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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Deng, Zhongyi Chen, C.L. Philip Zhang, Tong the Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition the School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China and is with the Pazhou Lab Guangzhou 510335 China and is with Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

ISBN: (纸本)9781665410205

Probability calibration is a method to improve the reliability of models by linking the predicted probability to accuracy. Most research follow a static strategy of full fine-tuning. These studies do not consider dynamic data and sparse parameters in Language Models as a Service(LMaaS), leading to limited effectiveness of probability calibration. To address above issues, we propose a dynamic probability calibration algorithm (DPCA) to consider both data flow and parameter freezing. DPCA consists of streaming annotation (SA) task and dynamic calibration (DC) task. The SA task takes a specified number of samples from the training data stream. The sampled data is automatically annotated according to the deviation between predicted probability and true label. The DC task injects the probability deviation of the SA task into next training epoch through adapter-tuning. DPCA achieves data augmentation in LMaaS through joint learning of sample labels and their predicted probability deviation. This work validates DPCA through the implementation on BERT architecutre. The proposed model achieves overall performance improvement on both Chinese and English NLP tasks. Experimental results demonstrate a 2.17% decrease in average ECE without de-creasing in accuracy. Experimental analysis demonstrate the effectiveness and generalizability of DPCA in LMaaS. © 2024 IEEE.

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Scavenger: Better Space-Time Trade-Offs for Key-Value Separated LSM-trees 40

Scavenger: Better Space-Time Trade-Offs for Key-Value Separa...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhang, Jianshun Wang, Fang Qiu, Sheng Wang, Yi Ou, Jiaxin Huang, Junxun Li, Baoquan Fang, Peng Feng, Dan School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China China Shenzhen Huazhong University of Science and Technology Research Institute China Bytefrance Inc.

ISBN: (纸本)9798350317152

Key-Value Stores (KVS) implemented with log-structured merge-tree (LSM-tree) have gained widespread ac-ceptance in storage systems. Nonetheless, a significant challenge arises in the form of high write amplification due to the compaction process. While KV-separated LSM-trees successfully tackle this issue, they also bring about substantial space am-plification problems, a concern that cannot be overlooked in cost-sensitive scenarios. Garbage collection (GC) holds significant promise for space amplification reduction, yet existing GC strategies often fall short in optimization performance, lacking thorough consideration of workload characteristics. Additionally, current KV-separated LSM-trees also ignore the adverse effect of the space amplification in the index LSM-tree. In this paper, we systematically analyze the sources of space amplification of KV-separated LSM-trees and introduce Scavenger, which achieves a better trade-off between performance and space amplification. Scavenger initially proposes an I/O-efficient garbage collection scheme to reduce I/O overhead and incorporates a space-aware compaction strategy based on compensated size to minimize the space amplification of index LSM-trees. Extensive experiments show that Scavenger significantly improves write performance and achieves lower space amplification than other KV-separated LSM-trees (including BlobDB, Titan, and TerarkDB). © 2024 IEEE.

关键词： Economic and social effects

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Discovering Cohesive Temporal Subgraphs with Temporal Density Aware Exploration

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Journal of computer science & Technology 2022年第5期37卷 1068-1085页

作者： Chun-Xue Zhu Long-Long Lin Ping-Peng Yuan Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Real-world networks,such as social networks,cryptocurrency networks,and e-commerce networks,always have occurrence time of interactions between *** networks are typically modeled as temporal *** cohesive subgraphs from temporal graphs is practical and essential in numerous data mining applications,since mining cohesive subgraphs gets insights into the time-varying nature of temporal ***,existing studies on mining cohesive subgraphs,such as Densest-Exact and k-truss,are mainly tailored for static graphs(whose edges have no temporal information).Therefore,those cohesive subgraph models cannot indicate both the temporal and the structural characteristics of *** this end,we explore the model of cohesive temporal subgraphs by incorporating both the evolving and the structural characteristics of temporal ***,the volume of time intervals in a temporal network is *** a result,the time complexity of mining temporal cohesive subgraphs is *** efficiently address the problem,we first mine the temporal density distribution of temporal *** by the distribution,we can safely prune many unqualified time intervals with the linear time ***,the remaining time intervals where cohesive temporal subgraphs fall in are examined using the greedy *** results of the experiments on nine real-world temporal graphs indicate that our model outperforms state-of-the-art solutions in efficiency and ***,our model only takes less than two minutes on a million-vertex DBLP and has the highest overall average ranking in EDB and TC metrics.

关键词： temporal network temporal feature distribution cohesive subgraph convex property

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Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning

arXiv

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arXiv 2024年

作者： Ding, Zezhong Xiang, Yongan Wang, Shangyou Xie, Xike Zhou, S. Kevin School of Data Science School of Computer Science and Technology School of Biomedical Engineering Data Darkness Lab MIRACLE Center Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou China

In the realm of distributed systems tasked with managing and processing large-scale graph-structured data, optimizing graph partitioning stands as a pivotal challenge. The primary goal is to minimize communication overhead and runtime cost. However, alongside the computational complexity associated with optimal graph partitioning, a critical factor to consider is memory overhead. Real-world graphs often reach colossal sizes, making it impractical and economically unviable to load the entire graph into memory for partitioning. This is also a fundamental premise in distributed graph processing, where accommodating a graph with non-distributed systems is unattainable. Currently, existing streaming partitioning algorithms exhibit a skew-oblivious nature, yielding satisfactory partitioning results exclusively for specific graph types. In this paper, we propose a novel streaming partitioning algorithm, the Skewness-aware Vertex-cut Partitioner (S5P), designed to leverage the skewness characteristics of real graphs for achieving high-quality partitioning. S5P offers high partitioning quality by segregating the graph’s edge set into two subsets, head and tail sets. Following processing by a skewness-aware clustering algorithm, these two subsets subsequently undergo a Stackelberg graph game. Our extensive evaluations conducted on substantial real-world and synthetic graphs demonstrate that, in all instances, the partitioning quality of S5P surpasses that of existing streaming partitioning algorithms, operating within the same load balance constraints. For example, S5P can bring up to a 51% improvement in partitioning quality compared to the top partitioner among the baselines. Lastly, we showcase that the implementation of S5P results in up to an 81% reduction in communication cost and a 130% increase in runtime efficiency for distributed graph processing tasks on *** Codes 97P30 Copyright © 2024, The Authors. All rights reserved.

关键词： Graph theory

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Improving Autoencoder-Based Recommendation Systems 7th

Improving Autoencoder-Based Recommendation Systems

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7th International Conference on data Management, Analytics and Innovation, ICDMai 2023

作者： Sinhababu, Nilanjan Sarma, Monalisa Samanta, Debasis Center for Computational and Data Sciences Indian Institute of Technology Kharagpur India Subir Chowdhury School of Quality and Reliability Indian Institute of Technology Kharagpur India Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9789819914135

Recommendation systems help in providing suggestions regarding the products/services that the users might be interested in by analyzing ratings, reviews, and other feedback obtained from the users on their previous purchases. These types of systems can be seen in domains like e-commerce, entertainment, news, etc. The ratings and reviews in e-commerce are obtained from user feedback which are far less in number when compared with the users and items count. This leads to data sparsity issues where the recommendation systems are prone to provide in-accurate recommendations. To counter this issue, in our study, we used both autoencoder and sentiment analysis on the reviews using the BERT language model to generate a more accurate version of the rating matrix. The empirical studies done on the Amazon baby product reviews dataset have shown that the approach has significantly increased the accuracy of predicted ratings. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Pelvic bone region segmentation (PBRS) from X-ray image using convolutional neural network (CNN) 26

Pelvic bone region segmentation (PBRS) from X-ray image usin...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ul Alam, Syed Alif Binte Alam, Saadia Saha, Sourav Haque, Mahmudul Rahman, Rashedur Kobashi, Syoji Independent University Bangladesh Department of Computer Science and Engineering Dhaka1229 Bangladesh Independent University Bangladesh Center for Computational & Data Sciences Dhaka1229 Bangladesh University of Hyogo Graduate School of Engineering Shosha Hyogo Himeji2167 Japan

ISBN: (纸本)9798350359015

Pelvic and hip fractures offer considerable public health risks with high morbidity and mortality rates. Because of the complicated bone structure of the pelvic bone region, detecting fractures is difficult. Though X-ray imaging is routinely utilised for detecting fractures, manual fracture diagnosis is prone to inaccuracies. This paper proposes the use of deep learning algorithms for automated segmentation of the pelvic bone region in X-ray images. In our work, we have investigated U-Net based pelvic area segmentation models with various convolutional neural network (CNN) backbones. The DenseNet121-based U-Net design emerged as the most optimal model, establishing a compromise between performance and computational efficiency. Although it had a modest loss in IoU and F1 scores when compared to InceptionNetV3, it had a remarkable 59.44% reduction in the number of parameters. © 2023 IEEE.

关键词： deep learning pelvic bone segmentation x-ray

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Grassland Mouse Hole Recognition Model Based on UAV Remote Sensing and Improved YOLOv7

Grassland Mouse Hole Recognition Model Based on UAV Remote S...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yu Han Xiangyu Bai Xiaochen Gao Haoran Cheng Kexin Zhou Engineering Research Center of Ecological Big Data Ministry of Education College of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education School of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

As the most widely distributed vegetation type on Earth, grasslands are the second largest ecosystem after forests, and are known as the “skin of the earth”. However, the frequency of rodent infestation has been increasing year by year in recent years due to climate change, loose soil and other factors. As the current traditional monitoring methods are time-consuming and laborious, while the deep learning method has the advantages of low cost, high efficiency and high accuracy. Therefore, this paper proposes a combination of UAV remote sensing and deep learning methods to monitor grassland mouse hole. First, because there is no publicly available grassland mouse hole dataset, this study used a UAV to capture images to create a grassland mouse hole dataset. Second, an improved YOLOv7 model, YOLOAPM, is proposed with the goal of improving the accuracy of small target recognition. Through ablation and comparison experiments, the validity of the improved model as well as the method of this paper are verified to be improved compared to other models. Finally, in order to facilitate the monitoring task of the researchers, a monitoring system is established and the mouse hole identification of this paper is embedded into the system.

关键词： Deep learning Earth Grasslands Accuracy Target recognition Rodents Rats Autonomous aerial vehicles Monitoring Remote sensing

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An Efficient Machine Learning Model for Identification of Seagrasses through Morphometrics 2

An Efficient Machine Learning Model for Identification of Se...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Dubey, Ashwani Kumar Thanikkal, Jibi G Dilipan, E. Sharma, Puneet Shukla, Manoj Kumar Amity University Uttar Pradesh Aset Dept.of Electronics and Communication Engineering UP Noida India Amity University Uttar Pradesh Aset Dept.of Computer Science and Engineering UP Noida India Saveetha University Center for Marine Omics Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai77 India Sr University School of Computer Science & Ai Telangana Warangal India

ISBN: (纸本)9798350360523

Seagrasses are marine flowering plants capable of completing their life cycle underwater. Globally, 72 species of seagrasses have been identified, including 15 species from India. Artificial intelligence/ Machine learning techniques are gaining wide use in many fields, and their applications in marine sciences are still in the initial stage. Considering this, a study is attempted to identify seagrass species through Artificial intelligence/ Machine learning techniques. Since the characteristics of seagrass vary depending on location and other environmental factors, identifying seagrass species is regarded as a difficult task. In the present study, a known seagrass species is taken as a reference and its leaf morphological features are considered for shape description and deep learning-based identification. Leaf morphometrics of seagrass species are drawn to develop the databases. Morphological feature extraction algorithm, descriptor generation algorithm, and deep learning-based identification are applied for species identification. Existing artificial intelligence/ machine learning models use image pixels as input to the deep learning model. However, this is the first attempt to recognize seagrasses through their morphological properties. Here, the leaf shape property is utilized for seagrass identification. The proposed machine learning model received 60% accuracy during testing. Additional investigation was possible with the same algorithm for future seagrass species identification. Utilizing Artificial intelligence/ Machine learning algorithms may lead to identifying new seagrass species. © 2024 IEEE.

关键词： Learning algorithms

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Test-Driven Feature Extraction of Web Components

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Journal of computer science & Technology 2022年第2期37卷 389-404页

作者： Yong-Hao Long Yan-Cheng Chen Xiang-Ping Chen Xiao-Hong Shi Fan Zhou School of Computer Science and Engineering National Engineering Research Center of Digital Life Sun Yat-sen UniversityGuangzhou 510006China School of Design The Hong Kong Polytechnic UniversityHong KongChina Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion The School of Communication and DesignSun Yat-sen UniversityGuangzhou 510006China School of Information Technology and Engineering Guangzhou College of CommerceGuangzhou 511363China

With the growing requirements of web applications,web components are developed to package the implementation of commonly-used features for *** some cases,the developer may want to reuse some features which cannot be customized by the component’s ***/she has to extract the implementation by *** is labor-intensive and *** the widely-used test cases which can be useful to specify the software features,a test-driven approach is proposed to extract the implementation of the desired features in web *** satisfaction of the user^requirements is transformed into the passing rate of user-specified test *** this way,the quality of the extraction result can be evaluated ***,a record/replay-based GUI test generation method is proposed to ensure that the extraction result has the correct GUI *** extract the feature implementation,a hierarchical genetic algorithm is proposed to find the code snippet that can pass all the tests and has the approximate smallest *** compare our method with two existing feature extraction *** result shows that our method can extract the correct implementation with the minimum size.A human-subject study is conducted to show the effectiveness and weaknesses of our method in helping users extract the features.

关键词： feature extraction genetic algorithm graphical user interface software reuse

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Understanding and Detecting Privacy Leakage Vulnerabilities in Hyperledger Fabric Chaincodes 35

Understanding and Detecting Privacy Leakage Vulnerabilities ...

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35th IEEE International Symposium on Software Reliability engineering, ISSRE 2024

作者： Chen, Ziming Li, Yue Gao, Jianbo Zhang, Jiashuo Wang, Ke Hu, Jianbin Guan, Zhi Chen, Zhong Peking University School of Computer Science Beijing China Taiyuan University of Technology College of Computer Science and Technology Taiyuan China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing China Peking University Chongqing Research Institute of Big Data Chongqing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350353884

The application on a blockchain cannot maintain secrecy because its data is replicated across all peers in the network. To remedy this problem, Hyperledger Fabric introduces private data collection (PDC) into its smart contract (i.e. chaincode) to facilitate applications that require privacy. However, recent studies have revealed that PDC is too complex for chaincode developers to fully understand and use correctly, leading to privacy leaks vulnerabilities. In this paper, we present an empirical study on the prevalence of PDC misuse in chaincodes by extracting privacy leakage cases from StackOverflow posts and Hyperledger Fabric repositories on GitHub. Subsequently, we systematically categorize the misuse of PDC into three categories of vulnerabilities resulting in the leakage of private data and provide formal definitions for them. Furthermore, we develop PDChecker, an automated security analysis framework for identifying the privacy and security vulnerabilities in Fabric chaincodes. We evaluated PDChecker on 956 real-world chaincodes applying PDC and found that 67.78% of them contain at least one privacy leakage vulnerability. In addition, PDChecker uncovered 10 zero-day vulnerabilities documented by the China National Vulnerability database. © 2024 IEEE.

关键词： Differential privacy

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