers who were registered Sail App users living in Japan. Sussex students were asked to have a total of 6 speaking and listening skills evaluations by 5 Japanese native speakers who were also Helte Co Ltd staff. The 5 Japanese native speakers evaluated 14 students on 4 occasions (the initial baseline evaluation, the second month, and the third month and final evaluation). The students also self-evaluated of their listening and speaking skills on 2 occasions (the initial baseline evaluation and the final evaluation). Results: After three months, only 1 student completed all 6 evaluations and 3 students completed 4 native speakers’ evaluations by Helte Co Ltd staff. The majority of students’ speaking ability remained unchanged. However, the perception discrepancies of speaking and listening skills between Sussex students and Japanese native speakers were found. Conclusion: The majority of Ab Initio and Intermediate level students had a tendency to evaluate their listening and speaking skills higher than those by native Japanese speakers. As their level of language went higher, this discrepancy became smaller.
THE DEPRESSION SYMPTOMS OF THE CHARACTER NIC SHEFF IN THE FILM BEAUTIFUL BOY BY FELIX VAN GROENINGEN.

The title of this research is Depression Symptoms of Nic Sheff in Beautiful Boy by Felix Van Groeningen. The research aims to find out the symptoms of Nic Sheff's depression in the film Beautiful Boy by Felix Van Groeningen. The problem in this study focused on the symptoms of depression of Nic's character, which analyzes the symptoms of depression through 6 aspects of depression according to Beck and Alford. The method used in this research is descriptive qualitative method in which presents the data in descriptive form. The approach used is a literary psychology approach while researchers use reading and note-taking techniques as data collection techniques. The data in this study used are dialog and scene cuts derived from primary data sources, namely the film Beautiful Boy by Felix Van Groeningen. Secondary data sources are obtained from reference books or documents related to the object of research. The data collection technique used is to collect data using reading and note-taking techniques. Based on the final results of the research conducted on the film Beautiful Boy shows that Nic there are symptoms of depression faced by the main character, namely emotional aspects, cognitive aspects, motivational aspects, physical aspects, delusions, and hallucinations.
Towards Robust Multi-user 3D Posture Tracking Through mmWave Signals

With the rapid growth of artificial intelligence-of-things (AIoT) in recent years, the market for 3D human posture tracking has expanded to encompass a wide range of application scenarios, including various VR/AR/MR applications. However, current mainstream solutions for posture tracking, which rely on vision-based methods, suffer from concerns regarding privacy leakage and are heavily dependent on favorable lighting conditions. Conversely, although RF-based posture-tracking solutions have been proposed in recent years to address privacy issues and handle varying lighting conditions, doubts have been raised regarding their robustness. To push the limit of 3D human posture tracking, we present a mmWave-based robust multi-user 3D posture tracking system, which leverages a single commercial off-the-shelf (COTS) mmWave radar to track multiple users’ 3D postures simultaneously and keep the tracking result robust in real-world scenarios.
Sensitivity Analysis of RF+clust for Leave-One-Problem-Out Performance Prediction

Leave-one-problem-out (LOPO) performance prediction requires machine learning (ML) models to extrapolate algorithms' performance from a set of training problems to a previously unseen problem. LOPO is a very challenging task even for state-of-the-art approaches. Models that work well in the easier leave-one-instance-out scenario often fail to generalize well to the LOPO setting. To address the LOPO problem, recent work suggested enriching standard random forest (RF) performance regression models with a weighted average of algorithms' performance on training problems that are considered similar to a test problem. More precisely, in this RF+clust approach, the weights are chosen proportionally to the distances of the problems in some feature space. Here in this work, we extend the RF+clust approach by adjusting the distance-based weights with the importance of the features for performance regression. That is, instead of considering cosine distance in the feature space, we consider a weighted distance measure, with weights depending on the relevance of the feature for the regression model. Our empirical evaluation of the modified RF+clust approach on the CEC 2014 benchmark suite confirms its advantages over the naive distance measure. However, we also observe room for improvement, in particular with respect to more expressive feature portfolios.
Triple-output phase unwrapping network with a physical prior in fringe projection profilometry

Deep learning has been attracting more and more attention in the phase unwrapping of fringe projection profilometry (FPP) in recent years. In order to improve the accuracy of the deep-learning-based unwrapped phase methods from a single fringe pattern, this paper proposes a single-input triple-output neural network structure with a physical prior. In the proposed network, a single-input triple-output network structure is developed to convert the input fringe pattern into three intermediate outputs: the wrapped phase, the fringe order, the coarse unwrapped phase, and the final output high-precision unwrapped phase from the three outputs. Moreover, a new, to the best of our knowledge, loss function is designed to improve the performance of the model using a physical prior about these three outputs in FPP. Numerous experiments demonstrated that the proposed network is able to improve the accuracy of the unwrapped phase, which can also be extended to other deep learning phase unwrapping models.
An End-to-End Two-Branch Network Towards Robust Video Fingerprinting

With the increasing number of edited videos, many robust video fingerprinting schemes have been proposed to solve the problem of video content authentication. However, most of them either deal with the temporal and spatial features symmetrically or insufficiently consider the temporal information. In this work, an end-to-end two-branch network towards robust video fingerprinting ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RVFNet</b> ) is proposed, where the two branches focus on the temporal and spatial information, respectively. The temporal branch aims to comprehensively capture complex motion patterns by combining subtle motion changes with the overall motion trend. The spatial branch exploits the pixel-level information obtained by multiple receptive fields while preserving significant structural features. Deep metric learning is employed in the training process, and we adopt hard triplet loss to constrain the generation of fingerprints. Furthermore, we construct a large-scale and complex dataset for the robust video fingerprinting task based on multiple video content-preserving manipulations in actual scenarios. The size of our dataset exceeds most datasets adopted in the current robust video fingerprinting schemes. Based on the proposed dataset, experimental results demonstrate that our scheme achieves outstanding performance improvements compared to the state-of-the-art.
AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices

Mobile video applications today have attracted significant attention. Deep learning model (e.g., deep neural network, DNN) compression is widely used to enable on-device inference for facilitating robust and private mobile video applications. The compressed DNN, however, is vulnerable to the agnostic data drift of the live video captured from the dynamically changing mobile scenarios. To combat the data drift, mobile ends rely on edge servers to continuously evolve and re-compress the DNN with freshly collected data. We design a framework, AdaEvo, that efficiently supports the resource-limited edge server handling mobile DNN evolution tasks from multiple mobile ends. The key goal of AdaEvo is to maximize the average quality of experience (QoE), i.e., the proportion of high-quality DNN service time to the entire life cycle, for all mobile ends. Specifically, it estimates the DNN accuracy drops at the mobile end without labels and performs a dedicated video frame sampling strategy to control the size of retraining data. In addition, it balances the limited computing and memory resources on the edge server and the competition between asynchronous tasks initiated by different mobile users. With an extensive evaluation of real-world videos from mobile scenarios and across four diverse mobile tasks, experimental results show that AdaEvo enables up to 34% accuracy improvement and 32% average QoE improvement.
ABFT: high-performance asynchronous Byzantine fault-tolerant consensus algorithm for electricity data metrology

In recent years, the adoption of blockchain technology in energy data metrology has become a hot research topic in energy IoT and blockchain. Blockchain is used to ensure measurement data authenticity, storage immutability, transaction reliability, communication security, etc. Due to the enormous data volume, short data uploading period, and high data uploading frequency in the electricity data metrology scenario, adopting an efficient and practical consensus algorithm becomes a key challenge to integrating blockchain into power grids. To address the problems of low transaction throughput and long consensus latency of practical Byzantine fault-tolerant (PBFT) algorithm under such a scenario, this paper proposes an Asynchronous Byzantine fault-tolerant consensus algorithm (ABFT). First, the transaction set collection phase in the consensus algorithm is improved, and SHA-MAP trees are used to store transaction sets so that blockchain nodes may broadcast, check, request, and validate the digest of transaction sets instead of the transactions themselves, reducing network redundancy and consumption; second, asynchronous parallel validation of transactions is achieved without reducing system liveness, effectively improving consensus efficiency; finally, we changed the view-change process so that nodes do not need to wait for leader node timeout to initiate a new view-change process, reducing the consensus delay. Experimental results showed that the performance of ABFT algorithm is substantially higher than that of PBFT. Therefore, ABFT can better meet the requirements of electricity data metrology.