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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoli Lin Yaoyu Chen Wenli Yang HaiPing Yu Zimo Wu Xiaolong Zhang Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China School of Information Wuhan Vocational College Professional Committee of Graphics and Big Data China Graphics Society Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-target interaction (DTI) prediction has an important role in drug discovery, significantly expediting the drug design and development process. This paper proposes a new model, ERW_BiAN, that seamlessly integrates graph embedding representations with deep learning network. Specifically, we utilize an improved graph embedding model, ERW, to generate comprehensive feature vectors for each node within the knowledge graph. Subsequently, these feature vectors are fed into the BiAN model, which leverages an attention mechanism to assign precise weights to sequences. Experimental results demonstrate the superior accuracy and predictive prowess of ERW_BiAN in DTI prediction tasks. Furthermore, the case about COVID-19 shows that ERW_BiAN has the better application potential for predicting drug-target interactions.

关键词： Deep learning Drugs COVID-19 Attention mechanisms Biological system modeling Knowledge graphs Predictive models Vectors Drug discovery Diffusion tensor imaging

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Space-Oblivious Compression and Wear Leveling for Non-Volatile Main Memories 36

Space-Oblivious Compression and Wear Leveling for Non-Volati...

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36th International Conference on Massive Storage systems and technology, MSST 2020

作者： Liu, Haikun Ye, Yuanyuan Liao, Xiaofei Jin, Hai Zhang, Yu Jiang, Wenbin He, Bingsheng National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computing National University of Singapore Singapore

Emerging Non-Volatile Main Memory (NVMM) technologies generally feature high density, low energy consumption and cost per bit compared to DRAM. However, the limited write endurance of NVMM poses a significant challenge of using NVMM as an substitute for DRAM. This paper proposes a space-oblivious compression and wear-leveling based memory architecture to improve the write endurance of NVMM. As memory blocks of many applications usually contain a large amount of zero bytes and frequent values, we propose a nonuniform compression encoding scheme that integrates Zero Deduplication with Frequent Value Compression (called ZD-FVC) to reduce bit-writes on NVMM. Moreover, we leverage the memory space available through compression to achieve an intra-block wear leveling policy. It rotates the writing positions of a compressed data block within the data’s initial memory space, and thus enhances write endurance by balancing the bit-writes per NVMM cell. ZD-FVC can be integrated into the NVMM module and implemented entirely by hardware, without any intervention of Operating systems. We implement ZD-FVC in Gem5 and NVMain simulators, and evaluate it with several programs from SPEC CPU2006. Experimental results shows that ZD-FVC is much better than several state-of-the-art approaches. Particularly, ZD-FVC can improve data compression ratio by 55% compared to Frequent Value Compression. Compared with the Data Comparison Write, ZD-FVC is able to significantly improve the lifetime of NVMM by 3.3X on average, and also reduces NVMM write latency by 31% and energy consumption by 19% on average. © 2020 36th International Conference on Massive Storage systems and technology, MSST 2020. All Rights Reserved.

关键词： Dynamic random access storage

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Automatic Generation of Specification from Natural Language Based on Temporal Logic 10th

Automatic Generation of Specification from Natural Language ...

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International Workshop on Structured Object-Oriented Formal Language and Method, SOFL+MSVL 2020

作者： Wang, Xiaobing Li, Ge Li, Chunyi Zhao, Liang Shu, Xinfeng Institute of Computing Theory and Technology and ISN Laboratory Xidian University Xi’an710071 China School of Computer Science and Technology Xi’an University of Posts and Communications Xi’an710061 China

ISBN: (纸本)9783030774738

Formal specifications are usually used for describing safety system properties and play an important role in formal verification. In order to improve the effectiveness of formal specification generation and formal verification, this paper proposes a framework for automatic conversion from natural language describing properties to temporal logic formulas, and implements a tool PPTLGenerator (Propositional Projection Temporal Logic formula Generator) for the conversion. First, PPTLGenerator is developed based on JavaCC for automatic conversion from natural language to PPTL. Then, the satisfiability of a PPTL formula generated by PPTLGenerator is checked by a tool PPTLSAT. Finally, to illustrate the principle and effectiveness of the framework, a case study of the safety property of Level 3 autonomous car is provided. © 2021, Springer Nature Switzerland AG.

关键词： Formal specification

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Free-space beam shaping and steering based on a silicon optical phased array

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Photonics Research 2023年第12期11卷 2093-2099页

作者： WENBO REN QINGQING LIANG JIJUN FENG HAIPENG LIU JIANHUA HU SHUO YUAN XINCHENG XIA WEI JIANG QIWEN ZHAN HEPING ZENG Shanghai Key Laboratory of Modern Optical System Engineering Research Center of Optical Instrument and System(Ministry of Education)School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal UniversityChongqing 401120China State Key Laboratory of Precision Spectroscopy East China Normal UniversityShanghai 200241China

In this study,we present a method for free-space beam shaping and steering based on a silicon optical phased array,which addresses the theoretical limitation of traditional bulk *** theoretically analyze the beam propagation properties with changes in the applied *** beam profiles can be shaped by varying the phase combination,while a high-order quasi-Bessel beam can be generated with a cubic change to the phase *** simulated results are validated further experimentally,and they match one another *** steering can be achieved with a field of view as large as 140°,which has potential benefits for practical *** presented method is expected to have broad application prospects for optical communications,free-space optical interconnects,and light detection and ranging.

关键词： optical beam optics

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Cone-Beam Computed Tomography Limited-Angle Image Reconstruction Using Planning CT-Guided Deep Diffusion Model

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wu, Zhan Yang, Yang Hu, Dianlin Lyu, Tianling Li, Yinsheng Tang, Hao Shu, Huazhong Xi, Yan Zhang, Yi Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Zhejiang University of Technology Academy for Advanced Interdisciplinary Science and Technology Hangzhou China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System China Capital Medical University Beijing Jishuitan Hospital China First-Imaging Tech Shanghai China Sichuan University School of Cyber Science and Engineering Chengdu China Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Laboratory of Image Science and Technology the School of Computer Science and Engineering Nanjing210096 China

For existing image-guided radiation therapy systems (IGRT), the limited angular range of the cone-beam computed tomography (CBCT) scanning improves the compactness of the system and compatibility between CBCT and linear accelerator configurations. However, the limited-angle CBCT reconstruction under incomplete projection data causes severe artifacts in the reconstructed images, further having a negative impact on the registration accuracy in IGRT. In this paper, we propose a planning CT-guided deep diffusion model, termed LARGE-DiffNet, to improve imaging quality and registration accuracy for limited-angle CBCT image reconstruction. In stark contrast to existing approaches leveraging hand-crafted regularization, such as local smoothness and/or sparsity of the image, LARGE-DiffNet learns the distribution-level prior knowledge to describe the features of ideal CBCT images via a planning CT-guided diffusion model. The proposed LARGE-DiffNet is evaluated on the clinical CBCT dataset, and extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods. The proposed LARGE-DiffNet technique has great potential in IGRT. © 1963-2012 IEEE.

关键词： Image reconstruction Computed tomography Planning Radiation therapy Diffusion models Biomedical imaging Image restoration Training Reconstruction algorithms Image quality

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TDiffSal: Text-Guided Diffusion Saliency Prediction Model for Images 27th

TDiffSal: Text-Guided Diffusion Saliency Prediction Model fo...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Nana Xiong, Min Zhu, Dandan Zhu, Kun Zhai, Guangtao School of Computer Science and Technology Donghua University No. 2999 Renmin North Road Songjiang District Shanghai201620 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China Institute of AI Education Shanghai East China Normal University No. 3663 Zhongshan North Road Putuo District Shanghai200333 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University No. 4800 Cao’an Highway Jiading District Shanghai201804 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9783031781858

Existing visual saliency prediction methods mainly focus on single-modal visual saliency prediction, while ignoring the significant impact of text on visual saliency. To more comprehensively explore the influence of text on human attention in images, we propose a text-guided diffusion saliency prediction model, named TDiffSal. In specific, recent studies on stable diffusion models have shown promising performance in unifying tasks due to their inherent generalization ability. Inspired by this, a novel diffusion model for generalized visual-text saliency prediction is proposed, which formulates the prediction issue as a conditional generative task of the saliency map by employing input visual and text as the conditions. Meanwhile, we introduce a multi-head fusion module to effectively integrate text features and image features, which can efficiently guide the image denoising process and progressively refine the generated saliency map to make it semantically relevant to the text. Additionally, we employ an efficient pre-training strategy to enhance the robustness and generalization of the proposed model. We conduct extensive experiments on benchmark datasets to demonstrate its superior performance compared to other state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Conditional Privacy-Preserving Transaction for the Unspent Transaction Output-Based Multi-Chain Blockchain system

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IEEE Transactions on computers 2025年第7期74卷 2473-2486页

作者： Cui, Jie Zhuang, Wenting Zhong, Hong Zhang, Qingyang Wang, Fengqun He, Debiao Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230039 China Anhui University Anhui Engineering Laboratory of IoT Security Technologies Hefei230039 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China MatrixElements Technologies Shanghai Key Laboratory of Privacy Preserving Computation Shanghai201204 China

The anonymity of blockchain may be exploited by criminals for illegal fund transfers, thus a conditional privacy-preserving scheme is important for blockchain regulation. Currently, sharding technology under a multi-chain architecture is used to improve blockchain scalability. However, current conditional privacy-preserving schemes cannot work on this architecture. To protect the privacy of the transaction, we present a conditional privacy-preserving transaction scheme (MC-CPPT) for multi-chain blockchain system. In this system, we proposed a zero-knowledge proof based anonymous transaction, in terms of the identities of transaction participants and amounts, which also enables the unlinkability of transactions and indistinguishability between cross-chain and intra-chain transactions in multi-chain blockchain system. In addition, a multi-node regulatory agency is introduced to control the transaction amount and frequency in the system without a single point of failure. Moreover, an ECC-based encryption scheme is proposed to achieve the traceability of suspicious transactions. A security model is defined and the security of MC-CPPT is demonstrated to meet the expected security goals. Evaluating the prototype revealed acceptable performance and additional security features. © 1968-2012 IEEE.

关键词： Anonymity

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Learning Adaptive and View-Invariant Vision Transformer with Multi-Teacher Knowledge Distillation for Real-Time UAV Tracking

arXiv

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arXiv 2024年

作者： Wu, You Li, Yongxin Liu, Mengyuan Wang, Xucheng Yang, Xiangyang Ye, Hengzhou Zeng, Dan Zhao, Qijun Li, Shuiwang College of Computer Science and Engineering Guilin University of Technology Guilin541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006 China School of Computer Science Fudan University Shanghai200082 China School of Artificial Intelligence Sun Yat-sen University Zhuhai510275 China College of Computer Science Sichuan University Chengdu610017 China

Visual tracking has made significant strides due to the adoption of transformer-based models. Most state-of-the-art trackers struggle to meet real-time processing demands on mobile platforms with constrained computing resources, particularly for real-time unmanned aerial vehicle (UAV) tracking. To achieve a better balance between performance and efficiency, we introduce AVTrack, an adaptive computation framework designed to selectively activate transformer blocks for real-time UAV tracking. The proposed Activation Module (AM) dynamically optimizes the ViT architecture by selectively engaging relevant components, thereby enhancing inference efficiency without significant compromise to tracking performance. Furthermore, to tackle the challenges posed by extreme changes in viewing angles often encountered in UAV tracking, the proposed method enhances ViTs’ effectiveness by learning view-invariant representations through mutual information (MI) maximization. Two effective design principles are proposed in the AVTrack. Building on it, we propose an improved tracker, dubbed AVTrack-MD, which introduces the novel MI maximization-based multi-teacher knowledge distillation (MD) framework. It harnesses the benefits of multiple teachers, specifically the off-the-shelf tracking models from the AVTrack, by integrating and refining their outputs, thereby guiding the learning process of the compact student network. Specifically, we maximize the MI between the softened feature representations from the multi-teacher models and the student model, leading to improved generalization and performance of the student model, particularly in noisy conditions. Extensive experiments on multiple UAV tracking benchmarks demonstrate that AVTrack-MD not only achieves performance comparable to the AVTrack baseline but also reduces model complexity, resulting in a significant 17% increase in average tracking speed. Codes is be available at https://***/wuyou3474/AVTrack. Copyright © 2024, The

关键词： Teaching

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DESIGN AND DEVELOPMENT OF MORPHOLOGICAL ANALYZER FOR TIGRIGNA VERBS USING HYBRID APPROACH

Research Square

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Research Square 2023年

作者： Gebremeskel, Hagos Gebremedhin Chong, Feng Heyan, Huang Beijing Engineering Research Center of High Volume Language Information Processing Cloud Computing Applications Laboratory School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science Mekelle University Tigray Mekelle231 Ethiopia

Morphological analyzer is the base for various high-level NLP applications such as information retrieval, spell checking, grammar checking, machine translation, speech recognition, POS tagging and automatic sentence construction. This paper is carefully designed for design and analysis of morphological analyzer Tigrigna verbs using hybrid of memory learning and rules based approaches. The experiment have conducted using Python 3 where TiMBL algorithms IB2 and TRIBL2, and Finite State Transducer rules are used. The performance of the system has been evaluated using 10 fold cross validation technique. Testing was conducted using optimized parameter settings for regular verbs and linguistic rules of the Tigrigna language allomorph and phonology for the irregular verbs. The accuracy of the memory based approach with optimized parameters of TiMBL algorithm IB2 and TRIBL2 was 93.24% and 92.31%, respectively. Finally, the hybrid approach had an actual performance of 95.6% using linguistic rules for handling irregular and copula verbs. © 2023, CC BY.

关键词： Machine learning

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FunctionFlow:coordinating parallel tasks

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Frontiers of computer science 2019年第1期13卷 73-85页

作者： Xuepeng FAN Xiaofei LIAO Hai JIN Services Computing Technology and System Lab (SCTS)&Cluster and Grid Computing Lab (CGCL) School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

With the growing allel programming,nowadays popularity of task-based partask-parallel programming libraries and languages are still with limited support for coordinating parallel *** limitation forces programmers to use additional independent components to coordinate the parallel tasks -the components can be third-party libraries or additional components in the same programming library or ***,mixing tasks and coordination components increase the difficulty of task-based programming,and blind schedulers for understanding tasks'dependencies. In this paper,we propose a task-based parallel programming library,Function Flow,which coordinates tasks in the purpose of avoiding additional independent coordination ***,we use dependency expression to represent ubiquitous tasks'*** key idea behind dependency expression is to use && for both task's termination and Ⅱ for any task termination,along with the combination of dependency ***,as runtime support,we use a lightweight representation for dependency expression. Also,we use suspended-task queue to schedule tasks that still have prerequisites to run. Finally,we demonstrate Function Flow's effectiveness in two aspects,case study about implementing popular parallel patterns with FunctionFIow,and performance comparision with state-of-the-art practice,*** demonstration shows that FunctionFIow can generally coordinate parallel tasks without involving additional components,along with comparable performance with TBB.

关键词： task parallel programming tasks dependency FunctionFlow coordination patterns

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