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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Yajun Qiu, Lulu Li, Aimin Fei, Rong Li, Junhuai Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Division of Biomedical Engineering Department of Mechanical Engineering Department of Computer Science Saskatoon Canada

ISBN: (纸本)9798350386226

With the development of deep sequencing, recent studies indicate that a miRNA precursor can generate multiple miRNA isoforms (isomiRs). The family prediction of canonical miRNAs and isomiRs could provide a basis for miRNA functional research. In this study, we propose a novel method for family identification of canonical miRNA and isomiRs based on incremental learning. First, a benchmark dataset is constructed by processing data based on miRNA sequences and their family annotation. Moreover, sequence embedding and RNN are used for capturing essential features and inherent dependencies within a sequence. Finally, incremental learning is applied to accommodate the continuous influx of miRNA sequencing data, enabling RNN to stay relevant and effective over time. Comparative experiments and ablation studies illustrate the effectiveness of our model, which can help to comprehensively understand miRNA's function. © 2024 IEEE.

关键词： Contrastive Learning

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CrossLinkNet: An Explainable and Trustworthy AI Framework for Whole-Slide Images Segmentation

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Computers, Materials & Continua 2024年第6期79卷 4703-4724页

作者： Peng Xiao Qi Zhong Jingxue Chen Dongyuan Wu Zhen Qin Erqiang Zhou Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Faculty of Data Science City University of MacaoMacao999078China

In the intelligent medical diagnosis area,Artificial Intelligence(AI)’s trustworthiness,reliability,and interpretability are critical,especially in cancer *** neural networks,while excellent at processing natural images,often lack interpretability and adaptability when processing high-resolution digital pathological *** limitation is particularly evident in pathological diagnosis,which is the gold standard of cancer diagnosis and relies on a pathologist’s careful examination and analysis of digital pathological slides to identify the features and progression of the ***,the integration of interpretable AI into smart medical diagnosis is not only an inevitable technological trend but also a key to improving diagnostic accuracy and *** this paper,we introduce an innovative Multi-Scale Multi-Branch Feature Encoder(MSBE)and present the design of the CrossLinkNet *** MSBE enhances the network’s capability for feature extraction by allowing the adjustment of hyperparameters to configure the number of branches and *** CrossLinkNet Framework,serving as a versatile image segmentation network architecture,employs cross-layer encoder-decoder connections for multi-level feature fusion,thereby enhancing feature integration and segmentation *** quantitative and qualitative experiments on two datasets demonstrate that CrossLinkNet,equipped with the MSBE encoder,not only achieves accurate segmentation results but is also adaptable to various tumor segmentation tasks and scenarios by replacing different feature ***,CrossLinkNet emphasizes the interpretability of the AI model,a crucial aspect for medical professionals,providing an in-depth understanding of the model’s decisions and thereby enhancing trust and reliability in AI-assisted diagnostics.

关键词： Explainable AI security trustworthy CrossLinkNet whole slide images

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A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning

A New Pipeline for Knowledge Graph Reasoning Enhanced by Lar...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chen, Zhongwu Bai, Long Li, Zixuan Huang, Zhen Jin, Xiaolong Dou, Yong National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761643

Conventional Knowledge Graph Reasoning (KGR) models learn the embeddings of KG components over the structure of KGs, but their performances are limited when the KGs are severely incomplete. Recent LLM-enhanced KGR models input KG structural information into LLMs. However, they require fine-tuning on open-source LLMs and are not applicable to closed-source LLMs. Therefore, in this paper, to leverage the knowledge in LLMs without fine-tuning to assist and enhance conventional KGR models, we propose a new three-stage pipeline, including knowledge alignment, KG reasoning and entity reranking. Specifically, in the alignment stage, we propose three strategies to align the knowledge in LLMs to the KG schema by explicitly associating unconnected nodes with semantic relations. Based on the enriched KGs, we train structure-aware KGR models to integrate aligned knowledge to original knowledge existing in KGs. In the reranking stage, after obtaining the results of KGR models, we rerank the top-scored entities with LLMs to recall correct answers further. Experiments show our pipeline can enhance the KGR performance in both incomplete and general situations. © 2024 Association for Computational Linguistics.

关键词： Semantics

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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Computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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Blockchain-Based Data Acquisition with Privacy Protection in UAV Cluster network

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 879-902页

作者： Lemei Da Hai Liang Yong Ding Yujue Wang Changsong Yang Huiyong Wang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information SecurityGuilin University of Electronic TechnologyGuilin541004China Cyberspace Security Research Center Pengcheng LaboratoryShenzhen518055China School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin541004China

The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information *** to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security *** paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition *** UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative *** successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for *** are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,*** security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution *** also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)*** performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.

关键词： Unmanned aerial vehicle cluster network certificateless signcryption certificateless signature batch verification source authentication data privacy blockchain

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key Management and Authentication Scheme Forward Trusted Pervasive Container computing 2

Key Management and Authentication Scheme Forward Trusted Per...

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2nd International Conference on Big Data and Privacy computing, BDPC 2024

作者： Hei, XinHong Xue, YuZhan Wang, YiChuan Liu, YiFan Peng, Chen Sun, Xiang School of Computer Science and Engineering Xi'an University of Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9798350307597

Cloud computing technology is an internet-based computing model that provides high reliability and security measures, including data backup, secure access control, etc. As Docker containers play a increasingly important role in traditional cloud computing architectures, the communication needs between containers become prominent. However, existing container to container communication mainly relies on network protocols for file transfer, making it difficult to achieve the expected results in cloud cluster security. Therefore, this paper primarily focuses on container based keys and proposes a container identity trusted key management method based on Pervasive container computing architecture. The experimental results demonstrate that the container identity trusted key management method not only meets the security transmission characteristics required in the container cloud architecture, but also adopts a strategy combined with trusted hardware, providing security guarantees. In addition, the method proposed in this article that combines trusted hardware for signature verification can defend against multiple network attacks compared to traditional container architectures without using security policies. © 2024 IEEE.

关键词： Cloud computing architecture

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge computing

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Ming Northeastern University School of Computer Science and Engineering Engineering Research Center of Security Technology of Complex Network System Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

Mobile edge computing (MEC) integrated with network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system information. We first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multi-per-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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ADLT: a novel method for effective detection of adversarial attacks in image retrieval

ADLT: a novel method for effective detection of adversarial ...

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2024 International Conference on Computer Vision and Image Processing, CVIP 2024

作者： Xu, Shiyue Zhang, Yu College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300350 China

ISBN: (纸本)9781510688384

The versatile application of deep learning has made it prone to vulnerabilities, particularly adversarial attacks, which have been increasingly prevalent as its scope expands. Attack methods based on adversarial queries pose significant challenges to image retrieval models, disrupting their functionality. While numerous defense methods have been proposed for adversarial query attacks in image classification, approaches to detecting or defending against such attacks in image retrieval remain limited due to inherent challenges in adapting these methods. Based on these observations, we introduce the Augmentation Detection based on Top-K-List (ADTL) method, the first effective approach for detecting adversarial attacks in image retrieval. ADTL applies image augmentation to a query and determines its adversarial nature by calculating the divergence in the Top-K list before and after augmentation. This paper presents a method that employs suitable image augmentation techniques and K-Reciprocal clustering for pre-classifying images in the gallery to ease divergence calculations, which has been tested on the Oxford5k and Paris6k datasets and has shown remarkable effectiveness with a high detection accuracy of 87.3%. © 2025 SPIE.

关键词： Adversarial perturbation Attack detection Image augmentation Image retrieval

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C3shartMark: A Chart Watermarking Scheme With Consecutive-Encoding and Concurrent-Decoding

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IEEE Transactions on Circuits and Systems for Video technology 2025年第1期35卷 492-507页

作者： Ma, Linfeng Fang, Han Ma, Zehua Jia, Zhaoyang Zhang, Weiming Yu, Nenghai University of Science and Technology of China Anhui Province Key Laboratory of Digital Security The Cas Key Laboratory of Electromagnetic Space Information Hefei230026 China National University of Singapore School of Computing Queenstown Singapore117417 Singapore

Chart images are widely employed as the intuitive form to express information, which renders them highly valuable. Consequently, there is an urgent demand to develop a watermarking algorithm for copyright protection and leakage prevention of chart images. Nevertheless, existing chart watermarking methods fail to thoroughly consider the chart image's special characteristics and simply rely on the previous natural image-based watermarking framework. Compared to natural images, the chart image generally exhibits relatively simple layouts and textures, containing fewer complex texture regions that watermarks are typically embedded in. Therefore, the embedding locations of watermarks for different distortions can be relatively dispersed in natural images, while for chart images, watermark embedding regions under various distortion conditions tend to be relatively concentrated and share more overlaps. Inspired by the above special characteristics of chart images, to sufficiently leverage them and design a better framework, this paper proposes C3hartMark, a chart watermarking scheme with consecutive-encoding and concurrent-decoding. Instead of using the combined noise layer as existing methods to ensure multiple robustness, a novel consecutive training framework is introduced in this paper, which efficiently utilizes the overlapping of embedded watermark features in chart images, and simultaneously, mitigates the poor convergence brought by the combined noise layer. During the extraction stage, multiple concurrent decoders are introduced to extract the potential embedded watermarks for different distortions independently. Moreover, we also incorporate two special noise layers, namely Captioning and Fusion, to address the corresponding realistic distortions in chart images, and an agnostic noise layer to accommodate potential channel transmission distortions unknown during training. Through extensive experiments, we demonstrate that with the better visual quality, C3hartMar

关键词： Image texture

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