Reviewerly/exHarmony huggingface dataset:
Air-GR: An Over-the-Air Handwritten Character Recognition System Based on Coordinate Correction YOLOv5 Algorithm and LGR-CNN

Traditional human-computer interaction technology relies heavily on input devices such as mice and keyboards, which limit the speed and naturalness of interaction and can no longer meet the more advanced interaction needs of users. With the development of computer vision (CV) technology, research on contactless gesture recognition has become a new research hotspot. However, current CV-based gesture recognition technology has the limitation of a limited number of gesture recognition and cannot achieve fast and accurate text input operations. To solve this problem, this paper proposes an over-the-air handwritten character recognition system based on the coordinate correction YOLOv5 algorithm and a lightweight convolutional neural network (LGR-CNN), referred to as Air-GR. Unlike the direct recognition of captured gesture pictures, the system uses the trajectory points of gesture actions to generate images for gesture recognition. Firstly, by combining YOLOv5 with the gesture coordinate correction algorithm proposed in this paper, the system can effectively improve gesture detection accuracy. Secondly, considering that the captured gesture coordinates may contain multiple gestures, this paper proposes a time-window-based algorithm for segmenting the gesture coordinates. Finally, the system recognizes user gestures by plotting the segmented gesture coordinates in a two-dimensional coordinate system and feeding them into the constructed lightweight convolutional neural network, LGR-CNN. For the gesture trajectory image classification task, the accuracy of LGR-CNN is 13.2%, 12.2%, and 4.5% higher than that of the mainstream networks VGG16, ResNet, and GoogLeNet, respectively. The experimental results show that Air-GR can quickly and effectively recognize any combination of 26 English letters and numbers, and its recognition accuracy reaches 95.24%.
Using clinical surveillance software to support effective infection prevention and control for managing COVID-19

The speed, severity and scale of the COVID-19 pandemic challenged infection prevention and control (IPC) efforts at hospitals worldwide. Dorset County Hospital NHS Foundation Trust in Dorchester had an established pandemic plan, which had been developed in response to the swine flu (H1N1) pandemic in 2009. However, the COVID-19 pandemic developed to a level that modern health care had not seen before and it remains the largest challenge for health care to date. This article outlines the experience of a rural district general hospital using digital solutions for infection prevention and control before and during the pandemic. The author will explore the experience of a hospital that implemented specialised clinical surveillance software, how it was applied to management and control of COVID-19 cases, and how the availability of that system allowed for continued focus on controlling other pathogens in the hospital environment, even at the height of the pandemic.
An Article On: Augmented Reality Contrast to Ergonomic Design

To describe the use of augmented reality and virtual reality (AR/VR) related to ergonomic applications across education, aircraft crew stations, and maintenance, practicing engineers involved with military applications undertook a literature review. This review analyses articles based on their relevance to ergonomic AR/VR applications as well as their breadth of applications. The review's findings were compiled across various industries to highlight particular use cases for AR/VR applications. The goal of this article is to inform ergonomics professionals about current AR/VR applications addressing ergonomic issues across a choice of three military-related usage areas. It also identifies key implementation strategies, research findings, and application-specific similarities.
Adaptive Coding is Optimal in Reinforcement Learning

Adaptive coding is defined as the adjustment of parameters of a cognitive process as a function of the task's statistical properties. Our aim is to demonstrate that adaptive coding maximizes performance in situations where decision-making follows a reinforcement learning process (RLM), and test empirically test the predictions of our model. We first provide a detailed model of adaptive coding of reward in a RLM and study its performance, measured in expected value obtained from chosen options. In our model, the parameters modulating learning adjust optimally according to the information on outcomes. Their values are modulated by the trade-off between exploration and exploitation. When feedback is complete, exploration is redundant, and parameters adjust to maximize exploitation. When feedback is partial (only provided on the chosen option) an optimal compromise is reached between exploration and exploitation. We test these predictions in a set of experiments with complete and partial feedback. We find support for the hypothesis that coding is adaptive, and has the linear form we hypothesize. The design allows us to discriminate between two possible formulations, finding support for the hypothesis that adaptation is specific to reward encoding. We also find that the representational bias introduced in choice by the adaptive coding is transitory and is corrected when appropriate information is provided to participants.
Time-optimal trajectory planning based on event-trigger and conditional proportional control

Trajectory planning is an important issue for manipulators and robots. To get a optimal trajectory, many constraints including actuators specifications, motion range of joints, workspace limitations, etc, and many objectives including the shortest time, the shortest distance, the lowest energy consumption, the minimum oscillations, obstacles-avoiding, etc, should be considered both. In this paper, firstly, the forward kinematics and inverse kinematics of a five axis manipulator are deduced. And, a simple method to choose one appropriate solution from multi solutions of inverse kinematics is proposed. Secondly, an easy-implemented optimization method of trajectory planning is proposed based on seventh order polynomial interpolation, event-trigger mechanism and conditional proportional control (P control). The proposed optimization method can capture the time optimal trajectory, and the actuators specifications including velocity, acceleration of motor can be guaranteed as well. Thirdly, comparative simulations and experiments validate the effectiveness and efficiency of proposed optimization method. The research provides an insight for the application of trajectory optimization on the micro controller with low computing capability and high real-time performance requirement.
Asymmetric volatility connectedness between cryptocurrencies and energy: Dynamics and determinants

We explore the dynamics and determinants of volatility connectedness between cryptocurrencies and energy. We employed a block dynamic equicorrelation model and a group volatility connectedness measurement to measure the cross-equicorrelation and volatility connectedness between cryptocurrencies and energy. We also adopted dynamic model averaging to identify the time-varying drivers. The results suggest that changes in cross-equicorrelation between the two groups were affected by influential global events and increased after the COVID-19 pandemic. Volatilities were transmitted in both directions between cryptocurrencies and energy, but the transmission from energy to cryptocurrencies is by far the strongest. The driver identification implies that the factors related to cryptocurrencies and global financial markets had important roles in explaining the volatility connectedness from cryptocurrencies to energy in some periods after the COVID-19 pandemic, but the effects were marginal. In contrast, factors such as electricity consumption, cryptocurrency turnovers, and VIX were important in affecting the volatility connectedness from energy to cryptocurrencies, and the effects depended on factors and changed over time.
PMU-Leaker

Performance Monitor Unit (PMU) is a special hardware module in processors that contains a set of counters to record various architectural and micro-architectural events. In this paper, we propose PMU-Leaker, a novel realization of all existing cache side-channel attacks where accurate execution time measurements are replaced by information leaked through PMU. The efficacy of PMU-Leaker is demonstrated by (1) leaking the secret data stored in Intel Software Guard Extensions (SGX) with the transient execution vulnerabilities including Spectre and ZombieLoad and (2) extracting the encryption key of a victim AES performed in SGX. We perform thorough experiments on a DELL Inspiron 15-7560 laptop that has an Intel® Core™ i5-7200U processor with the Kaby Lake architecture and the results show that, among the 176 PMU counters, 24 of them are vulnerable and can be used to launch the PMU-Leaker attack.
On the Long-term Archiving of Research Data

Accessing research data at any time is what FAIR (Findable Accessible Interoperable Reusable) data sharing aims to achieve at scale. Yet, we argue that it is not sustainable to keep accumulating and maintaining all datasets for rapid access, considering the monetary and ecological cost of maintaining repositories. Here, we address the issue of cold data storage: when to dispose of data for offline storage, how can this be done while maintaining FAIR principles and who should be responsible for cold archiving and long-term preservation.
A Parallel Multiobjective PSO Weighted Average Clustering Algorithm Based on Apache Spark

Multiobjective clustering algorithm using particle swarm optimization has been applied successfully in some applications. However, existing algorithms are implemented on a single machine and cannot be directly parallelized on a cluster, which makes it difficult for existing algorithms to handle large-scale data. With the development of distributed parallel computing framework, data parallelism was proposed. However, the increase in parallelism will lead to the problem of unbalanced data distribution affecting the clustering effect. In this paper, we propose a parallel multiobjective PSO weighted average clustering algorithm based on apache Spark (Spark-MOPSO-Avg). First, the entire data set is divided into multiple partitions and cached in memory using the distributed parallel and memory-based computing of Apache Spark. The local fitness value of the particle is calculated in parallel according to the data in the partition. After the calculation is completed, only particle information is transmitted, and there is no need to transmit a large number of data objects between each node, reducing the communication of data in the network and thus effectively reducing the algorithm’s running time. Second, a weighted average calculation of the local fitness values is performed to improve the problem of unbalanced data distribution affecting the results. Experimental results show that the Spark-MOPSO-Avg algorithm achieves lower information loss under data parallelism, losing about 1% to 9% accuracy, but can effectively reduce the algorithm time overhead. It shows good execution efficiency and parallel computing capability under the Spark distributed cluster.
Robust Structure-aware Semi-supervised Learning

We present a novel unified framework robust structure-aware semi-supervised learning called Unified RSSL (URSSL) which is robust to both outliers and noisy labels where the ground truth of the corrupted labels can be either within or out of training sets. Particularly, URSSL applies joint semi-supervised dimensionality reduction with robust estimators and network sparse regularization simultaneously on the graph Laplacian matrix iteratively to preserve the intrinsic graph structure and ensure robustness to the compound noise. First, in order to relieve the influence from outliers, a novel semi-supervised robust dimensionality reduction is applied relying on robust estimators to suppress outliers. Meanwhile, to tackle noisy labels, the denoised graph similarity information is encoded into the network regularization. Moreover, by identifying strong relevance of dimensionality reduction and network regularization in the context of robust semi-supervised learning (RSSL), a two-step alternative optimization is derived to compute optimal solutions with guaranteed convergence. Extensive experimental results demonstrate the promising performance of this framework when applied to multiple benchmark datasets with respect to state-of-the-art approaches for important applications in the areas of image classification.
Contemporary Role of Blockchain in Industry 4.0

Everything around us is transforming with advancement in time, as a result, new technologies are developing with upcoming industry revolution which is widely popular as Industry 4.0. It includes new trends related to cyber-physical system, Augmented Reality, artificial intelligence, Internet of Things (IoT), cloud computing, and many more. Many physical infrastructures are transformed to virtual ones which allows decision making and planning in a decentralized form. It also enabled users to accomplish real-time communications with help of integrated computing algorithms with the challenge of decentralization and heterogeneous systems. Progressive web technologies are involving varied characteristics in order to make technology very easy for the manufacturer communal. Blockchain version 4.0 is aiding business in terms of ensuring clearness in terms of supply, besides it promises conviction amongst the transaction establishments, and supply chain management is inculcating blockchain technology completely. The significant segment which is benefited with the adoption of Industry 4.0 moralities besides machineries is industrial or manufacturing business which will be generating smart industrial era involving progressive knowledge in addition to systems in Industry 4.0 which will be enhancing varied progressions amongst business value chain plus up surging proficiency and productivity. Though, Industry 4.0 necessitates actual incorporation of countless machineries and arrangements involving unified procedures amongst its constituents which generates numerous trials during creation of applications for smart industrial and manufacturing units which involves various factors like safety, conviction, traceability, dependability, and contract automation amongst manufacturing value chain. This chapter discusses various solutions correlated to union of blockchain as well as Industry 4.0. It also explores various pros and cons of traditional security solutions and provides evaluation of present blockchain-based security solutions. It also deliberates blockchain-based application areas of Industry 4.0.
The use of multimedia interactive to improve students science literacy in the new normal era

This study aims to describe the influence of interactive multimedia on students' science literacy in the new normal era and student responses after learning using interactive multimedia on energy flow materials. The type of research used is pre-experimental design research through one group pretest-postest design. The subjects in this study were 31 students of class VIIC in Muhammadiyah Junior High School 2 Taman Sidoarjo, even semester of the 2021/2022 academic year. Data collected by techniques use observation, tests, and questionnaires. Research instruments include observation sheets for the implementation of learning, science literacy tests, and student response questionnaire sheets. The data were analyzed descriptively using the N-Gain test, the implementation observation sheet, and the response questionnaire. The results showed that the implementation of learning using interactive multimedia on energy flow materials got the criteria for being carried out properly. Students' science literacy skills increased from an average pretest score of 38.5 to 81 on the posttest, with an average N-Gain score of 0.69, categorized as moderate. The learning process and the use of interactive multimedia applications on energy flow materials were also responded to positively by students. Based on the description above, learning using interactive multimedia on energy flow materials can improve students' science literacy skills in the new normal era.
The Implementation of the Marrakesh Treaty in the European Union: An Important Piece in the Accessibility Jigsaw?

The Marrakesh Treaty to Facilitate Access to Published Works for Persons Who Are Blind, Visually Impaired, or Otherwise Print Disabled (Marrakesh Treaty) aims to enable the creation and cross-border exchange of copyrighted works in formats that are accessible to individuals with disabilities. To that end, it requires Contracting Parties to introduce a set of limitations and exceptions to existing copyright rules. Following Opinion 3/15 of the Court of Justice, the Marrakesh Treaty was concluded by the European Union (EU) on behalf of its Member States. It was implemented by means of a Directive governing the substantive rights of reproduction, distribution and making available of published works in accessible formats, and a Regulation governing the cross-border exchange of accessible format works with Third Countries, both based on Article 114 of the Treaty on the Functioning of the European Union (TFEU). This article examines the role of the Marrakesh Directive and Regulation in enhancing access to printed material to persons with disabilities. In that connection, it discusses common trends and perceptions of such a Marrakesh framework on the basis of empirical research consisting of a set of semi-structured interviews conducted with key stakeholders across twelve Member States. It locates the Directive and Regulation within the growing body of EU legislation that aims to ensure accessibility of an array of materials, products and services for persons with disabilities, while driving forward economic integration. In doing so, it conceives of the Marrakesh Directive and Regulation as part of the broader remit of EU disability law, which is an emerging cross-cutting area of EU action. On the whole, this article argues that the Marrakesh Treaty and its implementing legislation contribute to the protection of the rights of persons with disabilities within the internal market, but form just one piece – albeit an important one – of the accessibility ‘jigsaw’. Marrakesh Treaty, Directive 2017/1564/EU (Marrakesh Directive), Disability, Copyright exceptions, Accessibility, European Accessibility Act, European Union Law, Implementation, Empirical Research
A Novel Smart Deepfake Video Detection System

Rapid advancements in deep learning-based technologies have developed several synthetic video and audio generation methods producing incredibly hyper-realistic deepfakes.These deepfakes can be employed to impersonate the identity of a source person in videos by swapping the source's face with the target one.Deepfakes can also be used to clone the voice of a target person utilizing audio samples.Such deepfakes may pose a threat to societies if they are utilized maliciously.Consequently, distinguishing either one or both deepfake visual video frames and cloned voices from genuine ones has become an urgent issue.This work presents a novel smart deepfake video detection system.The video frames and audio are extracted from given videos.Two feature extraction methods are proposed, one for each modality; visual video frames, and audio.The first method is an upgraded XceptionNet model, which is utilized for extracting spatial features from video frames.It produces feature representation for visual video frames.The second one is a modified InceptionResNetV2 model based on the Constant-Q Transform (CQT) method.It is employed to extract deep timefrequency features from the audio modality.It produces feature representation for the audio.The corresponding extracted features of both modalities are fused at a mid-layer to produce a bimodal information-based feature representation for the whole video.These three representation levels are independently fed into the Gated Recurrent Unit (GRU) based attention mechanism helping to learn and extract deep and important temporal information per level.Then, the system checks whether the forgery is only applied to video frames, audio, or both, and produces the final decision about video authenticity.The newly suggested method has been evaluated on the FakeAVCeleb multimodal videos dataset.The experimental results analysis assures the superiority of the new method over the current-stateof-the-art methods.
The Effects of Artificial Intelligence Education: A Meta-Analysis

Purpose: According to the government policy, interest in and research on artificial intelligenceeducation has been actively conducted in Korea since 2019. The purpose of this study was to synthesizeeffects of artificial intelligence education through meta-analysis.&#x0D; Methods: The researcher rigorously selected a total of 43 primary studies including 25 journal articlesand 18 theses and dissertations reporting effects of artificial intelligence education. To calculate theeffect size, first, the effect size for each study was calculated, and then the overall effect size wascalculated. Subgroup analyses were then performed to compare differences by study characteristics. CMA 3.0 program was used in data analysis.&#x0D; Results: First, the overall effect size of artificial intelligence education is 0.687, which can beinterpreted as a medium effect size. Second, effect sizes according to the school level were 0.947for college, 0.784 for high school, 0.784 for middle school, and 0.633 for elementary school. Third,effect sizes according to the learning environment were 0.915 for online tools, 0.777 for the unplugged,0.705 for physical computing, 0.684 for block coding, 0.493 for script coding, and 0.332 for others. Fourth, effect sizes according to dependent variables were 0.857 for the cognitive domain and 0.57for the affective domain.&#x0D; Conclusion: Based on the results of this study, it is expected that school administrators and educatorswill be able to develop effective and efficient artificial intelligence education programs. Therefore,the author hopes that artificial intelligence education will enable students at all school levels toexperience successful learning and achieve their educational goals.
Supporting Non-Native-English Speaking Graduate Students with Academic Writing Skills: A Case Study of the Explicit Instructional Use of Paraphrasing Guidelines writing frequently

In this study, we examined how the explicit instructional use of paraphrasing guidelines can help international graduate students who are non-native English speakers to paraphrase information in text sources. This case study involved 14 graduate students enrolled in an academic writing class at a university in the northwest United States. Data were collected through seven sources: a background questionnaire, video of instruction, pretest, posttest, student task documents, stimulated recall interviews, and teacher interviews, which together addressed the three research questions. The data show that the participants’ perceptions of using the guidelines were positive and that their paraphrases in the posttest had improved according to the guidelines. The study concludes that the use of the guidelines should be accompanied by meaningful support through explicit instruction and sufficient practice over time. The implications of this study include recommendations for paraphrasing instruction and future research.