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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jin, Qiangguo Cui, Hui Sun, Changming Huang, Jiapeng Xuan, Ping Xu, Yiyue Wang, Linlin Cao, Leilei Wei, Leyi Su, Ran Northwestern Polytechnical University School of Software Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Csiro Data61 Sydney Australia Shantou University School of Engineering Department of Computer Science Guangdong China Shandong First Medical University Shandong Academy of Medical Sciences Shandong Cancer Hospital and Institute Department of Radiation Oncology Shandong China Zhejiang University Innovation Center of Yangtze River Delta Zhejiang China Shandong University School of Software Shandong China Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China

ISBN: (纸本)9798350337488

Accurate tumor segmentation is crucial for esophageal cancer radiotherapy treatment planning. The low contrast among the esophagus, tumors, and surrounding tissues, and irregular tumor shapes limit the performance of automatic segmentation methods. In this paper, we aim to exploit the irregular shapes of tumors to facilitate accurate segmentation. We propose a simple and pluggable shape-aware contrastive deep supervision network (SCDSNet) with shape-aware regularization and voxel-to-voxel contrastive deep supervision. Specifically, the shape-aware regularization with an uncertainty minimization strategy encourages the precise predictions of an additional shape-aware head. The voxel-to-voxel contrastive deep supervision enhances the multi-scale shape-tumor contrast for better voxel-to-voxel prediction of shapes. The proposed method is simple and highly pluggable, which can easily be extended to other frameworks. Further, we establish a large in-house dataset on esophageal cancer to validate the effectiveness of our proposed method. The quantitative and qualitative experimental results demonstrate the effectiveness of SCDSNet on the esophageal cancer dataset. © 2023 IEEE.

关键词： Deep supervision Esophagus tumor segmentation Shape-aware regularization Voxel-to-voxel contrastive learning

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A New Class of Multiple Optical ZCZ Sequence Sets with Good Cross-Correlation

A New Class of Multiple Optical ZCZ Sequence Sets with Good ...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Yang Yan Rize Jin Qiuyan Wang Shimin Sun School of Information Technology and Engineering Tianjin University of Technology and Education Tianjin China School of Software Tiangong University Tianjin Chian School of Computer Science and Technology Tiangong University Tianjin China

Zero correlation zone (ZCZ) sequences have been widely applied to quasi-synchronous code division multiple access system (QS-CDMA) and therefore have been extensively studied in recent years. In this paper, ZCZ sequence sets are constructed. The presented ZCZ sequence sets have the following properties: 1) each sequence in the constructed sequence set is perfect; 2) each set is an optimal ZCZ sequence set with parameters (N 2 ,N,N); 3) the maximum inter-set cross-correlation of the given multiple sequence sets meets the Sarwate bound.

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Small sets of genuinely nonlocal Greenberger-Horne-Zeilinger states in multipartite systems

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Physical Review A 2024年第2期109卷 022428-022428页

作者： Zong-Xing Xiong Yongli Zhang Mao-Sheng Li Lvzhou Li Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-Sen University Guangzhou 510006 China School of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou 510665 China School of Mathematics South China University of Technology Guangzhou 510641 China

A set of orthogonal multipartite quantum states are called (distinguishability-based) genuinely nonlocal if they are locally indistinguishable across any bipartition of the subsystems. In this work, we consider the problem of constructing small genuinely nonlocal sets consisting of generalized Greenberger-Horne-Zeilinger (GHZ) states in multipartite systems. For system (C2)⊗N where N is large, using the language of group theory, we show that a tiny proportion Θ(1/2N) of the states among the N-qubit GHZ basis suffice to exhibit genuine nonlocality. Similar arguments also hold for the canonical generalized GHZ bases in systems (Cd)⊗N, wherever d is even and N is large. What is more, moving to the condition that any fixed N is given, we show that d+1 genuinely nonlocal generalized GHZ states exist in (Cd)⊗N, provided the local dimension d is sufficiently large. As an additional merit, within and beyond an asymptotic sense, the latter result also indicates some evident limitations of the “trivial othogonality-preserving local measurements” (TOPLM) technique that has been utilized frequently for detecting genuine nonlocality.

关键词： Nonlocality Quantum correlations in quantum information

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LSTM-Attention-Based Video Popularity in UAV-Assisted Vehicular Networks

LSTM-Attention-Based Video Popularity in UAV-Assisted Vehicu...

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IEEE International Conference on Communications in China (ICCC)

作者： Jie Jin Qichao Xu Zhou Su Dongfeng Fang School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China Department of Computer Science and Software Engineering California Polytechnic State University USA

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Recently, video contents application (e.g. multimedia on-demand service, short video playback, etc.) are provided by more and more automobile manufacturers in vehicular networks. However, the huge traffic poses a large burden on the limited wireless network resources. Edge caching technology is a promising solution, where base stations act as edge cache nodes. There are limitations that base stations have restricted coverage and the cost of deployment is a concern. Unmanned Aerial Vehicles(UAVs) has been widely used due to its lightweight and ease of deployment. Each vehicular user requests video contents of interest from the content provider and nearby UAVs which precache a certain video contents in advance deliver video contents to the requester, i.e., vehicular users. However, the storage capacity of UAVs is limited. In order to efficiently utilize the storage resources of UAVs, it is necessary for UAVs to proactively cache video contents with higher popularity. It is a challenge that the popularity of video contents are unknown and time-variant. Thus, we need to predict the popularity of video contents. In this paper, a prediction scheme based on LSTM-Attention method is proposed. Specifically, historical requests of video contents are used to make a prediction on the request of video contents in the next time step in the LSTM model. Attention mechanism is added to improve the accuracy of the prediction of the popularity of video contents. The real-world dataset is used to demonstrate that the proposed LSTM-Attention prediction scheme can enhance the prediction performance of popularity.

关键词： Base stations Attention mechanisms Accuracy Costs Wireless networks Simulation Predictive models

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Conjugate Surrogate for Expensive Multiobjective Optimization

Conjugate Surrogate for Expensive Multiobjective Optimizatio...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Yang, Qi-Te Luo, Liu-Yue Xu, Xin-Xin Chen, Chun-Hua Wang, Hua Zhang, Jun Zhan, Zhi-Hui School of Computer Science and Engineering South China University of Technology Guangzhou510006 China College of Artificial Intelligence Nankai University Tianjin300350 China School of Computer Science and Technology Ocean University of China Qingdao266100 China School of Software Engineering South China University of Technology Guangzhou510006 China Institute for Sustainable Industries and Liveable Cities Victoria University MelbourneVIC 8001 Australia Hanyang University Ansan15588 Korea Republic of

ISBN: (纸本)9781665430654

The Kriging surrogate (KS) has been widely used in surrogate-assisted multiobjective evolutionary algorithms (SAMOEAs) for solving expensive multiobjective optimization problems (EMOPs). Typically, when tackling an M-objective EMOP, a KS consists of M Kriging models, in which each model is used to approximate one objective function to replace the expensive fitness evaluation. Since such a KS is only efficient in solving low-dimensional EMOPs, the dimension reduction method has been adopted to construct the reduction surrogate (RS) to reduce training costs. However, both KS and RS can only approximate the mapping from variables to different objectives (i.e., objective function) but ignore the potential relationship between objectives. For practical applications, it is necessary to take into account the mapping between different objectives for the reliability of the surrogate. Therefore, we for the first time propose the concept of the conjugate surrogate (CS) and construct a simple CS to realize the approximated mapping from objectives to objectives. Different from KS or RS, all models in CS are conjugate symbiosis. In collaboration with RS, CS can not only benefit the light training cost, but also improve the convergence speed. Compared with five state-of-the-art SAMOEAs, the CS-assisted algorithm shows its effectiveness and competitiveness in solving EMOPs. © 2023 IEEE.

关键词： Evolutionary Computation Expensive Multiobjective Optimization Kriging Surrogate-assisted

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PEFN: A Patches Enhancement and Hierarchical Fusion Network for Robust Vehicle Re-Identification

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IEEE Internet of Things Journal 2025年

作者： He, Wenying Wang, Feiyu Bai, Yude Xiong, Neal N. Xu, Guangquan Guo, Fei Hebei University of Technology School of Artificial Intelligence Tianjin300401 China Hebei University of Technology Hebei Province Key Laboratory of Big Data Calculation Tianjin300130 China Tiangong University School of Software Tianjin300387 China Northeastern State University Department of Mathematics and Computer Science TahlequahOK74464 United States Tianjin300350 China Central South University School of Computer Science and Engineering Changsha410083 China

Vehicle Re-Identification (Re-ID), which is a significant application in the Internet of Things, aims to accurately retrieve the remaining images of a given vehicle across different cameras views. The improvement in vehicle Re-ID performance largely stems from better addressing the issues of inter-class similarity and intra-class variance. Existing methods, relying solely on max or average pooling after using attention modules, fail to obtain significantly complete and pure global and local features, and neglect the false guidance that some unique individual information on images bring to re-identification. Moreover, models combining global and local features have shown good results in vehicle Re-ID, but these successes neglect the interaction between features across different convolutional layers, resulting in the loss of crucial details for vehicle Re-ID. To tackle these issues, we introduce a Patches Enhancement and hierarchical Fusion Network (PEFN) based on a multi-branch architecture, divided into a Global and Local Attention Supplement (GLAS) branch, and an Enhanced Hierarchical feature fusion (EnHi) branch. The GLAS branch, through the Identity-related Feature Remodeling (IDFR) module’s staged supplementation of spatial and channel features, has achieved the enhancement of both global and local features and effectively mitigated the negative impacts of individual information. The EnHi branch enhances the robustness of feature representation by interacting hierarchical features. Extensive experiments on two large-scale vehicle re-identification datasets demonstrate that our PEFN method outperforms state-of-the-art vehicle re-identification approaches. Specifically, without utilizing extra data and re-ranking, our model achieves 85.15% mAP on the VeRi776 dataset. © 2014 IEEE.

关键词： Reamers

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ran Su Jin Xiao Hui Cui Ping Xuan Xikang Feng Leyi Wei Qiangguo Jin School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Computer Science School of Engineering Shantou University China School of Software Northwestern Polytechnical University China AIDD Faculty of Applied Science Macao Polytechnic University Macao SAR China School of Information Xiamen University Xiamen China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGA mm , which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer.

关键词： Knowledge engineering Representation learning Pathology Genomics Prediction methods Data models Gene expression Bioinformatics Prognostics and health management Cancer

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Privacy-Aware Web APIs Recommendation for Consumer Mashup Creation Based on Iterative Quantification

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Zhang, Ruowei Qi, Lianyong Yan, Chao Chen, Zige Gong, Wenwen Xu, Yanwei Rafique, Wajid School of Computer Science Qufu Normal University China China Tsinghua University China College of Intelligence and Computing Tianjin University China Department of Electrical and Software Engineering University of Calgary Canada

With the emergence of the "unmanned" field, unmanned supermarket software has entered consumers’ lives in line with the pace of development of the times. Nowadays, developers of unmanned supermarket software often tend to create feature-rich consumer mashup applications by invoking a variety of Web APIs (Application Programming Interfaces) in order to save time and costs. The number of APIs available on edge servers has significantly increased with the rise of mobile edge computing. To cope with the continuous growth in API volume and the increasing diversity of API functions in API sharing communities (e.g., ***), lightweight recommendation techniques have been employed to assist consumer mashup developers in finding their desired Web APIs from a vast pool of candidates. However, during the data processing in edge computing, traditional Web API recommendation approaches often prioritize the accuracy of recommended APIs, neglecting the potential privacy risks associated with disclosure. This significantly reduces the willingness and incentive of consumer mashup developers to share API information, such as historical edge mashup-API invocation records. To address this issue, we propose a privacy-preserving Web API recommendation approach, WARecITQ, based on Iterative Quantification (ITQ). Specifically, we convert the sensitive mashup-API invocation records into less-sensitive mashup indices using Iterative Quantization hash coding. We then utilize these less-sensitive mashup indices as the primary decision-making criteria for Web API recommendations, thereby achieving the goal of completing top-k API recommendations. Lastly, we crawled a real-world dataset of mashup-API invocation records from *** and conducted a series of experiments on the dataset to evaluate performance. The results of the experiments demonstrate the superiority of our WARecITQ approach in multiple performance metrics compared to other related approaches, while ensu

关键词： Iterative methods

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Understanding heterophily for graph neural networks 24

Understanding heterophily for graph neural networks

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Proceedings of the 41st International Conference on Machine Learning

作者： Junfu Wang Yuanfang Guo Liang Yang Yunhong Wang State Key Laboratory of Software Development Environment and School of Computer Science and Engineering and Shen Yuan Honors College Beihang University Beijing China State Key Laboratory of Software Development Environment and School of Computer Science and Engineering Beihang University Beijing China School of Artificial Intelligence Hebei University of Technology Tianjin China School of Computer Science and Engineering Beihang University Beijing China

Graphs with heterophily have been regarded as challenging scenarios for Graph Neural Networks (GNNs), where nodes are connected with dissimilar neighbors through various patterns. In this paper, we present theoretical understandings of heterophily for GNNs by incorporating the graph convolution (GC) operations into fully connected networks via the proposed Heterophilous Stochastic Block Models (HSBM), a general random graph model that can accommodate diverse heterophily patterns. Our theoretical investigation comprehensively analyze the impact of heterophily from three critical aspects. Firstly, for the impact of different heterophily patterns, we show that the separability gains are determined by two factors, i.e., the Euclidean distance of the neighborhood distributions and $\sqrt{\mathbb{E}\left[\operatorname{deg}\right]}$, where $\mathbb{E}\left[\operatorname{deg}\right]$ is the averaged node degree. Secondly, we show that the neighborhood inconsistency has a detrimental impact on separability, which is similar to degrading $\mathbb{E}\left[\operatorname{deg}\right]$ by a specific factor. Finally, for the impact of stacking multiple layers, we show that the separability gains are determined by the normalized distance of the l-powered neighborhood distributions, indicating that nodes still possess separability in various regimes, even when over-smoothing occurs. Extensive experiments on both synthetic and realworld data verify the effectiveness of our theory.

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Research on Multi-Model Fusion Method for Similar Multi-domain

Research on Multi-Model Fusion Method for Similar Multi-doma...

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Robotics and computer Vision (ICRCV), International Conference on

作者： Junhua Zhao Qingzeng Song Yongjiang Xue Jing Yu School of computer science and technology Tiangong University Tianjin China School of computer science and technology Tiangong University Haihe Lab of ITAI Tianjin China School of Telecommunication Engineering Beijing Polytechnic Beijing China

In order to improve the classification accuracy of test samples from different datasets in multi-domain image classification, this paper proposes a multi-domain sample classification method based on Baidu AI open platform image recognition and multi-model fusion. The classification task is divided into two steps: first, the domain to which the sample belongs is determined based on the predicted label and its probability output by the Baidu API, and then the deep learning model trained on that domain is used to predict the label of the sample. The effectiveness of the proposed method is verified through extensive experiments on three public datasets: Cifar-10, Cifar100, and Mini-ImageNet. In addition, compared with existing algorithms, the proposed method shows significant improvements in classification performance.

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