Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving TPE-Based JPEG Image Retrieval in Cloud-Assisted Internet of Things

Authors:
raw_author_name: Yakun Ma
raw_affiliation_string: School of Artificial Intelligence, Henan Engineering Research Center for Industrial Internet of Things, Henan University, Zhengzhou, China
raw_author_name: Xiuli Chai
raw_affiliation_string: School of Artificial Intelligence, Henan Engineering Research Center for Industrial Internet of Things, Henan University, Zhengzhou, China
raw_author_name: Zhihua Gan
raw_affiliation_string: School of Software, Intelligent Data Processing Engineering Research Center of Henan Province, Institute of Intelligent Network System, Henan University, Kaifeng, China
raw_author_name: Yushu Zhang
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Abstract: With the large-scale deployment of the Internet of Things, lots of images are generated and outsourced to the cloud to alleviate storage burdens. Encrypted image retrieval has been widely studied as a promising technique for balancing privacy and usability. However, existing schemes have primarily focused on retrieval usability but neglected visual usability, resulting in limitations in image management, even in scenarios where preserved visual information poses no hazard to privacy. In this paper, we are thus inspired to propose a privacy-preserving JPEG image retrieval scheme that effectively enhances retrieval efficiency and accuracy while ensuring low file expansion and excellent thumbnail-preserving accuracy for ciphertext images. Specifically, the well-designed thumbnail-preserving encryption (TPE) enables accurate thumbnail-preserving and high-quality decryption of encrypted images by integrating Huffman coding and information embedding techniques. Moreover, the proposed TPE method fully considers JPEG compression to decrease the ciphertext file expansion and gain good format compatibility. Additionally, adaptive encryption key generation is designed to minimize key storage and considerably bolster security. Also, the cloud can generate preview thumbnails for uploaded TPE-encrypted images, and then extract the Hue-Saturation-Value (HSV) and uniform Local Binary Pattern (ULBP) features from thumbnails instead of encrypted images to boost retrieval efficiency and accuracy. Experimental results show that the peak signal-to-noise ratio (PSNR) of thumbnail-preserving accuracy and decrypted image quality reaches 52dB and 61dB, respectively, the file expansion is minimally limited to 6%, and the mean average precision (mAP) of retrieval attains 64%, indicating that our scheme significantly outperforms the state-of-the-art ones.
Quadratic interpolation and a new local search approach to improve particle swarm optimization: Solar photovoltaic parameter estimation

Authors:
raw_author_name: Mohammed Qaraad
raw_affiliation_string: Department of Computer Science, Faculty of Science, Amran University, Amran, Yemen; TIMS, FS, Abdelmalek Essaadi University, Tetouan, Morocco
raw_author_name: Souad Amjad
raw_affiliation_string: TIMS, FS, Abdelmalek Essaadi University, Tetouan, Morocco
raw_author_name: Nazar K. Hussein
raw_affiliation_string: Department of Mathematics, College of Computer Sciences and Mathematics, Tikrit University, Iraq
raw_author_name: M.A. Farag
raw_affiliation_string: Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom, Egypt
raw_author_name: Seyedali Mirjalili
raw_affiliation_string: Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, South Korea
raw_author_name: Mostafa A. Elhosseini
raw_affiliation_string: College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt

Abstract: The Particle Swarm Optimization technique (PSO) is widely used in practical applications due to its flexibility and strong optimization performance. However, like other metaheuristic algorithms, PSO has limitations, such as a propensity to become trapped in local minima and an uneven distribution of effort between exploration and exploitation stages. A novel local search technique called QPSOL, based on PSO is the proposed solution to mitigate these issues. QPSOL aims to increase diversity and achieve a closer balance between the exploration and exploitation phases. The QPSOL incorporates a dynamic optimization strategy to enhance the method's efficiency. Unlike the novel local search strategy, which uses a new local search approach (LSA) to break out of local optima, QPSOL employs quadratic interpolation around the optimal search agent to enhance its exploitation capability and solution accuracy. These strategies complement each other and contribute to boosting PSO's convergence efficiency while seeking to balance exploration and exploitation. The proposed method is assessed using the IEEE CEC'2021 test suite, and its efficacy is evaluated against other metaheuristics and cutting-edge algorithms to determine its trustworthiness. The optimal parameters of three PV models are determined using the proposed technique and compared to different well-established algorithms. Systematic comparisons show that QPSOL is competitive with, and often outperforms, commonly used methods in research for predicting model parameters.
An Efficient Differential Grouping Algorithm for Large-Scale Global Optimization

Authors:
raw_author_name: Abhishek Kumar
raw_affiliation_string: Institute for Advancing Intelligence, TCG CREST, Kolkata, India
raw_author_name: Swagatam Das
raw_affiliation_string: Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India
raw_author_name: Rammohan Mallipeddi
raw_affiliation_string: Department of Artificial Intelligence, School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea

Abstract: Cooperative co-evolution (CC) is a practical and efficient evolutionary framework for solving large-scale global optimization problems (LSGOPs). The performance of CC depends on how variables are being grouped and can be improved through guided variable decomposition for various optimization problems. However, achieving a proper variable decomposition is computationally expensive. This paper proposes an effective yet efficient differential grouping (EDG) method to reduce the associated computational cost. Our method exploits the historical interrelationship information of previous variable groups to examine interactions between the remnant variable groups. This allows us to spend less computing resources without compromising the accuracy of the final grouping result. Our proposal utilizes the covariance matrix adaptation evolution strategy (CMA-ES) algorithm, in conjunction with EDG, to solve LSGOPs. Further, to reduce time complexity and improve the stability of CMA-ES, we substitute the complex matrix decomposition step with simpler matrix operations to compute the square root of the covariance matrix. Results from our experiments and analysis indicate that EDG is a competitive method to solve LSGOPs and improve the performance of CC. The proposed schemes significantly enhance the searchability of CMA-ES compared to the other large-scale variants of CMA-ES and state-of-the-art large-scale optimizers. Moreover, our EDG could be integrated with evolutionary optimizers of different flavors like Differential Evolution (DE).
A Similarity Measurement Method of Normal Cloud Models for the Operational Status Perception and Computing of Urban Rail Transit

Authors:
raw_author_name: Guangyu Zhu
raw_affiliation_string: School of Traffic and Transportation, and the Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing, China
raw_author_name: Yubo Yang
raw_affiliation_string: School of Traffic and Transportation, and the Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing, China
raw_author_name: Ranran Sun
raw_affiliation_string: School of Traffic and Transportation, and the Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing, China
raw_author_name: Edmond Q. Wu
raw_affiliation_string: Department of Automation, and the Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Rob Law
raw_affiliation_string: Asia-Pacific Academy of Economics and Management, University of Macau, Taipa, Macau, China

Abstract: The similarity measurement method is the key part of the normal cloud model as well as its applications. Too much attention has been paid on geometric and numerical features, leading to the weak interpretability and unreasonable results in the similarity measurement method of the normal cloud models. A bidirectional and weighted similarity measurement (BWSM) method is proposed by the number distribution and membership of cloud droplets on a normal cloud model. First, the cloud droplets are analyzed, and their characteristics of number distribution and membership that belong to different cloud models are obtained. Second, the similarity measurement strategy of the normal cloud models is analyzed and used to propose a BWSM method, which is compared with two commonly used similarity measurement methods of normal cloud models. The influence of expectation, entropy, and hyper entropy on the similarity measurement method of the normal cloud models is analyzed. Finally, the method is applied on the operational status perception and computing of urban rail transit. Results show the method has high rationality, validity, and applicability for perceiving and computing the operational status of urban rail transit. The method improves the interpretability of the similarity measurement of the normal cloud models and the credibility of the research based on the similarity measurement of the normal cloud models.
Multimodal Hierarchical Graph Collaborative Filtering for Multimedia-Based Recommendation

Authors:
raw_author_name: Kang Liu
raw_affiliation_string: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China
raw_author_name: Feng Xue
raw_affiliation_string: School of Software, Hefei University of Technology, Hefei, China
raw_author_name: Shuaiyang Li
raw_affiliation_string: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China
raw_author_name: Richang Hong
raw_affiliation_string: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China

Abstract: Multimedia-based recommendation (MMRec) is a challenging task, which goes beyond the collaborative filtering (CF) schema that only captures collaborative signals from interactions and explores multimodal user preference cues hidden in complex multimedia content. Despite the significant progress of current solutions for MMRec, we argue that they are limited by multimodal noise contamination. Specifically, a considerable amount of preference-irrelevant multimodal noise (e.g., the background, layout, and brightness in the image of the product) is incorporated into the representation learning of items, which contaminates the modeling of multimodal user preferences. Moreover, most of the latest researches are based on graph convolution networks (GCNs), which means that multimodal noise contamination is further amplified because noisy information is continuously propagated over the user–item interaction graph as recursive neighbor aggregations are performed. To address this problem, instead of the common MMRec paradigm which learns user preferences in an integrated manner, we propose a hierarchical framework to separately learn collaborative signals and multimodal preferences cues, thus preventing multimodal noise from flowing into collaborative signals. Then, to alleviate the noise contamination for multimodal user preference modeling, we propose to extract semantic entities from multimodal content that are more relevant to user interests, which can model semantic-level multimodal preferences and thus remove a large fraction of noise. Furthermore, we use the full multimodal features to model content-level multimodal preferences like the existing MMRec solutions, which ensures the sufficient utilization of multimodal information. Overall, we develop a novel model, multimodal hierarchical graph CF (MHGCF), which consists of three types of GCN modules tailored to capture collaborative signals, semantic-level preferences, and content-level preferences, respectively. We conduct extensive experiments to demonstrate the effectiveness of MHGCF and its components. The complete data and codes of MHGCF are available at.
TFC: Transformer Fused Convolution for Adversarial Domain Adaptation

Authors:
raw_author_name: Mengzhu Wang
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Junyang Chen
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Ye Wang
raw_affiliation_string: College of Computer, National University of Defense Technology, Changsha, China
raw_author_name: Zhiguo Gong
raw_affiliation_string: Department of Computer Information Science, State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China
raw_author_name: Kaishun Wu
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Victor C. M. Leung
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

Abstract: In unsupervised domain adaptation (UDA), a classifier is applied to the target domain without or with limited labels, when the target domain has no or few labels. Recently, inspired by their capabilities of long-distance feature dependencies, vision transformer (ViT)-based methods have been used in UDA, however, they ignore the fact that ViT lacks strength in extracting local feature details. To handle the above problems, the purpose of this article is to demonstrate how to take advantage of both convolutional operations and transformer mechanisms for adversarial UDA by using a hybrid network structure called transformer fused convolution (TFC). TFC integrates local features with global features to boost the representation capacity for UDA which can enhance the discrimination between foreground and background. Moreover, to improve the robustness of the TFC, we leverage an uncertainty penalty loss to make incorrect classes have consistently lower scores. Extensive experiments validate the significant performance gains compared to conditional adversarial domain adaptation (CDAN) on all five datasets including DomainNet ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 8.5%), VisDA-2017 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 14.9%), Office-Home ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 18.9%), Office-31 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 11.5%), and ImageCLEF-DA ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 5.5%).
An Overview of Advanced Deep Graph Node Clustering

Authors:
raw_author_name: Shiping Wang
raw_affiliation_string: College of Computer and Data Science and the Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China
raw_author_name: Jinbin Yang
raw_affiliation_string: College of Computer and Data Science and the Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China
raw_author_name: Jie Yao
raw_affiliation_string: College of Computer and Data Science and the Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China
raw_author_name: Yang Bai
raw_affiliation_string: School of Cyberspace Security, Chengdu University of Information Technology, Chengdu, China
raw_author_name: William Zhu
raw_affiliation_string: Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China

Abstract: Graph data have become increasingly important, and graph node clustering has emerged as a fundamental task in data analysis. In recent years, graph node clustering has gradually moved from traditional shallow methods to deep neural networks due to the powerful representation capabilities of deep learning. In this article, we review some representatives of the latest graph node clustering methods, which are classified into three categories depending on their principles. Extensive experiments are conducted on real-world graph datasets to evaluate the performance of these methods. Four mainstream evaluation performance metrics are used, including clustering accuracy, normalized mutual information, adjusted rand index, and F1-score. Based on the experimental results, several potential research challenges and directions in the field of deep graph node clustering are pointed out. This work is expected to facilitate researchers interested in this field to provide some insights and further promote the development of deep graph node clustering.
Ethical leadership and employee performance: The role of Islamic work ethics and knowledge sharing

Authors:
raw_author_name: Udin Udin
raw_affiliation_string: Faculty of Economics and Business, Universitas Muhammadiyah Yogyakarta, Special Region of Yogyakarta, Indonesia

Abstract: BACKGROUND: Many recent studies recommend ethical leadership as a key antecedent to improving employee performance in organizations. OBJECTIVE: Therefore, this study aims to examine how ethical leadership affects employee performance by focusing on the role of Islamic work ethics and knowledge sharing. METHODS: Based on data obtained from 196 Muslim employees who work in the three stone milling companies located in Central Java - Indonesia, this study uses a survey in the form of a questionnaire and Structural Equation Modeling (SEM) based on Amos 24.0 software to test hypotheses in the proposed research model. RESULTS: The results of this study prove that ethical leadership has a significant effect on employee performance, Islamic work ethics, and knowledge sharing. Furthermore, Islamic work ethics and knowledge sharing significantly improve employee performance. CONCLUSION: The findings in this study further confirm that Islamic work ethics and knowledge sharing play an important role in linking ethical leadership and employee performance.
A Hand Gesture-Operated System for Rehabilitation Using an End-to-End Detection Framework

Authors:
raw_author_name: H. Pallab Jyoti Dutta
raw_affiliation_string: Department of Electronics and Electrical Engineering, Indian Institute of Technology, Guwahati, Assam, India
raw_author_name: M. K. Bhuyan
raw_affiliation_string: Department of Electronics and Electrical Engineering and Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology, Guwahati, Assam, India
raw_author_name: Debanga Raj Neog
raw_affiliation_string: Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology, Guwahati, Assam, India
raw_author_name: Karl F. MacDorman
raw_affiliation_string: School of Informatics and Computing, Indiana University, Indiana, IN, USA
raw_author_name: R. H. Laskar
raw_affiliation_string: Department of Electronics and Communication Engineering, National Institute of Technology, Silchar, Assam, India

Abstract: We propose a hand gesture-operated system as an AI application to relieve discomfort and restore function in hand and arm movements caused by injuries and nerve and muscle complications. The system trains patients with hand exercises, such as performing hand gestures accurately, traversing within specified bounds, and operating a hand gesture calculator. However, the system requires accurate hand gesture detection, which is impeded by background clutter and variations in illumination and in the hand's size and angle. To address this, we developed a robust hand detection module that uses a single-stage transformer deep network. The transformer network encodes global information and uses bipartite matching to reduce the frequency of spurious detections. It drives a regression head and a classification head to localize the hand gesture in a bounding box and assign it a class label. Hand keypoints are also detected to support drawing, path traversal, and calculator use. The approach is evaluated on two benchmark datasets: OUHANDS and NUS. The method yields 89.6% accuracy for OUHANDS and 100% for NUS. These results indicate that precise hand detection can support a robust system for rehabilitation through hand exercises. Our experiments confirm that the users' hand function progressively improved.
Design and Practical Implementation of a High Efficiency Two-Layer Trajectory Planning Method for AGV

Authors:
raw_author_name: Runqi Chai
raw_affiliation_string: School of Automation, Beijing Institute of Technology, Beijing, China
raw_author_name: Senchun Chai
raw_affiliation_string: School of Automation, Beijing Institute of Technology, Beijing, China
raw_author_name: Yuanqing Xia
raw_affiliation_string: School of Automation, Beijing Institute of Technology, Beijing, China
raw_author_name: Antonios Tsourdos
raw_affiliation_string: School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford, U.K.

Abstract: This paper proposes a two-layer trajectory optimization method for the autonomous ground vehicle (AGV). This twolayer strategy includes an efficient path planning layer and a fast trajectory planning layer. In the first layer, a novel target area adaptive rapidly exploring random tree algorithm (TAA-RRT*) is proposed to search the shortest path. This layer mainly includes a preprocessing and a sampling planning process. In the preprocessing process, the generalized voronoi diagram (GVD) is used to construct the environment information and find the initial path. Then, the sampled target area (TA) is constructed based on this initial path to provide non-uniform sampling. In the sampling planning process, the improved adaptive RRT* algorithm is used to carry out sampling planning in the TA, and the direct connection strategy (DCS) is combined to quickly locate the optimal solution. In the trajectory planning layer, combined with the constraints of the unmanned vehicle and the path constraints obtained in the first layer, the speed planning and the trajectory optimization are addressed by solving the optimal control problem (OCP). After performing a large number of experiments, the feasibility and effectiveness of the proposed method is verified.