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DSANet: dilated spatial attention network for the detection of text, non-text and touching components in unconstrained handwritten documents

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Neural Computing and Applications 2024年第27期36卷 16959-16976页

作者： Bhowmik, Showmik Risat, Shaikh Sarkar, Bhaskar Research Lab Department of Computer Science and Engineering Ghani Khan Choudhury Institute of Engineering and Technology Malda India

Handwritten documents generated in our day-to-day office work, class room and other sectors of society carry vital information. Automatic processing of these documents is a pipeline of many challenging steps. The very first and crucial step is to identify text separately from the non-text as any OCR (optical character recognition) engine can only process the textual content. Separating text from non-text in unconstrained handwritten documents is a very complex task. In addition to other challenges, touching component is one of the major issues for text non-text separation in unconstrained handwritten documents. Detection of text, non-text along with touching component in such documents is an unexplored area of research. To address this issue, in this work, we develop a dilated spatial attention-based network for text, non-text and touching component detection. Additionally, in this work, we also prepare a realistic dataset for the said task. In the proposed dataset, the present model obtains overall accuracy of 87.85%. The performance of the present model is compared with seven feature-engineering-based methods and six deep learning-based methods. In most of the cases, the proposed model outperforms the comparing methods in the proposed dataset. The codes of our method are available here https://***/Showmik-Bhowmik/DSANet-Dilated-Spatial-Attention-.git. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep learning

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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Science China(Information Sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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A deep learning framework for enhanced mass spectrometry data analysis and biomarker screening

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computer Methods in Biomechanics and Biomedical engineering 2025年 1-13页

作者： Zhang, Shuyu Li, Zhiyu Peng, Weili Chen, Yuanyuan Wu, Yao Machine Intelligence Lab College of Computer Science Sichuan University Chengdu China National Engineering Research Center for Biomaterials School of Biomedical Engineering Sichuan University Chengdu China

Mass spectrometry (MS) serves as a powerful analytical technique in metabolomics. Traditional MS analysis workflows are heavily reliant on operator experience and are prone to be influenced by complex, high-dimensional MS data. This study introduces a deep learning framework designed to enhance the classification of complex MS data and facilitate biomarker screening. The proposed framework integrates preprocessing, classification, and biomarker selection, addressing challenges in high-dimensional MS analysis. Experimental results demonstrate significant improvements in classification tasks compared to other machine learning approaches. Additionally, the proposed peak-preprocessing module is validated for its potential in biomarker screening, identifying potential biomarkers from high-dimensional data. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Liver disease deep learning mass spectrometry pre-processing

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Precise control of page cache for containers

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Frontiers of computer Science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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Resource investment for DDoS attack resistant SDN: a practical assessment

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Science China(Information Sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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Partial Sum Attack on Round-Reduced AES Utilizing Parallel Computation Approaches

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SN computer Science 2025年第3期6卷 1-21页

作者： Pal, Debranjan Gupta, Ankit Das, Abhijit Chowdhury, Dipanwita Roy Crypto Research Lab Department of Computer Science and Engineering IIT Kharagpur West Bengal Kharagpur 721301 India

The Advanced Encryption Standard (AES) is a widely recognized, extensively utilized, and thoroughly studied industry-standard symmetric key block cipher. Consequently, cryptanalysis of AES is a highly relevant and significant task for cryptography researchers. In the field of cryptanalysis, the Square attack is a powerful key recovery technique used against symmetric ciphers. This study aims to provide two efficient implementations of the partial sum attack (PSA), a variant of the square attack, on reduced rounds of AES. The goal is to demonstrate the effectiveness of these implementations in terms of reduced time complexity for full key recovery. We demonstrate two efficient implementations of PSA on reduced rounds of AES. The first implementation is a GPU-based attack applied to five and six rounds of AES, achieving full key recovery in 0.8s and 7.58 days, respectively. The second implementation is cluster-based, applied to six rounds of AES, with an elapsed time of 2.5 days for full key recovery. Both implementations leverage parallelization to enhance performance, showing a significant reduction in attack time proportional to the number of working threads. The findings confirm that PSA effectively reduces the time complexity for full key recovery in reduced rounds of AES, demonstrating the practicality and efficiency of these implementations. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： AES Chosen plaintext Cluster GPU Parallel computing Partial sum attack Square attack

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NEXUS: A 28nm 3.3pJ/SOP 16-Core Spiking Neural Network with a Diamond Topology for Real-Time Data Processing

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IEEE Transactions on Biomedical Circuits and Systems 2024年第3期19卷 1-13页

作者： Sadeghi, Maryam Rezaeiyan, Yasser Khatiboun, Dario Fernandez Eissa, Sherif Corradi, Federico Augustine, Charles Moradi, Farshad Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands Circuit Research Lab Intel Hillsboro OR USA

The realization of brain-scale spiking neural networks (SNNs) is impeded by power constraints and low integration density. To address these challenges, multi-core SNNs are utilized to emulate numerous neurons with high energy efficiency, where spike packets are routed through a network-on-chip (NoC). However, the information can be lost in the NoC under high spike traffic conditions, leading to performance degradation. This work presents NEXUS, a 16-core SNN with a diamond-shaped NoC topology fabricated in 28-nm CMOS technology. It integrates 4096 leaky integrate-and-fire (LIF) neurons with 1M 4-bit synaptic weights, occupying an area of 2.16 mm2. The proposed NoC architecture is scalable to any network size, ensuring no data loss due to contending packets with a maximum routing latency of 5.1μs for 16 cores. The proposed congestion management method eliminates the need for FIFO in routers, resulting in a compact router footprint of 0.001 mm2. The proposed neurosynaptic core allows for increasing the processing speed by up to 8.5× depending on input sparsity. The SNN achieves a peak throughput of 4.7 GSOP/s at 0.9 V, consuming a minimum energy per synaptic operation (SOP) of 3.3 pJ at 0.55 V. A 4-layer feed-forward network is mapped onto the chip, classifying MNIST digits with 92.3% accuracy at 8.4Kclassification/ s and consuming 2.7-μJ/classification. Additionally, an audio recognition task mapped onto the chip achieves 87.4% accuracy at 215-μJ/classification. IEEE

关键词： Spatio-temporal data

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A hybrid memory architecture supporting fine-grained data migration

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Frontiers of computer Science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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Anti-saturation fault-tolerant adaptive torsional vibration control with fixed-time prescribed performance for rolling mill main drive system

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Journal of Iron and Steel research International 2024年第3期31卷 660-669页

作者： Shuang Liu Chen Du Cong Zhang Zhen-hua Bai Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao 066004HebeiChina National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Yanshan UniversityQinhuangdao 066004HebeiChina

An anti-saturation fault-tolerant adaptive torsional vibration control method with fixed-time prescribed performance for the rolling mill main drive system(RMMDS)was investigated,which is affected by control input saturation,actuator faults,sensor measurement errors,and parameter ***,we gave a continuously differentiable saturation function to approximate the control input saturation characteristic of the RMMDS,translating the saturation characteristic into the matched uncertainty and unknown time-varying gain in the ***,an RMMDS mathematical model with unmatched uncertainty and unknown time-varying gain was developed,taking into account the presence of control input saturation,actuator faults,sensor measurement errors,and parameter *** on the established mathematical model,an error transformation model of the roll speed tracking was constructed by the equivalent error transformation *** to the error transformation model,a barrier Lyapunov function and a novel adaptive controller were studied to ensure that the roll speed tracking error always evolves inside a fixed-time asymmetric ***,numerical simulations were performed in Matlab/Simulink to verify the effectiveness and superiority of the proposed control method in suppressing the RMMDS torsional vibration.

关键词： Fixed-time prescribed performance Control input saturation Actuator fault Unknown time-varying gain Adaptive control Asymmetric constraint

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Adaptive Water Quality Potability Prediction and Analysis Through GEM Continual Learning Algorithm for Sustainable Resource Management 10

Adaptive Water Quality Potability Prediction and Analysis Th...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Jeremy David, C. Shibin, D. Karunya Institute of Technology and Sciences Computer Security Research Lab Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

Ensuring the safety of drinking water on a worldwide scale is imperative, and accurate estimation of water quality is crucial for both environmental and public health. This study leverages machine learning, evolving a novel approach with the GEM Continual Learning Algorithm to enhance sustainable resource management through water quality prediction. The research systematically evaluates GEM's effectiveness in forecasting water quality features, comparing it with diverse contemporary machine learning models like Logistic Regression, Ridge, KNeighbors Classifier, and more. Results demonstrate GEM's superiority, highlighting its adaptability and efficiency in predicting water quality attributes. Insights for customized applications are provided by discussing model selection choices that address goals and limits. Future work aims to refine feature engineering, explore ensemble methods, and address data quality concerns within the GEM framework. Conclusively, the GEM Continual Learning Algorithm emerges as a robust solution for adaptive water quality prediction, promising continuous monitoring, early detection, and compliance with water quality regulations. Its deployment holds potential for ensuring safer water supplies, contributing to sustainable water management practices worldwide. © 2024 IEEE.

关键词： Resource allocation

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