Reviewerly/exHarmony huggingface dataset:
Interval Type-2 Mutual Subsethood Cauchy Fuzzy Neural Inference System (IT2MSCFuNIS)

Authors:
raw_author_name: Hesham A. Hefny
raw_affiliation_string: Department of Computer Science, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt

Abstract: Abstract An interval type-2 (IT2) mutual subsethood Cauchy fuzzy neural inference system has been proposed in this paper. The network architecture consists of 3-layers with all connection weights being IT2 Cauchy fuzzy membership functions (CMFs). The crisp inputs to the system are fuzzified into IT2CMFs with fixed centers and uncertain spreads. The hidden layer represents the rule-based knowledge. The firing degree of the antecedent part of each rule at the hidden layer is computed by aggregating the product of the mutual subsethood similarity measures between the inputs and the connection weights. A volume defuzzification is used to compute the numeric output. A gradient descent back-propagation algorithm is used to train the model. The novelty of the proposed model is threefold. First, is enriching the theory of the mutual subsethood fuzzy neural models by adopting the Cauchy membership function (CMF) as another powerful fuzzy basis function (FBF) rather than the classical choice of Gaussian fuzzy membership functions (GMFs). Second, is the success of computing the mutual subsethood similarity measure between the IT2CMFs and all the model parameters’ updating equations in analytic closed-form formulas, not numerically or approximately. Third, is the ability to extract the type-1 (T1) mutual subsethood Cauchy fuzzy neural inference system (T1MSCFuNIS) with all its analytic closed-form formulas directly as a special case from the general formulas of IT2MSCFuNIS model. Such a novelty makes the proposed model a concrete and effective development of the theory of mutual subsethood fuzzy neural models. Both IT2MSCFuNIS and T1MSCFuNIS models have been tested using different examples from the domains of function approximation, classification, and prediction. The results ensure the efficacy of both models compared with other models reported in the literature.
GeoViewMatch: A Multi-Scale Feature-Matching Network for Cross-View Geo-Localization Using Swin-Transformer and Contrastive Learning

Authors:
raw_author_name: Wenhui Zhang
raw_affiliation_string: College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
raw_author_name: Zhinong Zhong
raw_affiliation_string: College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
raw_author_name: Hao Chen
raw_affiliation_string: College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China; Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources, Changsha 410073, China
raw_author_name: Ning Jing
raw_affiliation_string: College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China; Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources, Changsha 410073, China

Abstract: Cross-view geo-localization aims to locate street-view images by matching them with a collection of GPS-tagged remote sensing (RS) images. Due to the significant viewpoint and appearance differences between street-view images and RS images, this task is highly challenging. While deep learning-based methods have shown their dominance in the cross-view geo-localization task, existing models have difficulties in extracting comprehensive meaningful features from both domains of images. This limitation results in not establishing accurate and robust dependencies between street-view images and the corresponding RS images. To address the aforementioned issues, this paper proposes a novel and lightweight neural network for cross-view geo-localization. Firstly, in order to capture more diverse information, we propose a module for extracting multi-scale features from images. Secondly, we introduce contrastive learning and design a contrastive loss to further enhance the robustness in extracting and aligning meaningful multi-scale features. Finally, we conduct comprehensive experiments on two open benchmarks. The experimental results have demonstrated the superiority of the proposed method over the state-of-the-art methods.
Artificial Intelligence in Hospitality and Tourism: Insights From Industry Practices, Research Literature, and Expert Opinions

Authors:
raw_author_name: Hyunsu Kim
raw_affiliation_string: Department of Management, College of Business and Economics, California State University, Fullerton, CA, USA
raw_author_name: Kevin Kam Fung So
raw_affiliation_string: College of Hotel & Tourism Management, Kyung Hee University, Seoul, Republic of Korea; William S. Spears Chair in Business, School of Hospitality and Tourism Management, Spears School of Business, Oklahoma State University, Stillwater, OK, USA
raw_author_name: Jing Li
raw_affiliation_string: Department of Hospitality and Retail Management, College of Human Sciences, Texas Tech University, Lubbock, TX, USA

Abstract: Given that artificial intelligence (AI) is significantly transforming businesses, it is crucial to examine how AI will change the future of the hospitality and tourism industry. By integrating multiple data sources (i.e., practitioner literature, research literature, and expert opinions), we suggest three trends constituting opportunities and challenges (AI applications in different business sectors, primary AI functions, emerging AI topics), three possible themes of change (adoption and acceptance, operations management, AI in marketing), as well as four directions for future research (AI interaction, AI and organizational decision making, organizational implications, and managerial issues). Our findings present a detailed picture of AI development and applications along with predictions regarding its place in the industry. Finally, we outline a research agenda that addresses key issues for stakeholders in hospitality and tourism: individuals, including customers and employees; organizations and businesses; and public policymakers and governments.
Design and Optimization of UAV Aerial Recovery System Based on Cable-Driven Parallel Robot

Authors:
raw_author_name: Jun Wu
raw_affiliation_string: School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150080, China
raw_author_name: Fei Yang
raw_affiliation_string: School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150080, China

Abstract: Aerial recovery and redeployment can effectively increase the operating radius and the endurance of unmanned aerial vehicles (UAVs). However, the challenge lies in the effect of the aerodynamic force on the recovery system, and the existing road-based and sea-based UAV recovery methods are no longer applicable. Inspired by the predatory behavior of net-casting spiders, this study introduces a cable-driven parallel robot (CDPR) for UAV aerial recovery, which utilizes an end-effector camera to detect the UAV's flight trajectory, and the CDPR dynamically adjusts its spatial position to intercept and recover the UAV. This paper establishes a comprehensive cable model, simultaneously considering the elasticity, mass, and aerodynamic force, and the static equilibrium equation for the CDPR is derived. The effects of the aerodynamic force and cable tension on the spatial configuration of the cable are analyzed. Numerical computations yield the CDPR's end-effector position error and cable-driven power consumption at discrete spatial points, and the results show that the position error decreases but the power consumption increases with the increase in the cable tension lower limit (CTLL). To improve the comprehensive performance of the recovery system, a multi-objective optimization method is proposed, considering the error distribution, power consumption distribution, and safety distance. The optimized CTLL and interception space position coordinates are determined through simulation, and comparative analysis with the initial condition indicates an 83% reduction in error, a 62.3% decrease in power consumption, and a 1.2 m increase in safety distance. This paper proposes a new design for a UAV aerial recovery system, and the analysis lays the groundwork for future research.
Building trustworthy NeuroSymbolic AI Systems: Consistency, reliability, explainability, and safety

Authors:
raw_author_name: Manas Gaur
raw_affiliation_string: University of Maryland, Baltimore County, Baltimore, Maryland, USA
raw_author_name: Amit Sheth
raw_affiliation_string: AI Institute, University of South Carolina, Columbia, South Carolina, USA

Abstract: Abstract Explainability and Safety engender trust. These require a model to exhibit consistency and reliability. To achieve these, it is necessary to use and analyze data and knowledge with statistical and symbolic AI methods relevant to the AI application––neither alone will do. Consequently, we argue and seek to demonstrate that the NeuroSymbolic AI approach is better suited for making AI a trusted AI system. We present the CREST framework that shows how C onsistency, R eliability, user‐level E xplainability, and S afety are built on NeuroSymbolic methods that use data and knowledge to support requirements for critical applications such as health and well‐being. This article focuses on Large Language Models (LLMs) as the chosen AI system within the CREST framework. LLMs have garnered substantial attention from researchers due to their versatility in handling a broad array of natural language processing (NLP) scenarios. As examples, ChatGPT and Google's MedPaLM have emerged as highly promising platforms for providing information in general and health‐related queries, respectively. Nevertheless, these models remain black boxes despite incorporating human feedback and instruction‐guided tuning. For instance, ChatGPT can generate unsafe responses despite instituting safety guardrails. CREST presents a plausible approach harnessing procedural and graph‐based knowledge within a NeuroSymbolic framework to shed light on the challenges associated with LLMs.
Religiosity and financial development in Africa: evidence from panel quantile regression

Authors:
raw_author_name: Godfred Amewu
raw_affiliation_string: Department of Finance, School of Business, University of Ghana, Legon, Ghana

Abstract: As secularism is increasing and religious diversity is regaining momentum worldwide, it is important to understand how systemic religious differences have influenced financial development in Africa. To this end, this study seeks to investigate the impact of religiosity on financial development in Africa by using freedom of religion to proxy religiosity and domestic credit to the private sector to proxy financial development covered from 2000 to 2020. The results from the panel quantile regression show that religiosity is negatively associated with the use of financial services across the quantile, whereas freedom of association, assembly, and civil society, and security and safety are marginally stronger within the lower, middle, and upper quantiles. The findings underscore that, fundamental human rights exert significant influence on financial development in Africa. Our findings contribute to literature by expanding knowledge on the role of personal freedom on economic activities hence, African governments and policymakers constitutionalize freedom as a fundamental human right, as freedom matters for financial development in Africa.
Human-Centered Explanations: Lessons Learned from Image Classification for Medical and Clinical Decision Making

Authors:
raw_author_name: Bettina Finzel
raw_affiliation_string: Cognitive Systems, University of Bamberg, Bamberg, Germany

Abstract: Abstract To date, there is no universal explanatory method for making decisions of an AI-based system transparent to human decision makers. This is because, depending on the application domain, data modality, and classification model, the requirements for the expressiveness of explanations vary. Explainees, whether experts or novices (e.g., in medical and clinical diagnosis) or developers, have different information needs. To address the explanation gap, we motivate human-centered explanations and demonstrate the need for combined and expressive approaches based on two image classification use cases: digital pathology and clinical pain detection using facial expressions. Various explanatory approaches that have emerged or been applied in the three-year research project “Transparent Medical Expert Companion” are shortly reviewed and categorized in expressiveness according to their modality and scope. Their suitability for different contexts of explanation is assessed with regard to the explainees’ need for information. The article highlights open challenges and suggests future directions for integrative explanation frameworks.
AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography

Authors:
raw_author_name: Amy McGovern
raw_affiliation_string: School of Computer Science, University of Oklahoma, Norman, Oklahoma, USA; School of Meteorology, University of Oklahoma, Norman, Oklahoma, USA
raw_author_name: Imme Ebert‐Uphoff
raw_affiliation_string: Electrical and Computer Engineering & Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, USA
raw_author_name: Elizabeth A. Barnes
raw_affiliation_string: Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
raw_author_name: Ann Bostrom
raw_affiliation_string: Evans School of Public Policy and Governance, University of Washington, Seattle, Washington, USA
raw_author_name: Mariana G. Cains
raw_affiliation_string: National Center for Atmospheric Research, Boulder, Colorado, USA
raw_author_name: Phillip Davis
raw_affiliation_string: Del Mar College, Corpus Christi, Texas, USA
raw_author_name: Julie L. Demuth
raw_affiliation_string: National Center for Atmospheric Research, Boulder, Colorado, USA
raw_author_name: Dimitrios I. Diochnos
raw_affiliation_string: School of Computer Science, University of Oklahoma, Norman, Oklahoma, USA
raw_author_name: Philippe Tissot
raw_affiliation_string: Conrad Blucher Institute Texas A&amp;M University—Corpus Christi Corpus Christi Texas USA
raw_author_name: John K. Williams
raw_affiliation_string: The Weather Company, IBM Business, Armonk, New York, USA

Abstract: Abstract The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) focuses on creating trustworthy AI for a variety of environmental and Earth science phenomena. AI2ES includes leading experts from AI, atmospheric and ocean science, risk communication, and education, who work synergistically to develop and test trustworthy AI methods that transform our understanding and prediction of the environment. Trust is a social phenomenon, and our integration of risk communication research across AI2ES activities provides an empirical foundation for developing user‐informed, trustworthy AI. AI2ES also features activities to broaden participation and for workforce development that are fully integrated with AI2ES research on trustworthy AI, environmental science, and risk communication.
Web Semantic-Based Robust Graph Contrastive Learning for Recommendation via Invariant Learning

Authors:
raw_author_name: Yujun Wang
raw_affiliation_string: College of Computer and Information Science, Southwest University, China

Abstract: The use of contrastive learning (CL) in recommendation has advanced significantly. Recently, some works use perturbations in the embedding space to obtain enhanced views of nodes. This makes the representation distribution of nodes more even and then improve recommendation effectiveness. In this article, the authors provide an explanation on the role of added noises in the embedding space from the perspective of invariant learning and feature selection. Guided by this thinking, the authors devise a more reasonable method for generating random noises and put forward web semantic based robust graph contrastive learning for recommendation via invariant learning, a novel graph CL-based recommendation model, named RobustGCL. RobustGCL, randomly zeros the values of certain dimensions in the noise vectors at a fixed ratio. In this way, RobustGCL can identify invariant and variant features and then learn invariant and variant representations. Tests on publicly available datasets show that our proposed approach can learn invariant representations and achieve better performance.
Convergence analysis of particle swarm optimization algorithms for different constriction factors

Authors:
raw_author_name: Surafel Luleseged Tilahun
raw_affiliation_string: Department of Applied Mathematics, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia

Abstract: Particle swarm optimization (PSO) algorithm is an optimization technique with remarkable performance for problem solving. The convergence analysis of the method is still in research. This article proposes a mechanism for controlling the velocity by applying a method involving constriction factor in standard swarm optimization algorithm, that is called CSPSO. In addition, the mathematical CSPSO model with the time step attractor is presented to study the convergence condition and the corresponding stability. As a result, constriction standard particle swarm optimization that we consider has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, CSPSO modifies all terms of the PSO velocity equation. We test the effectiveness of the CSPSO algorithm based on constriction coefficient with some benchmark functions and compare it with other basic PSO variant algorithms. The theoretical convergence and experimental analyses results are also demonstrated in tables and graphically.
Use of Autoencoders for Improving the Performance of Classification and Interpretation of Convolutional Neural Networks in Histopathological Images

Authors:
raw_author_name: Daniel C. Macedo
raw_affiliation_string: Centro de Informática, Universidade Federal de Pernambuco, Av. Jorn. Aníbal Fernandes, Recife, Pernambuco 50740-560, Brazil
raw_author_name: Fernando M. de Paula Neto
raw_affiliation_string: Centro de Informática, Universidade Federal de Pernambuco, Av. Jorn. Aníbal Fernandes, Recife, Pernambuco 50740-560, Brazil
raw_author_name: Tasso L. O. Moraes
raw_affiliation_string: Centro de Informática, Universidade Federal de Pernambuco, Av. Jorn. Aníbal Fernandes, Recife, Pernambuco 50740-560, Brazil
raw_author_name: Vinicius D. Santos
raw_affiliation_string: Centro de Informática, Universidade Federal de Pernambuco, Av. Jorn. Aníbal Fernandes, Recife, Pernambuco 50740-560, Brazil

Abstract: Breast cancer is one of the most common types of cancer and it presents itself as being the leading cause of death among women. If its diagnosis occur soon enough, the probability to achieve the cure of the patient can be increased. Recently, it has been more common the use of deep neural network techniques to aid pathologists in their prognosis, but they still do not fully trust them because they lack interpretability. In light of that, this work investigates if previous training of the models as encoders could enhance their accuracy in both classification and interpretability. There were implemented three models to the BreakHis and BreCaHAD dataset: NASNet Mobile, DenseNET201, and MobileNetV2. The experiments have shown that the three models increased the classification performance and two models improved their interpretability using the proposed strategy. DenseNet201 encoder has performed almost 23% better than its vanilla version in classifying a tumor and the NASNet Mobile encoder has improved 28.5% in its tumor interpretation.
Influence of Quality, Procedures, and Service Facilities on Community Satisfaction with Hydrological Data Services at the Sulawesi II Gorontalo River Basin Center

Authors:
raw_author_name: Deby Rita Karundeng
raw_affiliation_string: Universitas Gorontalo
raw_author_name: Mohamad Afan Suyanto
raw_affiliation_string: Universitas Gorontalo

Abstract: This research aims to determine and analyze the influence of service quality, service procedures and service facilities on community satisfaction with hydrological data services at the Sulawesi II Gorontalo River Regional Office. The multiple linear regression quantitative analysis model is used to analyze the influence of the independent variable on the dependent variable. The research results show that overall service quality, service procedures and service facilities influence public satisfaction, but for service quality the influence is not that significant. This shows that increasing community satisfaction is jointly influenced by service quality, service procedures and service facilities.
Application of Multimedia Simulation Teaching in Youth Physical Fitness Training

Authors:
raw_author_name: So Hee Yoon
raw_affiliation_string: Hannam University, South Korea

Abstract: The multimedia simulation teaching mode introduces students into virtual scenes for learning. Whether it is enhancing students' interest in learning or enhancing their physical fitness, it is a new teaching mode. This article discusses the establishment of a BP neural network model to study the prediction of students' physical fitness and conducts experiments on predicting and evaluating adolescent physical fitness. The results indicate that the BP neural network model better reflects the functional relationship between various indicators of student physical fitness and the total test score, providing a more reasonable mathematical model for evaluating adolescent physical fitness.
Classical-Quantum Dual Encoding for Laser Communications in Space

Authors:
raw_author_name: Mathew Winnel
raw_affiliation_string: School of Mathematics and Physics, University of Queensland, Brisbane, Queensland, AUSTRALIA
raw_author_name: Ziqing Wang
raw_affiliation_string: School of EE&T, University of New South Wales, Sydney, New South Wales, 2052, AUSTRALIA
raw_author_name: Robert Malaney
raw_affiliation_string: University of New South Wales, Sydney, New South Wales, AUSTRALIA
raw_author_name: Ryan Aguinaldo
raw_affiliation_string: Northrop Grumman Corporation San Diego, San Diego, California, UNITED STATES
raw_author_name: Jonathan Green
raw_affiliation_string: Northrop Grumman Corporation San Diego, San Diego, California, UNITED STATES
raw_author_name: Timothy C Ralph
raw_affiliation_string: School of Mathematics and Physics, University of Queensland, School of Mathematics and Physics, University of Queensland, Brisbane, Queensland, 4072, AUSTRALIA

Abstract: Abstract In typical laser communications classical information is encoded by modulating the amplitude of the laser beam and measured via direct detection. We add a layer of security using quantum physics to this standard scheme, applicable to free-space channels. We consider a simultaneous classical-quantum communication scheme where the classical information is encoded in the usual way and the quantum information is encoded as fluctuations of a sub-Poissonian noise-floor. For secret key generation, we consider a continuous-variable quantum key distribution protocol (CVQKD) using a Gaussian ensemble of squeezed states and direct detection. Under the assumption of passive attacks secure key generation and classical communication can proceed simultaneously. Compared with standard CVQKD. which is secure against unrestricted attacks, our added layer of quantum security is simple to implement, robust and does not affect classical data rates. We perform detailed simulations of the performance of the protocol for a free-space atmospheric channel. We analyse security of the CVQKD protocol in the composable finite-size regime.