Article
Computer Science, Interdisciplinary Applications
Yuzhi Fang, Li Liu
Summary: This paper introduces a novel scalable supervised online hashing method to solve the problems of learning compact binary codes for new data streams and updating hash functions. By establishing a similarity matrix and using an alternate optimization algorithm, the method effectively addresses data imbalance and achieves superior performance in terms of accuracy and scalability for online retrieval tasks.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Tao Yao, Ruxin Wang, Jintao Wang, Ying Li, Jun Yue, Lianshan Yan, Qi Tian
Summary: This paper proposes an efficient supervised cross-media hashing scheme that combines semantic embedding and graph embedding to generate a sharing semantic subspace. By incorporating class labels, the generated binary codes are optimized to improve the retrieval performance. The proposed approach outperforms existing cross-media hashing methods in terms of both accuracy and efficiency.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Information Systems
Si-chao Lei, Xing Tian, Wing W. Y. Ng, Yue-Jiao Gong
Summary: Image hashing methods have been proven effective and efficient for large-scale image retrieval. However, existing methods often manually select fixed hash code lengths, which may not optimize retrieval performance. In this paper, a length adaptive hashing method is proposed to optimize hash code lengths adaptively using a multiobjective evolutionary algorithm. Experimental results show significant improvement in retrieval performance compared to traditional methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Zheng Zhang, Jianning Wang, Lei Zhu, Guangming Lu
Summary: This article proposes a novel deep tripartite semantically interactive hashing framework, called Semantically Cycle-consistent Hashing Networks (SCHNs), for discriminative hash code learning. The framework constructs a flexible semantic space and a transitive latent space, in conjunction with the visual space, to jointly deduce the privileged discriminative hash space. A cyclic adversarial learning module is proposed to preserve semantic consistency across multiple spaces. Experimental results on large-scale datasets demonstrate the superiority of the proposed method.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Zheng Zhang, Jianning Wang, Lei Zhu, Yadan Luo, Guangming Lu
Summary: The deep hashing algorithm achieves remarkable success in large-scale image retrieval, thanks to its powerful discriminative representation of deep learning and the attractive computational efficiency of compact hash code learning. However, existing semantic-preserving hashing strategies fail to capture the interactive correlations between visual semantics and category-level labels, and they lack flexibility in semantic representation and adaptive knowledge communication in hash code learning. This paper proposes a novel Deep Collaborative Graph Hashing (DCGH) method, which considers multi-level semantic embeddings, latent common space construction, and intrinsic structure mining for discriminative hash codes learning.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Information Systems
Minghua Wana, Xueyu Chen, Cairong Zhao, Tianming Zhan, Guowei Yang
Summary: This paper presents a new weakly supervised discrete discriminant hashing (WDDH) algorithm, which ensures a more effective representation of data and better retrieval of information by considering the nearest neighbour relationship between samples and constructing new neighbourhood graphs.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Zijian Chao, Shuli Cheng, Yongming Li
Summary: This paper proposes a deep internally connected Transformer hashing (DICTH) approach to address the issue of limited global dependency information in image retrieval using convolutional neural networks (CNN). By introducing the internally connected Transformer block (ICT) and an improved cross-entropy loss function, DICTH demonstrates improved performance on multiple datasets.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Environmental Sciences
Xiaoyan Tan, Yun Zou, Ziyang Guo, Ke Zhou, Qiangqiang Yuan
Summary: Hashing is widely used in large-scale remote sensing image retrieval due to its advantages in storage and search speed. However, training deep hashing networks requires labeled images, which are scarce and expensive in remote sensing datasets. To address this issue, a deep unsupervised hashing method called deep contrastive self-supervised hashing (DCSH) is proposed, which learns accurate hash codes using only unlabeled images. By maximizing the consistency of different views of the same image, the need for label annotation is eliminated. The proposed network, consisting of four parts, achieves semantic similarity-preserved hash codes through an end-to-end training process.
Article
Computer Science, Artificial Intelligence
Yao Xiao, Wei Zhang, Xiangguang Dai, Xiangqin Dai, Nian Zhang
Summary: In this paper, a robust supervised hashing framework, called Robust Supervised Discrete Hashing (RSDH), is proposed based on the Cauchy loss function and Supervised Discrete Hashing (SDH) to learn a subspace consisted of binary codes. RSDH can reduce outliers and noise of the hashing codes and achieve better retrieval performance.
Article
Computer Science, Information Systems
Muhammad Umair Hassan, Dongmei Niu, Mingxuan Zhang, Xiuyang Zhao
Summary: This research proposes a novel asymmetric learning-based generative adversarial network (AGAN) for image retrieval, integrating feature learning with hashing and introducing three loss functions that significantly improve retrieval performance.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
P. Arulmozhi, S. Abirami
Summary: Deep supervised hashing has been successful in solving large-scale image retrieval challenges. However, there is a need to further improve retrieval accuracy. A novel deep supervised hashing method based on Pooled Feature map (DSHPoolF) is proposed to generate compact hash codes by exploring spatial information and enhancing retrieval performance.
Article
Computer Science, Artificial Intelligence
Jesus Fonseca-Bustos, Kelsey Alejandra Ramirez-Gutierrez, Claudia Feregrino-Uribe
Summary: Content identification systems use perceptual hashing and self-supervised learning to enhance robustness, achieving excellent identification performance and generalization without the need for re-training.
Article
Geochemistry & Geophysics
Jiayi Tang, Dali Wang, Xiaochong Tong, Chunping Qiu, Weiming Yang, Yi Lei
Summary: This study proposes a hashing-based image retrieval approach that learns hash codes from open and representative self-supervised features, and enhances performance by exploiting a small set of labeled datasets. Experimental results on two commonly used remote sensing image datasets demonstrate the effectiveness and balance of the proposed approach between retrieval accuracy and utilized annotations.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Abid Hussain, Heng-Chao Li, Danish Ali, Muqadar Ali, Fakhar Abbas, Mehboob Hussain
Summary: As the amount of multimedia data continues to grow, researchers are facing challenges in searching and retrieving relevant images. This paper proposes an optimized deep supervised hashing approach based on a teacher-student method for swift and precise image retrieval. Through knowledge distillation and model pruning, the computational and storage costs of the model are effectively reduced, improving response time and efficiency.
IMAGE AND VISION COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Bolin Zhang, Jiangbo Qian, Xijiong Xie, Yu Xin, Yihong Dong
Summary: With the advancement of Internet technology, hashing-based approximate nearest neighbor retrieval methods have gained attention. One of the most advanced hashing methods involves using deep neural networks, particularly convolutional neural networks (CNN), along with the introduction of capsule networks.
CSH method incorporates a hashing layer for hash learning, optimizing the defined objective function to simultaneously learn feature representations, hashing functions, and classification results.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Yingjian Li, Zheng Zhang, Bingzhi Chen, Guangming Lu, David Zhang
Summary: Cross-domain Facial Expression Recognition (FER) is challenging due to the subtle difference between expressions and the large discrepancy between domains. Existing methods focus on reducing domain shift, but fail to learn discriminative representations. To address this, we propose a novel DMSRL framework, which extracts multi-level discriminative features during semantic-aware domain adaptation. We design a semantic metric learning module based on source data and pseudo labels of target data, enlarge the category margin, and develop a mutual information minimization module. We also formulate a multi-level feature extracting module to capture detailed information. Experimental results show that our DMSRL outperforms state-of-the-art baselines.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Information Systems
Wenjue He, Zheng Zhang, Yongyong Chen, Jie Wen
Summary: This paper proposes a novel method to solve the problem of incomplete multi-view spectral clustering. The method uses structural anchor and paired anchor samples to handle missing cases, and a fast learning algorithm is designed to optimize the clustering performance.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Lei Zhu, Tianshi Wang, Jingjing Li, Zheng Zhang, Jialie Shen, Xinhua Wang
Summary: Deep cross-modal hashing retrieval models are vulnerable to adversarial attacks. A new efficient query-based black-box attack method (EQB(2)A) is proposed to generate adversarial examples efficiently.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Automation & Control Systems
Jie Wen, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu, Zhao Zhang, Jinxing Li
Summary: Conventional multiview clustering methods fail when not all views of samples are available in practical applications. Incomplete multiview clustering (IMC) is developed to address this issue. Recent years have seen significant advances in IMC research, but there are still open problems to be solved.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hengxin Li, Xiaoshuang Shi, Xiaofeng Zhu, Shuihua Wang, Zheng Zhang
Summary: This paper proposes a novel dual interpretable graph convolutional network, FSNet, to simultaneously select significant features and samples for medical diagnosis and interpretation. Extensive experiments demonstrate its superior classification performance and interpretability over the recent state-of-the-art methods.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Jie Zhang, Shengbin Liao, Haofeng Zhang, Yang Long, Zheng Zhang, Li Liu
Summary: Traditional GAN-based GZSL methods suffer from ignoring class differences and incomplete adversarial training. This paper proposes a data-driven recurrent adversarial generative network. It synthesizes visual prototypes for unseen classes and generates samples using corresponding semantic attributes. A recurrent GAN is designed to enhance representative visual features. Extensive experiments demonstrate the effectiveness and superiority of the proposed method.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Qi Zhu, Jing Yang, Shuihua Wang, Daoqiang Zhang, Zheng Zhang
Summary: Brain network analysis is an effective method for brain disease diagnosis. This paper proposes a multi-modal non-Euclidean brain network analysis method based on community detection and convolutional autoencoder. It can address the challenges of the non-Euclidean nature of brain networks and the suboptimal utilization of complementary information from distinct modalities.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Tianshi Wang, Lei Zhu, Zheng Zhang, Huaxiang Zhang, Junwei Han
Summary: In this paper, we propose a targeted adversarial attack method against deep cross-modal hashing retrieval. By utilizing a progressive fusion module, semantic adaptation network, and semantic translator, we achieve the interaction between target semantics and the attacked model's implicit semantics. Extensive experiments on widely tested cross-modal retrieval datasets demonstrate the superiority of our proposed method.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Mixiao Hou, Zheng Zhang, Chang Liu, Guangming Lu
Summary: The article introduces a Semantic Alignment network based on Multi-Spatial learning (SAMS), which is used for multi-modal emotion recognition. SAMS achieves local and global alignment between modalities by using high-level emotion representations as supervisory signals and constructs a self-modal interaction module in the learning space to improve emotion recognition performance.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Business
Yuan Sun, Zhebin Ding, Zuopeng Zhang
Summary: In recent years, China has made great progress in the adoption and use of emerging information technologies. The popularity of enterprise social media (ESM) in Chinese enterprises and the resulting innovation practices provide an opportunity to explore relevant theories and assumptions. This article applies the organizational information processing theory (OIPT) to study the innovative use cases of ESM in China and identify contributing factors through a multicase-analysis approach. The research contributes to the literature by studying the innovative use cases in the ESM context and provides valuable insights for practitioners in designing and implementing ESM effectively.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2023)
Article
Computer Science, Information Systems
Qiming Yang, Qi Zhu, Mingming Wang, Wei Shao, Zheng Zhang, Daoqiang Zhang
Summary: The multi-site approach has attracted attention for brain disease diagnosis. However, privacy disclosure is a concern in the transmission of subject's images or features between sites. To address this, the authors propose a self-supervised federated adaptation framework (S2FA) that can reduce privacy risks. The proposed approach achieves accurate cross-site prediction of brain disease.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Artificial Intelligence
Lin Yuanbo Wu, Lingqiao Liu, Yang Wang, Zheng Zhang, Farid Boussaid, Mohammed Bennamoun, Xianghua Xie
Summary: In this paper, a method for cross-resolution person re-identification is proposed, which learns resolution-adaptive representations and utilizes resolution-adaptive mechanisms to improve the performance. Experimental results demonstrate that the proposed method outperforms existing approaches and achieves state-of-the-art performance on multiple CRReID benchmarks.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Computer Science, Theory & Methods
Qi Zhu, Guangnan Xin, Lunke Fei, Dong Liang, Zheng Zhang, Daoqiang Zhang, David Zhang
Summary: Contactless palmprint recognition is a promising biometric technology that has gained attention for its noninvasive and practical characteristics. However, two challenges that limit its performance are the inconsistency in acquisition devices used for training and testing, and incomplete data due to subjects being unable to be imaged on each device. To address these issues, we propose a self-paced CycleGAN with self-attention modules that synthesizes missing data and reduces the influence of different imaging devices. Experimental results on contactless palmprint datasets collected from different smartphones demonstrate the effectiveness of our approach in classifying contactless palmprint images.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Artificial Intelligence
Zheng Zhang, Wen-Jue He
Summary: The success of current multi-view clustering lies in the assumption of completeness on each view, but this assumption is rarely satisfied in real-world applications. Incomplete multi-view clustering (IMC) focuses on clustering partially observed instances, caused by data corruption or sensor failure. However, the current research faces obstacles including the reliance on pairwise similarity measurement, the challenge of recovering and enhancing high-order similarities, and the limitation to dual-view IMC. This paper proposes a Tensorized Topological Graph Learning (TTGL) approach for generalized incomplete multi-view clustering, which considers topological graph construction, missing feature completion, and high-order uncertain correlation enhancement. Experimental results on benchmark datasets show the effectiveness of TTGL compared to state-of-the-art IMC clustering algorithms. Source code for TTGL is available at: https://github.com/DarrenZZhang/TTGL.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Bingzhi Chen, Yishu Liu, Zheng Zhang, Guangming Lu, Adams Wai Kin Kong
Summary: This article proposes a Transformer-based Attention Guided Network, called TransAttUnet, for enhancing the performance of semantical segmentation in medical images. By incorporating self-aware attention modules and multi-scale skip connections, the network successfully learns non-local interactions among encoder features and strengthens the representation ability of multi-scale context information. Experimental results show that the proposed method consistently outperforms existing state-of-the-art methods.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
