Reviewerly/exHarmony huggingface dataset:
Social support of virtual characters reduces pain perception

Psychosocial factors, such as social support, can reduce pain. Virtual reality (VR) is a powerful tool to decrease pain, but social factors in VR-based pain analgesia have rarely been studied. Specifically, it is unclear whether social support by virtual characters can reduce pain and whether the perceived control behind virtual characters (agency) and varying degrees of social cues impact pain perception.Healthy participants (N = 97) received heat pain stimulation while undergoing four within-subject conditions in immersive VR: (1) virtual character with a low number of social cues (virtual figure) provided verbal support, (2) virtual character with a high number of social cues (virtual human) provided verbal support, (3) no social support (hearing neutral words), (4) no social support. Perceived agency of the virtual characters served as between-subjects factor. Participants in the avatar group were led to believe that another participant controlled the virtual characters. Participants in the agent group were told they interacted with a computer. However, in both conditions, virtual characters were computer-controlled. Pain ratings, psychophysiological measurements and presence ratings were recorded.Virtual social support decreased pain intensity and pain unpleasantness ratings but had no impact on electrodermal activity nor heart rate. A virtual character with a high number of social cues led to lower pain unpleasantness and higher feelings of presence. Agency had no significant impact.Virtual characters providing social support can reduce pain independent of perceived agency. A more human visual appearance can have beneficial effects on social pain modulation by virtual characters.Social influences are important factors in pain modulation. The current study demonstrated analgesic effects through verbal support provided by virtual characters and investigated modulating factors. A more human appearance of a virtual character resulted in a higher reduction of pain unpleasantness. Importantly, agency of the virtual characters had no impact. Given the increasing use of digital health interventions, the findings suggest a positive impact of virtual characters for digital pain treatments.
The Scope of Security

Security. It is an incredibly overloaded word in the IT, OT, and IoT world. It means so many different things in many different contexts, but in the end, it is all about protecting sensitive and valuable resources against malicious usage.
Longing for touch post-COVID-19: current observations and future directions

Abstract Previous studies have reported an association between the COVID-19 pandemic, social distancing regulations and longing for touch (LFT; i.e., a discrepancy between actual touch frequency and one’s desire to be touched). However, less is known about the prevalence and severity of LFT in the general population in the absence of social distancing regulations. The aim of this study was therefore to exploratively compare data collected during and after the pandemic. Pandemic data was collected online in an international sample (n = 1982), of which a matched subsample (n = 115) was used in the reported analyses. Post-pandemic data was collected one week after social distancing regulations restrictions were lifted in the Netherlands (n = 60) and when virtually no restrictions were in place (n = 55). The severity of LFT was significantly higher during the pandemic than afterwards. Although there were no significant differences in the general prevalence of LFT, significantly more participants reported high levels of LFT (score of 75–100) during the pandemic. We cautiously conclude that, although LFT may have peaked during the pandemic, a large portion of the general population desires to experience more interpersonal touch, even in the absence of social distancing regulations.
Exploration on the Curriculum System and Mode of Innovative Talents Training in Big Data Specialty Based on AI Concept

The rapid development of big data and cloud computing technology, artificial intelligence (Artificial Intelligence, AI) technology, and wireless sensor technology provides support for the digital transformation of various industries and industrial economies in society, and also puts forward the comprehensive quality of big data professionals. Higher requirements. Starting from the “AI concept” of the integration and development of “big data + artificial intelligence”, this paper explores the problems of unclear educational objectives, imperfect teaching system, outdated teaching knowledge content and insufficient innovation of teaching mode in the current big data professional courses in colleges and universities, and puts forward the “1 + X” open teaching system with big data professional teaching materials as the main part and other subject teaching as the supplement, the establishment of network Massive Open Online Course (MOOC) platform, school-enterprise integration practice training base to deepen the orientation education of big data professional students, the formation of big data curriculum theory, employment application practice integration teaching mode, so as to achieve the goal of comprehensive application ability training of big data professionals.
The Relationship Between Technological Self-Efficacy and Organizational Citizenship Behavior During Covid-19: Analyzing the Moderating Role of Trust

In this study, we examine the intricate relationship between technological self-efficacy, organizational citizenship behavior (OCB), and the moderating influence of trust—a facet largely unexplored in existing literature. The COVID-19 pandemic has intensified the reliance on technologies like Zoom and Microsoft Teams, creating a unique backdrop. Amid the elevated anxiety surrounding the adoption of "new" technology, we hypothesize that trust within the workplace will play a pivotal role in moderating the link between technological self-efficacy and OCB. Our research aims to shed light on this uncharted territory, offering insights into how trust shapes employees' responses to technological challenges, particularly in the context of a global crisis.
Intermediate Domain Based Meta Learning Framework for Adaptive Object Detection

Deep learning based object detection methods have made significant progress in recent years. However, these methods often suffer from a substantial performance drop when domain shifts occur, making it difficult to generalize a source domain trained object detector to a new target domain. To address this problem, we propose an Online Meta Learning Framework (OMLF) for unsupervised domain adaptive object detection. In our proposed framework, we adopt the Polar Harmonic Fourier Moment (PHFM) to generate target-like intermediate data. The purpose is to construct a two-pair framework that learns meta knowledge (i.e. model initial parameters) from the pair of “source-to-intermediate” to assist another pair of “intermediate-to-target”. Moreover, the optimizing process requires a heavy computational load due to triggering higher-order gradients. To alleviate this problem, we introduce a shortest-path update strategy that accelerates optimization. When evaluated on several benchmark adaptation scenarios (i.e. normal-to-foggy weather, cross cameras, synthetic-to-real, and real-to-artistic), our OMLF achieves state-of-the-art results, demonstrating its effectiveness.
Delay-based reservoir computing using Mackey–Glass oscillator and Arduino board for edge intelligence applications

This article introduces the experimental demonstration of the Mackey–Glass oscillator (MGO)/Arduino-based reservoir computing system as a novel versatile platform for several applications. Performance evaluations conducted on benchmark prediction tasks demonstrate the system’s capabilities with exceptional normalized mean square error (NMSE) values of up to 0.050 [log10(NMSE) ≃ −1.29] for Santa Fe and 0.0034 [log10(NMSE) ≃ −2.46] for electrocardiogram tasks. In addition, we achieve outstanding classification accuracy of up to 96.67% in the chaos recognition task. Our MGO/Arduino-based reservoir computing approach offers many advantages, such as cheapness, affordability, accessibility, and versatility, positioning it as a valuable and efficient solution in advancing neuromorphic computing for edge intelligence applications.
Python-Powered Precision: Unraveling Consumer Price Index Trends in Makassar City through a Duel of Long Short-Term Memory and Gated Recurrent Unit Models

This research aims to carry out a predictive analysis of the Consumer Price Index in the city of Makassar to anticipate possible impacts on inflation and deflation in the future. The Consumer Price Index is an indicator that can be used as a basis for measuring changes in the prices of goods and services purchased by consumers which have an impact on inflation in a region. The CPI is very useful for knowing the level of increase in prices, services, and income, as well as measuring the amount of production costs. This data was obtained through the official website of the Central Statistics Agency (BPS) for the Makassar city area. The methods used in this research are Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results of this research show that based on analysis and testing, the LSTM model has an MAE of 1.0849 and the GRU model has an MAE of 0.9915, which shows that there is no significant difference between the two methods and both methods can work very well, however, The lowest error value was obtained in the GRU model using a 70:30 dataset ratio, 9 number of sequences, 16 neurons in hidden layer 1 and 32 neurons in hidden layer 2, and 1000 number of epochs.
On-chip phonon-magnon reservoir for neuromorphic computing

Abstract Reservoir computing is a concept involving mapping signals onto a high-dimensional phase space of a dynamical system called “reservoir” for subsequent recognition by an artificial neural network. We implement this concept in a nanodevice consisting of a sandwich of a semiconductor phonon waveguide and a patterned ferromagnetic layer. A pulsed write-laser encodes input signals into propagating phonon wavepackets, interacting with ferromagnetic magnons. The second laser reads the output signal reflecting a phase-sensitive mix of phonon and magnon modes, whose content is highly sensitive to the write- and read-laser positions. The reservoir efficiently separates the visual shapes drawn by the write-laser beam on the nanodevice surface in an area with a size comparable to a single pixel of a modern digital camera. Our finding suggests the phonon-magnon interaction as a promising hardware basis for realizing on-chip reservoir computing in future neuromorphic architectures.
A Systematic Literature Review of AI-based Software Requirements Prioritization Technique

Software requirements show what the customer desires his software to do. They are the first stepping stone towards a successful software development project. With the increasing complexity of the software due to its size and feature base, it is vital to prioritize the requirements for efficient utilization of development resources. To achieve this, industrial organizations are devising new strategies and improved solutions even with the help of artificial intelligence (AI) tool set. Existing requirements prioritization techniques are human-intensive and suffer from several limitations like overlapping outcomes, scalability problems, time consumption, inaccuracy, and so on. Some of the problems can be solved with the inclusion of artificial intelligence algorithms and strategies. For this several AI-based requirements prioritization techniques have been proposed by applying Genetic Algorithms, Fuzzy Logic, Ant Colony Optimization, and Machine Learning. Literature witnesses some good review studies and surveys on conventional prioritization techniques but there exists none for AI-based techniques that identify not only their strengths but also their weaknesses, advantages of machine learning techniques over other AI-based requirements prioritization techniques, and limitations of applying AI-based techniques in requirements prioritization. This study presents a systematic literature review (SLR) of AI-based requirements prioritization approaches covering 46 papers published from 2000 to 2021.We have given this literature review a new dimension by conducting a parametric analysis of AI-based requirements prioritization techniques and we have identified these parameters after a thorough literature study. Some of the chosen parameters are generic (related to the prioritization process) and some are specific (related to AI techniques). This study has greatly helped us draw a clear line among AI-based techniques to show their domain of application to gain maximum advantage. Our findings will assist researchers, requirement analysts, and other stakeholders in making a wise decision to select the best requirements prioritization technique to gain optimal results.
LiDAR-Camera Fusion in Perspective View for 3D Object Detection in Surface Mine

LiDAR-Camera fusion can effectively provide the complementary geometric and appearance information for 3D object detection task of autonomous driving system. However, current dominant methods designed for urban scenes often fuse multi-modal information into a Bird's-Eye-View (BEV) feature representation and have a relatively short perception range, which limits their application in surface mine. To achieve long-range perception in surface mine, we utilize a solid-state LiDAR (i.e. Livox Horizon) that has a large perception range (over 200 meters). Accordingly, we propose PVFusion, a novel 3D object detection method that only fuses LiDAR and camera information in Perspective View (PV). Specifically, we first generate the multi-channel depth map via point cloud projection and Depth Map Densification (DMD). Then, we extract visual-depth features and local geometry features from depth-enhanced camera image and point cloud respectively. To better fuse multi-modal information, we propose an Attentional Feature Fusion (AFF) module to dynamically fuse visual-depth features and local geometry features into fused PV features. Moreover, we design a three-branch detection head that leverages a Depth Decoupling Strategy (DDS) to improve the depth estimation for distant objects. We conduct extensive experiments on our surface mine dataset and verify the effectiveness of the proposed PVFusion.
Application of Decision Tree Algorithm for the Classification Problem in Bank Telemarketing

This paper describes the application of the Classification and Regression Tree (CART) for a data mining problem in bank telemarketing. Tasks of telemarketing include questions such as which customer segments to target, which prices and promotions to offer, or when to make a phone call to the customer. We make use of a common dataset that is publicly available and construct a decision tree for the classification of customers with or without deposits. By eliminating certain attributes of the dataset to get a reasonable performance of the decision tree algorithm, we show that domain knowledge of an industry sector is important in a data mining project. It is shown that after removing unnecessary features that may cause bias, we improve the structure of the decision tree (DT) and the performance. The metric for measuring information impurity used in this work is the Gini index. The results show that our DT model performs effectively with values of 92, 99, 86 and 92% for AS, Recall, Precision, and F1-score. Our findings revealed interesting and valuable knowledge. The attribute “previous” has a relatively strong impact on the classification, resulting in it being chosen as the root node of the tree. This means that customers who are contacted for the first time would have a higher probability of accepting the subscription.
Automatic measurement system for large-aperture-angle non-holonomic spherical stitching with laser differential confocal interference

In response to the urgent need for highly precise and efficient stitching measurements of large-aperture-angle non-holonomic spherical surfaces, a differential confocal interference automatic stitching measurement system for large-aperture-angle non-holonomic spherical surfaces was developed. The system realizes precise positioning of the confocal position through differential confocal precise focusing technology. Through the stitching model, coordinate transformation and error compensation were performed on subaperture data, and the stitching measurement of the spherical surface shape was realized. The positions and postures of the tested samples were adjusted automatically using an automatic adjustment workbench. The stitching measurement accuracy of this measurement system can attain 0.0013λ, relative error can attain 1.36%, and measurement time for eight subaperture stitching is 6 min. This system achieves automatic and rapid adjustment of large-aperture-angle spherical elements and high-precision, nondestructive, fast, and automatic measurement of surface stitching.
Investigation on the monolithically integrated chaotic optical transmitting chip based on the parallel EAMs

Chaotic optical communication ensures information security at the physical layer. However, the monolithic integration of lasers and lithium niobate Mach-Zehnder modulators remains a challenge, limiting the progress of integrated chaotic optical communication systems based on an electro-optic feedback. Here, we propose the monolithically integrated chaotic optical transmitting chip based on the parallel EAMs and validate its performance from the perspectives of phase portraits, fast Fourier transform (FFT), probability density function (PDF), largest Lyapunov exponents, and bifurcation. The results demonstrate the feasibility of the chip, which is beneficial for the miniaturization and integration of the system.
Image processing setting adaptions according to image dose and radiologist preference can improve image quality in computed radiography of the equine distal limb: A cadaveric study

Abstract Image processing (IP) in digital radiography has been steadily refined to improve image quality. Adaptable settings enable users to adjust systems to their specific requirements. This prospective, analytical study aimed to investigate the influence of different IP settings and dose reductions on image quality. Included were 20 cadaveric equine limb specimens distal to the metacarpophalangeal and metatarsophalangeal joints. Images were processed with the Dynamic Visualization II system (Fujifilm) using five different IP settings including multiobjective frequency processing, flexible noise control (FNC), and virtual grid processing (VGP). Seven criteria were assessed by three veterinary radiology Diplomates and one veterinary radiology resident in a blinded study using a scoring system. Algorithm comparison was performed using an absolute visual grading analysis. The rating of bone structures was improved by VGP at full dose ( P &lt; .05; AUC VGC = 0.45). Überschwinger artifact perception was enhanced by VGP ( P &lt; .001; AUC VGC = 0.66), whereas image noise perception was suppressed by FNC ( P &lt; .001; AUC VGC = 0.29). The ratings of bone structures were improved by FNC at 50% dose ( P &lt; .05; AUC VGC = 0.44), and 25% dose ( P &lt; .001; AUC VGC = 0.32), and clinically acceptable image quality was maintained at 50% dose (mean rating 2.16; 95.8% ratings sufficient or better). The favored IP setting varied among observers, with higher agreement at lower dose levels. These findings supported using individualized IP settings based on the radiologist's preferences and situational image requirements, rather than using default settings.
Capital flight and public health outcomes in Africa

Abstract Motivated by the momentous concerns over the development challenges associated with capital flight and the persistent lack of funding to improve healthcare in Africa, this study investigates the effects of capital flight on public health outcomes. Exploiting regional variations in capital flight stemming from differences in exchange rate regimes across CFA (Communaute Financiere Africaine) zone countries and non‐CFA zone countries, I construct a shift‐share instrumental variable to infer the causal effect of capital flight on health outcomes in Africa. The results show some headways toward considering capital flight as a potential threat to achieving better public health outcomes. This finding is robust to various robustness tests, including pre‐existing trend analysis, falsification tests, heterogeneity analysis, and alternative instrumental variable specifications. The study demonstrates that government spending on health and living standards are the main routes through which capital flight influences public health outcomes.
Blockchain-Based Lightweight Authentication Mechanisms for Industrial Internet of Things and Information Systems

The industrial internet of things (IIoT) necessitates robust cross-domain authentication to secure sensitive on-site equipment data. This paper presents a refined reputation-based lightweight consensus mechanism (LRBCM) tailored for IIoT's distributed network structures. Leveraging node reputation values, LRBCM streamlines ledger consensus, minimizing communication overhead and complexity. Comparative experiments show LRBCM outperforms competing mechanisms. It maintains higher throughput as the number of participating nodes increases and achieves a throughput approximately 10.78% higher than ReCon. Moreover, runtime analysis demonstrates LRBCM's scalability, surpassing ReCon by approximately 12.79% with equivalent nodes and transactions. In addition, as a combination of LRBCM, the proposed distributed lightweight authentication mechanism (ELAM) is rigorously evaluated against the security of various attacks, and its resilience is confirmed. Experiments show that ELAM has good efficiency while maintaining high security.
Motivasi Belajar Lansia Pada Program Keaksaraan Di Kampung Bungaraya Kecamatan Bungaraya Kabupaten Siak

This research was conducted in Bungaraya Village, Bungaraya District, Siak Regency, Riau Province. The aim was to determine the learning motivation of elderly people in the literacy program in Bungaraya Village, and to determine the results of the literacy education program in Bungaraya Village. This research method uses descriptive qualitative research methods and data collection using in-depth interview techniques, direct observation and documentation. With a total of seven subjects coming from the community, with the condition that subjects other than village people include transmigrants who have lived in the village for decades, and are deemed capable of defining and explaining a problem solving process based on existing facts. The theories used are social action theory and motivation theory. Field research found that the learning motivation of elderly people in literacy programs is quite high and the results of literacy programs are very effective in eradicating cases of illiteracy. The government, administrators and tutors work together to continue efforts to eradicate illiteracy. The literacy program is an effective step for the government to always pay attention to its people in social, development and educational matters.