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Chinese Control Conference (CCC)

作者： Lili Pan Bing Hu Zhe Sun Lei Xu Guangfu Gong Xiangpeng Xie Zhixin Sun Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology) Nanjing University of Posts and Telecommunications Nanjing P. R. China Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing P. R. China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing P. R. China Anhui Yougu Express Intelligent Technology Co. LTD Nanling Anhui P. R. China

The problem of vehicle routing planning (VRP) has far-reaching influence, and is widely used in vehicle scheduling, industrial production, transportation, logistics and distribution. The study of VRP and its associated derivations has grown significantly in recent years. So, starting with the fundamental VRP, this paper categorizes the VRP based on its traits and real-world applications. It then in-depth analyses the capacity constrained VRP, time window VRP, batched delivery VRP, dynamic VRP, and stochastic demand VRP, and provides an update on each type of VRP's research. Additionally, a summary of the VRP problem-solving methods is provided. The study of evolutionary algorithms is the main topic of this paper, and the fundamental solution method is provided in accordance with the development of the algorithms.

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NVMM-oriented hierarchical persistent client caching for lustre

NVMM-oriented hierarchical persistent client caching for lus...

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作者： Cheng, Wen Li, Chunyan Zeng, Lingfang Qian, Yingjin Li, Xi Brinkmann, André Huazhong University of Science and Technology Wuhan National Lab for Optoelectronics Luoyu Road 1037 Wuhan430074 China Data Direct Networks No. 99 Danba Road Shanghai200062 China Johannes Gutenberg-Universität Mainz Zentrum für Datenverarbeitung Saarstr. 21 Mainz55122 Germany Zhejiang Lab Research Center for Intelligent Computing System Wenyi West Road 1818 Hangzhou Zhejiang311121 China

In high-performance computing (HPC), data and metadata are stored on special server nodes and client applications access the servers' data and metadata through a network, which induces network latencies and resource contention. These server nodes are typically equipped with (slow) magnetic disks, while the client nodes store temporary data on fast SSDs or even on non-volatile main memory (NVMM). Therefore, the full potential of parallel file systems can only be reached if fast client side storage devices are included into the overall storage architecture. In this article, we propose an NVMM-based hierarchical persistent client cache for the Lustre file system (NVMM-LPCC for short). NVMM-LPCC implements two caching modes: a read and write mode (RW-NVMM-LPCC for short) and a read only mode (RO-NVMM-LPCC for short). NVMM-LPCC integrates with the Lustre Hierarchical Storage Management (HSM) solution and the Lustre layout lock mechanism to provide consistent persistent caching services for I/O applications running on client nodes, meanwhile maintaining a global unified namespace of the entire Lustre file system. The evaluation results presented in this article show that NVMM-LPCC can increase the average read throughput by up to 35.80 times and the average write throughput by up to 9.83 times compared with the native Lustre system, while providing excellent scalability. © 2021 Association for computing Machinery. All rights reserved.

关键词： Virtual storage

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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µVulDeePecker: A deep learning-based system for multiclass vulnerability detection

arXiv

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arXiv 2020年

作者： Zou, Deqing Wang, Sujuan Xu, Shouhuai Li, Zhen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed µVulDeePecker. The key insight underlying µVulDeePecker is the concept of code attention, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of µVulDeePecker. Experimental results show that µVulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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LCCG: A Locality-Centric Hardware Accelerator for High Throughput of Concurrent Graph Processing

LCCG: A Locality-Centric Hardware Accelerator for High Throu...

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Supercomputing Conference

作者： Jin Zhao Yu Zhang Xiaofei Liao Liaang He Binzsheng He Haikun Liu Hai Jin Huazhong University of Science and Technology China 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 Wuhan China University of Warwick United Kingdom National University of Singapore Singapore

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

In modern data centers, massive concurrent graph processing jobs are being processed on large graphs. However, existing hardware/-software solutions suffer from irregular graph traversal and intense resource contention. In this paper, we propose LCCG, a Locality-entric programmable accelerator that augments the many-core processor for achieving higher throughput of Concurrent Graph processing jobs. Specifically, we develop a novel topology-aware execution approach into the accelerator design to regularize the graph traversals for multiple jobs on-the-fly according to the graph topology, which is able to fully consolidate the graph data accesses from concurrent jobs. By reusing the same graph data among more jobs and coalescing the accesses of the vertices' states for these jobs, LCCG can improve the core utilization. We conduct extensive experiments on a simulated 64-core processor. The results show that LCCG improves the throughput of the cutting-edge software system by 11.3~23.9 times with only 0.5% additional area cost. More-over, LCCG gains the speedups of 4.7~10.3, 5.5~13.2, and 3.8~8.4 times over state-of-the-art hardware graph processing accelerators (namely, HATS, Minnow, and PHI, respectively).

关键词： data centers Costs High performance computing Throughput Software systems Topology Hardware acceleration

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data-Driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

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data Driven Control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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Investigating 16 Open Clusters in the Kepler/K2 - Gaia DR3 field. I. Membership, Binary, and Rotation

arXiv

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arXiv 2023年

作者： Long, Liu Bi, Shaolan Zhang, Jinghua Zhang, Xianfei Zhang, Liyun Ge, Zhishuai Li, Tanda Chen, Xunzhou Li, YaGuang Ye, LiFei Sun, TianCheng Zhou, JianZhao Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China South-Western Institute for Astronomy Research Yunnan University Chenggong District Kunming650500 China College of Physics Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China Beijing Planetarium Beijing Academy of Science and Technology Beijing100044 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China School of Physics University of Sydney NSW2006 Australia Stellar Astrophysics Centre Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus CDK-8000 Denmark

Using data from the Gaia data Release 3 (Gaia DR3) and Kepler/K2, we present a catalog of 16 open clusters with ages ranging from 4 to 4000 Myr, which provides detailed information on membership, binary systems, and rotation. We assess the memberships in 5D phase space, and estimate the basic parameters of each cluster. Among the 20,160 members, there are 4,381 stars identified as binary candidates and 49 stars as blue straggler stars. The fraction of binaries vary in each cluster, and the range between 9% to 44%. We obtain the rotation periods of 5,467 members, of which 4,304 are determined in this work. To establish a benchmark for the rotation-age-color relation, we construct color-period diagrams. We find that the rotational features of binaries are similar to that of single stars, while features for binaries are more scattered in the rotation period. Moreover, the morphology of the color-period relationship is already established for Upper Scorpius at the age of 19 Myr, and some stars of varying spectral types (i.e. FG-, K-, and M-type) show different spin-down rates after the age of ∼ 110 Myr. By incorporating the effects of stalled spin-down into our analysis, we develop an empirical rotation-age-color relation, which is valid with ages between 700 - 4000 Myr and colors corresponding to a range of 0.5 BP − GRP)0 Copyright © 2023, The Authors. All rights reserved.

关键词： Color

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Fast Subspace Clustering Based on the Kronecker Product

Fast Subspace Clustering Based on the Kronecker Product

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International Conference on Pattern Recognition

作者： Lei Zhou Xiao Bai Liang Zhang Jun Zhou Edwin Hancock School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China School of Information and Communication Technology Griffith University Nathan Australia University of York York U.K.

Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension. Spectral clustering, as one of the main approaches to subspace clustering, often takes on a sparse representation or a low-rank representation to learn a block diagonal self-representation matrix for subspace generation. However, existing methods require solving a large scale convex optimization problem with a large set of data, with computational complexity reaches O(N³) for N data points. Therefore, the efficiency and scalability of traditional spectral clustering methods can not be guaranteed for large scale datasets. In this paper, we propose a subspace clustering model based on the Kronecker product. Due to the property that the Kronecker product of a block diagonal matrix with any other matrix is still a block diagonal matrix, we can efficiently learn the representation matrix which is formed by the Kronecker product of k smaller matrices. By doing so, our model significantly reduces the computational complexity to O(kN³/^k). Furthermore, our model is general in nature, and can be adapted to different regularization based subspace clustering methods. Experimental results on two public datasets show that our model significantly improves the efficiency compared with several state-of-the-art methods. Moreover, we have conducted experiments on synthetic data to verify the scalability of our model for large scale datasets.

关键词： Adaptation models Computational modeling Scalability Clustering methods Face recognition Memory management data models

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Malicious Family Identify Combining Multi-channel Mapping Feature Image and Fine-Tuned CNN

Malicious Family Identify Combining Multi-channel Mapping Fe...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Chenghua Tang Chen Zhou Min Hu Mengmeng Yang Baohua Qiang Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore Guangxi Cloud Computing and Big Data Collaborative Innovation Center Guilin University of Electronic Technology Guilin China

Using the features of malicious family to detect malicious code can improve the analysis efficiency and reduce the workload. Aiming at the problems of low efficiency and low accuracy of classifiers in analyzing and detecting malicious code using traditional machine learning, a new malicious family identification method 2MFI-FT is proposed. Firstly, the more comprehensive and essential features of malicious code are obtained by extracting local feature information, assembly instruction set information and visible string information. Secondly, in order to solve the problem of inconsistent images generated by feature sequences, different feature grayscale images are fused into a multi-channel mapping feature image (2MFI) based on the signature matrix. Thirdly, in view of the high cost of collecting and labeling enough data, a classification model with high recognition accuracy and strong generalization ability is realized, based on the fine-tuned Convolutional Neural Network (CNN). The regularization technique is also used to improve the robustness of the model. It is tested on Microsoft Malware Classification Challenge dataset. The experimental results show that the proposed 2MFI method for multi-channel feature image visualization has good identification effect and low time consumption when used as the input of the network model. At the same time, the 2MFI-FT method combined with fine-tuned CNN can accurately identify malicious families, and the accuracy on small training sets and large training sets can reach about 98.25% and 99.68% respectively, which provides a feasible solution for effective identification of malicious families.

关键词： Training Codes Instruction sets Semantics Feature extraction Malware Robustness

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Msnoa-Catboost: A Novel Pan-Cancer Diagnosis Model with Feature Subset Selection and Hyperparameter Optimization

SSRN

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SSRN 2024年

作者： Xiao, Tianyun Zhang, Zichen Kong, Shanshan Liu, Fengchun Yang, Aimin Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China

Cancer, as a global epidemic disease, remains a significant threat to human life and health. Prevention and early diagnosis are widely recognized as effective ways to tackle cancer. With the development of big data technology, many data-driven cancer diagnosis models have been proposed. However, the accuracy and generalization ability of existing models often fail to meet practical application needs. In order to address these challenges, this paper proposes a novel and improved protocol for high-precision pan-cancer diagnosis based on five publicly available cancer datasets from the UCI database and Kaggle, covering different cancer types such as breast, lung, and glioma. To achieve feature subset selection and hyperparameter optimization, we first introduce the nutcracker optimization algorithm (NOA), and improve it by using a multi-strategy method (MSNOA) to enhance optimization performance and efficiency. In the comparative experiment phase, we compare several popular machine learning models and identify MSNOA-CatBoost as the proposed model. The experimental results show that MSNOA-CatBoost improves the accuracy of the five datasets by an average of 9.12% compared to the baseline optimal model, and outperforms existing models in terms of performance. In summary, this study presents a promising approach to achieving high-precision pan-cancer diagnosis by leveraging the power of big data and advanced machine learning techniques. The proposed MSNOA-CatBoost model shows remarkable performance improvement over existing models, and has the potential to be applied in clinical practice for cancer diagnosis and treatment planning. © 2024, The Authors. All rights reserved.

关键词： Optimization

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