Reviewerly/exHarmony huggingface dataset:
A Computer Vision-Based Yoga Pose Grading Approach Using Contrastive Skeleton Feature Representations

The main objective of yoga pose grading is to assess the input yoga pose and compare it to a standard pose in order to provide a quantitative evaluation as a grade. In this paper, a computer vision-based yoga pose grading approach is proposed using contrastive skeleton feature representations. First, the proposed approach extracts human body skeleton keypoints from the input yoga pose image and then feeds their coordinates into a pose feature encoder, which is trained using contrastive triplet examples; finally, a comparison of similar encoded pose features is made. Furthermore, to tackle the inherent challenge of composing contrastive examples in pose feature encoding, this paper proposes a new strategy to use both a coarse triplet example-comprised of an anchor, a positive example from the same category, and a negative example from a different category, and a fine triplet example-comprised of an anchor, a positive example, and a negative example from the same category with different pose qualities. Extensive experiments are conducted using two benchmark datasets to demonstrate the superior performance of the proposed approach.
Multimodal Phased Transformer for Sentiment Analysis

Multimodal Transformers achieve superior performance in multimodal learning tasks.However, the quadratic complexity of the selfattention mechanism in Transformers limits their deployment in low-resource devices and makes their inference and training computationally expensive.We propose multimodal Sparse Phased Transformer (SPT) to alleviate the problem of self-attention complexity and memory footprint.SPT uses a sampling function to generate a sparse attention matrix and compress a long sequence to a shorter sequence of hidden states.SPT concurrently captures interactions between the hidden states of different modalities at every layer.To further improve the efficiency of our method, we use Layer-wise parameter sharing and Factorized Co-Attention that share parameters between Cross Attention Blocks, with minimal impact on task performance.We evaluate our model with three sentiment analysis datasets and achieve comparable or superior performance compared with the existing methods, with a 90% reduction in the number of parameters.We conclude that (SPT) along with parameter sharing can capture multimodal interactions with reduced model size and improved sample efficiency.
Perceived Security Risk Based on Moderating Factors for Blockchain Technology Applications in Cloud Storage to Achieve Secure Healthcare Systems

Due to the high amount of electronic health records, hospitals have prioritized data protection. Because it uses parallel computing and is distributed, the security of the cloud cannot be guaranteed. Because of the large number of e-health records, hospitals have made data security a major concern. The cloud’s security cannot be guaranteed because it uses parallel processing and is distributed. The blockchain (BC) has been deployed in the cloud to preserve and secure medical data because it is particularly prone to security breaches and attacks such as forgery, manipulation, and privacy leaks. An overview of blockchain (BC) technology in cloud storage to improve healthcare system security can be obtained by reading this paper. First, we will look at the benefits and drawbacks of using a basic cloud storage system. After that, a brief overview of blockchain cloud storage technology will be offered. Many researches have focused on using blockchain technology in healthcare systems as a possible solution to the security concerns in healthcare, resulting in tighter and more advanced security requirements being provided. This survey could lead to a blockchain-based solution for the protection of cloud-outsourced healthcare data. Evaluation and comparison of the simulation tests of the offered blockchain technology-focused studies can demonstrate integrity verification with cloud storage and medical data, data interchange with reduced computational complexity, security, and privacy protection. Because of blockchain and IT, business warfare has emerged, and governments in the Middle East have embraced it. Thus, this research focused on the qualities that influence customers’ interest in and approval of blockchain technology in cloud storage for healthcare system security and the aspects that increase people’s knowledge of blockchain. One way to better understand how people feel about learning how to use blockchain technology in healthcare is through the United Theory of Acceptance and Use of Technology (UTAUT). A snowball sampling method was used to select respondents in an online poll to gather data about blockchain technology in Middle Eastern poor countries. A total of 443 randomly selected responses were tested using SPSS. Blockchain adoption has been shown to be influenced by anticipation, effort expectancy, social influence (SI), facilitation factors, personal innovativeness (PInn), and a perception of security risk (PSR). Blockchain adoption and acceptance were found to be influenced by anticipation, effort expectancy, social influence (SI), facilitating conditions, personal innovativeness (PInn), and perceived security risk (PSR) during the COVID-19 pandemic, as well as providing an overview of current trends in the field and issues pertaining to significance and compatibility.
Deep Q-Learning-Based Neural Network with Privacy Preservation Method for Secure Data Transmission in Internet of Things (IoT) Healthcare Application

The healthcare industry is being transformed by the Internet of Things (IoT), as it provides wide connectivity among physicians, medical devices, clinical and nursing staff, and patients to simplify the task of real-time monitoring. As the network is vast and heterogeneous, opportunities and challenges are presented in gathering and sharing information. Focusing on patient information such as health status, medical devices used by such patients must be protected to ensure safety and privacy. Healthcare information is confidentially shared among experts for analyzing healthcare and to provide treatment on time for patients. Cryptographic and biometric systems are widely used, including deep-learning (DL) techniques to authenticate and detect anomalies, andprovide security for medical systems. As sensors in the network are energy-restricted devices, security and efficiency must be balanced, which is the most important concept to be considered while deploying a security system based on deep-learning approaches. Hence, in this work, an innovative framework, the deep Q-learning-based neural network with privacy preservation method (DQ-NNPP), was designed to protect data transmission from external threats with less encryption and decryption time. This method is used to process patient data, which reduces network traffic. This process also reduces the cost and error of communication. Comparatively, the proposed model outperformed some standard approaches, such as thesecure and anonymous biometric based user authentication scheme (SAB-UAS), MSCryptoNet, and privacy-preserving disease prediction (PPDP). Specifically, the proposed method achieved accuracy of 93.74%, sensitivity of 92%, specificity of 92.1%, communication overhead of 67.08%, 58.72 ms encryption time, and 62.72 ms decryption time.
A new image encryption scheme based on a hyperchaotic system &amp; multi specific S-boxes

This paper studies a new image encryption scheme based on a hyperchaotic system & multi-specific S-boxes. First, the hyperchaotic system generates a sequence that is used to create a specific S-box. The S-box is obtained by sorting the values of the sequence of hyperchaotics of increasing sort, and then taking their index as a new sequence to create the S-box. The number of specific S-boxes needed to encrypt the image depends on the rows of the plain image. Second, each row of the plain image is substituted by a different S-box to obtain the rows of the pre-encrypted image. Third, to increase the level of security, each specific S-box is considered as a column of the mask matrix. Then, a permutation is applied to the mask matrix, which used the last sequence of the hyperchaotic to permuted the rows of the mask matrix to obtain the permuted mask matrix. Finally, the XOR operation is applied between the permuted mask matrix and the pre-encrypted image to obtain the final cipher image. Experimental and analysis results show that the proposed algorithm has a good performance in terms of security, high sensitivity, as well as low time complexity.
Non-Pharmaceutical Interventions against COVID-19 Pandemic: Review of Contact Tracing and Social Distancing Technologies, Protocols, Apps, Security and Open Research Directions

The COVID-19 Pandemic has punched a devastating blow on the majority of the world's population. Millions of people have been infected while hundreds of thousands have died of the disease throwing many families into mourning and other psychological torments. It has also crippled the economy of many countries of the world leading to job losses, high inflation, and dwindling Gross Domestic Product (GDP). The duo of social distancing and contact tracing are the major technological-based non-pharmaceutical public health intervention strategies adopted for combating the dreaded disease. These technologies have been deployed by different countries around the world to achieve effective and efficient means of maintaining appropriate distance and tracking the transmission pattern of the diseases or identifying those at high risk of infecting others. This paper aims to synthesize the research efforts on contact tracing and social distancing to minimize the spread of COVID-19. The paper critically and comprehensively reviews contact tracing technologies, protocols, and mobile applications (apps) that were recently developed and deployed against the coronavirus disease. Furthermore, the paper discusses social distancing technologies, appropriate methods to maintain distances, regulations, isolation/quarantine, and interaction strategies. In addition, the paper highlights different security/privacy vulnerabilities identified in contact tracing and social distancing technologies and solutions against these vulnerabilities. We also x-rayed the strengths and weaknesses of the various technologies concerning their application in contact tracing and social distancing. Finally, the paper proposed insightful recommendations and open research directions in contact tracing and social distancing that could assist researchers, developers, and governments in implementing new technological methods to combat the menace of COVID-19.
Improving the Cryptocurrency Price Prediction Performance Based on Reinforcement Learning

During recent developments, cryptocurrency has become a famous key factor in financial and business opportunities. However, the cryptocurrency investment is not visible regarding the market’s inconsistent aspect and volatility of high prices. Due to the real-time prediction of prices, the previous approaches in price prediction doesn’t contain enough information and solution for forecasting the price changes. Based on the mentioned problems in cryptocurrency price prediction, we proposed a machine learning-based approach to price prediction for a financial institution. The proposed system contains the blockchain framework for secure transaction environment and Reinforcement Learning algorithm for analysis and prediction of price. The main focus of this system is on Litecoin and Monero cryptocurrencies. The results show the presented system accurate the performance of price prediction higher than another state-of-art algorithm.
Development and Comparison of Two Novel Hybrid Neural Network Models for Hourly Solar Radiation Prediction

There are a lot of developing countries with inadequate meteorological stations to measure solar radiation. This has been a major drawback for solar power applications in these countries as the performance of the solar-powered system cannot be accurately forecasted. In this study, two novel hybrid neural networks namely; convolutional neural network/artificial neural network (CNN-ANN) and convolutional neural network/long short-term memory/artificial neural network (CNN-LSTM-ANN), have been developed for hourly global solar radiation prediction. ANN models are also developed and the performance of the hybrid neural network models is compared with it. This study contributes to the search for more accurate solar radiation estimation methods. The hybrid neural network models are trained/tested with data from ten different countries across Africa. Results from this study indicate that the performance of all the hybrid models developed in this study is superior to what has been presented in existing literature with their r values ranging from 0.9662 to 0.9930. CNN-ANN model is the best for solar radiation forecasting in Southern, Central, and West Africa. CNN-LSTM-ANN is better for East Africa while both CNN-ANN and CNN-LSTM-ANN are suitable for North Africa. CNN-ANN application for solar radiation prediction in Chad had the overall best performance with an r-value, MAE, RMSE, and MAPE of 0.9930, 15.70 W/m2, 46.84 W/m2, and 4.98% respectively. The integration of CNN and LSTM algorithms with an ANN model enhanced long-term computational dependency and reduce error terms for the model.
Phishing Websites Detection using Machine Learning

Phishing is a common attack on credulous people by making them to disclose their unique information using counterfeit websites. The objective of phishing website URLs is to purloin the personal information like user name, passwords and online banking transactions. Phishers use the websites which are visually and semantically similar to those real websites. As technology continues to grow, phishing techniques started to progress rapidly and this needs to be prevented by using anti-phishing mechanisms to detect phishing. Machine learning is a powerful tool used to strive against phishing attacks. This paper surveys the features used for detection and detection techniques using machine learning
The Effects of Smart Home Interface Touch Button Design Features on Performance among Young and Senior Users

Touch technology-based smart homes have become increasingly prevalent, as they can help people with independent daily life, especially for the elderly. The aim of this study was to investigate the effects of button features (i.e., button size, graphics/text ratio, and icon style) in smart home interfaces on user performance across two age groups. Participants in the young group (n = 15) and senior group (n = 15) completed a clicking task. Button size ranged from 10 mm to 25 mm with 5 mm increments. The three levels of graphics/text ratio were 3:1, 1:1, and 1:3, while icon style was either flat or skeuomorphic. Results showed that button size and graphics/text ratio had significant effects on user performance in both groups, whereas icon style only had an effect in the senior group. It was observed that the elderly were fond of buttons with a larger size of 20 mm with larger texts and skeuomorphic icons, whereas the young preferred a button size of 15 mm with equal-sized graphics and text. These results may help to improve the accessibility and usability of smart home interface design.
Bringing Your Own View

Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph representations beneficial to downstream tasks; but its success could hinge on domain knowledge for handcraft or the often expensive trials and errors. Even its state-of-the-art representative, graph contrastive learning (GraphCL), is not completely free of those needs as GraphCL uses a prefabricated prior reflected by the ad-hoc manual selection of graph data augmentations. Our work aims at advancing GraphCL by answering the following questions: How to represent the space of graph augmented views? What principle can be relied upon to learn a prior in that space? And what framework can be constructed to learn the prior in tandem with contrastive learning? Accordingly, we have extended the prefabricated discrete prior in the augmentation set, to a learnable continuous prior in the parameter space of graph generators, assuming that graph priors per se, similar to the concept of image manifolds, can be learned by data generation. Furthermore, to form contrastive views without collapsing to trivial solutions due to the prior learnability, we have leveraged both principles of information minimization (InfoMin) and information bottleneck (InfoBN) to regularize the learned priors. Eventually, contrastive learning, InfoMin, and InfoBN are incorporated organically into one framework of bi-level optimization. Our principled and automated approach has proven to be competitive against the state-of-the-art graph self-supervision methods, including GraphCL, on benchmarks of small graphs; and shown even better generalizability on large-scale graphs, without resorting to human expertise or downstream validation. Our code is publicly released at https://github.com/Shen-Lab/GraphCL_Automated.
Enhance Teaching-Learning-Based Optimization for Tsallis-Entropy-Based Feature Selection Classification Approach

Feature selection is an effective method to reduce the number of data features, which boosts classification performance in machine learning. This paper uses the Tsallis-entropy-based feature selection to detect the significant feature. Support Vector Machine (SVM) is adopted as the classifier for classification purposes in this paper. We proposed an enhanced Teaching-Learning-Based Optimization (ETLBO) to optimize the SVM and Tsallis entropy parameters to improve classification accuracy. The adaptive weight strategy and Kent chaotic map are used to enhance the optimal ability of the traditional TLBO. The proposed method aims to avoid the main weaknesses of the original TLBO, which is trapped in local optimal and unbalance between the search mechanisms. Experiments based on 16 classical datasets are selected to test the performance of the ETLBO, and the results are compared with other well-established optimization algorithms. The obtained results illustrate that the proposed method has better performance in classification accuracy.
Privacy-Preserving Encrypted Traffic Inspection With Symmetric Cryptographic Techniques in IoT

To ensure the security of Internet of Things (IoT) communications, one can use deep packet inspection (DPI) on network middleboxes to detect and mitigate anomalies and suspicious activities in network traffic of IoT, although doing so over encrypted traffic is challenging. Therefore, in this article, an efficient and privacy-preserving encrypted traffic detection scheme is proposed. The scheme uses only lightweight cryptographic operations (i.e., symmetric encryption, hash functions, and pseudorandom functions) to achieve both privacy and security within an inspection round. A dispute resolution mechanism is also designed to address potential disputes between client(s) and server(s). We also present the corresponding security proof and experimental evaluation, which demonstrate that our proposed scheme achieves strong security and privacy preservation and good performance.
Assessment of the effects of Covid-19 pandemic on the prospects of e-learning in higher learning institutions: The mediating role of academic innovativeness and technological growth

This paper assesses the antecedents (prospects) geared toward the implementation of e-learning strategy in the HLI’s of Ghana (a developing country) amidst the Covid-19 pandemic. The study again espouses the enabling factors that influence the e-learning system usage during the Covid-19 pandemic in a less digitalized economy. In doing so, the authors relied wholly on a quantitative research paradigm by leveraging on structural equation modelling (SEM) technique to achieve the study goal(s). The findings from 563 valid responses showed that the outbreak of the novel Covid-19 has positively and significantly strengthened the adoption of e-learning strategies across HLI’s in Ghana. It also revealed that “academic innovativeness” and “technological growth & development” have a significant mediation effect on the relationship between the Covid-19 pandemic and the adoption of e-learning strategy. In practice, the study offers contributions for universities’ management and policymakers to understand the relevant factors needed urgently by students and instructors of HLI’s for ensuring the successful usage of e-learning systems. The conclusion and future research direction has been presented in the paper.
Federated Learning Algorithms to Optimize the Client and Cost Selections

In recent years, federated learning has received widespread attention as a technology to solve the problem of data islands, and it has begun to be applied in fields such as finance, healthcare, and smart cities. The federated learning algorithm is systematically explained from three levels. First, federated learning is defined through the definition, architecture, classification of federated learning, and comparison with traditional distributed knowledge. Then, based on machine learning and deep learning, the current types of federated learning algorithms are classified, compared, and analyzed in-depth. Finally, the communication from the perspectives of cost, client selection, and aggregation method optimization, the federated learning optimization algorithms are classified. Finally, the current research status of federated learning is summarized. Finally, the three major problems and solutions of communication, system heterogeneity, and data heterogeneity faced by federated learning are proposed and expectations for the future.
AFLPC: An Asynchronous Federated Learning Privacy-Preserving Computing Model Applied to 5G-V2X

Federated learning can effectively protect local data privacy in 5G-V2X environment and ensure data protection in Internet of vehicles environment. The advantages of low delay of 5G network should be better utilized in the vehicle-road cooperative system. But the existing asynchronous federated learning obtains a local model through different node training and completes the update of the global model through the central server. There are problems such as single point of failure, privacy leakage, and deviation of aggregation parameters. In response to the above problems, we proposed a 5G-V2X-oriented asynchronous federated learning privacy-preserving computing model (AFLPC). We used an adaptive differential privacy mechanism to reduce noise while protecting data privacy. A weight-based asynchronous federated learning aggregation update method is proposed to reasonably control the proportion of parameters submitted by users with different training speeds in the aggregation parameters and actively update the aggregation parameters of lagging users, so as to effectively reduce the negative impact on the model caused by the different speed of finding you. Experiments show that the proposed method can effectively ensure the credibility and privacy of asynchronous federated learning in 5G-V2X scenarios and at the same time improve the utility of the model.