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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ziyu Fan Yuanpeng Zhang Yahan Li Zeyu Zhong Lei Deng School of Computer Science and Engineering Central South University Changsha China School of Software Xinjiang University Wulumuqi China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

This study investigates the role of circular RNAs (circRNAs) in drug sensitivity, with a focus on their potential to inform personalized medicine. While current methods for identifying circRNA-drug sensitivity associations are resource-intensive, we propose LSNSCDA, a novel prediction algorithm that integrates Local Smoothing Graph Neural Networks (LS-GNN) and Credible Negative Sampling (CNS) to improve prediction accuracy. Our approach overcomes the challenges of fixed-length propagation in graph neural networks and the unreliability of randomly sampled negative instances. Experimental results show that LSNSCDA outperforms existing models, providing more reliable predictions and valuable insights into cancer treatment. Extensive evaluation confirms the effectiveness of each component of our model, while case studies further demonstrate its practical applicability. The source code and dataset are available at https://***/ZiyuFanCSU/LSNSCDA.

关键词： Drugs Sensitivity Smoothing methods Accuracy RNA Precision medicine Predictive models Prediction algorithms Graph neural networks Reliability

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Thermal Simulation Computing Network Based on Deep Learning

Thermal Simulation Computing Network Based on Deep Learning

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International Conference on Digital Home (ICDH)

作者： Ruomei Wang Yi Teng Yaoquan Zhang Fan Zhou School of Software and Engineering School of Computer Science Guangdong University of Education Guangzhou China National Engineering Research Center of Digital Life School of Computer Science Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331509873

ISBN: (纸本)9798331509880

The heat and moisture transfer simulation about human body and clothing is a method to simulate the regulation mechanism of human body, and heat and moisture performance of clothing in thermal environment by using computer technology. According to the simulation results, it can be used to the monitor and predicate the key physiological data of human body, and the functional clothing design. Some mathematical models about this area have been built. However, a large number of differential equations are involved to be used to the numerical computation in the simulation process, which leads to high computational complexity, large memo-ry consumption and long computing time. With the expansion of computing scale, these problems will become more obvious and directly affect the practical application of simulation models. The main work of this paper is to study the efficient solution method based on neural network, and proposed a deep learning-based differential equation computing network. The network uses differential equation as the constraint of the loss function of neural network, and integrates the differential equation into the training and prediction process of the network. The experimental results show that the proposed network can reduce the computational complexity of the simulation model and improve the computational efficiency.

关键词： Training Computational modeling Clothing Neural networks Moisture Differential equations Mathematical models Numerical models Computational complexity Heat transfer

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Fuzzy Overlapping Community Guided Hierarchical Self-Attention Pooling

Fuzzy Overlapping Community Guided Hierarchical Self-Attenti...

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Intelligent Computing and Human-computer Interaction (ICHCI), International Conference on

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

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Graph pooling aims at extracting vital information for graph coarsening, and thus helping graph neural networks to improve their graph representation ability. However, existing methods either compress similar nodes by a clustering algorithm or discard unimportant nodes by an importance score, resulting in the coarsened graph suffering from different issues. Therefore, aiming at the goal of breaking through their limitation, this paper develops a fuzzy overlapping community guided hierarchical self-attention pooling, namely FOCoHSAP. FOCoHSAP performs node selection within every detected community rather than the entire graph, which allows for an explicit preservation of both topology hierarchy and detailed node information. Especially, FOCoHSAP introduces fuzzy overlapping community detection algorithm to explore those fine-grained relationships between nodes and communities, allowing for a more promising learning of graph representation that can be utilized for accurate graph classification.

关键词： Human computer interaction Accuracy Clustering algorithms Graph neural networks Topology Data mining Detection algorithms

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A Comparative Study of Different Optimisers on Convolutional Neural Networks for Bird Classification

A Comparative Study of Different Optimisers on Convolutional...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Singh, Neelam Srivastava, Rohit Mahdi, Hussain Falih Sati, Subhangi Joshi, Purvika Sar, Ayan Singh, Devansh Choudhury, Tanupriya Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun 248002 India School of Computer Science University of Petroleum and Energy Studies Bidholi Uttarakhand Dehradun 248007 India University of Diyala Department of Computer and Software Engineering Baquba Iraq Thapar University Punjab Patiala 147001 India

ISBN: (纸本)9798350379716

The advantage of CNNs in classifying bird species through images has received public attention in the past several years due to the capability to get minute details from images. Still, CNN performance is greatly dependent on the type of optimiser selected during the training phase. This paper is focused on an experimental comparison of several optimisers like SGD, Adam and Rmsprop, as well as Adagrad, to see how they affect the classification accuracy and detection of bird species using CNNs. A large dataset of images featuring different bird species, which encompasses the different types of birds, is used to create robust results. Through extensive tests and evaluations, we estimate the speed of convergence, determination of stability, and classification performance of each optimiser and, finally, will assess the effectiveness of the algorithms. The findings of this work give great information about deciding on the best optimiser to fit the purpose of training a CNN, which the automation of a classification system depends on;the progress of such systems is one step closer to the automation of the bird species identification system. © 2024 IEEE.

关键词： Adagrad Adam Bird classification Convolutional Neural Networks empirical evaluation Gradient Descent model performance Nadam RSM props SGD

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Motif-driven Subgraph Structure Learning for Graph Classification

arXiv

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arXiv 2024年

作者： Zhou, Zhiyao Zhou, Sheng Mao, Bochao Chen, Jiawei Sun, Qingyun Feng, Yan Chen, Chun Wang, Can College of Computer Science Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China School of Computer Science and Engineering Beihang University China

To mitigate the suboptimal nature of graph structure, Graph Structure Learning (GSL) has emerged as a promising approach to improve graph structure and boost performance in downstream tasks. Despite the proposal of numerous GSL methods, the progresses in this field mostly concentrated on node-level tasks, while graph-level tasks (e.g., graph classification) remain largely unexplored. Notably, applying node-level GSL to graph classification is non-trivial due to the lack of find-grained guidance for intricate structure learning. Inspired by the vital role of subgraph in graph classification, in this paper we explore the potential of subgraph structure learning for graph classification by tackling the challenges of key subgraph selection and structure optimization. We propose a novel Motif-driven Subgraph Structure Learning method for Graph Classification (MOSGSL). Specifically, MOSGSL incorporates a subgraph structure learning module which can adaptively select important subgraphs. A motif-driven structure guidance module is further introduced to capture key subgraph-level structural patterns (motifs) and facilitate personalized structure learning. Extensive experiments demonstrate a significant and consistent improvement over baselines, as well as its flexibility and generalizability for various backbones and learning procedures. The code is available in the https://***/OpenGSL/OpenGSL. © 2024, CC BY.

关键词： Structural optimization

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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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EDF and G/G/c Queue Model-Based Auto-scaling Algorithm for Elastic Cloud Workflow Service 19th

EDF and G/G/c Queue Model-Based Auto-scaling Algorithm for...

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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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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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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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