Reviewerly/exHarmony huggingface dataset:
Personal space increases during the COVID-19 pandemic in response to real and virtual humans

Personal space is the distance that people tend to maintain from others during daily life in a largely unconscious manner. For humans, personal space-related behaviors represent one form of non-verbal social communication, similar to facial expressions and eye contact. Given that the changes in social behavior and experiences that occurred during the COVID-19 pandemic, including "social distancing" and widespread social isolation, may have altered personal space preferences, we investigated this possibility in two independent samples. First, we compared the size of personal space measured before the onset of the pandemic to its size during the pandemic in separate groups of subjects. Personal space size was significantly larger in those assessed during (compared to those assessed before) the onset of the pandemic (all d > 0.613, all p < 0.007). In an additional cohort, we measured personal space size, and discomfort in response to intrusions into personal space, longitudinally before and during the pandemic, using both conventional and virtual reality-based techniques. Within these subjects, we found that measurements of personal space size with respect to real versus virtual humans were significantly correlated with one another (r = 0.625-0.958) and similar in magnitude. Moreover, the size of personal space, as well as levels of discomfort during personal space intrusions, increased significantly during (compared to before) the COVID-19 pandemic in response to both real and virtual humans (all d > 0.842, all p < 0.01). Lastly, we found that the practice of social distancing and perceived (but not actual) risk of being infected with COVID-19 were linked to this personal space enlargement during the pandemic (all p < 0.038). Taken together, these findings suggest that personal space boundaries expanded during the COVID-19 pandemic independent of actual infection risk level. As the day-to-day effects of the pandemic subside, personal space preferences may provide one index of recovery from the psychological effects of this crisis.
Recent developments of smart energy networks and challenges

In the previous decade, smart meters have been placed all over the globe. By collecting load profiles and facilitating bidirectional information flow, the advanced metering infrastructure, which comprises smart meters, a communication network, and a data management system, plays a vital role in power delivery systems. Because smart meters are so widely used, a vast amount of fine-grained energy use data may be collected. Smart meters may soon be able to do more than just charge you. High-resolution data from smart meters deliver adequate information about consumers' energy consumption patterns and lives. Meanwhile, power sector deregulation, particularly on the delivery side, continues to advance in several countries throughout the world. These countries are now concentrating all of their efforts on energy retail reform. Retailers, consumers, and aggregators are all working to make the retail business more prosperous, dynamic, and competitive. The utilization of massive amounts of data from smart meters to promote and enhance demand side efficiency and sustainability has become a global hot topic. In recent years, major advances in data analytics have been made in the power sector's production, transmission, equipment, and consumption operations. There are an increasing number of smart meter data analytics projects being created.
An OpenCL-Based FPGA Accelerator for Faster R-CNN

In recent years, convolutional neural network (CNN)-based object detection algorithms have made breakthroughs, and much of the research corresponds to hardware accelerator designs. Although many previous works have proposed efficient FPGA designs for one-stage detectors such as Yolo, there are still few accelerator designs for faster regions with CNN features (Faster R-CNN) algorithms. Moreover, CNN's inherently high computational complexity and high memory complexity bring challenges to the design of efficient accelerators. This paper proposes a software-hardware co-design scheme based on OpenCL to implement a Faster R-CNN object detection algorithm on FPGA. First, we design an efficient, deep pipelined FPGA hardware accelerator that can implement Faster R-CNN algorithms for different backbone networks. Then, an optimized hardware-aware software algorithm was proposed, including fixed-point quantization, layer fusion, and a multi-batch Regions of interest (RoIs) detector. Finally, we present an end-to-end design space exploration scheme to comprehensively evaluate the performance and resource utilization of the proposed accelerator. Experimental results show that the proposed design achieves a peak throughput of 846.9 GOP/s at the working frequency of 172 MHz. Compared with the state-of-the-art Faster R-CNN accelerator and the one-stage YOLO accelerator, our method achieves 10× and 2.1× inference throughput improvements, respectively.
COMPARATIVE ANALYSIS OF SOLUTIONS TO PROVIDE CONTROL AND MANAGEMENT OF PRIVILEGED ACCESS IN THE IT ENVIRONMENT

Abuse of privileges in the IT environment is defined as one of the threats to the information assets of the business at the present stage. The article examines and analyzes these problems, which are closely related to the leakage of information due to legitimate access to it and / or unauthorized access to it. Reports, research, acts, surveys at various enterprises contain a large amount of analytical and statistical materials that confirm the relevance and importance of this work. Based on the scientific literature, a review of key definitions on this issue, namely: characterized the definition of "privileged access"; the main examples of privileged access in the IT environment are considered; describes the risks and threats of information from attack vectors associated with privileged access to the IT environment. The mechanism for control and management of privileged access - RAM is presented, the steps of this process are highlighted and its expediency is substantiated. Experimental techniques allowed to choose the most applicable solutions of RAM: WALLIX Bastion PAM, One Identity Safeguard PAM, CyberArk PAM. The essence and functionality of each of these solutions are revealed. The advantages and disadvantages of each technology are established. As a result of research of technical and functional characteristics the comparative analysis of data of three decisions is carried out: obligatory components of the decision on control and management of privileged access are the manager of passwords and the manager of sessions (sessions), and additional - the module with analytics of privileged sessions and the access manager. use a VPN to access privileged assets. It can also be noted that the functionality of all products is very similar, so the implementation plays a big role, namely the practical approach during operation, internal algorithms, additional opportunities for integration and innovation. PAM solutions are recommended for organizations as a means to mitigate information security risks and threats due to insider activities of company employees who have privileged access to the IT environment.
Data poisoning attacks on federated learning by using adversarial samples

Federated learning has grown increasingly in academia and industry. It has the strong ability to train joint models among numerous parties without local data exchange. Since federated learning does not have access to the training process of participants, it is vulnerable to poisoning attacks by attackers, that is, attackers can compromise the accuracy of jointly trained models by uploading elaborate malicious local updates to the server in the guise of normal participants. However, traditional attacks such as label flipping attack and Gaussian noise attack are less effective in untargeted attacks against robust federation learning. To address above issues, we focus on applying adversarial samples to jeopardize the accuracy of the training model, which is rarely explored for verifying the security of federated learning. In this paper, we propose a novel poisoning attack algorithm Fed-MIFGSM against the robust federated learning framework to investigate the impact of adversarial samples on federated learning. Experiments show that federated learning is vulnerable to attacks from adversarial samples with more than 5% reduction of the accuracy under different scenarios.
Latency Analysis of Blockchain-Based SSI Applications

Several revolutionary applications have been built on the distributed ledgers of blockchain (BC) technology. Besides cryptocurrencies, many other application fields can be found in smart systems exploiting smart contracts and Self Sovereign Identity (SSI) management. The Hyperledger Indy platform is a suitable open-source solution for realizing permissioned BC systems for SSI projects. SSI applications usually require short response times from the underlying BC network, which may vary highly depending on the application type, the used BC software, and the actual BC deployment parameters. To support the developers and users of SSI applications, we present a detailed latency analysis of a permissioned BC system built with Indy and Aries. To streamline our experiments, we developed a Python application using containerized Indy and Aries components from official Hyperledger repositories. We deployed our experimental application on multiple virtual machines in the public Google Cloud Platform and on our local, private cloud using a Docker platform with Kubernetes. We evaluated and compared their performance benchmarked by Read and Write latencies. We found that the local Indy ledger reads and writes 30–50%, and 65–85% faster than the Indy ledger running on the Google Cloud Platform, respectively.
A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction

Medical records generated in hospitals are treasures for academic research and future references. Medical Image Retrieval (MIR) Systems contribute significantly to locating the relevant records required for a particular diagnosis, analysis, and treatment. An efficient classifier and effective indexing technique are required for the storage and retrieval of medical images. In this paper, a retrieval framework is formulated by adopting a modified Local Binary Pattern feature (AvN-LBP) for indexing and an optimized Fuzzy Art Map (FAM) for classifying and searching medical images. The proposed indexing method extracts LBP considering information from neighborhood pixels and is robust to background noise. The FAM network is optimized using the Differential Evaluation (DE) algorithm (DEFAMNet) with a modified mutation operation to minimize the size of the network without compromising the classification accuracy. The performance of the proposed DEFAMNet is compared with that of other classifiers and descriptors; the classification accuracy of the proposed AvN-LBP operator with DEFAMNet is higher. The experimental results on three benchmark medical image datasets provide evidence that the proposed framework classifies the medical images faster and more efficiently with lesser computational cost.
The Effect of Organizational Trust on Turnover Intention of Rural Kindergarten Teachers: The Mediating Role of Teaching Efficacy and Job Satisfaction

Recent studies have focused on turnover among rural kindergarten teachers. However, none of these studies have shown a clear connection between turnover intention and organizational trust, although there are studies in other areas showing that organizational trust can affect turnover intention. Drawing on a sample of 330 kindergarten teachers in rural areas, this study explores the mechanism of influence between organizational trust and turnover intention with teaching efficacy and job satisfaction as mediators. We found that organizational trust negatively impacted teachers' turnover intention, and this relationship was mediated by a significant chain mediating effect of teaching efficacy and job satisfaction. The findings enrich knowledge about turnover among rural kindergarten teachers and inspire us to create a more supportive organizational environment against the backdrop of the COVID-19 pandemic to improve job satisfaction and alleviate turnover among rural kindergarten teachers.
Robot Guidance and Control Using Global Sliding Modes and Acceleration Velocity Obstacles

This paper is concerned with the robust guidance and control of fully actuated mobile robots subject to velocity and control constraints in dynamic environments involving external disturbances. The overall method consists of an outer-loop guidance based on the acceleration-velocity-obstacles strategy and a stabilizing control loop based on a global sliding mode policy. The guidance strategy generates velocity commands aiming to reach a target position while avoiding collision with moving obstacles and respecting velocity and acceleration bounds. On the other hand, the global sliding mode ensures that the velocity commands are, in theory, exactly tracked all the time. The proposed method is numerically evaluated using two-dimensional robots and shows to be effective.
Overview of Blockchain Data Storage and Privacy Protection

Blockchain nowadays is finding great application in many areas, based on its security, decentralized mode of operation, however many things during its use remain challenging include the issue of data privacy, such as safe and effective data management, maintaining data integrity and data quality, etc. Through this paper we will try to make an overview of existing blockchain technologies, their use today and sublimating existing and future trends. We will briefly describe application of blockchain technology in various fields suggesting solutions, advantages and disadvantages of blockchain, cloud platforms for blockchain data storage. We will focus on data privacy by making a description of the strategies used in this regard. We will conclude the paper by giving the latest research regarding data storage, the importance of data storage and our views regarding the future of data management and storage.
Spatio-temporal dynamic clustering modeling for solar irradiance resource assessment

Nowadays, with the development of international policies and agreements to promote the integration of renewable energy sources, mainly solar and wind, modeling the solar resource by including the spatio-temporal variability is crucial to determine future PV power plant locations and estimate potential power generation performances. However, contributions involving long-term periods and different time windows to explore such potential solar resource variability are generally scarce. Under this framework, the present paper proposes a methodology focused on characterizing and clustering the spatio-temporal solar resource variability through the global horizontal irradiance analysis. Hierarchical clustering technique is firstly used to classify the spatial data. Different time windows — from short-term to long-term data — can be subsequently evaluated by using various sources of information. The Spanish territory is selected as case study, considering 22-year period data (1999–2020) and 1,936,917 observations from online satellite database. Spatial variability and geographical clustering differences are discussed and compared depending on the selected time windows, identifying relevant spatial variations for some specific months. Additionally, some years present more variability as well, in line with the sunspot peak of the solar cycles. The proposed approach gives an alternative comprehensive spatio-temporal clustering and characterization of GHI evolution, providing a suitable methodology to help the current European sustainable energy transition.
Explainable AI lacks regulative reasons: why AI and human decision-making are not equally opaque

Abstract Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that this argument overlooks that human decision-making is sometimes significantly more transparent and trustworthy than algorithmic decision-making. This is because when people explain their decisions by giving reasons for them, this frequently prompts those giving the reasons to govern or regulate themselves so as to think and act in ways that confirm their reason reports. AI explanation systems lack this self-regulative feature. Overlooking it when comparing algorithmic and human decision-making can result in underestimations of the transparency of human decision-making and in the development of explainable AI that may mislead people by activating generally warranted beliefs about the regulative dimension of reason-giving.
Muslim Responses to the COVID-19 Pandemic in Western Sydney: Understanding the Role of Community-Specific Communication Infrastructure

In Australia, the COVID-19 pandemic rendered visible and exacerbated socio-economic inequalities and divides. Migrant and religious minority communities in Australia faced particular challenges during the pandemic when government responses were not made culturally accessible, appropriate or relatable. This paper examines the role played by community-specific communication infrastructures in addressing government deficiencies to meet the needs of Muslim communities. Drawing on semi-structured interviews with Muslims in an area hit hard by the pandemic – Western Sydney – we consider the role communication infrastructures played in combatting misinformation, providing up-to-date information and creating spaces for locally and culturally informed responses to be developed. We propose that community-specific communication infrastructures were critical to Muslims seeking to address gaps, needs and inequalities during the pandemic and they were effective because they were designed with an understanding of Muslim communities’ social and cultural norms, practices, experiences and needs. The research offers insights relevant to migrant and religious communities, not-for-profits, and government agencies seeking to develop effective strategies to respond to challenges and crises as they emerge.
Mapping of Suitable Sites for Concentrated Solar Power Plants in the Philippines Using Geographic Information System and Analytic Hierarchy Process

Solar energy is a renewable source of energy harnessed from the sun. Concentrated solar power (CSP) plants harness this energy by focusing sunlight on a limited area to heat a working fluid, which is used to generate steam and power a thermodynamic cycle that produces electricity. There are currently no CSP plants in the Philippines, and this study aimed to locate the most suitable sites for this type of power plant. The first step was to mask out areas totally unsuitable as plant sites; we identified five exclusion factors for this: protected areas, slope, direct normal irradiance (DNI), water bodies, and land cover type. A scoring gradient was then applied to the remaining suitable areas according to seven ranking factors: DNI, slope, typhoon frequency, capacity of the nearest grid line, distance to the nearest grid line, distance to the nearest road, and distance to the nearest water body. Next, to reflect the actual degrees of influence of the factors to site suitability, we determined their relative numeric weights using analytic hierarchy process, with the weights derived from inputs from interviews with academic and industry experts. Finally, using ArcGIS Pro, we used a weighted sum of the ranking scores to produce a suitability map covering the entire Philippines. Suitable sites in the following provinces were found: Ilocos Sur, Pampanga, Mindoro, Masbate, and Maguindanao. These areas have a total area of 27.9 km2 and a projected total power output of 733 MW.
Voxel-wise adversarial semi-supervised learning for medical image segmentation

Semi-supervised learning for medical image segmentation is an important area of research for alleviating the huge cost associated with the construction of reliable large-scale annotations in the medical domain. Recent semi-supervised approaches have demonstrated promising results by employing consistency regularization, pseudo-labeling techniques, and adversarial learning. These methods primarily attempt to learn the distribution of labeled and unlabeled data by enforcing consistency in the predictions or embedding context. However, previous approaches have focused only on local discrepancy minimization or context relations across single classes.In this paper, we introduce a novel adversarial learning-based semi-supervised segmentation method that effectively embeds both local and global features from multiple hidden layers and learns context relations between multiple classes. Our voxel-wise adversarial learning method utilizes a voxel-wise feature discriminator, which considers multilayer voxel-wise features (involving both local and global features) as an input by embedding class-specific voxel-wise feature distribution. Furthermore, our previous representation learning method is improved by overcoming information loss and learning stability problems, which enables rich representations of labeled data.In the experiments, we used the Left Atrial Segmentation Challenge dataset and the Abdominal Multi-Organ dataset to prove the effectiveness of our method in both single class and multiclass segmentation. The experimental results demonstrate that our method outperforms current best-performing state-of-the-art semi-supervised learning approaches. Our proposed adversarial learning-based semi-supervised segmentation method successfully leveraged unlabeled data to improve the network performance by 2% in Dice score coefficient for multi-organ dataset.We compare our approach to a wide range of medical datasets, and showed our method can be adapted to embed class-specific features. Furthermore, visual interpretation of the feature space demonstrates that our proposed method enables a well-distributed and separated feature space from both labeled and unlabeled data, which improves the overall prediction results.
Medical image enhancement in health care applications using modified sun flower optimization

Image enhancement (IE) is a process which improves the contrast of image by sharpening the edge pixels intensity. This technique has attained much attention in medical field and several enhancement techniques are proposed by researchers. In image processing, the enhancement is regarded as complex optimization issues. This work introduces an efficient model to solve optimization issues using a modified optimization approach. Initially, the input medical images are denoised using Modified median filter (MMF) filter. Then these denoised images are enhanced for the further process. The enhancement is carried out by pixel intensity of image. The parameters like entropy, edge information and intensity are optimized by modified sun flower optimization (MSFO). This optimization is used for increasing the convergence speed. The overall evaluation is carried in Matlab platform. The image quality is analyzed on six performance metrics and compared over several approaches and provided better results. The experimentation is evaluated on five medical images and the Mean square error (MSE) and peak signal noise ratio (PSNR) achieved by the medical image 1 are 0.02 and 43.7 respectively.