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International Joint Conference on Neural networks (IJCNN)

作者： Yanfeng Jiang Ning Sun Zhengxian Lu Shuang Peng Yi Zhang Fei Yang Tao Li College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Zhejiang Lab Hangzhou China Haihe Lab of ITAI Tianjin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Protein language models are currently experiencing a surge in demand owing to their remarkable accuracy in protein structure prediction. Nevertheless, their applications are hindered by the significant computation and memory requirements. The existing optimization strategies primarily focus on computational efficiency while often neglecting memory optimization, thereby restricting their suitability for devices with limited resources. In this paper, we propose MEFold, a novel memory-efficient optimization framework for protein language models that enables efficient inference on resource-constrained devices. MEFold consists of Look-up Table Chunk and Fine-grained Quantization. Look-up Table Chunk reduces the memory of intermediate activations by chunk and avoids the overhead of obtaining the optimal chunk size configuration through pre-computing. For the memory of model parameters, Fine-grained Quantization, delicately controls the scope of quantization to ensure that memory reduction is achieved while preventing declines in accuracy and computational speed. Experimental results show that, compared to the original model, for protein sequences ranging from 74 to 1024 in length, our method significantly reduces the peak memory during inference from 14.7-54.2GB to 6.0-14.4GB, while minimizing the impact on inference latency. On CASP14 and CAMEO datasets, the accuracy loss compared to the original model is below 1%. Moreover, our optimization provides various memory-saving alternatives. Our code is available at https://***/llwx593/MEFold.

关键词： Proteins Quantization (signal) Accuracy Computational modeling Memory management Neural networks Predictive models

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EVPIR: Efficient and Verifiable Privacy-Preserving Image Retrieval in Cloud-assisted Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Li, Mingyue Li, Yuntao Du, Ruizhong Jia, Chunfu Shao, Wei Hebei University School of Cyber Security and Computer Baoding071000 China Key Lab on High Trusted Information Sys tem in Hebei Province Hebei China College of Cyber Sciences Nankai university Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China Jinan250014 China

With the proliferation of mobile devices and the advancement of cloud computing capabilities, cloud-assisted Internet of Things (IoT) attracts increased attention based on its computational and storage advantages. Upon these conveniences, there also raises privacy concerns that numerous solutions have been proposed to solve it. However, existing methods suffer from challenges such as low retrieval accuracy, inefficiency in large-scale image retrieval, and lack of efficient result verification. In this paper, we propose an Efficient Verifiable Privacy-preserving Image Retrieval scheme (EVPIR). Specifically, we design a hierarchical graph index to significantly enhance retrieval efficiency, which organizes image feature vectors into a multi-level structure, establishing connections between neighboring nodes within each layer and creating a highly structured and efficient retrieval framework. During the retrieval process, we employ greedy search algorithm to navigate these connections and identify the closest neighbors across different levels, which make the proposed multi-level approach reduce the search space at each level, achieving faster and more accurate retrieval. Furthermore, we design an efficient dynamic verifiable framework leveraging Chameleon hash functions and BLS signatures where we utilize Chameleon hash nodes based on Merkle Hash Trees (MHT) to enable dynamic updates of the verification tree and employ BLS signatures to construct multiple verification nodes for effectively shortening the verification path. Finally, security analysis shows that EVPIR can defend various threat models and extensive experiments further demonstrates that EVPIR can improve retrieval and verification efficiency. (Figure presented) © 2014 IEEE.

关键词： Differential privacy

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Cerebrovascular segmentation from mesoscopic optical images using Swin Transformer

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Journal of Innovative Optical Health Sciences 2023年第4期16卷 120-133页

作者： Yuxin Li Qianlong Zhang Hang Zhou Junhuai Li Xiangning Li Anan Li Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048P.R.China School of Computer Science Chengdu University of Information Technology Chengdu 610225P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan 430074 P.R.China HUST-Suzhou Institute for Brainsmatics Suzhou 215123 P.R.China

Vascular segmentation is a crucial task in biomedical image processing,which is significant for analyzing and modeling vascular networks under physiological and pathological *** advances in fluorescent labeling and mesoscopic optical techniques,it has become possible to map the whole-mouse-brain vascular networks at capillary ***,segmenting vessels from mesoscopic optical images is a challenging *** problems,such as vascular signal discontinuities,vessel lumens,and background fluorescence signals in mesoscopic optical images,belong to global semantic information during vascular *** vascular segmentation methods based on convolutional neural networks(CNNs)have been limited by their insufficient receptive fields,making it challenging to capture global semantic information of vessels and resulting in inaccurate segmentation ***,we propose SegVesseler,a vascular segmentation method based on Swin *** adopts 3D Swin Transformer blocks to extract global contextual information in 3D *** approach is able to maintain the connectivity and topology of blood vessels during *** evaluated the performance of our method on mouse cerebrovascular datasets generated from three different labeling and imaging *** experimental results demonstrate that the segmentation effect of our method is significantly better than traditional CNNs and achieves state-of-the-art performance.

关键词： Vascular segmentation Swin Transformer mesoscopic optical imaging fMOST

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HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

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IEEE Transactions on computers 2025年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong Xidian University School of Computer Science and Technology China Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China Jiangxi University of Finance and Economics School of Computing Nanchang330013 China Jiangxi University of Finance and Economics Nanchang330013 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. © 1968-2012 IEEE.

关键词： Scalability

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A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection

arXiv

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arXiv 2023年

作者： Li, Kaiyu Cao, Xiangyong Meng, Deyu The School of Computer Science and Technology Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Network Security Xi’an Jiaotong University Xi’an710049 China

Change detection (CD) is a critical task to observe and analyze dynamic processes of land cover. Although numerous deep learning-based CD models have performed excellently, their further performance improvements are constrained by the limited knowledge extracted from the given labelled data. On the other hand, the foundation models that emerged recently contain a huge amount of knowledge by scaling up across data modalities and proxy tasks. In this paper, we propose a Bi-Temporal Adapter network (BAN), which is a universal foundation model-based CD adaptation framework aiming to extract the knowledge of foundation models for CD. The proposed BAN contains three parts, i.e. frozen foundation model (e.g., CLIP), bi-temporal adapter branch (Bi-TAB), and bridging modules between them. Specifically, BAN extracts general features through a frozen foundation model, which are then selected, aligned, and injected into Bi-TAB via the bridging modules. Bi-TAB is designed as a model-agnostic concept to extract task/domain-specific features, which can be either an existing arbitrary CD model or some handcrafted stacked blocks. Beyond current customized models, BAN is the first extensive attempt to adapt the foundation model to the CD task. Experimental results show the effectiveness of our BAN in improving the performance of existing CD methods (e.g., up to 4.08% IoU improvement) with only a few additional learnable parameters. More importantly, these successful practices show us the potential of foundation models for remote sensing CD. The code is available at https://***/likyoo/BAN and will be supported in our Open-CD† Copyright © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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Formal Modeling and Defense Methods for 5G network Endpoint Access Denial of Service Attacks

Formal Modeling and Defense Methods for 5G Network Endpoint ...

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International Conference on networking and network Applications (NaNA)

作者： Tong Zhang Wei Wei Yichuan Wang Xi Deng Lei Zhu Wenjiang Ji School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technolog Xi'an China School of Computer Science Xi'an Shiyou University Xi'an China

5G terminal access denial-of-service attack is one of the more common types of 5G signaling network attacks. It manifests itself as a large number of abnormal access requests occupying core network resources. Normal requests cannot obtain resources, resulting in unstable network services, and users will be unable to connect or drop connections, eventually leading to frequent terminal takeoffs and landings. This paper proposes a formal modeling scheme for a 5G terminal access denial-of-service attack. The attack principle is analyzed, a defense scheme is designed and modeled, and analyzed, and then quantitative experiments are conducted, and the experimental results show the effectiveness of the defense scheme.

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Software Module Clustering Using the Hierarchical Clustering Combination Method 7

Software Module Clustering Using the Hierarchical Clustering...

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7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Xia, Hong Zhang, Yongkang Chen, Yanping Zhang, Hengshan Wang, Zhongmin Wang, Fengwei School of Computer Science and Technology Xi'An Univ. of P. and Telecom. Shaanxi Key Lab. of Network Data Analysis and Intelligent Processing Xi'an China Xi'An University of Posts and Telecommunications School of Computer Science and Technology Xi'an China ZTECorporation Shenzhen China

ISBN: (纸本)9781665487115

The scale of software applications has increased dramatically. Hierarchical clustering is a good method for modular recovery of software architecture. Because the different evaluation criteria of software types and clustering results, a single hierarchical clustering algorithm cannot integrate the number of clusters, arbitrary decision-making, recovery quality and other indicators on different evaluation standards, there is no comprehensive clustering algorithm selection method. We propose a hierarchical clustering combination algorithm and use principal component analysis to combine the results of multiple hierarchical clusters, the combined result retain the basic information of each clustering algorithm as much as possible. The experimental results show that compared with the single software hierarchical clustering method and other combined methods, the result of our method is closer to expert decomposition, and it has good performance on a variety of indicators. © 2022 IEEE.

关键词： Cloud computing Software architecture Clustering methods Software algorithms Decision making Clustering algorithms Big Data

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Enhancing Underwater Imaging with 4-D Light Fields: Dataset and Method

arXiv

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arXiv 2024年

作者： Lin, Yuji Lyu, Xianqiang Hou, Junhui Zhao, Qian Meng, Deyu The Department of Computer Science City University of Hong Kong Hong Kong The School of Mathematics and Statistics Xi'an Jiaotong University Shaanxi Xi'An710049 China The School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi 710049 China The School of Mathematics and Statistics The Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'An710049 China Guangdong Guangzhou510555 China

In this paper, we delve into the realm of 4-D light fields (LFs) to enhance underwater imaging plagued by light absorption, scattering, and other challenges. Contrasting with conventional 2-D RGB imaging, 4-D LF imaging excels in capturing scenes from multiple perspectives, thereby indirectly embedding geometric information. This intrinsic property is anticipated to effectively address the challenges associated with underwater imaging. By leveraging both explicit and implicit depth cues present in 4-D LF images, we propose a progressive, mutually reinforcing framework for underwater 4-D LF image enhancement and depth estimation. Specifically, our framework explicitly utilizes estimated depth information alongside implicit depth-related dynamic convolutional kernels to modulate output features. The entire framework decomposes this complex task, iteratively optimizing the enhanced image and depth information to progressively achieve optimal enhancement results. More importantly, we construct the first 4-D LF-based underwater image dataset for quantitative evaluation and supervised training of learning-based methods, comprising 75 underwater scenes and 3675 high-resolution 2K pairs. To craft vibrant and varied underwater scenes, we build underwater environments with various objects and adopt several types of degradation. Through extensive experimentation, we showcase the potential and superiority of 4-D LF-based underwater imaging vis-a-vis traditional 2-D RGB-based approaches. Moreover, our method effectively corrects color bias and achieves state-of-the-art performance. The dataset and code will be publicly available at https://***/linlos1234/LFUIE. Copyright © 2024, The Authors. All rights reserved.

关键词： Underwater imaging

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ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers

arXiv

引用

arXiv 2024年

作者： Jiang, Yanfeng Sun, Ning Xie, Xueshuo Yang, Fei Li, Tao College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Zhejiang Lab Zhejiang Hangzhou China Haihe Lab of ITAI Tianjin China

Vision Transformers (ViTs) have exhibited exceptional performance across diverse computer vision tasks, while their substantial parameter size incurs significantly increased memory and computational demands, impeding effective inference on resource-constrained devices. Quantization has emerged as a promising solution to mitigate these challenges, yet existing methods still suffer from significant accuracy loss at low-bit. We attribute this issue to the distinctive distributions of post-LayerNorm and post-GELU activations within ViTs, rendering conventional hardware-friendly quantizers ineffective, particularly in low-bit scenarios. To address this issue, we propose a novel framework called Activation-Distribution-Friendly post-training Quantization for Vision Transformers, ADFQ-ViT. Concretely, we introduce the Per-Patch Outlier-aware Quantizer to tackle irregular outliers in post-LayerNorm activations. This quantizer refines the granularity of the uniform quantizer to a per-patch level while retaining a minimal subset of values exceeding a threshold at full-precision. To handle the non-uniform distributions of post-GELU activations between positive and negative regions, we design the Shift-Log2 Quantizer, which shifts all elements to the positive region and then applies log2 quantization. Moreover, we present the Attention-score enhanced Module-wise Optimization which adjusts the parameters of each quantizer by reconstructing errors to further mitigate quantization error. Extensive experiments demonstrate ADFQ-ViT provides significant improvements over various baselines in image classification, object detection, and instance segmentation tasks at 4-bit. Specifically, when quantizing the ViT-B model to 4-bit, we achieve a 10.23% improvement in Top-1 accuracy on the ImageNet dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Distribution transformers

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Adfq-Vit: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers

SSRN

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SSRN 2024年

关键词： Chemical activation

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