Reviewerly/exHarmony huggingface dataset:
Feature interaction and two-stage cross-modal fusion for RGB-D salient object detection

Authors:
raw_author_name: Ming Yu
raw_affiliation_string: School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
raw_author_name: Yi Liu
raw_affiliation_string: School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
raw_author_name: Gang Yan
raw_affiliation_string: School of Artificial Intelligence, Hebei University of Technology, Tianjin, China

Abstract: Most existing RGB-D salient object detection (SOD) methods extract features of both modalities in parallel or adopt depth features as supplementary information for unidirectional interaction from depth modality to RGB modality in the encoder stage. These methods ignore the influence of low-quality depth maps, and there is still room for improvement in effectively fusing RGB features and depth features. To address the above problems, this paper proposes a Feature Interaction Network (FINet), which performs bi-directional interaction through feature interaction module (FIM) in the encoder stage. The feature interaction module is divided into two parts: depth enhancement module (DEM) filters the noise in the depth features through the attention mechanism; and cross enhancement module (CEM) effectively interacts RGB features and depth features. In addition, this paper proposes a two-stage cross-modal fusion strategy: high-level fusion adopts the semantic information of high level for coarse localization of salient regions, and low-level fusion makes full use of the detailed information of low level through boundary fusion, and then we progressively refine high-level and low-level cross-modal features to obtain the final saliency prediction map. Extensive experiments show that the proposed model achieves better performance than eight state-of-the-art models on five standard datasets.
An LDP-Based Privacy-Preserving Longitudinal and Multidimensional Range Query Scheme in IoT

Authors:
raw_author_name: Yun Ni
raw_affiliation_string: College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China
raw_author_name: Jinguo Li
raw_affiliation_string: College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China
raw_author_name: Jifei Xiao
raw_affiliation_string: College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China

Abstract: Range queries are extensively used in various Internet of Things (IoT) applications as an essential functional requirement to provide intelligent and personalized services to users. In IoT environments, diverse types of data are generated, necessitating the design of range query schemes for multidimensional data. Privacy preservation is a key concern for range queries, leading to the proposal of several privacy-preserving solutions. However, most of these solutions are either inefficient or impractical. Moreover, existing approaches often suffer from the problem of longitudinal data privacy leakage, posing a serious threat to user privacy. Although some efforts have addressed the privacy issues of longitudinal data, practical implementations have been hesitant. To tackle these challenges, we propose a Local Differential Privacy-based (LDP) privacy-preserving scheme called the Privacy-Preserving Longitudinal and Multidimensional Range Query (PLMRQ) for IoT. Our scheme focuses on lightweight privacy preservation and eliminates the need for a trusted third party (TTP). Firstly, it is designed based on a double randomizer, ensuring effective privacy preservation of longitudinal data over time. Secondly, to mitigate excessive noise injection, PLMRQ dynamically constructs a binary tree structure by hierarchically decomposing the entire domain. Finally, through the utilization of a post-processing technique, the mean square error is efficiently reduced. Theoretical and experimental results demonstrate that the proposed PLMRQ maintains competitive utility while rigorously satisfying lneϵ1+tϵ2+1/eϵ1+etϵ2-LDP with an upper bound of ϵ1 and a lower bound of ϵ2.
The Application of Artificial Intelligence in Alzheimer's Research

Authors:
raw_author_name: Qing Zhao
raw_affiliation_string: Beijing University of Technology,Faculty of Information Technology,Beijing,China,100124
raw_author_name: Hanrui Xu
raw_affiliation_string: School of Medicine, Tsinghua University,Beijing,China,100084
raw_author_name: Jianqiang Li
raw_affiliation_string: Beijing University of Technology,Faculty of Information Technology,Beijing,China,100124
raw_author_name: Faheem Akhtar Rajput
raw_affiliation_string: Sukkur IBA University,Department of Computer Science,Sukkur,Pakistan,65200
raw_author_name: Liyan Qiao
raw_affiliation_string: Tsinghua University Yuquan Hospital,Department of Neurology,Beijing,China,100040

Abstract: Alzheimer's disease (AD) is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function, but the etiology of AD is still unclear. With the explosive growth of electronic health data, the application of artificial intelligence (Al) in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches, and improving the disease's diagnostic and prognostic outcome. This paper first briefly introduces Al technologies and applications in medicine, and then presents a comprehensive review of Al in AD. In simple, it includes etiology discovery based on genetic data, computer-aided diagnosis (CAD), computer-aided prognosis (CAP) of AD using multi-modality data (genetic, neuroimaging and linguistic data), and pharmacological or non-pharmacological approaches for treating AD. Later, some popular publicly available AD datasets are introduced, which are important for advancing Al technologies in AD analysis. Finally, core research challenges and future research directions are discussed.
Joint Sample Position Based Noise Filtering and Mean Shift Clustering for Imbalanced Classification Learning

Authors:
raw_author_name: Lilong Duan
raw_affiliation_string: Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China; School of Computer Science and Technology, Anhui University of Technology,Maanshan,China,243032
raw_author_name: Wei Xue
raw_affiliation_string: Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China; School of Computer Science and Technology, Anhui University of Technology,Maanshan,China,243032
raw_author_name: Jun Huang
raw_affiliation_string: Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China; School of Computer Science and Technology, Anhui University of Technology,Maanshan,China,243032
raw_author_name: Xiao Zheng
raw_affiliation_string: Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China; School of Computer Science and Technology, Anhui University of Technology,Maanshan,China,243032

Abstract: The problem of imbalanced data classification learning has received much attention. Conventional classification algorithms are susceptible to data skew to favor majority samples and ignore minority samples. Majority weighted minority oversampling technique (MWMOTE) is an effective approach to solve this problem, however, it may suffer from the shortcomings of inadequate noise filtering and synthesizing the same samples as the original minority data. To this end, we propose an improved MWMOTE method named joint sample position based noise filtering and mean shift clustering (SPMSC) to solve these problems. Firstly, in order to effectively eliminate the effect of noisy samples, SPMSC uses a new noise filtering mechanism to determine whether a minority sample is noisy or not based on its position and distribution relative to the majority sample. Note that MWMOTE may generate duplicate samples, we then employ the mean shift algorithm to cluster minority samples to reduce synthetic replicate samples. Finally, data cleaning is performed on the processed data to further eliminate class overlap. Experiments on extensive benchmark datasets demonstrate the effectiveness of SPMSC compared with other sampling methods.
Multilingual personalized hashtag recommendation for low resource Indic languages using graph-based deep neural network

Authors:
raw_author_name: Shubhi Bansal
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology, Indore, India
raw_author_name: Kushaan Gowda
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology, Indore, India
raw_author_name: Nagendra Kumar
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology, Indore, India

Abstract: Users from different cultures and backgrounds often feel comfortable expressing their thoughts on trending topics by generating content in their regional languages. Recently, there has been an explosion in multilingual information, and a massive amount of multilingual textual data is added daily on the Internet. Using hashtags for multilingual low-resource content can be an effective way to overcome language barriers because it allows content to be discovered by a wider audience and makes it easier for people interested in the topic to find relevant content, regardless of the language in which it was written. To account for linguistic diversity and universal access to information, hashtag recommendation for multilingual low-resource content is essential. Several approaches have been put forth to recommend content-based and personalized hashtags for multimodal content in high-resource languages. Data availability and linguistic differences often limit the development of hashtag recommendation methods for low-resource Indic languages. Hashtag recommendation for tweets disseminated in low-resource Indic languages has seldom been addressed. Moreover, personalization and language usage aspects to recommend hashtags for tweets posted in low-resource Indic languages have yet to be explored. In view of the foregoing, we propose an automated hashtag recommendation system for tweets posted in low-resource Indic languages dubbed as TAGALOG, capable of recommending personalized and language-specific hashtags. We employ user-guided and language-guided attention mechanisms to distill indicative features from low-resource tweets according to the user's topical and linguistic preferences. We propose a graph-based neural network to mine users' posting behavior by connecting historical tweets of a particular user and language relatedness by linking tweets according to language families, i.e., Indo-Aryan and Dravidian. Experimental results on the curated dataset from Twitter demonstrate that the proposed model outperformed recognized pre-trained language models and extant research, showing an average improvement of 12.3% and 12.8% in the F1-score, respectively. TAGALOG recommends hashtags that align with the user's interests and linguistic predilections, leading to a heightened level of tailored and engaging user experience. Personalized and multilingual hashtag recommendation systems for low-resource Indic languages can help to improve the discoverability and relevance of content in these languages.
A hybrid search mode-based differential evolution algorithm for auto design of the interval type-2 fuzzy logic system

Authors:
raw_author_name: Yongbin Yu
raw_affiliation_string: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China
raw_author_name: Xiangxiang Wang
raw_affiliation_string: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China
raw_author_name: Jingye Cai
raw_affiliation_string: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China
raw_author_name: Shouming Zhong
raw_affiliation_string: School of Mathematical Science, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China
raw_author_name: Hao Wang
raw_affiliation_string: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China
raw_author_name: Kaibo Shi
raw_affiliation_string: School of Electronic Information and Electrical Engineering and Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan, Chengdu University, Chengdu 610106, Sichuan, China

Abstract: This article proposed a hybrid search mode-based differential evolution (HSM-DE) algorithm to overcome the complex design of an interval type-2 fuzzy logic system (IT2FLS). First, transform complex design processes of the IT2FLS into optimization problems for our proposed evolutionary algorithm by decoding the whole system into design parameters regarded as search objectives. Then, the DE algorithm with a rapid convergence characteristic is adopted as a global search method for exploration according to a memetic algorithm (MA) structure. Considering the conventional DE algorithm is lack of escaping ability for the local optimal solution, a local search method combined with a simulated binary crossover (SBX) and polynomial mutation (PM) is utilized to enhance the exploitation behavior of our proposed method. In addition, simple constraints on the design parameters of the membership function are added to balance the searching freedom of the HSM-DE algorithm and the readability of the optimal IT2FLS. Finally, a simulation of a mobile two-wheel vehicle (MTWV) under a goal search task is performed to verify the effectiveness of the optimal IT2FLS auto designed by HSM-DE. All results demonstrate that the HSM-DE realizes auto design for the IT2FLS with high-performance control and outperforms the comparative methods. Moreover, four comparison methods are performed to validate the high-performance of the proposed HSM-DE on the search ability of the optimal solution.
Accelerating Federated Learning With Data and Model Parallelism in Edge Computing

Authors:
raw_author_name: Yunming Liao
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Yang Xu
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Hongli Xu
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Lun Wang
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Chunming Qiao
raw_affiliation_string: Department of Computer Science and Engineering, University at Buffalo (The State University of New York), Buffalo, NY, USA

Abstract: Recently, edge AI has been launched to mine and discover valuable knowledge at network edge. Federated Learning, as an emerging technique for edge AI, has been widely deployed to collaboratively train models on many end devices in data-parallel fashion. To alleviate the computation/communication burden on the resource-constrained workers (e.g., end devices) and protect user privacy, Spilt Federated Learning (SFL), which integrates both data parallelism and model parallelism in Edge Computing (EC), is becoming a practical and popular approach for model training over distributed data. However, apart from the resource limitation, SFL still faces two other critical challenges in EC, i.e., system heterogeneity and context dynamics. To overcome these challenges, we present an efficient SFL method, named, which controls both local updating frequency and batch size to better accelerate model training. We theoretically analyze the model convergence rate and obtain a convergence upper bound regarding local updating frequency given a fixed batch size. Upon this, we develop a control algorithm to determine adaptive local updating frequency and diverse batch sizes for heterogeneous workers to enhance the training efficiency. The experimental results show that can reduce the completion time by about 43% and the network traffic consumption by about 31% for achieving the similar test accuracy, compared to the baselines.
IoMT: A Medical Resource Management System Using Edge Empowered Blockchain Federated Learning

Authors:
raw_author_name: Tasiu Muazu
raw_affiliation_string: College of Computer and Information, Hohai University, Nanjing, China
raw_author_name: Mao Yingchi
raw_affiliation_string: College of Computer and Information and the Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, China
raw_author_name: Abdullahi Uwaisu Muhammad
raw_affiliation_string: College of Computer and Information, Hohai University, Nanjing, China
raw_author_name: Muhammad Ibrahim
raw_affiliation_string: College of Environment, Hohai University, Nanjing, China
raw_author_name: Omaji Samuel
raw_affiliation_string: Department of Computer Science, Edo State University Uzairue, Auchi, Nigeria
raw_author_name: Prayag Tiwari
raw_affiliation_string: Department of Computer Science, Aalto University, Espoo, Finland

Abstract: As data sharing on the Internet of Medical Things (IoMT) become more complicated, the problems of divergent interests, unregulated policies, privacy and security, and the resource constraints of data owners have drawn the attention of researchers. To address the problems, this paper provides resource management in the IoMT using a proposed edge-empowered blockchain federated learning system. Also, an improved linear regressor model is proposed as the global learning model for the federated learning system. Gradient parameters are encrypted using Paillier encryption on the federated server side before they are shared by the federated clients. Blockchain is deployed to provide new security features for IoMT and edge computing. Moreover, all transactions of IoMT and edge devices are stored on the blockchain for secure cataloguing and auditing. Edge computing is employed to handle complex computing tasks on behalf of IoMT devices. Extensive simulations are conducted to validate the efficacy of the proposed system model. The results show that computing costs are minimized while still achieving the benefits of security and privacy in the proposed system. Furthermore, security analysis shows that the proposed system is protected from security attacks.
Secure and optimized satellite image sharing based on chaotic eπ map and Racah moments

Authors:
raw_author_name: Hicham Karmouni
raw_affiliation_string: Laboratory of Engineering, Systems and Applications, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco; National School of Applied Sciences, Cadi Ayyad University, Marrakech, Morocco
raw_author_name: Mohamed Amine Tahiri
raw_affiliation_string: Laboratory of Electronic Signals and Systems of Information LESSI, Dhar El Mahrez Faculty of Science, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco
raw_author_name: Hicham Amakdouf
raw_affiliation_string: Sidi Mohamed Ben Abdellah University, Institute of Sports Sciences, Fez, Morocco
raw_author_name: Mohamed Ouazzani Jamil
raw_affiliation_string: Systems and Sustainable Environment Laboratory (SED), Faculty of Engineering Sciences (FSI) Private University of Fez (UPF), Fez, Morocco
raw_author_name: Hassan Qjidaa
raw_affiliation_string: Laboratory of Electronic Signals and Systems of Information LESSI, Dhar El Mahrez Faculty of Science, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco
raw_author_name: Mhamed Sayyouri
raw_affiliation_string: Laboratory of Engineering, Systems and Applications, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

Abstract: The objective of this paper is to contribute to the sharing, transfer, and secured indexing of large color images. Watermarking and digital encryption have emerged as alternative and complementary solutions to ensure authorized access, facilitate authentication of content, or prevent illegal reproduction. For this reason, we suggest a new system based on four techniques derived from various transformation and decomposition methods, such as the discrete orthogonal Racah moment transformations (DORMT), which allows the information capacity to be reduced to a number of coefficients, as well as the discrete wavelet transform (DWT), which makes it possible to decompose into a certain number of multilevel sub-bands, to have the sub-band that represents most of the image information. In addition, Schur decomposition (SD) and singular value decomposition (SVD) have been used to increase security. The proposed system consists of two main phases. The first phase is the integration of a color watermark, first applying DORMT-SD-SVD to a color host image and then applying DWT-SD-SVD to achieve a compromise between capacity, invisibility and robustness in terms of quality and privacy. The second phase is the encryption of the watermarked image with an eπ map to increase security for sharing, transfer and indexing. This system uses an Aquila Optimization Algorithm (AOA) as an optimization procedure to find the best set of DORMT parameters and provide a dynamic and adaptive scale factor. The experimental results, in which the proposed system's properties are tested and compared to those of other systems, make it clear that the proposed system is useful in terms of its level of security, capacity, invisibility, and resistance to attacks that use signal processing.
Semi-fragile neural network watermarking for content authentication and tampering localization

Authors:
raw_author_name: Zihan Yuan
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Xinpeng Zhang
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Zichi Wang
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Zhaoxia Yin
raw_affiliation_string: School of Communication and Electronic Engineering, East China Normal University, Shanghai 200444, China

Abstract: As an emerging digital product, artificial intelligence models face the risk of being modified. Malicious tampering will severely damage model functions, which is different from normal modifications. In addition, tampering localization for targeted repair can effectively reduce the cost. Therefore, it is crucial to achieve model content authentication and locate the tampering location. We proposed a novel semi-fragile neural network watermarking method in this paper to address these issues. Specifically, with the precondition of maintaining model performance, we proposed a method that generates a set of semi-fragile samples for a model to achieve the content authentication and tampering localization. The experiment results show that the content authentication of the model can be achieved by analyzing the output results of the model for the semi-fragile samples. When the model is processed normally, the output results are consistent with the expected label, while when the model is maliciously tampered with, the model produces unstable output. Furthermore, the tamper localization of the model can be further achieved through the information hidden in the semi-fragile samples, resulting in an average accuracy of more than 99.42%. In addition, our method is also effective for other deep neural networks.