Reviewerly/exHarmony huggingface dataset:
Contrastive Representation Learning for Gaze Estimation.

Self-supervised learning (SSL) has become prevalent for learning representations in computer vision. Notably, SSL exploits contrastive learning to encourage visual representations to be invariant under various image transformations. The task of gaze estimation, on the other hand, demands not just invariance to various appearances but also equivariance to the geometric transformations. In this work, we propose a simple contrastive representation learning framework for gaze estimation, named Gaze Contrastive Learning (GazeCLR). GazeCLR exploits multi-view data to promote equivariance and relies on selected data augmentation techniques that do not alter gaze directions for invariance learning. Our experiments demonstrate the effectiveness of GazeCLR for several settings of the gaze estimation task. Particularly, our results show that GazeCLR improves the performance of cross-domain gaze estimation and yields as high as 17.2% relative improvement. Moreover, the GazeCLR framework is competitive with state-of-the-art representation learning methods for few-shot evaluation. The code and pre-trained models are available at https://github.com/jswati31/gazeclr.
Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks

Intelligent devices, which significantly improve the quality of life and work efficiency, are now widely integrated into people’s daily lives and work. A precise understanding and analysis of human motion is essential for achieving harmonious coexistence and efficient interaction between intelligent devices and humans. However, existing human motion prediction methods often fail to fully exploit the dynamic spatial correlations and temporal dependencies inherent in motion sequence data, which leads to unsatisfactory prediction results. To address this issue, we proposed a novel human motion prediction method that utilizes dual-attention and multi-granularity temporal convolutional networks (DA-MgTCNs). Firstly, we designed a unique dual-attention (DA) model that combines joint attention and channel attention to extract spatial features from both joint and 3D coordinate dimensions. Next, we designed a multi-granularity temporal convolutional networks (MgTCNs) model with varying receptive fields to flexibly capture complex temporal dependencies. Finally, the experimental results from two benchmark datasets, Human3.6M and CMU-Mocap, demonstrated that our proposed method significantly outperformed other methods in both short-term and long-term prediction, thereby verifying the effectiveness of our algorithm.
ReIPS: A Secure Cloud-Based Reputation Evaluation System for IoT-Enabled Pumped Storage Power Stations

Reputation evaluation is an effective measure for maintaining secure Internet of Things (IoT) ecosystems, but there are still several challenges when applied in IoT-enabled pumped storage power stations (PSPSs), such as the limited resources of intelligent inspection devices and the threat of single-point and collusion attacks. To address these challenges, in this paper we present ReIPS, a secure cloud-based reputation evaluation system designed to manage intelligent inspection devices’ reputations in IoT-enabled PSPSs. Our ReIPS incorporates a resource-rich cloud platform to collect various reputation evaluation indexes and perform complex evaluation operations. To resist single-point attacks, we present a novel reputation evaluation model that combines backpropagation neural networks (BPNNs) with a point reputation-weighted directed network model (PR-WDNM). The BPNNs objectively evaluate device point reputations, which are further integrated into PR-WDNM to detect malicious devices and obtain corrective global reputations. To resist collusion attacks, we introduce a knowledge graph-based collusion device identification method that calculates behavioral and semantic similarities to accurately identify collusion devices. Simulation results show that our ReIPS outperforms existing systems regarding reputation evaluation performance, particularly in single-point and collusion attack scenarios.
Improved Robot Path Planning Method Based on Deep Reinforcement Learning

With the advancement of robotics, the field of path planning is currently experiencing a period of prosperity. Researchers strive to address this nonlinear problem and have achieved remarkable results through the implementation of the Deep Reinforcement Learning (DRL) algorithm DQN (Deep Q-Network). However, persistent challenges remain, including the curse of dimensionality, difficulties of model convergence and sparsity in rewards. To tackle these problems, this paper proposes an enhanced DDQN (Double DQN) path planning approach, in which the information after dimensionality reduction is fed into a two-branch network that incorporates expert knowledge and an optimized reward function to guide the training process. The data generated during the training phase are initially discretized into corresponding low-dimensional spaces. An “expert experience” module is introduced to facilitate the model’s early-stage training acceleration in the Epsilon–Greedy algorithm. To tackle navigation and obstacle avoidance separately, a dual-branch network structure is presented. We further optimize the reward function enabling intelligent agents to receive prompt feedback from the environment after performing each action. Experiments conducted in both virtual and real-world environments have demonstrated that the enhanced algorithm can accelerate model convergence, improve training stability and generate a smooth, shorter and collision-free path.
Significance of E-Commerce in the Growth and Development of Small Businesses: A Qualitative Study

E-commerce has been a big contributor to the expansion and development of small businesses, bringing about a sea change in the manner in which these companies function while also extending their reach beyond the confines of geographic location.The opportunity to have an online presence and compete on an equal playing field with larger and more established firms is one of the most important benefits that small businesses can receive from e-commerce.Ecommerce also provides small businesses with a number of other essential benefits.Small businesses have the ability to expose their goods and services to a global audience by establishing an e-commerce website, which in turn increases both their visibility and their number of customers.Because of this level of accessibility, it is now possible for small businesses to avoid the limitations provided by the presence of physical shops and obtain access to a sizable pool of potential customers.On the other hand, platforms for electronic commerce include solutions for inventory management, payment processing, and transportation that are both cost-effective and scalable.This removes the requirement for a substantial amount of infrastructure and brings down the overall cost of operations.Because of the cost-effectiveness of this solution, small firms are now able to more effectively allocate their resources, make investments in marketing tactics, and improve their overall competitiveness.E-commerce also provides important insights into consumer behaviour and preferences through the use of data analytics.This enables small businesses to adjust their products and services as well as their marketing efforts to particularly target particular client categories.E-commerce also provides substantial insights into consumer behaviour and preferences through the use of data analytics.E-commerce has given small businesses access to the tools and opportunities they need to be successful in a digital economy.This has not only helped to the general development of small 813 businesses but has also encouraged their growth, grown their customer base, and promoted the overall expansion of small enterprises.
Prograde metamorphism provides gold and base metals to orogenic gold deposits in southern Tibet: Insights from thermodynamic modeling

The metamorphic devolatilization model is presently the most widely accepted model for the formation of orogenic gold deposits. According to this model, Au is likely derived from metamorphic rocks that experienced metamorphism from greenschist to amphibolite facies. Nevertheless, in recent years, some researchers have suggested that Au may have originated from different source regions, such as the mantle. As one of China's most significant orogenic gold provinces, the Indus-Yarlung Zangbo suture zone (IYZSZ) in southern Tibet plays a critical role in this discussion. Orogenic gold deposits within the IYZSZ are typically hosted in Triassic metasedimentary rocks called the Langjiexue group. This paper aims to investigate the suitability of metasedimentary rocks of the Langjiexue group as a source region of Au and base metals. Furthermore, we also examine whether the metamorphic devolatilization model is applicable to the IYZSZ. Our study involves reporting the mineral compositions and evolution of metamorphic zones (chlorite to sillimanite zones) in metasedimentary rocks obtained from the IYZSZ and providing detailed petrographic descriptions of these rocks. Thermodynamic modeling based on metamorphic mineral compositions was used to reveal the behaviors of H2O, Au, S, Cu, Pb, and Zn within prograde metamorphism. Our modeling results proved that the breakdown of chlorite and muscovite, both of which are the main mechanisms of Au and base metals release in metasedimentary rocks, occurs under different geothermal gradients. Given the observed metamorphic mineral compositions, it is the breakdown of chlorite that is the predominant reaction responsible for releasing Au and base metals under the real geothermal gradient of lower crustal metasedimentary rocks (∼25 °C/km). This gradient is capable of releasing at least 1.5 ppb Au, 5.2 ppm Cu, 4.3 ppm Pb, 5.0 ppm Zn, and 20 ppm S (as part of the bulk rock) into the ore fluids. Through the mass balance calculations, we found that over 4.05 t of Au, 14,040 t of Cu, 11,600 t of Pb, and 13,500 t of Zn can be mobilized from 1 km3 of metasedimentary rocks. Therefore, lower crustal metasedimentary rocks can play a significant role in contributing to Au and base metals in the IYZSZ and other metasediment-hosted orogenic gold deposits worldwide.
Developing Cybersecurity in an Industrial Environment by Using a Testbed Environment

Critical infrastructure protection requires a testing environment that allows the testing of different kinds of equipment, software, networks, and tools to develop vital functions of the critical industrial environment. Used electrical equipment must be reliable, capable and maintain a stable critical industrial ecosystem. An industrial business needs to develop cybersecurity capabilities that detect and prevent IT/ICT and OT/ICS threats in an industrial environment. The emerging trend has been to create security operations center (SOC) services to detect ICS-related threats in enterprise networks. The energy supply sector must consist of crucial elements for safe business continuity and supply chain management in the industrial sector. Threats have changed into a combination of threat types. Hybrid threats may prevent everyday industrial activities, processes, and procedures so that supply chain problems may become long-lasting and affects business continuity management. The project CSG belongs to the (Cybersecurity governance of operational technology in the sector connected smart energy) research project consortium of Business Finland’s Digital Trust Programme. The first research paper regarding the CSG (Cyber Security Governance) project concentrates on the applied theory background of this project. The research provides a research approach for investigating cyber security at the operational and technical levels. It answers the questions of where to concentrate on OT-related cyber security research and how we aim to deploy a testbed to develop a governance model in the CSG project. The study's primary purpose is to describe the operating OT-SOC environment and analyze system requirements for optimizing situational awareness in the testbed environment.
EXTRACTING LATENT NEEDS FROM ONLINE REVIEWS THROUGH DEEP LEARNING BASED LANGUAGE MODEL

Abstract Aspect-based sentiment analysis (ABSA) provides an opportunity to systematically generate user's opinions of specific aspects to enrich the idea creation process in the early stage of product/service design process. Yet, the current ABSA task has two major limitations. First, existing research mostly focusing on the subsets of ABSA task, e.g. aspect-sentiment extraction, extract aspect, opinion, and sentiment in a unified model is still an open problem. Second, the implicit opinion and sentiment are ignored in the current ABSA task. This article tackles these gaps by (1) creating a new annotated dataset comprised of five types of labels, including aspect, category, opinion, sentiment, and implicit indicator (ACOSI) and (2) developing a unified model which could extract all five types of labels simultaneously in a generative manner. Numerical experiments conducted on the manually labeled dataset originally scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, scalability, and potentials of the framework developed. Several directions are provided for future exploration in the area of automated aspect-based sentiment analysis for user-centered design.
Designing Security for the Sixth Generation: About Necessity, Concepts and Opportunities

Intelligent, comprehensive and, above all, secure wireless interconnection is the driving force behind technological progress. To ensure this, the development towards Sixth Generation (6G) Wireless Systems has been launched and is scheduled to be operational by 2030. This data technology of the future turns 6G into the infrastructure of a new generation of mobile, intelligent, and context-sensitive services, available everywhere and featuring high trustworthiness and performance, relying on both, network-side and off-network context sources. In addition, the networks themselves ought to become intelligent and thus more efficient and resource-saving, which requires a high degree of automated utilization of Artificial Intelligence (AI). Building upon the principles of information and communication theory for both the physical (bit)-transmission layer (PHY) and media access, new communication concepts for 6G will be developed providing the foundations for research into new single and multi-user operation, access and core networks. The flip side of this coin of opportunities: Sophisticated technology inevitably leads to additional security vulnerabilities, open access systems and Open-Radio Access Network (O-RAN) approaches imply new attack vectors. The holistic interconnection of everything renders it ever more attractive to attackers to harm systems, and create damage. Furthermore, enhanced computational power along with quantum computers make conventional systems more vulnerable than ever, and the value of the transmitted data increases tremendously: It is not only machine and sensor data, but also very personal and healthcare data transmitted with 6G. Therefore, the aim is to build a resilient and secure 6G system capable of recognizing attacks and uncertainties, flexibly absorbing them, recovering in a timely and sustainable manner, and compensating for impaired functionality through transformation. This holistic resilience-by-design approach is based, among other things, on technology such as Quantum Key Distribution (QKD) and Post Quantum-Crypto to achieve end-to-end security, Reconfigurable Intelligent Surfaces (RISs) to rely, control and manipulate the wireless transmission channel, Wireless Optical Communication (WOC), Physical Layer Security (PhySec), but also Body Area Networks (BANs), the integration of the human body relying on biometrics and the Tactile Internet (TI). These concepts will be discussed and shed light on in the scope of this work.
SOCIO-EMOTIONAL LEARNING AS AN ESSENTIAL MECHANISM OF DEVELOPING SOFT SKILLS IN STUDENTS: MODERN CHALLENGES AND UKRAINIAN EXPERIENCE

The paper presents the findings of a study conducted to authenticate the need and feasibility of introducing socio-emotional learning (SEL) and its revisited and enhanced version – Social, Emotional and Ethical Learning (SEE Learning) – into the Ukrainian education system as an effective method for developing soft (social and emotional) skills in students in times of new challenges for child education and whilst implementing a competency-based approach in education, as well as highlighting the experience of promoting such an approach by the civic sector of Ukraine.The paper considers the emergence of the «soft skills» concept in the mainstream of embodying the ideas of human well-being and happy life as the ultimate goal of education against the backdrop of the Fourth Industrial Revolution (4IR); next, it justifies the importance of SEL and SEE Learning as methods of soft skills development in students. In the context of the New Ukrainian School reform, these practices have been established to serve as a tool for developing soft skills, with educators as the key figures for its successful implementation.The article analyzes the level of educators' awareness and their readiness to implement SEL based on the sociological study «Socio-Emotional Learning in Ukraine: Today Life and Prospects», which was conducted in 2021 with 3,657 respondents (including 3,237 teachers and 420 heads of educational institutions) with Public Organization «EdCamp Ukraine» and the paper author among the research team. The survey established a great demand for developing socio-emotional skills and relevant training among teachers.The paper further reveals the importance of discussing SEL topics as a basis for the systemic restructuring of the entire educational process, including teacher training and professional development in terms of implementing the concept of the New Ukrainian School. It summarizes various programs that implement socio-emotional learning on the local level in Ukraine and demonstrates the prospects and added value of the SEE learning program in Ukraine.Finally, the article analyzes the experience of implementing the SEE Learning program on a national scope by EdCamp Ukraine and outlines the prospects for actualizing SEE Learning in Ukraine during the full-scale russian military aggression of and the post-war reconstruction of Ukraine
Application of J48 and Naïve Bayes Algorithms to Predict Ream Bookings at PT. Nippon Presisi Teknik

In the field of goods production, demand prediction is important. By doing sales predictions, companies can make calculations and forecasts for what raw materials are mostly ordered. J48 and Naïve Bayes algorithm are two popular machine learning technique. By using these two algorithms, this study aims to develop an accurate and more reliable predictive model that help the company to make data driven decision. This study focuses on the application of quantitative methods, specifically the J48 algorithm and Naïve Bayes algorithm. This research conducted 4 times testing on each algorithm. This study produces high accuracy values ​​with the Naïve Bayes and J48 algorithms. Both algorithm results have a fairly high accuracy value of 94% for Naïve Bayes and 98% for J48. The findings of this study implicate that by using J48 and Naïve Bayes algorithm, company can make informed decisions lead to improved operational efficiency, cost-effective, and resource utilization.
Developing an eMental health monitoring module for older mourners using fuzzy cognitive maps

Objective Effective internet interventions often combine online self-help with regular professional guidance. In the absence of regularly scheduled contact with a professional, the internet intervention should refer users to professional human care if their condition deteriorates. The current article presents a monitoring module to recommend proactively seeking offline support in an eMental health service to aid older mourners. Method The module consists of two components: a user profile that collects relevant information about the user from the application, enabling the second component, a fuzzy cognitive map (FCM) decision-making algorithm that detects risk situations and to recommend the user to seek offline support, whenever advisable. In this article, we show how we configured the FCM with the help of eight clinical psychologists and we investigate the utility of the resulting decision tool using four fictitious scenarios. Results The current FCM algorithm succeeds in detecting unambiguous risk situations, as well as unambiguously safe situations, but it has more difficulty classifying borderline cases correctly. Based on recommendations from the participants and an analysis of the algorithm's erroneous classifications, we propose how the current FCM algorithm can be further improved. Conclusion The configuration of FCMs does not necessarily demand large amounts of privacy-sensitive data and their decisions are scrutable. Thus, they hold great potential for automatic decision-making algorithms in mental eHealth. Nevertheless, we conclude that there is a need for clear guidelines and best practices for developing FCMs, specifically for eMental health.
Automated player identification and indexing using two-stage deep learning network

Abstract American football games attract significant worldwide attention every year. Identifying players from videos in each play is also essential for the indexing of player participation. Processing football game video presents great challenges such as crowded settings, distorted objects, and imbalanced data for identifying players, especially jersey numbers. In this work, we propose a deep learning-based player tracking system to automatically track players and index their participation per play in American football games. It is a two-stage network design to highlight areas of interest and identify jersey number information with high accuracy. First, we utilize an object detection network, a detection transformer, to tackle the player detection problem in a crowded context. Second, we identify players using jersey number recognition with a secondary convolutional neural network, then synchronize it with a game clock subsystem. Finally, the system outputs a complete log in a database for play indexing. We demonstrate the effectiveness and reliability of player tracking system by analyzing the qualitative and quantitative results on football videos. The proposed system shows great potential for implementation in and analysis of football broadcast video.
Effects of user factors on user experience in virtual reality: age, gender, and VR experience as influencing factors for VR exergames

Abstract As virtual reality (VR) technology is extensively developing in past years, more and more people are using it in different fields. One of the fast-developing areas in VR is exergaming, a combination of physical exercise and a game. VR exergames that aim to engage people in physical activity should look and feel good for users regardless of their age, gender, or their previous VR experience with similar technologies. However, recent studies showed that those factors are influencing the user experience (UX) with virtual reality. Building on top of the initial study that has reported on the effect of human influencing factors for exergaming, with this work, we investigated the influence of user parameters (such as age, gender, and previous VR experience) on their motivation for sports and VR exergaming. The study was done using a crowdsourcing platform to recruit a diverse set of participants, with the aim to explore how different user factors are connected to sports motivation. Results show significant differences in the user’s sports motivation and affinity for technology interaction depending on the age group, gender, previous experience with VR, their weekly exercise routine, and how much money they spend on sports yearly.