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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Yamashita, Aoi Tanaka, Masahiro Bessho, Yutaro Fujiwara, Yasuhiro Kawashima, Hideyuki Graduate School of Keio University Japan Ntt Computer and Data Science Laboratories Japan Ntt Communication Science Laboratories Japan Keio University Japan

ISBN: (纸本)9798350306941

Raft is known as a replicating state machine that tolerates crash faults, and its use enables various distributed systems, including distributed transaction processing systems. The original Raft protocol creates an intra-cluster consensus by having the leader generate a log of each incoming client request and replicate it to its followers. When requests arrive frequently, the number of replications increases, increasing the number of communications and possibly leading to performance degradation. To solve this problem, we propose a bulk-transfer protocol that replicates multiple requests at once. We implemented the Raft system to verify the effect of the proposed method. We measured the performance of the implemented system when multiple requests were transferred in bulk and observed an increase in throughput with an increase in the amount of data replicated at a time. The highest throughput of 214 MB/sec was achieved when replicating 12.5 MB at a time. This is 12,066 times higher than the performance when replicating one request (18 B) at a time. Besides, the size of the log is reduced to 5/9 of the conventional method. © 2023 IEEE.

关键词： Distributed computer systems

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of computer science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG 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 State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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Elevating Cystic Fibrosis Detection in Lungs using HRCT Images with a Cutting-Edge CNN-Based Approach 2

Elevating Cystic Fibrosis Detection in Lungs using HRCT Imag...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Ganjam, Amrutha Sriya Reddy C, Kishor Kumar Radhika, D. Ganjam, Karthik Singh.t, Monika Akuthota, Keerthi Sai Stanley College of Engineering and Technology for Women abids Department of Computer Science Hyderabad India University of Edinburgh Department School of Informatics MSc Data Science Edinburgh United Kingdom Stanley College of Engineering and Technology for Women Deptartment. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331519056

The creation of an algorithm for recognizing pathological abnormalities in cystic fibrosis is investigated in this paper using the CNN model with a modified psp-net. Currently, Decision Trees, Random Forests, PSP Nets, and Neural Networks are utilized in the diagnosis of cystic fibrosis. Since convolutional neural networks (CNNs) can process complicated picture data rapidly and efficiently, the goal of this study is to use CNNs for the detection of anomalies associated with cystic fibrosis. The method groups distinct annotated images into a simple and efficient structure, runs the set of images through a multiscale CNN procedure, and precisely locates the lung region affected by cystic fibrosis. The result of this paper demonstrated that differences in the training dataset can impact performance, but annotating CT images and categorizing them in terms of similar pathologies can improve the accuracy of the model. The proposed CNN model achieved an Accuracy of 84 %, Precision of 74%, Recall of 79%, F1-Score of 72%, error rate of 16% Which is better when compared with existing approaches. © 2024 IEEE.

关键词： Convolutional neural networks

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A Two-Stage Hybrid GA-Cellular Encoding Approach to Neural Architecture Search

A Two-Stage Hybrid GA-Cellular Encoding Approach to Neural A...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Londt, Trevor Gao, Xiaoying Andreae, Peter Mei, Yi Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand

ISBN: (纸本)9781665430654

Neural Architecture Search (NAS) aims to automate the creation of Artificial Neural Networks, including Convolutional Neural Networks (CNN), lessening the reliance on labour-intensive manual design by human experts. A CNN architecture can be decomposed into a micro- and macro-architecture, each influenced by distinct design and optimisation strategies to con-tribute to the overall construction and performance of the CNN. Cellular Encoding (CE), an evolutionary computation technique, has been successfully used to represent diverse network topologies of varying complexities. Recently, CE has been applied to evolve CNN architectures, showing promising results. However, current CE-based NAS approaches focus on evolving either the micro-or macro-architectures without considering the evolution of both in the same algorithm. Evolving the micro- and macro-architecture together can increase the performance of evolved CNN architectures. This research introduces a novel two-stage hybrid approach, combining Genetic Algorithms (GA) and CE to evolve both the micro- and macro-architectures to synthesise CNNs for classification tasks. Candidate macro-architectures are evolved using a CE approach, while a GA approach is used to explore the micro-architecture search space. The proposed algorithm is evaluated across four commonly used datasets and compared against six NAS peer competitors and five state-of-the-art manually designed CNN architectures. The results validate the approach's high competitiveness, outperforming several peer competitors on image and text classification tasks. © 2023 IEEE.

关键词： Cellular Encoding Convolutional Neural Networks Neural Architecture Search

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Generative Forgery Image Detection Based on Diffusion Reconstruction Loss and Frequency Domain Feature 11

Generative Forgery Image Detection Based on Diffusion Recons...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Hu, Xinyang Haihong, E. Liu, Jianhua Hu, Tianyi Qiao, Xiaodong Ding, Junpeng Huang, Jiayu School of Computer Science Beijing University of Posts and Telecommunications Beijing China Beijing Wanfang Data Co. Ltd. Beijing China

ISBN: (纸本)9798331531904

Since the advent of generative models such as Generative Adversarial Networks (GANs), image generation techniques have made significant progress in the field of digital image processing. However, the development of these techniques has also brought about challenges in research integrity, especially the growing problem of forged images in research papers. Currently, there are no mature research results related to the generation of forged image detection in the field of research integrity detection. Existing detection techniques are difficult to adapt to the rapidly evolving image generation models, especially when dealing with research paper images that have a single structure and are quickly learned by the generation models. Existing methods fail to effectively identify high-quality forged images produced by advanced generative models, which poses a severe threat to scientific integrity. In this study, we propose a new detection method that combines diffusion reconfiguration loss and frequency-domain features to identify and differentiate generative forged images from fundamental scientific research images. We developed a technique to detect frequency-domain artifacts specific to generated images using frequency-domain analysis and combined it with image reconstruction error features of the diffusion model to enhance the accuracy and robustness of detection. Through testing on a newly constructed public dataset, our method shows superior performance in effectively identifying forged images produced by multiple generative models. © 2024 IEEE.

关键词： Generative adversarial networks

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AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation 23

AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Fan, Rui Pu, Yuanhao Chen, Jin Zhu, Zhihao Lian, Defu Chen, Enhong School of Computer Science University of Science and Technology of China Hefei China School of Data Science University of Science and Technology of China Hefei China University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9781450394161

The Embarrassingly Shallow Autoencoders (EASE and SLIM) are strong recommendation methods based on implicit feedback, compared to competing methods like iALS and VAE-CF. However, EASE suffers from several major shortcomings. First, the training and inference of EASE can not scale with the increasing number of items since it requires storing and inverting a large dense matrix;Second, though its optimization objective - the square loss- can yield a closed-form solution, it is not consistent with recommendation goal - predicting a personalized ranking on a set of items, so that its performance is far from optimal w.r.t ranking-oriented recommendation metrics. Finally, the regularization coefficients are sensitive w.r.t recommendation accuracy and vary a lot across different datasets, so the fine-tuning of these parameters is important yet time-consuming. To improve training and inference efficiency, we propose a Similarity-Structure Aware Shallow Autoencoder on top of three similarity structures, including Co-Occurrence, KNN and NSW. We then optimize the model with a weighted square loss, which is proven effective for ranking-based recommendation but still capable of deriving closed-form solutions. However, the weight in the loss can not be learned in the training set and is similarly sensitive w.r.t the accuracy to regularization coefficients. To automatically tune the hyperparameters, we design two validation losses on the validation set for guidance, and update the hyperparameters with the gradient of the validation losses. We finally evaluate the proposed method on multiple real-world datasets and show that it outperforms seven competing baselines remarkably, and verify the effectiveness of each part in the proposed method. © 2023 ACM.

关键词： Collaborative filtering

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AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading 23

AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Duan, Zhuohui Feng, Hao Liu, Haikun Liao, Xiaofei Jin, Hai Li, Bangyu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133458

The key-value separation is renowned for its significant mitigation of the write amplification inherent in traditional LSM trees. However, KV separation potentially increases performance overhead in the management of Value region, especially for garbage collection (GC) operation that is used to reduce the redundant space occupation. In response, many efforts have been made to optimize the GC mechanism for KV separation. However, our analysis indicates that such solution based on trade-offs between CPU and I/O overheads cannot simultaneously satisfy the three requirements of KV separated systems in terms of throughput, tail latency, and space usage. This limitation hinders their real-world application. In this paper, we introduce AegonKV, a "three-birds-one-stone" solution that comprehensively enhances the throughput, tail latency, and space usage of KV separated systems. AegonKV first proposes a SmartSSD-based GC offloading mechanism to enable asynchronous GC operations without competing with LSM read/write for bandwidth or CPU. AegonKV leverages offload-friendly data structures and hardware/software execution logic to address the challenges of GC offloading. Experiments demonstrate that AegonKV achieves the largest throughput improvement of 1.28-3.3 times, a significant reduction of 37%-66% in tail latency, and 15%-85% in space overhead compared to existing KV separated systems. © 2025 FAST. All Rights Reserved.

关键词： Digital storage

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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Multi-agent Scheduling for Shared Manufacturing Systems 6

Multi-agent Scheduling for Shared Manufacturing Systems

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Duran, Ege Ozturk, Cemalettin O'Sullivan, Barry SFI Centre for Research Training in AI School of Computer Science IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments. © 2025 The Authors.

关键词： Mixed-integer linear programming

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Cross-Language Multimodal Detection: A Novel Model for Identifying Duplicate Publications in Scientific Papers 11

Cross-Language Multimodal Detection: A Novel Model for Ident...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Huang, Jiayu Haihong, E. Liu, Jianhua Hu, Tianyi Qiao, Xiaodong Ding, Junpeng Hu, Xinyang School of Computer Science Beijing University of Posts and Telecommunications Beijing China Beijing Wanfang Data Co. Ltd. Beijing China

ISBN: (纸本)9798331531904

This paper introduces a novel multimodal, cross-language detection model for identifying duplicate publications in academia, a problem where authors publish similar content across different languages without citation. Traditional detection methods are primarily text-based and struggle with linguistic variations and deliberately altered structures. Our model uniquely integrates textual, image, and structural modalities using a knowledge distillation SBERT for semantic similarity calculations between Chinese and English, and Vision Transformer (ViT) for image comparisons. We developed a new ChineseEnglish multimodal dataset in collaboration with the Chinese Journal of Electronics and Wanfang data. On this dataset, our model has an AUC of 0.931 and an F1 of 0.82. The model's effectiveness is demonstrated through the identification and subsequent verification of two suspected duplicate publications. This research advances duplicate detection by combining multiple modalities and supports comprehensive academic content analysis. © 2024 IEEE.

关键词： Semantics

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