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19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Zeng, Guoshu Yu, Yang Pan, Maolin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China

ISBN: (纸本)9789819623754

According to the three-tier structure of the cloud computing system, the request initiated by the tenant is first processed through the request scheduling system, and then forwarded to the cloud application service for execution. The resource scheduling system monitors the real-time status of cloud application service and then scales them accordingly. However, cloud workflow engine service, the key component in the cloud business process management system, is characterized by long startup times and high resource consumption. Unreasonable scaling strategies often result in problems such as scaling late, scaling excessively and so on. Therefore, to achieve elastic scaling of the cloud workflow engine, it is essential to predict future request traffic in advance and calculate corresponding future resource demands. For the future request traffic, since all requests initiated by tenants will pass through the request scheduling system, this paper believes that the future request traffic can be predicted using the status information from both request scheduling system and cloud application service. So finally, this paper proposed a G/G/c queue model-based scaling algorithm, which works with the EDF-based request scheduling system, to resolve the issue of elastic scaling in cloud workflow services. The scaling algorithm predicts the future resource demands using real-time data from the request scheduling system and cloud application service. This algorithm reduces average cloud resource supply while maintaining low SLA violation rate and QoS over-provision rate, which can fully meet the requirements of cloud service providers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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DTA-Net: Dual-Task Attention Network for Medical Image Segmentation and Classification

DTA-Net: Dual-Task Attention Network for Medical Image Segme...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haifeng Qi Siyuan Shen Zhijian Huang Lei Deng School of Software Xinjiang University Urumqi China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Medical image segmentation and classification are fundamental tasks in computer-aided diagnosis, where accurate segmentation plays a key role in identifying disease-related features and regions of interest, thus aiding subsequent classification. In this paper, we propose a novel Dual-Task Attention Network (DTA-Net), which simultaneously generates high-quality segmentation masks and performs image classification by incorporating Electronic Medical Records (EMR). The DTA-Net architecture introduces an innovative Shared Proxy Attention Module (SPAM), which leverages a shared mapping function to effectively encode the attention weights of both queries and keys for spatial and channel attention. This dual attention mechanism facilitates complementary learning and interdependence between spatial and channel features. Notably, the proxy projection mechanism within SPAM significantly reduces the computational complexity of spatial attention to a linear level. We validated our approach on three datasets: MSD Prostate for prostate segmentation, the RICORD dataset for lung segmentation, and the iCTCF COVID-19 dataset for both segmentation and classification tasks. Experimental results demonstrate that the proposed network achieves promising performance across all datasets. The source code is available at: https://***/HaifengQi/DTA-Net.

关键词： Training Image segmentation Computational modeling Source coding Biological systems Electronic medical records Computational complexity Medical diagnostic imaging Diseases Image classification

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SKDLog: Self-Knowledge Distillation-based CNN for Abnormal Log Detection 19

SKDLog: Self-Knowledge Distillation-based CNN for Abnormal L...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Han, Ningning Lu, Siyang Wang, Dongdong Wang, Mingquan Tan, Xiaoman Wei, Xiang Beijing Jiaotong University School of Computer and Information Technology Beijing China University of Central Florida Department of Computer Science OrlandoFL United States Beijing Jiaotong University School of Software Engineering Beijing China Southwest University College of Computer and Information Science College of Software Chongqing China

ISBN: (纸本)9798350346558

System logs are critical to system health diagnosis, especially for big data distributed systems. To detect anomalies in logs, log analysis has become a popular and practical approach. However, with the increasing complexity of system logs in real-world systems, more noisy data are generated. The noise disturbs the regular detection procedure and decreases the detection quality. Hence, it is more difficult to improve detection performance under such uncertain circumstance. To tackle this challenge, we propose SKDLog, a novel and effective log anomaly detection method equipped with self-knowledge distillation. This approach leverages the knowledge from soft labels and refined feature maps through knowledge distillation. Moreover, it helps the detection model better acquire latent information. To better exploit feature maps, an auxiliary self-teacher branch is incorporated into the framework. After the integration, the model achieves performance gain in log anomaly detection. To demonstrate the effectiveness, we compare SKDLog with the state-of-the-art log-based anomaly detection approaches on HDFS and Hadoop Application datasets. Our model outperforms other approaches on both recall and F1-score. Furthermore, we challenge problem solving by performing experiments on an industry dataset and an unstable dataset. The experimental results show that SKDLog more accurately detects abnormal logs from noisy data with high recall and F1-score. © 2022 IEEE.

关键词： Distillation

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IoT Device Fingerprinting From Periodic Traffic Using Locality-Sensitive Hashing

IoT Device Fingerprinting From Periodic Traffic Using Locali...

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

作者： Jianhui Ming Weiping Wang Linlin Zhang Yingjie Hu Shigeng Zhang School of Software Xinjiang University Urumqi China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the widespread adoption of IoT devices, their inadequate security measures make them increasingly susceptible to malicious attacks. Consequently, accurate device identification has become a critical task for safeguarding network security and privacy. This paper introduces IFPH, a novel method for IoT device fingerprinting and identification based on periodic traffic payload hashing. By exploiting the inherent periodicity in idle traffic, IFPH uses Discrete Fourier Transform (DFT) to extract the traffic's periodicity and applies Locality-Sensitive Hashing (LSH) to process packet payloads within each period. This method generates distinctive device fingerprints, facilitating efficient and reliable device identification. Unlike previous methods, IFPH addresses the inaccuracies associated with fixed-time window fingerprinting and eliminates the need for complex feature extraction or model training. Experimental results reveal that IFPH surpasses existing techniques, achieving accuracy and recall rates exceeding 95% on publicly available datasets.

关键词： Training Privacy Accuracy Discrete Fourier transforms Fingerprint recognition Feature extraction Object recognition Internet of Things Reliability Payloads

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Challenges of Processing Data Clumps within Plugin Architectures of Integrated Development Environment

arXiv

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arXiv 2024年

作者： Baumgartner, Nils Pulvermüller, Elke Software Engineering Research Group School of Mathematics/Computer Science/Physics Osnabrück University Germany

In this study, we explore advanced strategies for enhancing software quality by detecting and refactoring data clumps, special types of code smells. Our approach transcends the capabilities of integrated development environments, utilizing a novel method that separates the detection of data clumps from the source access. This method facilitates data clump processing. We introduce a command-line interface plugin to support this novel method of processing data clumps. This research highlights the efficacy of modularized algorithms and advocates their integration into continuous workflows, promising enhanced code quality and efficient project management across various programming and integrated development environments. © 2024, CC BY.

关键词： Project management

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ADiffGDA: Exploring Gene-Drug Associations via Adaptive Graph Diffusion Networks

ADiffGDA: Exploring Gene-Drug Associations via Adaptive Grap...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Che Zhang Yanhao Fan Xiaowen Hu Yurong Qian Lei Deng School of Software Xinjiang University Urumqi China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Exploring gene-drug associations is a key step in identifying new drug candidates, but traditional experimental methods are often expensive and time-consuming. While Graph Neural Network (GNN)-based models have demonstrated effectiveness in association prediction tasks, they face challenges in the information aggregation process. Existing GNN models either treat all nodes uniformly or rely on simple attention mechanisms to assign weights to neighboring nodes, limiting their capacity to capture complex relationships and improve performance. To address these limitations, we propose a novel adaptive graph diffusion network, ADiffGDA, for gene-drug association prediction. The model begins by randomly initializing embeddings for genes and drugs, which are then updated through neighborhood information aggregation. A key feature of ADiffGDA is the incorporation of a heat kernel, enabling each node to dynamically adjust aggregation weights based on its local structure. This approach allows the model to better capture variations in node types and local neighborhood patterns. Through extensive comparative experiments, we show that ADiffGDA outperforms existing state-of-the-art methods. Furthermore, case studies validate its effectiveness as a predictive tool, offering valuable insights for future biological experiments. The code and datasets for ADiffGDA are freely available at https://***/one-melon/ADiffGDA.

关键词： Drugs Heating systems Convolutional codes Adaptation models Adaptive systems Limiting Predictive models Graph neural networks Kernel Faces

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One Gray Code Fits All: Optimizing Access Time with Bi-Directional Programming for QLC SSDs

One Gray Code Fits All: Optimizing Access Time with Bi-Direc...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Shaoqi Li Tianyu Wang Yongbiao Zhu Chenlin Ma Yi Wang Zhaoyan Shen Zili Shao College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Technology Shandong University Qingdao China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Gray code, a voltage-level-to-data-bit translation scheme, is widely used in QLC SSDs. However, it causes the four data bits in QLC to exhibit significantly different read and write performance with up to 8 × latency variation, severely impacting the worst-case performance of QLC SSDs. This paper presents BDP, a novel Bi-Directional Programming scheme. Based on a fixed Gray code, BDP combines both the normal (forward) and reverse programming directions to enable runtime programming direction arbitration. Experimental results show that BDP can effectively improve the read and write performance of SSD compared to representative schemes.

关键词： Translation Runtime Europe Bidirectional control Programming Reflective binary codes

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FGRL-Net: Fine-Grained Personalized Patient Representation Learning for Clinical Risk Prediction Based on EHRs

FGRL-Net: Fine-Grained Personalized Patient Representation L...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Chio, KaKit Zhu, Wenhao He, Lihua Zhang, Dian Yang, Xu Luo, Wuman Macao Polytechnic University Faculty of Applied Sciences S.A.R Macao China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Province China Shenzhen University Department of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350337020

Personalized patient representation learning (PPRL) is a critical element in clinical risk prediction. It aims to obtain a complete portrait of each patient based on Electronic Health Records (EHR). Although existing works have achieved remarkable progress in healthcare prediction, there are still three major issues. First, feature correlation is crucial for risk prediction, but it has not yet been fully exploited by existing works. Second, variation pattern of dynamic feature contains useful information about patient's physical status, but adaptive pattern recognition is still a challenge. Third, existing works usually adopt a two-stage embedding process to process each dimension of the EHR data. However, some useful low-level information for PPRL will be lost. To address these issues, in this paper, we propose a fine-grained PPRL architecture named FG RL- N et for clinical risk prediction based on EHR. Specifically, we propose a Medical Feature Correlation Detection Module (FCM) to effectively learn the feature correlations for each patient and a Temporal Variation Pattern Recognition Module (TVM) to effectively detect the variation patterns of each dynamic feature. Moreover, we design a Fine-Grained Representation Mechanism (FGRM) to preserve the low-level information (from both feature and visit dimensions) useful for risk prediction. In addition, in the stage of data preprocessing, We utilize generic medical classification knowledge to classify numerical dynamic data. We conduct the in-hospital mortality experiment and the decompensation experiment on a real-world dataset. The experiment results show that the FGRL-Net outperforms state-of-the-art approaches. The source code is provided in github https://***/JackyChio/FGRL-Net. © 2023 IEEE.

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Fuzzy Overlapping Community Guided Subgraph Neural Network for Graph Classification

Fuzzy Overlapping Community Guided Subgraph Neural Network f...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xin Liu Yao Xiao Bo Huang Yan Zhang Zuping Zhang School of Computer Science and Engineering Central South University Changsha China School of Software Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Graph classification is of great significance to many real-world applications like drug discovery, for which subgraph neural networks are gaining increasing attention due to their strong power in capturing node attributes along with finer substructures for graph representation learning. In this study, focusing on the crucial substructure of community inherent in complex networks, a fuzzy overlapping community guided subgraph neural network, called FOCsubGNN, is innovatively proposed to learn their fine-grained representation. Specifically, fuzzy overlapping community detection enables the most realistic node relationships can be discovered for neighbor aggregation, which provides great potential to achieve accurate graph representation. On this basis, to extract discriminative features for more promising classification, we also bring out an intra-subgraph node discarding based pooling strategy, which allows the graph intrinsic hierarchy can be well integrated into these extracted features, and thus playing a positive role in promoting more superior classification performance. Extensive experiments on 6 popular benchmark datasets demonstrate the effectiveness of FOCsubGNN.

关键词： Representation learning Visualization Accuracy Neural networks Focusing Complex networks Benchmark testing

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DeTable: Table data extraction model based on deep 10

DeTable: Table data extraction model based on deep

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10th International Conference on Networks, Communication and Computing, ICNCC 2021

作者： Fan, Yuxuan Tan, Huobin Liu, Yu Zhang, Jingxuan School Of Software Beihang University China School Of Computer Science And Engineering China

ISBN: (纸本)9781450385848

The rapid development of the information age leads to the mass production and frequent transmission of data, which is difficult to deal with by human alone. With the rise and development of artificial intelligence, the use of data is becoming more efficient. Table, as a special data form, has attracted wide attention gradually. However, extracting data from table subimages presents a number of challenges, including accurately detecting table regions in the image, and then detecting and extracting information from detected table rows and columns. While some progress has been made in table detection, extracting table contents remains a challenge because it involves more fine-grained table structure identification. In this paper, DeTable: table data extraction model based on deep learning is proposed. The model uses the interdependence between the twin tasks of table detection and table structure recognition to divide the text region and the box-line region of the table. Then, rows based on semantic rules are extracted from the identified table regions. The proposed models and extraction methods were evaluated on publicly available ICDAR 2019 and Marmot table datasets and the most advanced results were obtained. © 2021 ACM.

关键词： Semantics

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