Reviewerly/exHarmony huggingface dataset:
Discovering Factors that Influence the Use of Augmented Reality for Communication on Active Highway Construction Sites

The construction industry requires effective communication between project stakeholders in various locations. This communication can be facilitated by in-person site visits or phone calls. However, these methods can lead to wasted travel time or the omission of critical visual site details, respectively. In theory, augmented reality (AR) can support consistent understandings of site environments in a similar way to in-person visits but with the efficiency of phone calls. However, most studies exploring AR for communication in construction have done so in controlled environments. This work investigates the efficacy of AR calls in uncontrolled settings (i.e., active construction sites) by interviewing and recording practitioners who are using AR, to identify behaviors and perceptions that influence communication efficiency and effectiveness. The authors conducted qualitative analyses and identified 14 factors that can affect information exchanges between professionals using AR. The factors found in this study include: virtual manipulation; visual aids; shared action plan; hands-free operation; decision-making; site interaction; working remotely; repetition; locomotion; performing different job types; device discomfort; background noise; lighting conditions; and disconnection in hardware physical and feature components. Some factors represent beneficial opportunities for AR, while others pose challenges. Some of the identified challenges may be resolved through technological innovations and user training, as discussed in the paper. Furthermore, this work provides evidence of factors for which current AR technologies are sufficiently developed to guide near-term implementation in practice.
A fine-grained intrusion protection system for inter-edge trust transfer

The phenomenal popularity of smart mobile computing hardware is enabling pervasive edge intelligence and ushering us into a digital twin era. However, the natural barrier between edge equipment owned by different interested parties poses unique challenges for cross-domain trust management. In addition, the openness of radio access and the accessibility of edge services render edge intelligence systems vulnerable and put sensitive user data in jeopardy. This paper presents an intrusion protection mechanism for edge trust transfer to address the inter-edge trust management issue and the conundrum of detecting indistinguishable malevolent nodes launching weak attacks. First, an inter-edge reputation transfer framework is established to leverage the trust quality of different edges to retain the accumulated trust histories of users when they roam in multi-edge environments structurally. Second, a fine-grained intrusion protection system is proposed to reduce the negative impact of attacks on user interactions and improve the overall trust quality and system security of edge intelligence services. The experimental results validate the effectiveness and superior performance of the proposed intrusion protection for edge trust transfer in securing, enhancing, and consolidating edge intelligence services.
Transferable feature filtration network for multi-source domain adaptation

Compared to conventional unsupervised domain adaptation, multi-source domain adaptation (MSDA) is suitable for more practical scenarios but is more challenging because the knowledge transferred to the target domain is learned from multiple source domains. Existing adversarial-based domain adaptation methods facilitate the obfuscation of domain distributions and cross-domain alignment for representations by deceiving the discriminator. However, traditional methods neglect the distinction in the transferability of the different features, resulting in untransferable domain-varying features, such as those extracted from the background, also being forced to align between domains. To this end, we propose a feature filtration mechanism and design a corresponding neural network to achieve a selective feature alignment based on the transferability of features; this is termed a transferable feature filtration network (TFFN). We construct a transferable feature learning framework containing two subprocesses, filtering, in which the attention mechanism-based filtration network automatically extracts transferable features under additional supervision, and repairing, in which we suggest enhancing the adaptability of the filtration network by repairing it with the help of an additional classifier focused on the target knowledge. Furthermore, to facilitate the matching of the source and target distributions at the class level, we propose a filtration consistency loss to enhance the cross-domain consistency of the filtering weights. Extensive experiments conducted on MSDA benchmark datasets show that the proposed method has significant advantages over the existing methods.
The Effect of Atmospheric Transmittance of UVB Solar Irradiance to Broadband Solar Radiation at Different Climate Sites

In the present research, the continuous measurements of global solar radiation (G) and UVB solar radiation are taken in different climate sites during the period time from 1986 to 2020 for all weather conditions. The differences between measured and calculated UVB solar energy values vary from 1% to 2.2%. The average hourly UVB radiation intensity ratio to the current study's total global solar radiation was 0.294%. In contrast, the ratio of UVBext. to the corresponding Gext., UVBext./Gext., was 1.563%, demonstrating the much greater degree of attenuation of UVB relative to global solar radiation. The UVB transmission through the climate can be evaluated during the current investigation, where the typical hourly estimations of UVB transmission are declining due to the air as a daytime component. The change in the monthly average of hourly UVB/G% may be caused by the increase and decrease of both G and UVB's monthly average hourly values. The monthly average mean UVB/G varies from 0.15% in January to 0.30% in June. The average hourly UVB/G% was also lower in the afternoon compared to the morning. The effect of aerosols on the fluctuation in air transparency may have contributed to the lower UVB/G% levels in the afternoon. The average values of UVB solar energy reduction throughout this research were 92.21%, 90%, 90.2%, and 90.1 for Hurghada, El-Kharga, Dammam, and Hail, respectively, in the present study. Inversely, the relationship between UVI levels and solar zenith angle (SZA) at all selected locations. The monthly mean values of RAF under clear skies were equal to 1.058, 1.003, 1.027, and 1.087 for Hurghada, El-Kharga, Dammam, and Hail sites in the present research. The monthly mean values under clear skies, UVB values rise by 1.058%, 1.003%, 1.027%, and 1.087% if the slant ozone value at all examined sites decreases by 1%. Ozone variations at high angles result in non-linear UVB transmission.
Recursive rules with aggregation: a simple unified semantics

Abstract Complex reasoning problems are most clearly and easily specified using logical rules, but require recursive rules with aggregation such as count and sum for practical applications. Unfortunately, the meaning of such rules has been a significant challenge, leading to many disagreeing semantics. This paper describes a unified semantics for recursive rules with aggregation, extending the unified founded semantics and constraint semantics for recursive rules with negation. The key idea is to support simple expression of the different assumptions underlying different semantics, and orthogonally interpret aggregation operations using their simple usual meaning. We present a formal definition of the semantics, prove important properties of the semantics and compare with prior semantics. In particular, we present an efficient inference over aggregation that gives precise answers to all examples we have studied from the literature. We also apply our semantics to a wide range of challenging examples, and show that our semantics is simple and matches the desired results in all cases. Finally, we describe experiments on the most challenging examples, exhibiting unexpectedly superior performance over well-known systems when they can compute correct answers.
Geospatial Machine Learning Prediction of Arsenic Distribution in the Groundwater of Murshidabad District, West Bengal, India: Analyzing Spatiotemporal Patterns to Understand Human Health Risk

Arsenic (As) contamination of groundwater in parts of South and Southeast Asia is a public health disaster. Millions of people living in these regions could be chronically exposed to drinking water with As concentrations above the World Health Organization's provisional guideline of 10 μg/L. Recent field investigations have shown that the distribution of groundwater As in some shallow aquifers in India and Bangladesh is evolving rapidly because of massive irrigation pumping. This study compares a decade-old dataset of As concentration measurements in groundwater with a dataset of recent measurements using geospatial machine learning techniques. We observed that the probability of As concentrations >10 μg/L was much greater in the regions between two major rivers than in the regions close to the Ganges River on the eastern border of the study area, where higher proportions of As concentrations >10 μg/L had been observed prior to 2005. The greater likelihood that toxic concentrations of As are present away from the river channel and is found instead in the interfluvial regions could be attributed to the transport and flushing of aquifer As from intense irrigation pumping. We estimated that about 2.8 million people could be chronically exposed to As concentrations >10 μg/L. This high population-level exposure to elevated As concentrations could be reduced through targeted well-testing campaigns, promoting well-switching, provisions for safe water access, and developing plans for raising public awareness. Policymakers could use the ternary hazard map presented here to target high-risk localities for priority implementation of piped water supply strategies to help reduce human suffering.
Robust dimensionality reduction method based on relaxed energy and structure preserving embedding for multiview clustering

Existing multiview clustering methods often learn inferior quality affinity graphs from original data, which suffer from excessive dimensionality and contain noise and outliers. Therefore, dimensionality reduction must be performed on the original data, which leads to the following problems: 1) the learned projection matrix does not preserve the main discriminative information of the original data; 2) the local and global structures are not preserved simultaneously; and 3) the negative effects of noise, outliers, and corruption cannot be overcome. To alleviate the aforementioned problems, a robust dimensionality reduction method based on a relaxed energy-preserving and structure-preserving embedding for multiview clustering is proposed in this study. In particular, the proposed architecture is a double-relaxed energy-preserving embedding model, which uses two projection matrices to extract the main discriminant features from the original data using two energy-preserving embedding routes, yielding clean data and a representation matrix in low-dimensional space. Subsequently, an affinity matrix that captures both local and global data structures is learned by performing locality-preserving projection and global low-rank-preserving embedding on the clean data and the representation matrix, respectively. Experimental results on benchmark datasets validate the effectiveness of the proposed algorithm.
Charisma™ virtual social training: A digital health platform and protocol

Low immersion virtual reality (LIVR) is a computer-generated, three-dimensional virtual environment that allows for authentic social interactions through a personal avatar, or digital representation of oneself. Lab-based delivery of LIVR social skills intervention has been shown to support social learning through controlled, targeted practice. Recent remote technological advancements allow LIVR-based social skills training to potentially overcome accessibility barriers by delivering to youth in their home. This study investigated the impact of 10-h of Charisma™ Virtual Social Training (CHARISMA-VST), a LIVR-based intervention, on social skill changes in children and adolescents who struggle socially via either in-person or remote training protocols. Specifically, the aims examined both the impact of training location (in-person vs remote access) and diagnosis (parent report of autism spectrum disorder (ASD) diagnosis versus parent report of other non-ASD diagnosis) on objective measures of social skill change following CHARISMA-VST. Researchers delivered the CHARISMA-VST via Charisma 1.0, a customized virtual gaming environment. Sixty-seven participants (49 males, 18 females) between the ages of 9–17, with parent reported social challenges, completed 10, 1-h CHARISMA-VST sessions during which nine social cognitive strategies were taught and then practiced within a LIVR environment with interspersed social coaching. Four social cognitive domains were measured pre-post training: emotion recognition, social inferencing, social attribution, and social self-schemata. Results revealed improvements in emotion recognition, social inferencing, social attribution, and social self-schemata with medium to large effect sizes following the CHARISMA-VST. There was no moderating effect of training location on emotion recognition, social inferencing, and social self-schemata, suggesting comparable gains whether participants accessed the technology in their own homes or from a school or specialty center. There was no moderating effect of ASD versus non-ASD diagnosis on performance measures, suggesting CHARISMA-VST may be effective in improving social skills in individuals beyond its initially designed use focused on individuals with ASD. These encouraging findings from this pilot intervention study provide some of the first evidence of potential new virtual technology tools, as exemplified by CHARISMA-VST, to improve one of the most important aspects of human behavior—social skills and human connectedness in youth with a range of social competency challenges.
Eye problems and musculoskeletal pain in Pokémon Go players

Individuals benefit from Pokémon Go (PG) gaming because this mobile augmented reality geolocation video game provides an attractive opportunity to increase physical activity outdoors and socialize. However, based on gaming and electronic media studies, intense involvement with PG is likely related to adverse phenomena, such as arm pain or eye-related problems. We aimed to test how PG use (problematic vs. non-problematic gaming and gaming time) is related to physical symptoms (pain in three body regions and computer vision syndrome). To dissect game-specific effects, we controlled for general problematic smartphone use, phubbing, and electronic media usage. PG players (N = 455) completed an online survey. We found that PG players with problematic game use reported more pain and vision problems. Problematic PG use was a better predictor of physical symptoms than PG gaming time and variables related to electronic media use. Problematic PG use and problematic smartphone use were correlated but independent predictors of physical symptoms. We conclude that the type of participation rather than the time spent playing predicts poorer physical health among PG players.
Particle swarm optimization with Chebychev functional-link network model for engineering design problems

In recent years, many improved particle swarm optimization (PSO) algorithms have been developed to improve the performance of PSO. These improved algorithms have greatly improved PSO performance, but PSO still has some shortcomings. So, an particle swarm optimization with Chebychev functional-link network model is proposed (APSOCFLN) in this paper. Firstly, in order to make up for the shortcomings of the canonical PSO update search, a novel Chebychev functional-link network (CFLN) elite guidance strategy is proposed. Two different update mechanisms are executed alternately, increasing the diversity of the algorithm population and making the algorithm more capable of jumping out of local optimization. Secondly, in order to better balance the exploration and exploitation of the algorithm, an adaptive probability strategy is proposed. Thirdly, an adaptive weighting strategy is proposed. It will be used for CFLN elite guidance strategy and give different weights to the two alternating update strategies, which can effectively solve the problem of premature convergence of PSO. Fourthly, the APSOCFLN and comparison algorithms are used to solve the practical engineering problems, and the results show that APSOCFLN has high precision, fast convergence , and excellent performance. Finally, the performance of the algorithm is tested with CEC2017 and CEC2022 benchmark functions. The comparative algorithm includes the classical PSO improvement algorithms and the classical other algorithms, and the experimental results show the effectiveness of the proposed strategy and the good performance of the improved algorithm.
Blockchain-Based System for Storing Warranty Receipts

With the proliferation in the number of documents these days, it has become an arduous task to store and verify the authenticity of each document. The traditional storage system enforces either paper printed documents or a naive centralized cloud-based storing system. Every product that a person acquires comes with a warranty receipt issued in the above-mentioned manner. Replicating such documents and committing fraud, germane to the issuance of the receipts and the respective payout has been of paramount concern for both the customers and the vendors. To mitigate this, we have proposed a system that eliminates the need for physical warranty receipts and leverages blockchain technology to provide a fraud-proof and transparent solution for storing warranty related receipts over a decentralized network. This paper has developed the proposed system and tested it over the existing blockchain networks.
Bengali Review Analysis for Predicting Popular Cosmetic Brand Using Machine Learning Classifiers

Nowadays, online platform has become one of the most popular media to express people's thought of all ages. That made the online platform a precious source for getting almost every kinds of information. As online shopping is rising in no time in recent years, as a result millions of comments are generating every single day. These users generated opinions on social media and different websites has made it easier for the people choosing the right product for them. Hence, sentimental analysis is a sought-after research topic nowadays. Our research paper has portrayed an experimental study on different cosmetics products review. To do so, we have selected ten popular cosmetic brands for analyzing their product review and chosen to analyze Bengali comments or sentences. The main focus of our work was to get out the most popular cosmetic brands among ten chosen brands. We have applied four classification algorithm such as naive Bayes, random forest, decision tree, and support vector machine for analyzing the final outcome and found vaseline and clear are the most popular brands.
Towards Using Artificial Intelligence in Neuromarketing

AI, when applied to the domain of neuromarketing, can help to model consumers' preferences, which can be leveraged by marketers in creating products or brand strategies that yield a good market value. Unlike focus groups and surveys where there is a possibility of masking the actual opinions and feelings of people to certain products and stimuli, AI can help to effectively capture data regarding impact of brain signals in response to certain stimuli. This study will be largely focused on connecting the dots between/forming linking/developing a connection between artificial intelligence and neuromarketing. Using artificial intelligence (AI) can help to bring personalization instead of standardization, which when combined with neuromarketing, can help to build effective advertising campaigns and influence customers' buying decisions. The content posted by consumers on social media, browsing behavior, and online shopping provides valuable personal information. This data when mined and processed using AI technologies can help to recognise patterns in consumer behavior.
Deep Learning Approach for Pedestrian Detection, Tracking, and Suspicious Activity Recognition in Academic Environment

Pedestrian detection, tracking, and suspicious activity recognition have grown increasingly significant in computer vision applications in recent years as security threats have increased. Continuous monitoring of private and public areas in high-density areas is very difficult, so active video surveillance that can track pedestrian behavior in real time is required. We present an innovative and robust deep learning system as well as a unique pedestrian dataset that includes student behavior like as test cheating, laboratory equipment theft, student disputes, and danger situations in institutions. It is the first of its kind to provide pedestrians with a unified and stable ID annotation. Again, we also presented a comparative analysis of result achieved by the recent deep learning approach of pedestrian detection, tracking, and suspicious activity recognition methods on a recent benchmark dataset. Our investigation will provide new research directions in vision-based surveillance for practitioners and research scholars.
Optimizing Augmented Reality and Virtual Reality for Customer Engagement

Technological developments have advanced significantly. Augmented reality and virtual reality are emerging technologies and rapidly gaining popularity. Augmented reality combines the virtual world with real environment using digital visual elements, sound, or other sensory stimuli delivered via technology and creates an interactive experience. Virtual reality creates a 3D-simulated immersive environment. AR and VR are two sides of a coin. Virtual reality replaces what people see and experience whereas augmented reality actually adds to it. The study aims at understanding applications of augmented reality and virtual augmented technologies for optimizing customer engagement and challenges in implementing these technologies for developing customer engagement. The study being conceptual in nature is based on secondary sources. Efforts have been made to collect literature from every possible source including websites, journals, books, theses, etc.