期刊：
IEEE TRANSACTIONS ON CLOUD COMPUTING,2025年13(1):118-129 ISSN：2168-7161
作者机构：
[Tingting He] College of Educational Science, Hengyang Normal University, Hengyang, China;[Zhiyong Xu] Math and Computer Science, Suffolk University, Boston, MA, USA;[Dewen Zeng; Wenlong Tian; Xuming Ye] School of Computer Science and Technology, University of South China, Hengyang, China;[Ruixuan Li] School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
摘要：
Post-deduplication in traditional cloud environments primarily focuses on single-node, where delta compression is performed on the same deduplication node located on server side. However, with data explosion, the multi-node post-deduplication, also called global deduplication, has become a hot issue in research communities, which aims to simultaneously execute delta compression on data distributed across all nodes. Simply setting up single-node deduplication systems on multi-node environments would significantly affect storage utilization and incur secondary overhead from file migration. Nevertheless, existing global deduplication solutions suffer from lower data compression ratios and high computational overhead due to their resemblance detection's inherent limitations and overly coarse granularities. Similar blocks typically have high correlations between sub-blocks; inspired by this observation, we propose IBNR (Intra-Block Neighborhood Relationship-Based Resemblance Detection for High-Performance Multi-Node Post-Deduplication), which introduces a novel resemblance detection based on relationships between sub-blocks and determines the ownership of blocks in entry stage to achieve efficient global deduplication. Furthermore, the by-products of IBNR have shown powerful scalability by replacing internal resemblance detection scheme with existing solutions on practical workloads. Experimental results indicate that IBNR outperforms state-of-the-art solutions, achieving an average 1.99× data reduction ratio and varying degrees of improvement across other key metrics.
摘要：
Traditional object detection methods usually underperform when locating tiny or small drones against complex backgrounds, since the appearance features of the targets and the backgrounds are highly similar. To address this, inspired by the magnocellular motion processing mechanisms, we proposed to utilize the spatial-temporal characteristics of the flying drones based on spiking neural networks, thereby developing the Magno-Spiking Neural Network (MG-SNN) for drone detection. The MG-SNN can learn to identify potential regions of moving targets through motion saliency estimation and subsequently integrates the information into the popular object detection algorithms to design the retinal-inspired spiking neural network module for drone motion extraction and object detection architecture, which integrates motion and spatial features before object detection to enhance detection accuracy. To design and train the MG-SNN, we propose a new backpropagation method called Dynamic Threshold Multi-frame Spike Time Sequence (DT-MSTS), and establish a dataset for the training and validation of MG-SNN, effectively extracting and updating visual motion features. Experimental results in terms of drone detection performance indicate that the incorporation of MG-SNN significantly improves the accuracy of low-altitude drone detection tasks compared to popular small object detection algorithms, acting as a cheap plug-and-play module in detecting small flying targets against complex backgrounds.
期刊：
Journal of Hazardous Materials,2025年490:137774 ISSN：0304-3894
通讯作者：
Peng, Guowen;Chen, Yinxiang;Zhang, Ye
作者机构：
[Rongshuo Guo; Xinyi Zhang; Ye Zhang; Linghua Jin] Lab of Optoelectronic Technology for Low Dimensional Nanomaterials, School of Chemical and Chemical Engineering, University of South China, Hengyang 421001, China;[Xinyi Zhang] School of Resources Environment and Safety Engineering, University of South China, Hengyang 421001, China;[AgrenHans Ågren; Artem Kuklin] Department of Physics and Astronomy Uppsala University, Box 516, Uppsala SE-751 20, Sweden;[Guowen Peng] School of Resources Environment and Safety Engineering, University of South China, Hengyang 421001, China. Electronic address: pgwnh78@163.com;[Yinxiang Chen] School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
通讯机构：
[Peng, Guowen; Zhang, Ye; Chen, Yinxiang] S;School of Resources Environment and Safety Engineering, University of South China, Hengyang 421001, China. Electronic address:;School of Computer Science and Technology, University of South China, Hengyang 421001, China. Electronic address:;School of Resources Environment and Safety Engineering, University of South China, Hengyang 421001, China. Electronic address:
关键词：
2e(−)-WOR-H(2)O(2);Density functional theory;Fe(2+)/Fe(3+) interconversion;SPF system;Synergistic oxidation and reduction
摘要：
Simultaneous organic pollutant oxidation and heavy metal ion reduction may enhance ocean and medical wastewater treatment. Here a self-cycling piezo-photocatalytic Fenton (SPF) system was developed using an Na-Sm bimetal-doped layered ferroelectric perovskite SrBi 2 Nb 2 O 9 (NS-SBNO) and Fe 2+ ions. The experimental results and density functional theory calculations demonstrated that bimetal doping induced an enhanced internal electric field, improving electron–hole pair separation and transmission. NS-SBNO exhibited increased adsorption energy and electron transfer capacity of H 2 O, leading to H 2 O 2 production at 497 μmol g −1 h −1 through a two-electron water oxidation reaction without any sacrificial reagent. Furthermore, compared with traditional Fenton systems, the SPF system afforded higher Fe 2+ /Fe 3+ interconversion efficiency and hydroxyl and superoxide radical yields, highlighting the system’s excellent redox capabilities. The SPF system also demonstrated enhanced oxidation and reduction of complex pollutants in wastewater [e.g., BPA, TC, 2, 4-DCP, NOR, LVX, U(VI), and Cr(VI)], demonstrating promising practical applicability.
Simultaneous organic pollutant oxidation and heavy metal ion reduction may enhance ocean and medical wastewater treatment. Here a self-cycling piezo-photocatalytic Fenton (SPF) system was developed using an Na-Sm bimetal-doped layered ferroelectric perovskite SrBi 2 Nb 2 O 9 (NS-SBNO) and Fe 2+ ions. The experimental results and density functional theory calculations demonstrated that bimetal doping induced an enhanced internal electric field, improving electron–hole pair separation and transmission. NS-SBNO exhibited increased adsorption energy and electron transfer capacity of H 2 O, leading to H 2 O 2 production at 497 μmol g −1 h −1 through a two-electron water oxidation reaction without any sacrificial reagent. Furthermore, compared with traditional Fenton systems, the SPF system afforded higher Fe 2+ /Fe 3+ interconversion efficiency and hydroxyl and superoxide radical yields, highlighting the system’s excellent redox capabilities. The SPF system also demonstrated enhanced oxidation and reduction of complex pollutants in wastewater [e.g., BPA, TC, 2, 4-DCP, NOR, LVX, U(VI), and Cr(VI)], demonstrating promising practical applicability.
摘要：
Recently, Oblivious Storage has been proposed to prevent privacy leakage from user access patterns, which obfuscates and makes it computationally indistinguishable from the random sequences by fake accesses and probabilistic encryption. The same data exhibits distinct ciphertexts. Thus, it seriously impedes cloud providers' efforts to improve storage utilization to remove user redundancy, which has been widely used in the existing cloud storage scenario. Inspired by the successful adoption of removing duplicate data in cloud storage, we attempt to integrate obliviousness, remove redundancy, and propose a practical oblivious storage, PEO-Store. Instead of fake accesses, introducing delegates breaks the mapping link between a valid access pattern and a specific client. The cloud interacts only with randomly authorized delegates. This design leverages non-interactive zero-knowledge-based redundancy detection, discrete logarithm problem-based key sharing, and secure time-based delivery proof. These components collectively protect access pattern privacy, accurately eliminate redundancy, and prove the data delivery among delegates and the cloud. Theoretical proof demonstrates that, in our design, the probability of identifying the valid access pattern with a specific client is negligible. Experimental results show that PEO-Store outperforms state-of-the-art methods, achieving an average throughput of up to 3 times faster and saving 74% of storage space.
摘要：
The concept of data locality is crucial for distributed systems (e.g., Spark and Hadoop) to process Big Data. Most of the existing research optimized the data locality from the aspect of task scheduling. However, as the execution container of Spark's tasks, the executor launched on different nodes can directly affect the data locality achieved by the tasks. This article tries to improve the data locality of tasks by executor allocation in Spark framework. First, because of different communication modes at stages, we separately model the communication cost of tasks for transferring input data to the executors. Then formalize an optimal executor allocation problem to minimize the total communication cost of transferring all input data. This problem is proven to be NP-hard. Finally, we present a greed dropping heuristic algorithm to provide solution to the executor allocation problem. Our proposals are implemented in Spark-3.4.0 and its performance is evaluated through representative micro-benchmarks (i.e., WordCount , Join , Sort ) and macro-benchmarks (i.e., PageRank and LDA ). Extensive experiments show that the proposed executor allocation strategy can decrease the network traffic and data access time by improving the data locality during the task scheduling. Its performance benefits are particularly significant for iterative applications.
摘要：
U-network is a comprehensive convolutional neural network that is widely utilized in medical image segmentation domain. However, it is not accurate enough in detail segmentation and resulting in unsatisfactory segmentation results. To solve this problem, this paper proposes an enhanced U-network that combines an improved Pyramid Pooling Module (PPM) and a modified Convolutional Block Attention Module (CBAM). Its whole network is U-Net architecture, where the PPM is improved by reducing the number of bin species and increasing the pooling connection multiples. It is used in the downsampling part of the network, which can extract input image features of various dimensions. And the CBAM is modified by using $1\times 1$ convolutional layers instead of the original fully connected layers. It is used in the upsampling part of the network, which can combine convolution and attention mechanism. This pays attention to the image from two aspects of space and channel. Besides, the network is trained with novel RGB training to further improve the segmentation ability of the network. Experimental results show that our network outperforms traditional U-shaped segmentation networks by 30% to 40% in metrics Dice, IoU, MAE, and BFscore respectively. What‘s more, it is better than U-Net ++, U2-Net, ResU-Net, ResU-Net++, and UNeXt in terms of segmentation effect and training time.
摘要：
<jats:p>It is crucial to detect high-severity defects, such as memory leaks that can result in system crashes or severe resource depletion, in order to reduce software development costs and ensure software quality and reliability. The primary cause of high-severity defects is usually resource scheduling errors, and in the program source code, these defects have contextual features that require defect context to confirm their existence. In the context of utilizing machine learning methods for defect automatic confirmation, the single-feature label method cannot achieve high-precision defect confirmation results for high-severity defects. Therefore, a multi-feature fusion defect automatic confirmation method is proposed. The label generation method solves the dimensionality disaster problem caused by multi-feature fusion by fusing features with strong correlations, improving the classifier’s performance. This method extracts node features and basic path features from the program dependency graph and designs high-severity contextual defect confirmation labels combined with contextual features. Finally, an optimized Support Vector Machine is used to train the automatic detection model for high-severity defects. This study uses open-source programs to manually implant defects for high-severity defect confirmation verification. The experimental results show that compared with existing methods, this model significantly improves the efficiency of confirming high-severity defects.</jats:p>
作者机构：
[Zhuo Tang] School of of Information Science and Engineering, Hunan University National Supercomputing Center, Changsha, Hunan, China;[Hejian Chen; Zhongming Fu; Li Liu; Mengsi He; Jiayi Deng] School of Computer Science and Technology, University of South China, Hengyang, Hunan, China
会议名称：
2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)
会议时间：
17 December 2023
会议地点：
Ocean Flower Island, China
会议论文集名称：
2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)
摘要：
U-network is a kind of full convolutional neural network, which is widely used in the field of medical image segmentation. However, it still has the problem of small targets being unable to be segmented and resulting in unsatisfactory segmentation effect. In order to solve this problem, this paper proposes an enhanced U-network by combining pyramid pooling module (PPM) and convolutional block attention module (CBAM). It’s whole network is U-Net architecture, where PPM with bin sizes of 1×1, 2×2 and 3×3 are used in the downsampling part of the network, which can extract input image features of various dimensions. And CBAM is used in the downsampling part of the network, which combines convolution and attention mechanism, and can pay attention to the image from two aspects of space and channel to improve the segmentation ability of the network. Experimental results show that our network outperforms traditional Ushaped segmentation networks by 30% to 40% in metrics IoU, MAE, and Dice, respectively.
摘要：
With the increasing number of big data applications, large amounts of valuable data are distributed in different organizations or regions. Federated Learning (FL) enables collaborative model training without sharing sensitive data and is widely used in AI medical diagnosis, economy, and autonomous driving scenarios. However, it still leaks the privacy from the gradient exchange in federated learning. What's worse, state-of-the-art work, such as Batchcrypt, still suffers from computational overhead due to a considerable amount of computation and communication costs caused by homomorphic encryption. Therefore, we propose a novel symmetric key-based homomorphic encryption scheme, Sym-Fed. To unleash the power of symmetric encryption in federated learning, we combine random masking with symmetric encryption and keep the homomorphic property during the gradient exchange in the federated learning process. Finally, the security analysis and experimental results on real workloads show that our design achieves performance improvement 6x to 668x and reduces the communication overhead 1.2x to 107x compared with the state-of-the-art work, BatchCrypt and FATE, without model accuracy degradation and security compromise.
作者机构：
[Ouyang, Chunping; Tian, Wenlong; Liu, Yongbin; Liu, Qifei; Li, Jing; Geng, Yuqing] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.;[Tian, Wenlong] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.;[Ouyang, Chunping; Tian, Wenlong; Liu, Yongbin] Hunan Prov Base Sci & Technol Innovat Cooperat, Hengyang, Peoples R China.;[Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.;[Xiao, Weijun] Virginia Commonwealth Univ, Elect & Comp Engn, Richmond, VA 23284 USA.
会议名称：
IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) / 9th International Conference on Big Data Computing, Applications and Technologies (BDCAT)
会议时间：
DEC 06-09, 2022
会议地点：
Vancouver, WA
会议主办单位：
[Geng, Yuqing;Tian, Wenlong;Ouyang, Chunping;Liu, Yongbin;Liu, Qifei;Li, Jing] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.^[Tian, Wenlong] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.^[Tian, Wenlong;Ouyang, Chunping;Liu, Yongbin] Hunan Prov Base Sci & Technol Innovat Cooperat, Hengyang, Peoples R China.^[Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.^[Xiao, Weijun] Virginia Commonwealth Univ, Elect & Comp Engn, Richmond, VA 23284 USA.^[Xu, Zhiyong] Suffolk Univ, Math & Comp Sci Dept, Boston, MA 02114 USA.
会议论文集名称：
2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
关键词：
Cloud Storage;Resemblance Detection;Context-Aware;Deduplication Ratio Prediction
摘要：
With the prevalence of cloud storage, people prefer to outsource their data to the cloud for flexibility and reliability. Undoubtedly, there are lots of redundancy among these data. However, high-end storage with deduplication costs heavy computation and increases the data management complexity. Potential customers need the redundancy proportion information of their outsourced data to decide whether high-end storage with deduplication is worthwhile. Thus, many researchers have previously attempted to predict the redundant ratio. However, existing mechanisms ignore the redundancy proportion among similar chunks containing many duplicate data. Although resemblance detection, detecting the duplicate parts among similar data, has become a hot issue, it is hardly applied to the conventional deduplication ratio estimation because of unacceptable calculation cost. Therefore, we analyze the limitations and challenges of deduplication ratio prediction in prediction scope and response time and further propose a novel prediction scheme. By leveraging the context-aware resemblance detection, and confidence interval theory, our method can achieve faster estimation speed with higher accuracy in deduplication ratio compared with the state-of-the-art work. Finally, the results show that our method can efficiently and effectively estimate the proportion of duplicate chunks and redundant data among similar chunks by conducting experiments on real workloads.
通讯机构：
[Zhigang Xu; Shiguang Zhang; Wenlong Tian; Hongmu Han; Xinhua Dong] S;[Haitao Wang; Zhiqiang Zheng] N;Narcotics Control Bureau of Department of Public Security of Guangdong Province,Guangzhou 510050,null,China<&wdkj&>School of Computer Science and Technology,University of South China,Hengyang 421001,China<&wdkj&>School of Computer Science,Hubei University of Hubei University of Technology,Wuhan 430068,China
关键词：
Introduction;Materials and Methods;Results;Discussion;Conclusion;Abstract;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interests;Authors’ Contributions;Funding Statement;Acknowledgements;Acknowledgments;Supplementary Materials;Reference;Dataset Description;Dataset Files;Abstract;Introduction;Introduction and Materials;Introduction and Methods;Materials;Materials and Methods;Methods;Results;Discussion;Results and Discussion;Discussion and Conclusion;Results and Conclusion;Conclusion;Conclusions;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interest;Authors’ Contributions;Funding Statement;Acknowledgements;Supplementary Materials;References;Appendix;Abbreviations;Preliminaries;Introduction and Preliminaries;Notation;Proof of Theorem;Proofs;Analysis of Results;Examples;Numerical Example;Applications;Numerical Simulation;Model;Model Formulation;Systematic Palaeontology;Nomenclatural Acts;Taxonomic Implications;Experimental;Synthesis;Overview;Characterization;Background;Experimental;Theories;Calculations;Model Verification;Model Implementation;Geographic location;Study Area;Geological setting;Data Collection;Field Testing;Data and Sampling;Dataset;Literature Review;Related Works;Related Work;System Model;Methods and Data;Experimental Results;Results and Analysis;Evaluation;Implementation;Case Presentation;Case Report;Search Terms;Case Description;Case Series;Background;Limitations;Additional Points;Case;Case 1;Case 2 etc.;Concern Details;Retraction Details;Copyright;Related Articles
摘要：
In the Internet of Things (IoT), data sharing security is important to social security. It is a huge challenge to enable more accurate and secure access to data by authorized users. Blockchain access control schemes are mostly one-way access control, which cannot meet the need for ciphertext search, two-way confirmation of users and data, and secure data transmission. Thus, this paper proposes a blockchain-aided searchable encryption-based two-way attribute access control scheme (STW-ABE). The scheme combines ciphertext attribute access control, key attribute access control, and ciphertext search. In particular, two-way access control meets the requirement of mutual confirmation between users and data. The ciphertext search avoids information leakage during transmission, thus improving overall efficiency and security during data sharing. Moreover, user keys are generated by the coalition blockchain. Besides, the ciphertext search and pre-decryption are outsourced to cloud servers, reducing the computing pressure on users and adapting to the needs of lightweight users in the IoT. Security analysis proves that our scheme is secure under a chosen-plaintext attack and a chosen keyword attack. Simulations show that the cost of encryption and decryption, keyword token generation, and ciphertext search of our scheme are preferable.
期刊：
Proceedings of SPIE - The International Society for Optical Engineering,2022年12331 ISSN：0277-786X
通讯作者：
Li, Meng(mlemon@usc.edu.cn)
作者机构：
[He, Chen] School of Computer Science and Technology, University of South China, Hunan, Hengyang;421001, China;[Yang, Xiaohua; Li, Meng; Yan, Shiyu] CNNC Key Laboratory on High Trusted Computing, Computer School, University of South China, Hunan, Hengyang, China;[He, Chen] 421001, China
