Reviewerly/exHarmony huggingface dataset:
GeoViewMatch: A Multi-Scale Feature-Matching Network for Cross-View Geo-Localization Using Swin-Transformer and Contrastive Learning

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

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

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

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

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

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

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

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

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

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

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

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

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.
A federated learning approach to QoS forecasting in cellular vehicular communications: Approaches and empirical evidence

QoS forecasting for cellular vehicular communications allows cooperative, connected and automated mobility applications to tailor their behavior to the expected communication conditions on the road. In a nutshell, vehicles may, for example, execute cooperative maneuvers if the communication quality of service is only above a certain quantitative level whereas if not they revert to the individual autonomous mode. In this paper, we propose and show empirical methods for estimating packet-based QoS metrics obtained from 5G network measurements with a direct application to vehicular applications. As many distributed vehicular applications possess strict QoS requirements, we focus here on bounding packet-based statistical QoS quantiles, specifically for latency and loss. Our approach is based on training regression neural networks in a federated learning fashion and show that it can obtain predictions on par with centralized training without the vehicles needing to transmit raw measurement data. In contrast to QoS prediction using physical layer information, We briefly discuss the embedding of such much simpler application-level service within the 5G architecture. We also validate our approach through recovering classical closed-form delay quantiles that are obtained from analytical models of simple queueing systems. We show that our approach goes beyond these simple models in that it provides quantile estimates for the complex scenario of cellular vehicle communications and under different application traffic patterns including empirical data traffic traces as well as 5G testbed measurements.
Group Behavior Recognition Algorithm for Basketball Video Based on Hidden Tag Mining Strategy under Gamification Teaching and Learning

The rapid development of Artificial Intelligence has facilitated the development of multimedia teaching and learning in the field of education, as well as providing intelligent methods for gamified teaching methods in the field of basketball. Currently, research on recognizing basketball video behavior primarily focuses on group behavior recognition methods for key individuals. However, there is currently no group behavior recognition method that can effectively distinguish features that reflect different events for temporal interactive behaviors such as dunks and steals in videos, as well as situations where key players are obstructed. The paper suggests a group behavior identification algorithm for basketball game videos based on the hidden tag mining technique in order to perform intelligent analysis in the fields of supplemental training and tactical analysis of gamified instruction. And this algorithm is integrated with the fusion of individual features to create the basketball video's group behavior identification algorithm with hidden label mining approach. According to the study's findings, the distribution of motion pattern features is more agglomerated and regular when the merging threshold is 0.6 and the best hyperparameter is 3. After the study applied the crucial individual feature extraction module, the overall group behavior identification accuracy increased by 6.44%. The group behavior recognition algorithm for basketball videos that incorporates individual features and hidden label mining techniques outperforms the most recent patio-temporal domain-based group behavior recognition algorithm by 15.56% when compared to the accuracy of commonly used algorithms. In summary, the study proposes a basketball video group behavior recognition algorithm with better performance. This algorithm achieves group behavior recognition in basketball game videos, promotes auxiliary training for students in gamified teaching mode, and provides a practical and feasible approach for intelligent analysis of other sports events. It also provides technical support for coaches and referees.
Revisiting code smell severity prioritization using learning to rank techniques

Code Smell Severity Prioritization (CSSP) is crucial in helping software developers minimize software maintenance costs and enhance software quality, particularly when faced with limited refactoring resources. Traditional code smell prioritization methods rely heavily on manual and semi-automatic approaches based on developer experience, often demanding considerable time and effort from experienced experts. Leveraging automated machine learning techniques can effectively overcome these limitations. However, most existing machine learning-based CSSP works have only considered limited pointwise Learning To Rank (LTR) algorithms and have used inappropriate metrics (e.g., Accuracy, Spearman, and MAE) to assess the performance of models. To address these limitations, we make a comprehensive comparison of 41 pointwise, 4 pairwise, and 4 listwise LTR algorithms for CSSP on four code smell severity datasets. Furthermore, we propose the adoption of Severity@20% and Cumulative Lift Chart (CLC) as the primary evaluation metrics to assess CSSP models more effectively. The results show that: (1) The ordinal Bagging (O-Bagging) algorithm demonstrates the highest performance for CSSP, achieving superior results in Severity@20% and CLC. (2) The ordinal classification method can help the top-performing base classification algorithms Bagging and XGBoost achieve better performance for CSSP tasks. (3) A higher (lower) Accuracy, higher (lower) Spearman, and lower (higher) MAE do not reliably indicate better (worse) performance for CSSP. This further underscores that Accuracy, Spearman, and MAE are unsuitable metrics for evaluating CSSP models’ effectiveness. To summarize, our study suggest that developers employ the O-Bagging algorithm for CSSP, with Severity@20% and CLC serving as the primary evaluation metrics.
Deep neural network-based robust hologram watermarking using guided attack module

A digital hologram (DH) is an ultra-high-value video content that includes three-dimensional information in two-dimensional data. Therefore, for the distribution of this content, the intellectual property rights must be protected. This paper proposes robust hologram watermarking based on deep neural networks using guided attack modules and attack rate control techniques. The proposed method robustly embeds the watermark into the original hologram to cope with various attacks while keeping the invisibility that the watermarked hologram is similar to the original hologram. Previous work considered PSNR during training. However, hologram exists in the complex domain. Thus SNR for complex numbers should be considered to confirm the reconstruction results.. In addition, we propose an enhanced network with increased depth to improve performance compared with previous work. Experiments show that the proposed method outperforms the previous work regarding robustness and invisibility against various attacks, such as Crop Out, Salt & Pepper Noise, and Rotation. Therefore, this study is expected to be widely used in various fields, such as digital image authentication, copyright protection, and data security.
Defense against adversarial attacks based on color space transformation

Deep Learning algorithms have achieved state-of-the-art performance in various important tasks. However, recent studies have found that an elaborate perturbation may cause a network to misclassify, which is known as an adversarial attack. Based on current research, it is suggested that adversarial examples cannot be eliminated completely. Consequently, it is always possible to determine an attack that is effective against a defense model. We render existing adversarial examples invalid by altering the classification boundaries. Meanwhile, for valid adversarial examples generated against the defense model, the adversarial perturbations are increased so that they can be distinguished by the human eye. This paper proposes a method for implementing the abovementioned concepts through color space transformation. Experiments on CIFAR-10, CIFAR-100, and Mini-ImageNet demonstrate the effectiveness and versatility of our defense method. To the best of our knowledge, this is the first defense model based on the amplification of adversarial perturbations.