Reviewerly/exHarmony huggingface dataset:
Extremely Lightweight Skeleton-Based Action Recognition With ShiftGCN++

In skeleton-based action recognition, graph convolutional networks (GCNs) have achieved remarkable success. However, there are two shortcomings of current GCN-based methods. Firstly, the computation cost is pretty heavy, typically over 15 GFLOPs for one action sample. Some recent works even reach ~100 GFLOPs. Secondly, the receptive fields of both spatial graph and temporal graph are inflexible. Although recent works introduce incremental adaptive modules to enhance the expressiveness of spatial graph, their efficiency is still limited by regular GCN structures. In this paper, we propose a shift graph convolutional network (ShiftGCN) to overcome both shortcomings. ShiftGCN is composed of novel shift graph operations and lightweight point-wise convolutions, where the shift graph operations provide flexible receptive fields for both spatial graph and temporal graph. To further boost the efficiency, we introduce four techniques and build a more lightweight skeleton-based action recognition model named ShiftGCN++. ShiftGCN++ is an extremely computation-efficient model, which is designed for low-power and low-cost devices with very limited computing power. On three datasets for skeleton-based action recognition, ShiftGCN notably exceeds the state-of-the-art methods with over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> less FLOPs and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> practical speedup. ShiftGCN++ further boosts the efficiency of ShiftGCN, which achieves comparable performance with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6\times $ </tex-math></inline-formula> less FLOPs and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> practical speedup.
Integrated design of solar photovoltaic power generation technology and building construction based on the Internet of Things

At the same time of economic development, the increasing scarcity of energy has had a great impact on people's development. People's production and life demand for electricity is also increasing rapidly, and solar power technology has received more and more attention. As a new energy source, solar energy has the advantages of environmental protection and sustainability, and it has no regional restrictions, can be used on-site, and designed to scale. Solar power generation is an important way to use solar energy. As the main component of the grid-connected power generation system, solar grid-connected inverters complete the tracking problem of the maximum power point in the photovoltaic array and transmit electrical energy to the grid through a set of control algorithms. The electrical energy is transmitted to the grid through the inverter, consistent with solar energy. In order to solve the problems of low integration, low energy efficiency, low reliability, high power consumption and lack of effective monitoring measures for solar energy devices. This article starts with the design of the solar cell integrated system, and through detailed analysis of the solar production system and building integrated planning, establishes the shadow radiant energy model of the solar cell system building electrical and solar cell system based on the Internet of Things, and designs an object-based Networked comprehensive auxiliary platform. The use of the Internet of Things and ZigBee wireless sensor network to study distributed solar energy devices and realize the joint design of solar energy devices and buildings is of great significance to the development of photovoltaic construction industry.
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health

Privacy protection is an ethical issue with broad concern in artificial intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across users or organisations without direct access to the data. It has great potential to be the next-general AI model training framework that offers privacy protection and therefore has broad implications for the future of digital health and healthcare informatics. Implementing an open innovation framework in the healthcare industry, namely open health, is to enhance innovation and creative capability of health-related organisations by building a next-generation collaborative framework with partner organisations and the research community. In particular, this game-changing collaborative framework offers knowledge sharing from diverse data with a privacy-preserving. This chapter will discuss how federated learning can enable the development of an open health ecosystem with the support of AI. Existing challenges and solutions for federated learning will be discussed.
Analog Optical Computing for Artificial Intelligence

The rapid development of artificial intelligence (AI) facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data. Optical computing provides a distinctive perspective to address this bottleneck by harnessing the unique properties of photons including broad bandwidth, low latency, and high energy efficiency. In this review, we introduce the latest developments of optical computing for different AI models, including feedforward neural networks, reservoir computing, and spiking neural networks (SNNs). Recent progress in integrated photonic devices, combined with the rise of AI, provides a great opportunity for the renaissance of optical computing in practical applications. This effort requires multidisciplinary efforts from a broad community. This review provides an overview of the state-of-the-art accomplishments in recent years, discusses the availability of current technologies, and points out various remaining challenges in different aspects to push the frontier. We anticipate that the era of large-scale integrated photonics processors will soon arrive for practical AI applications in the form of hybrid optoelectronic frameworks.
Siamese Earthquake Transformer: A Pair‐Input Deep‐Learning Model for Earthquake Detection and Phase Picking on a Seismic Array

Abstract Earthquake detection and phase picking play a fundamental role in studying seismic hazards and the Earth’s interior. Many deep‐learning‐based methods, including the state‐of‐the‐art model called Earthquake Transformer (EqT), have made considerable progress. However, the processing of low signal‐to‐noise ratio (SNR) seismograms remains a challenge. Here, we present a pair‐input deep‐learning model called Siamese Earthquake Transformer (S‐EqT), which achieves good performance on low SNR seismograms using the latent information in the deep‐learning black box of the pre‐trained EqT model on a seismic array. We compare the EqT and S‐EqT models on 2 weeks of continuous seismograms recorded by stations around northern Los Angeles region in California. In addition to showing a good performance similar to the EqT model on high SNR seismograms, the S‐EqT model retrieves ∼40% more reliable picks from low SNR seismograms, resulting in better earthquake characterizations. Our method provides a novel perspective on earthquake monitoring by highlighting the importance of hidden responses inside a deep‐learning model and shows its great potential for seismology.
ARPS: An Autonomic Resource Provisioning and Scheduling Framework for Cloud Platforms

With Cloud computing becoming mainstream for the execution of various applications, the multi-objective scheduling algorithms for providing the most suitable services to users have gained much attention. As provisioning Cloud services that satisfy end-users quality of service (QoS) requirements is complex and challenging, scheduling algorithms for cloud computing tend to focus on optimizing the execution cost or the execution time within user-defined deadline constraints. This paper addresses the problem of efficiently allocating Cloud services among competing jobs to achieve multiple end-users QoS. We design and develop a framework called Autonomic Resource Provisioning and Scheduling (ARPS) framework. ARPS framework has the decision-making capability to schedule the jobs at the best resources within the deadline and optimizes both the execution time and the cost simultaneously. The ARPS framework is also integrated with the spider monkey optimization (SMO) algorithm based scheduling mechanism. Our proposed mechanism is intended to solve a multi-objective optimization problem, including minimizing processing time, cost, and energy consumption. We study the effectiveness of the proposed scheduling mechanism through extensive simulation analysis using Cloudsim To assess the relative performance of our method, we compare it against four existing mechanisms. Experimental results show that the proposed mechanism outperforms its counterparts.
The impact of the COVID-19 pandemic on the adoption of e-learning among academics in Saudi Arabia

Saudi universities have suspended the traditional face-to-face classes and moved to online learning to ensure the continuation of the educational process during the COVID-19 pandemic. The speed at which the move to online learning has happened was unprecedented and staggering. Academics, had to improvise and try different e-learning solutions in less-than-ideal circumstances. No matter how powerful and easy-to-use e-learning solutions might be, many academics understandably found the transition to e-learning stressful. This research project looks at the transition experience to online learning from the viewpoint of academics in Saudi universities. It investigates how the COVID-19 pandemic has affected the adoption of e-learning solutions and how this forced experience would affect the long-term adoption of e-learning solutions. The study employed an online survey administered to academics from all the 24 public universities. The results were quantitatively analysed by using structural equation modelling (SEM). It was found that attitudes toward e-learning, self-efficacy and perceived reliability have significant positive effects on behavioural intention to adopt e-learning. It was also found that COVID-19 has positively affected the long-term adoption of e-learning. Therefore, it is recommended that universities take immediate actions and capitalise on the e-learning experience that academics have gained during COVID-19 pandemic.
A Traffic-Sign Detection Algorithm Based on Improved Sparse R-cnn

Automatic traffic-sign detection is a hot topic in computer vision and one of the critical technologies of intelligent transportation. The Transformer structure has recently become a research hotspot due to its excellent performance. We hope to apply this structure to the design of traffic sign detection algorithms. Therefore, we make some improvements to Sparse R-cnn, a neural network model inspired by Transformer. Sparse R-cnn is a novel model, and its core idea is to replace hundreds of thousands of candidate anchors in the RPN network with a small set of proposal boxes. The experiments in our paper proved that the performance of the Sparse R-cnn model is better than other existing general object detection models. Based on the original Sparse R-cnn inspiration, an improved Sparse R-cnn model is proposed. First, a novel backbone for the task of traffic-sign detection is proposed. Multi-scale fusion structure is the essential method of improving the algorithm for small target detection, so improving the multi-scale capability of the backbone is a required method for designing traffic sign detection. So, we made further improvements to the existing backbone ResNest. We enhanced the multi-scale representation ability of the backbone by constructing hierarchical residual-like connections within each single radix block in the original ResNest. We call the improved backbone Res2Nest. The novel backbone proposed by us shows better performance without introducing excessive computational costs to the model. In addition, the attention mechanism is also an effective method to improve the detection of traffic signs, so we set up a branch network for recalibrating the channel feature response adaptively through the Global Average Pooling (GAP) operation and a fully connected layer. It can also be seen as the implementation of the cross-channel self-attention mechanism. After experiments by TT100K dataset, our method would attain a better accuracy and robustness.
Etemadi multiple linear regression

Regression modeling is one of the most widely used statistical processes to estimate the relationships between dependent and independent variables, which have been frequently applied in a wide range of applications successfully. This method includes many techniques for modeling and analyzing several variables to cover real-world problems. The performance basis in conventional regression modeling is based on the assumption that maximum accuracy in inaccessible data is obtained from models with the least amount of error in modeling available data. In this type of regression modeling, in order to maximize the generalization ability of simulations, which are the main factor influencing the quality of decisions made in real-world problems, the principle of maximization of the accuracy of available historical data is used. However, in this type of modeling process, the model's reliability and results have not been considered. On the other, the generalization capability of a model is simultaneously dependent on the accuracy of the model and the reliability level of the accuracy. In this paper, a new methodology is proposed for multiple linear regression (MLR) modeling in which in contrast to traditionally developed models, the models' reliability is maximized instead of its accuracy. To comprehensively evaluate the proposed model's performance, 30 benchmark data sets are considered from the UCI. Empirical results indicate that, from a general perspective, in 19 cases, i.e., 63.333% of cases, the proposed model has better generalization ability than traditional ones. It is clearly illustrated the importance of the reliability of results and their accuracy that is considered in none of the conventional MLR modeling procedures. Therefore, the proposed MLR model can be regarded as an appropriate alternative in modeling fields, especially when more generalization is desired.
Elephant clan optimization: A nature-inspired metaheuristic algorithm for the optimal design of structures

The current study proposes a new metaheuristic algorithm based on the clan behavior of elephants, called elephant clan optimization (ECO), to solve structural optimization problems. This method is a new version of the previously developed algorithm; namely, the elephant herding optimization (EHO). While the EHO algorithm has been inspired by the behavior of elephants, the theory behind this method is based on the herding behavior of the elephants, and also the selection of random members to replace the worst members, which is far from the real-life behavior of this animal. Since elephants are animals with powerful memories and a high capability for learning, it seems that by accurately modeling the real-life behavior of this animal, a more powerful algorithm can be developed. The proposed ECO algorithm attempts to simulate the most essential individual and collective behaviors of elephants. The performance of the ECO method is evaluated by solving several structural optimization problems, including the size optimization of truss structures. The findings of the study confirm the reliable performance of the proposed ECO algorithm to expedite the convergence rate and achieve superior solutions in comparison with the EHO. Moreover, the ECO method produces better or very competitive results by consuming less computational effort compared to well-known metaheuristic methods.
Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

Cloud computing is an Information Technology deployment model established on virtualization.Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment.However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing.First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm.This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length.Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling.In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment.A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO.Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.
K and starting means for k-means algorithm

The k-means method aims to divide a set of N objects into k clusters, where each cluster is represented by the mean value of its objects. This algorithm is simple and converges to local minima quickly. It has linear time complexity, but it requires the number of clusters in advance which requires some knowledge in advance, in addition to selecting the initial centers which affect the quality of the final result and the number of iterations. The quality of the final result and the number of iterations depend on both k and initial centers. Many papers tried to detect a suitable value for k (the number of clusters) or introduced a better method for selecting the initial centers only. This research introduces a method able to detect a near-optimal value for k and near-optimal initial centers. The proposed method adds a preprocessing step to get the number of clusters and the initial centers before applying the k-means method. The idea is to get initial clusters using a density-based method that does not require the number of clusters in advance and computes the mean values for objects in each cluster and uses this knowledge in k-means. This leads to improving the quality of the final result as presented in the experimental results. The proposed method will use the DBSCAN “Density-based spatial clustering of application with noise” method as a preprocessing step. So, the paper concentrates on the DBSCAN and k-means. The proposed method will converge to global minima which improve the quality of the final result. The proposed method requires the two input parameters for the DBSCAN method and its time complexity is o(n log n) which is the same as that of DBSCAN.
Development of a yoga posture coaching system using an interactive display based on transfer learning

Yoga is a form of exercise that is beneficial for health, focusing on physical, mental, and spiritual connections. However, practicing yoga and adopting incorrect postures can cause health problems, such as muscle sprains and pain. In this study, we propose the development of a yoga posture coaching system using an interactive display, based on a transfer learning technique. The 14 different yoga postures were collected from an RGB camera, and eight participants were required to perform each yoga posture 10 times. Data augmentation was applied to oversample and prevent over-fitting of the training datasets. Six transfer learning models (TL-VGG16-DA, TL-VGG19-DA, TL-MobileNet-DA, TL-MobileNetV2-DA, TL-InceptionV3-DA, and TL-DenseNet201-DA) were exploited for classification tasks to select the optimal model for the yoga coaching system, based on evaluation metrics. As a result, the TL-MobileNet-DA model was selected as the optimal model, showing an overall accuracy of 98.43%, sensitivity of 98.30%, specificity of 99.88%, and Matthews correlation coefficient of 0.9831. The study presented a yoga posture coaching system that recognized the yoga posture movement of users, in real time, according to the selected yoga posture guidance and can coach them to avoid incorrect postures.
A Survey Of differential privacy-based techniques and their applicability to location-Based services

The widespread use of mobile devices such as smartphones, tablets, and smartwatches has led users to constantly generate various location data during their daily activities. Consequently, a growing interest has been seen in location-based services (LBSs), which aim to provide services adjusted to the current locations of users. However, location information may contain sensitive data; therefore, most users are reluctant to provide their exact location data to service providers. This has been identified as the most significant challenge in LBSs. Recently, differential privacy (DP) has emerged as a de facto standard for privacy-preserving data processing. With its strong privacy guarantees, DP has been used in diverse areas such as the collection, analysis, and release of sensitive private data, and several variants of DP have been proposed in the literature. The main objective of this paper is to investigate the applicability of DP-based approaches in an LBS setting. In this paper, we first describe the basic concept of DP and then survey its three variants: (a) geo-indistinguishability, (b) private spatial decomposition, and (c) local differential privacy, which are designed or can be used to protect location privacy in LBSs. Furthermore, we explore the applicability of DP-based schemes in protecting location privacy in different location data processing, collection, and publishing scenarios in LBSs. Finally, certain promising future research directions are discussed to spur further research in this area.
360-degree video in education: An overview and a comparative social media data analysis of the last decade

Abstract Due to its immersive and interactive nature, 360-degree video is becoming ever more popular. However, little is known about the public’s viewpoints and attitudes regarding the use of this emerging technology in educational contexts. This study reviews the research evidence for 360° video, virtual learning environments and social media and presents related studies. Moreover, the methodology, the tools and the analysis process used to comparatively analyze social media data are presented and the results that arose are showcased. The use of 360° video in education is discussed and directions for future research are given. Based on the data from the last 10 years, the main findings of the study show that 360° video and its use in educational settings are recognized positively by the public who mostly express anticipation, trust and joy when referring to it. Nonetheless, teachers are unfamiliar with 360° video and do not have the necessary technical skills to develop educational material using it or incorporating it into teaching activities. Finally, 360° video is proven to be an effective educational tool which satisfies the emerging educational needs, enriches the teaching and learning process and promotes students’ motivation, active participation and engagement, rendering, thus, learning more effective.