Reviewerly/exHarmony huggingface dataset:
Development, Application, And Regulation of Web3.0

This paper explores the development, applications, and regulatory issues of Web3.0 as the next generation of internet technology. Web3.0 is based on technologies such as distributed computing, blockchain, artificial intelligence, and smart contracts, and it features decentralization, intelligence, and high security. This paper introduces the development, characteristics, technical architecture, and key technologies of Web3.0, as well as its applications and future development trends in areas such as digital currencies, decentralized finance, supply chain management, and digital identity. However, the decentralized nature of Web3.0 increases regulatory challenges, and the emergence of new businesses like digital currencies and smart contracts also poses challenges to traditional regulatory institutions. Therefore, this paper discusses the regulatory status and challenges of Web3.0 and proposes solutions. The aim of this paper is to provide references and insights for the research and application of Web3.0.
A Speech Recognition Method Based on Domain-Specific Datasets and Confidence Decision Networks

This paper proposes a speech recognition method based on a domain-specific language speech network (DSL-Net) and a confidence decision network (CD-Net). The method involves automatically training a domain-specific dataset, using pre-trained model parameters for migration learning, and obtaining a domain-specific speech model. Importance sampling weights were set for the trained domain-specific speech model, which was then integrated with the trained speech model from the benchmark dataset. This integration automatically expands the lexical content of the model to accommodate the input speech based on the lexicon and language model. The adaptation attempts to address the issue of out-of-vocabulary words that are likely to arise in most realistic scenarios and utilizes external knowledge sources to extend the existing language model. By doing so, the approach enhances the adaptability of the language model in new domains or scenarios and improves the prediction accuracy of the model. For domain-specific vocabulary recognition, a deep fully convolutional neural network (DFCNN) and a candidate temporal classification (CTC)-based approach were employed to achieve effective recognition of domain-specific vocabulary. Furthermore, a confidence-based classifier was added to enhance the accuracy and robustness of the overall approach. In the experiments, the method was tested on a proprietary domain audio dataset and compared with an automatic speech recognition (ASR) system trained on a large-scale dataset. Based on experimental verification, the model achieved an accuracy improvement from 82% to 91% in the medical domain. The inclusion of domain-specific datasets resulted in a 5% to 7% enhancement over the baseline, while the introduction of model confidence further improved the baseline by 3% to 5%. These findings demonstrate the significance of incorporating domain-specific datasets and model confidence in advancing speech recognition technology.
Generalized measurement error: Intrinsic and incidental measurement error

In this paper, we generalize the notion of measurement error on deterministic sample datasets to accommodate sample data that are random-variable-valued. This leads to the formulation of two distinct kinds of measurement error: intrinsic measurement error, and incidental measurement error. Incidental measurement error will be recognized as the traditional kind that arises from a set of deterministic sample measurements, and upon which the traditional measurement error modelling literature is based, while intrinsic measurement error reflects some subjective quality of either the measurement tool or the measurand itself. We define calibrating conditions that generalize common and classical types of measurement error models to this broader measurement domain, and explain how the notion of generalized Berkson error in particular mathematicizes what it means to be an expert assessor or rater for a measurement process. We then explore how classical point estimation, inference, and likelihood theory can be generalized to accommodate sample data composed of generic random-variable-valued measurements.
Exploring artificial intelligence from a clinical perspective: A comparison and application analysis of two facial age predictors trained on a large‐scale Chinese cosmetic patient database

Abstract Background Age prediction powered by artificial intelligence (AI) can be used as an objective technique to assess the cosmetic effect of rejuvenation surgery. Existing age‐estimation models are trained on public datasets with the Caucasian race as the main reference, thus they are impractical for clinical application in Chinese patients. Methods To develop and select an age‐estimation model appropriate for Chinese patients receiving rejuvenation treatment, we obtained a face database of 10 529 images from 1821 patients from the author's hospital and selected two representative age‐estimation algorithms for the model training. The prediction accuracies and the interpretability of calculation logic of these two facial age predictors were compared and analyzed. Results The mean absolute error (MAE) of a traditional support vector machine‐learning model was 10.185 years; the proportion of absolute error ≤6 years was 35.90% and 68.50% ≤12 years. The MAE of a deep‐learning model based on the VGG‐16 framework was 3.011 years; the proportion of absolute error ≤6 years was 90.20% and 100% ≤12 years. Compared with deep learning, traditional machine‐learning models have clearer computational logic, which allows them to give clinicians more specific treatment recommendations. Conclusion Experimental results show that deep‐learning exceeds traditional machine learning in the prediction of Chinese cosmetic patients' age. Although traditional machine learning model has better interpretability than deep‐learning model, deep‐learning is more accurate for clinical quantitative evaluation. Knowing the decision‐making logic behind the accurate prediction of deep‐learning is crucial for deeper clinical application, and requires further exploration.
Factors Affecting the Creativity of Young Lecturers

Purpose: The study was conducted to determine the factors affecting the creative capacity of young lecturers in the Vietnamese higher education system. Theoretical framework: Creativity is the creation of new and helpful ideas in the field of science, art, business, and everyday activities (Amabile, 1996; Amabile, 1997). According to Woodman et al. (1993), creativity is the creation of new products, services, ideas, procedures, or processes that are useful and valuable. Kreitner &amp; Kinicki (2004) argued that creativity is defined as the process of using imagination and skills to develop a new, unique idea, product, or process. Creativity is a difficult concept to define, researchers do not fully agree with any single definition (DiLiello &amp; Houghton, 2006). Design/methodology/approach: An official survey was conducted from March to April 2022. The selected subjects are young lecturers (under 40 years old) working at 15 universities in the higher education system in Vietnam. The number of survey questionnaires achieved was 328, and applying structural equation modeling (SEM) to test the research hypotheses. Findings: The research has pointed out four factors that have positive impacts on the creative capacity of young lecturers, including intrinsic motivation, creative self-efficacy, thinking style, and the support environment. Among these, intrinsic motivation is the factor that has the most influence on the creative ability of young lecturers. Research, Practical &amp; Social implications: Several managerial implications are proposed to promote the creativity of young lecturers. Firstly, universities should have policies to encourage young lecturers to accept challenges and come up with new ideas. Secondly, universities should build a system to receive, evaluate, support, and provide practical suggestions for creative ideas from young lecturers. Thirdly, universities should develop a policy of recognizing and rewarding their efforts. Originality/value: In general, the study has achieved the set goals. The study has demonstrated four factors affecting the creative capacity of young lecturers in the higher education system in Vietnam. All these factors have a positive impact on creative capacity.
Classification of Natural Disaster Reports from Social Media using K-Means SMOTE and Multinomial Naïve Bayes

Disasters can occur anytime and anywhere. Floods and forest fires are two types of disasters that occur in Indonesia. South Kalimantan Province is an area that frequently experiences floods and forest fires. The dataset used for previous research's flood and forest fire disaster data is unbalanced. Unbalanced data conditions can complicate the classification method in carrying out the classification process. The sampling method for the data level approach that can be used to solve imbalance problems is oversampling, one of the derivatives of oversampling, namely SMOTE. The K-Means SMOTE method is a modification of SMOTE. One Naïve Bayes model often used in text classification is Multinomial Naïve Bayes. Multinomial Naïve Bayes has a good performance in classifying text. The research results on flood disaster data using K-Means SMOTE with Multinomial Naïve Bayes yielded an f1 score of 66.04%, and forest fire disaster data using K-Means SMOTE with Multinomial Naïve Bayes produced an f1 score of 66.31%.
Prioritizing Key Success Factor of the Internet of Things Application in Tourism Enterprise

Purpose: The objective of this study is to explore critical success factors in Internet of Things applying and their importance level in tourism companies using the fuzzy theory. Theoretical framework: This paper uses the TOE framework and adds a customer security variable. Therefore, the framework includes four variables technology, organization, environment, and customer security (TOEC) which are the four aspects of this research framework. Design/methodology/approach: By integrating FAHP and FAHP extension methods, the study finds that the critical success factors for IoT application in tourism companies, including technology, organization, environment, and customer security. Findings: The result show that, the first ranking is organization factor, the second ranking belongs to technology, customer securities come with third ranking, and the fourth ranking is environment. This result also indicates that IT Human resources, Technology infrastructure, Top management support, and organization readiness are the prioritized critical success factors for IoT applications in tourism companies. Research, Practical &amp; Social implications: This paper contributes to the understanding of IoT, its features and highlights the importance of new technology and solutions in tourism industry. Originality/value: This study fills the gap in the TEO model by adding the factor of customer securities so-called TEOC model.
Corporate Environmental Performance as a Mediator Between Eco-Efficiency Strategy and Financial Performance in Jordanian Industrial Sectors

Purpose: The purpose of this study is to examine examine corporate environmental performance (CEP) as a mediator between eco-efficiency strategy and financial performance. Theoretical framework: The application of the corporate environmental performance item is weak since it is not documented at that time, and there are also financial and eco-efficiency plan weaknesses. Also, the goal of the present study is to advance, add to the body of literature, and fill in any gaps. Design/methodology/approach: The study employed a survey-based cross-sectional study that included industrial, service, and real estate firms registered under the Amman Stock Exchange. A total of 230 questionnaires were distributed to the managers in health and safety, finance, and operational firms. Ultimately, 209 (90.8%) of the questionnaires were included for further analysis. Findings: The results revealed that efficiency strategy and CEP significantly influenced corporate financial performance, whereas efficiency strategy significantly influenced CEP. Moreover, CEP mediated the relationship between efficiency strategy and corporate financial performance. Nonetheless, the study was limited to eco-efficiency strategy. Research, Practical &amp; Social implications: The study of Jordanian industry, service, and real estate companies' Eco-Efficiency Strategy scores includes their environmental performance, which may be utilized to determine the primary influence factor on their financial success. This study offers insightful information for policymakers. Originality/value: The study's significance is that, in order to increase the financial success of these businesses, governments must pay more attention to the environment and the factors that influence it.
The Impact of Applying Quality Standards on the Internal Operations in the Public Sector in the Sultanate of Oman

Purpose: The aim of this study is to identify the impact of applying quality standards on the internal operations efficiency in public sector institutions in the Sultanate of Oman along with measuring the effectiveness and efficiency of operations after applying quality standards. Theoretical framework: This study focuses on the definition of quality and its history, quality standards, its types, rational behind their application, continuous improvement methodology which is Deming cycle (plan, Do, check, Act), and defining internal processes and their dimensions. Design/Methodology/Approach: To achieve the objective of this study , the questionnaire was used as a main tool for data collection. Questionnaires were distributed to a simple random sample of 115; 107, questionnaires were retrieved and (102) were suitable for analysis. The data was then analyzed using a variety of statistical techniques (arithmetic means and standard deviations, multiple linear regression analysis using the stepwise method, and Cronbach alpha for stability). Findings: The main findings in this study was identifies that prove there is a gap between quality standereds applications in planning of operations, readiness, and outputs. Research /Practical /Social Implications: This study is significant since it focuses on the influence of applying quality standards in terms of results and effect, specifically on processes and whether there is continuous process improvement. In social indicator it is impacting individuals satisfactions specially when speaking about the customers experience. Moreover, in enconmoical indocators it is indicates improvement of the institutional performance. Originality/ Value: This study is distinguished from previous studies in that it focuses on the impact of applying quality standards in terms of results and effect, specifically on processes. Moreover, this study specially build based on Oman experience.
Model Checking for Adversarial Multi-Agent Reinforcement Learning with Reactive Defense Methods

Cooperative multi-agent reinforcement learning (CMARL) enables agents to achieve a common objective. However, the safety (a.k.a. robustness) of the CMARL agents operating in critical environments is not guaranteed. In particular, agents are susceptible to adversarial noise in their observations that can mislead their decision-making. So-called denoisers aim to remove adversarial noise from observations, yet, they are often error-prone. A key challenge for any rigorous safety verification technique in CMARL settings is the large number of states and transitions, which generally prohibits the construction of a (monolithic) model of the whole system. In this paper, we present a verification method for CMARL agents in settings with or without adversarial attacks or denoisers. Our method relies on a tight integration of CMARL and a verification technique referred to as model checking. We showcase the applicability of our method on various benchmarks from different domains. Our experiments show that our method is indeed suited to verify CMARL agents and that it scales better than a naive approach to model checking.
An efficient method for maximal area coverage in the context of a hierarchical controller for multiple unmanned aerial vehicles

Computing the exact area of the union of an arbitrary number of circles is a challenging problem, since the union is generally non-convex and may be composed of multiple non-overlapping regions. In this paper, we propose tackling this problem by using graph-theoretical concepts and Green’s Theorem for exact area computation. Moreover, we show the implementation of this method for a rapid area coverage application with unmanned aerial vehicles (UAVs). Maximizing the area covered using multiple agents is difficult because fast solutions to large-scale optimization problems are sought. In our solution method, we present a hierarchical control framework. On the upper layer, a high-level controller performs centralised computation to determine the optimal UAV locations to maximize the area covered. On the bottom level, we adopt a decentralised approach by implementing multiple local controllers to tackle the trajectory planning and collision avoidance for each agent individually using Nonlinear Model Predictive Control (NMPC). Numerical experiments show that our method for computing the covered area can reduce the computational time required to solve the optimal positioning problem by more than two orders of magnitude when compared to a Monte-Carlo method. The trajectory planning problem was tested for up to 13 agents and the run-time was on the order of milliseconds, demonstrating the suitability for real-time implementation of the presented framework.
Intelligent Video Streaming at Network Edge: An Attention-Based Multiagent Reinforcement Learning Solution

Video viewing is currently the primary form of entertainment for modern people due to the rapid development of mobile devices and 5G networks. The combination of pervasive edge devices and adaptive bitrate streaming technologies can lessen the effects of network changes, boosting user quality of experience (QoE). Even while edge servers can offer near-end services to local users, it is challenging to accommodate a high number of mobile users in a dynamic environment due to their restricted capacity to maximize user long-term QoE. We are motivated to integrate user allocation and bitrate adaptation into one optimization objective and propose a multiagent reinforcement learning method combined with an attention mechanism to solve the problem of multiedge servers cooperatively serving users. Through comparative experiments, we demonstrate the superiority of our proposed solution in various network configurations. To tackle the edge user allocation problem, we proposed a method called attention-based multiagent reinforcement learning (AMARL), which optimized the problem in two directions, i.e., maximizing the QoE of users and minimizing the number of leased edge servers. The performance of AMARL is proved by experiments.
Assessing Students’ Attitude Towards Artificial Intelligence with Respect to Gender and Use of Computer and Mobile Devices

Artificial Intelligence is playing a very crucial role nowadays. Students are so much into the use of technology due to one or the other reasons and spend a lot of time on computer and mobile devices so this study is an attempt to find out the attitude of senior secondary students towards Artificial Intelligence (AI). The sample in this study consists of 200 senior secondary students from Prayagraj. For this study, a self-constructed Artificial Intelligence attitude scale has been used. The finding of the study revealed that there is a significant difference in the attitude of male and female senior secondary students towards Artificial Intelligence and even it shows that 42% of the students are spending more than 3 hours on computer and mobile devices as well as the study revealed that there is significant difference in the attitude towards Artificial Intelligence of students spending different hours on computer and mobile devices.
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving

In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible way to solve these issues. However, currently few-shot semantic segmentation methods focus on camera data, and most of them only predict the novel classes without considering the base classes. This setting cannot be directly applied to autonomous driving due to safety concerns. Thus, we propose a few-shot 3D LiDAR semantic segmentation method that predicts both novel and base classes simultaneously. Our method tries to solve the background ambiguity problem in generalized few-shot semantic segmentation. We first review the original cross-entropy and knowledge distillation losses, then propose a new loss function that incorporates the background information to achieve 3D LiDAR few-shot semantic segmentation. Extensive experiments on SemanticKITTI demonstrate the effectiveness of our method.
Unidirectional-Road-Network-Based Global Path Planning for Cleaning Robots in Semi-Structured Environments

Practical global path planning is critical for commercializing cleaning robots working in semi-structured environments. In the literature, global path planning methods for free space usually focus on path length and neglect the traffic rule constraints of the environments, which leads to high-frequency re-planning and increases collision risks. In contrast, those for structured environments are developed mainly by strictly complying with the road network representing the traffic rule constraints, which may result in an overlong path that hinders the overall navigation efficiency. This article proposes a general and systematic approach to improve global path planning performance in semi-structured environments. A unidirectional road network is built to represent the traffic constraints in semi-structured environments and a hybrid strategy is proposed to achieve a guaranteed planning result. Cutting across the road at the starting and the goal points are allowed to achieve a shorter path. Especially, a two-layer potential map is proposed to achieve a guaranteed performance when the starting and the goal points are in complex intersections. Comparative experiments are carried out to validate the effectiveness of the proposed method. Quantitative experimental results show that, compared with the state-of-art, the proposed method guarantees a much better balance between path length and the consistency with the road network.
Context-aware robot control using gesture episodes

Collaborative robots became a popular tool for increasing productivity in partly automated manufacturing plants. Intuitive robot teaching methods are required to quickly and flexibly adapt the robot programs to new tasks. Gestures have an essential role in human communication. However, in human-robot-interaction scenarios, gesture-based user interfaces are so far used rarely, and if they employ a one-to-one mapping of gestures to robot control variables. In this paper, we propose a method that infers the user's intent based on gesture episodes, the context of the situation, and common sense. The approach is evaluated in a simulated table-top manipulation setting. We conduct deterministic experiments with simulated users and show that the system can even handle the personal preferences of each user.