Reviewerly/exHarmony huggingface dataset:
SBI-DHGR: Skeleton-based intelligent dynamic hand gestures recognition

Hand gesture recognition (HGR) plays a significant role in interpreting the meaning of sign language, human–computer interaction, and robot control. This paper proposes a real-time skeleton-based intelligent dynamic hand gesture recognition (SBI-DHGR) approach, which comprises three modules: Palm Centroid (PC), Data augmentation with the Tenet Effect, and Deep learning architecture. The palm Centroid (PC) module is introduced to identify the proposed palm center joint. Data augmentation with a tenet effect module is designed to improve the CNN model’s generalizability. Further, a novel deep learning model is proposed for temporal 3D-HGR by exploiting the capabilities of a multi-channel convolutional neural network (CNN) and long short-term memory (LSTM) recurrent network. The multi-channel CNN is introduced to learn the cardinal positions of hand joints by capturing the low, average, and high-level features. LSTM is embedded to learn the temporal characteristics of hand joints. The effectiveness of the SBI-DHGR framework is evaluated over five challenging datasets: SHREC-14, SHREC-28, DHG-14, DHG-28, and FPHA, by adopting person-dependent and person-independent validation setups.
Types, Modes and so Much More – The Prolog Way

We present in a tutorial way some ideas developed in the context of the Ciao Prolog system that we believe could be useful for the future evolution of Prolog. We concentrate primarily on one area: the use of assertions with types, modes, and other properties, and how the unique characteristics of Prolog have made early advances possible in the area of combining static and dynamic language features. However, we also address briefly some other issues related to extending the expressiveness and functionality of the language.
Embedding Sustainable Mathematics Higher Education in the Fourth Industrial Revolution Era Post-COVID-19: Exploring Technology-Based Teaching Methods

Higher Education Institutions have adopted technology-based teaching methods to prevent the spreading of the contagious coronavirus (COVID-19). In the Fourth Industrial Revolution (4IR) era, technology-based teaching methods are indispensable for scaffolding teaching, learning and assessment. Mathematics is an important discipline in education, and provides a basis for problem solving, critical thinking and analytical skills, which is important to consider when focusing on sustainability. Thus, to add knowledge to the field about integrating technology-based teaching methods in mathematics, Higher Education environments during and post-COVID-19 need to be interrogated. This added knowledge is be valuable in the 4IR era post-COVID-19 for sustaining mathematics in Higher Education. This study explored participants’ experiences, views, implications and suggestions for technology-based teaching methods for mathematics. These participants (N = 45) were postgraduate students and mathematics school teachers at the research site in KwaZulu-Natal, South Africa. The research location for this qualitative study was a South African Higher Education Institution, and the study was conducted during the COVID-19 pandemic. The Community of Inquiry theoretical framework and the Substitution, Augmentation, Modification and Redefinition (SAMR) theoretical model guided this study. Participants were invited to two interactive online workshops. At these workshops, participants were introduced to different technology-based teaching methods. Then, they were invited to individual online interviews. The findings of this study suggest important experiences, views and suggestions for using technology-based teaching methods in Higher Education mathematics contexts in the 4IR era. These findings provide important implications and further research possibilities for embedding sustainable mathematics in Higher Education in the 4IR era, post-COVID-19.
Stack-YOLO: A Friendly-Hardware Real-Time Object Detection Algorithm

Efficient real-time object detection on edge devices, CPUs, GPU clusters and cloud GPUs is still a very challenging problem due to factors such as computational power, storage capacity and image resolution. To address these issues, this paper proposes a fast and efficient one-stage object detection algorithm - Stack-YOLO. Stack-YOLO uses a backbone network constructed by CSS units, which improve the gradient propagation path and enrich the gradient combination through three stages: Cross, Split and Shuffle. It can also greatly reduce the computation, reduce memory usage and memory access cost, improve detection accuracy, speed up convergence during training, perform model reparameterization during inference, fuse multibranch structure, save memory, and reduce resource requirements. In addition, to improve the computational efficiency of spatial pyramid pooling and to improve the existing pooling strategies that are prone to loss of detail information, this paper proposes the ASPPF pooling method, which replaces the time-consuming parallel pooling with serial pooling operations, automatically learns the feature weight matrix based on contextual information during downsampling, adaptively maintains important feature information, and maintains gradient better than maximum pooling during backpropagation. To improve the computational efficiency of the OTA method and SimOTA using local prior knowledge, this paper proposes the fast dynamic label matching method Fast-OTA based on the global matching cost. In addition, a composite factor model scaling method is proposed to systematically balance resource supply and demand. Based on various comparison experiments on MSCOCO and PASCAL VOC, Stack-YOLO outperforms the best existing algorithms, including Faster RCNN, DETR, YOLOX, and YOLOv6, in terms of speed and accuracy.
Small-Scale Ship Detection for SAR Remote Sensing Images Based on Coordinate-Aware Mixed Attention and Spatial Semantic Joint Context

With the rapid development of deep learning technology in recent years, convolutional neural networks have gained remarkable progress in SAR ship detection tasks. However, noise interference of the background and inadequate appearance features of small-scale objects still pose challenges. To tackle these issues, we propose a small ship detection algorithm for SAR images by means of a coordinate-aware mixed attention mechanism and spatial semantic joint context method. First, the coordinate-aware mixed attention mechanism innovatively combines coordinate-aware channel attention and spatial attention to achieve coordinate alignment of mixed attention features. In this way, attention with finer spatial granularity is conducive to strengthening the focusing ability on small-scale objects, thereby suppressing the background clutters accurately. In addition, the spatial semantic joint context method exploits the local and global environmental information jointly. The detailed spatial cues contained in the multi-scale local context and the generalized semantic information encoded in the global context are used to enhance the feature expression and distinctiveness of small-scale ship objects. Extensive experiments are conducted on the LS-SSDD-v1.0 and the HRSID dataset. The results with an average precision of 77.23% and 90.85% on the two datasets show the effectiveness of the proposed methods.
Encryption algorithm based on fractional order chaotic system combined with adaptive predefined time synchronization

An image encryption and decryption method of fractional order chaotic systems (FOCS) with predefined time synchronization is proposed in this article. Compared with the existing integer order chaotic systems (IOCS), fractional order chaotic systems has the advantage of increasing the complexity of the ciphertext. At the same time, by using the predefined synchronization time, the key space is expanded, the complexity of the key is increased, and the security of the algorithm is improved. To further improve the security of encryption and decryption process, this article uses a combination of DNA encoding, row/column cyclic shift and XOR diffusion, position scrambling and Arnold scrambling. The simulation tests of image encryption and decryption are carried out, and the effectiveness and advantages of the proposed encryption/decryption method are verified by histogram analysis, correlation analysis, entropy analysis, key sensitivity analysis and plaintext sensitivity analysis.
Local Data Spaces: Leveraging trusted research environments for secure location-based policy research in the age of coronavirus disease-2019

Abstract This work explores the use of Trusted Research Environments for the secure analysis of sensitive, record-level data on local coronavirus disease-2019 (COVID-19) inequalities and economic vulnerabilities. The Local Data Spaces (LDS) project was a targeted rapid response and cross-disciplinary collaborative initiative using the Office for National Statistics’ Secure Research Service for localized comparison and analysis of health and economic outcomes over the course of the COVID-19 pandemic. Embedded researchers worked on co-producing a range of locally focused insights and reports built on secure secondary data and made appropriately open and available to the public and all local stakeholders for wider use. With secure infrastructure and overall data governance practices in place, accredited researchers were able to access a wealth of detailed data and resources to facilitate more targeted local policy analysis. Working with data within such infrastructure as part of a larger research project involved advanced planning and coordination to be efficient. As new and novel granular data resources become securely available (e.g., record-level administrative digital health records or consumer data), a range of local policy insights can be gained across issues of public health or local economic vitality. Many of these new forms of data however often come with a large degree of sensitivity around issues of personal identifiability and how the data is used for public-facing research and require secure and responsible use. Learning to work appropriately with secure data and research environments can open up many avenues for collaboration and analysis.
Game-theoretic peer-to-peer solar energy trading on blockchain-based transaction infrastructure

The growing share of distributed, renewable resources in energy mix to combat climate change calls for a significant efficiency improvement through market-based solutions such as peer-to-peer (P2P) transactive energy systems. This paradigm shift requires an energy market that supports its functions through well-designed market rules and transaction infrastructure—the two ingredients that form a marketplace. Blockchain technology can facilitate the implementation of both market rules and transaction infrastructure for newly emerging P2P transactive energy markets. In this paper, we propose a model for the design of a transactive energy marketplace using blockchain as transaction infrastructure based on game-theoretic market rules that incentivize a more sustainable energy generation and consumption behavior while preserving the privacy of economic agents in the market. We propose a novel consensus mechanism named Proof-of-Reserve (PoR) to perform consumer-prosumer transactions and block mining activities on a distributed ledger. The model is numerically analyzed on a test system to illustrate its effectiveness. The results demonstrate the effectiveness of game-theoretic modeling in establishing market equilibrium and showcase the efficiency of blockchain technology in facilitating a functional, decentralized transaction settlement process.
Surface defect detection of wind turbine based on lightweight YOLOv5s model

The surface defect detection and maintenance of wind turbines affect the efficiency of wind power generation. However, traditional detection methods suffer from insufficient accuracy and large numbers of calculations. To solve this problem, we propose a surface defect detection model of wind turbines based on the lightweight YOLOv5s. Firstly, the YOLOv5s backbone network was replaced by the MobileNetv3 lightweight network for feature extraction to coordinate and balance the lightweight and accuracy relationship of the model. The neck network was realized as a weighted bi-directional feature pyramid network to enhance the model’s multi-scale adaptability and improve its fusion speed and efficiency. To adaptively adjust the feature weights and further improve the feature extraction ability, the convolution block attention module was used as the space and channel attention mechanism. Finally, the loss function was changed from border regression to the efficient intersection over union loss, which improved the accuracy of the bounding box regression. Compared with the original YOLOv5s model, the mean average precision was increased by 5.51 %, and the detection time was reduced by 10.79 frames per second, which shows that the improved model can complete the real-time target detection task on embedded devices with high accuracy and low energy consumption.
High-dimensional conditionally Gaussian state space models with missing data

We develop an efficient sampling approach for handling complex missing data patterns and a large number of missing observations in conditionally Gaussian state space models. Two important examples are dynamic factor models with unbalanced datasets and large Bayesian VARs with variables in multiple frequencies. A key observation underlying the proposed approach is that the joint distribution of the missing data conditional on the observed data is Gaussian. Furthermore, the inverse covariance or precision matrix of this conditional distribution is sparse, and this special structure can be exploited to substantially speed up computations. We illustrate the methodology using two empirical applications. The first application combines quarterly, monthly and weekly data using a large Bayesian VAR to produce weekly GDP estimates. In the second application, we extract latent factors from unbalanced datasets involving over a hundred monthly variables via a dynamic factor model with stochastic volatility.
Applications of immersive technologies for occupational safety and health training and education: A systematic review

Immersive technologies (ImTs) have emerged as a viable pathway to address poor occupational safety and health (OSH) performance through training and education of workers. This study aimed to gain a holistic view of the applications of ImTs for OSH training and education. A review of the application of ImTs for OSH training and education is conducted using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) approach and bibliometric analysis. This resulted in the evaluation of 67 relevant journal articles collected from Scopus, Web of Science, and Engineering Village. The review revealed that ImTs have been applied for OSH training and education in various industries including transportation, construction, mining, and healthcare. It was also revealed that the OSH hazards addressed by ImT-based training and education include but are not limited to fire, fall, electrical and chemical hazards in order to prevent or reduce injuries, illnesses and fatalities. In addition, it was revealed that one of the benefits of ImTs for OSH training and education is better retention of concepts when compared to conventional training and education. Challenges associated with the use of ImTs for OSH training and education include insufficient display brightness for users to effectively see virtual objects in a brightly luminated environment. Among the recommendations for future work is research into how to develop effective communication methods between trainers and trainees immersed in a virtual environment for trainers to fully understand the difficulties trainees experience in operating the developed ImT-based platform and provide solutions to such difficulties.
Eye Tracking, Usability, and User Experience: A Systematic Review

Usability and user experience (UX) are emerging concerns around not only application development but everything designed to be used by people. Evaluation of the UX is, by nature, intensely subjective and time-consuming. The article focuses mainly on Eye Tracking, Usability, and User Experience from a general point of view, with an emphasis on automatic data processing. In recent years, new technological approaches have been emerging to quantify usability testing data and improve process automation. Eye tracking technology is a great way to analyze users' interaction with the product, allowing researchers to discover usability issues and even leverage the power of machine learning to recognize various kinds of emotions linked to users' interactions. Existing research concerned with these three main topics has been methodically explored. For this review, we extensively searched 1988 theme-related articles. One hundred and forty-four articles were selected based on meticulous screening, from which 90 were included in this systematic review. The outcomes reveal a significant shift toward a more technologically advanced evaluation of user experience and usability in various areas. The review proposes several opportunities for future research and missing areas connecting user experience, eye tracking, and machine learning into more products focused on problem pattern identification.
A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron

Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction.
Exposing Deepfakes using Dual-Channel Network with Multi-Axis Attention and Frequency Analysis

This paper proposes a dual-channel network for DeepFake detection. The network comprises two channels: one using a stacked Maxvit block to process the downsampled original images, and the other using a stacked ResNet basic block to capture features from the discrete cosine transform of the image spectrums. The components extracted from the two channels are concatenated using a linear layer to train the entire model for exposing DeepFakes. Experimental results demonstrate that the proposed method could achieve satisfactory forensics performance. Besides, the experiments of cross-dataset evaluations prove it is also high in generalizability.
Graph Sample and Aggregate Attention Network optimized with Barnacles Mating Algorithm based Sentiment Analysis for Online Product Recommendation

Big data analytics is important in many businesses that use computing applications like real-time shopping and e-commerce. Big data is employed for promoting goods and improve connectivity betwixt retailers and customers. People today frequently utilize online promotions to learn about the best stores to visit in order to purchase higher-quality goods. This purchasing experience along opinion regarding the shopper's shop can be observing through the customer-experience published across social media. A new customer requires information's about manufacturing date (MRD), manufacturing price (MRP), offers, quality, and suggestions while searching a shop that are presented by prior customer experience. The product cover or label already has MRP and MRD. The product details have been predicted using a variety of methods, however the information is not correct. To overcome these issues, Graph Sample and Aggregate Attention Network (GSAAN) Optimized with Barnacles Mating Algorithm (BMA) based Sentiment Analysis (SA) is proposed for Online Product Recommendation (GSAAN-BMA-SA-OPR). The data's are collected from the data set of Amazon Product recommendation. With the help of GSAAN with BMA classify product recommendation as excellent, good, very good, bad, very bad. The proposed model is implemented in MATLAB, its efficiency of the proposed system is examined under performance metrics, such as mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), accuracy, F-Score, recall, precision. The proposed technique attains higher MAPE 23.77%, 65.3% and 10.42% and lower MSE 42.15%, 29.64% and 32.48% comparing to the existing models.
Why blockchain needs graph: A survey on studies, scenarios, and solutions

The popularity of blockchain platforms and their applications in industry and academia keeps rising. The multifarious requirements stimulate another technique, graph data and algorithms, to join the blockchains; thus, studies, scenarios, and solutions about graph-related blockchains have emerged. This paper aims to see whether the state-of-the-art studies satisfy the applications through a comprehensive survey on graph-related blockchains. To answer why a blockchain needs graphs in general, we analyze literature about blockchain and graph, as well as use cases on the application-oriented and graph-related scenarios collected from practical blockchain projects. The paper summarizes three graph-related blockchain studies: graph algorithms for blockchains, graph data in blockchains, and graph applications on blockchains. Based on these summarization, it figures out the gaps between the studies and the applications, that is, few of studies natively integrate graph computing into a blockchain. Here, the “graph integration” means processing on-chain graph data, which contains blockchain information, in a real-time, distributed, and consensual manner. We propose the prospect of the Graph-integrated Blockchain Platform (GiBP for short), and explain why a GiBP is inevitable, the challenges of a GiBP, and the GiBPs' main functions and features people expect for future research.
Strategies for Implementing Metaverse in Education

The purpose of the metaverse, a term that refers to the next generation of the internet, is to develop a fully immersive, identity virtual world in which individuals may work, learn, and engage. The purpose of this research is to focus on education in the metaverse. A comparison between learning in the metaverse and traditional learning is explored, as well as the use of structure and technology in the development of the educational system.
The Viability of Blockchain in Corporate Governance

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