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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lei, Haijun Xie, Hai Zhao, Jia Zhao, Danrui Huang, Limin Lei, Baiying Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China Southwest University School of Computer Information & Science Shenzhen China

ISBN: (纸本)9798350337488

Fundus images are commonly used to document the presence and severity of various retinal degenerative diseases, where the fovea, optic disc (OD), and optic cup (OC) serve as important anatomical landmarks. Locating and segmenting these landmarks are crucial for clinical diagnosis and treatment. Many existing methods treat the recognition of the fovea, OD, and OC as separate tasks without incorporating any clinical prior knowledge related to various anatomical structures. In this paper, we propose a prior information guided coarse-to-fine dual-branch encoding network, which enables fovea localization and OD/OC segmentation. In coarse stage, we employ a dual-branch network consisting of convolutional neural network (CNN) and Transformer to encode local and global features, and then utilize multi-scale feature fusion techniques to merge the extracted semantic features, aiming to enhance the localization accuracy. In addition, we effectively use the distance information from each pixel to the landmark of interest, and output the results of distance map and heat map regression as prior information to further guide the network to learn the positional relationship between fovea and OD. In fine stage, we refine the region of interest (ROI) of the OD, balance the distribution of the OD and OC using polar coordinate transformation (PCT), extract critical boundary features using the boundary attention module (BAM), and improve the generalization performance of our method through model ensemble strategy. Extensive experimental results demonstrate that our proposed method outperforms existing state-of-the-art (SOTA) methods on the publicly available GAMMA and REFUGE datasets. © 2023 IEEE.

关键词： coarse-to-fine dual-branch fovea localization optic disc and cup segmentation Prior information

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Adversarial Machine Learning for Detecting Advanced Threats Inspired by StuxNet in Critical Infrastructure Networks 12

Adversarial Machine Learning for Detecting Advanced Threats ...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Ahmad, Hafiz Bilal Gao, HaiChang Latif, Naila Aziiz, Abdul Auraangzeb, Muhammad Khan, Muhammad Tanveer School of Computer Science and Technology Xidian University Shaanxi 710071 China School of Telecommunications and Engineering Xidian University Shaanxi 710071 China School of Computer Science and Engineering Southeast University Nanjing China School of Electrical Engineering Southeast University Nanjing China School of Software Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350330366

Inspired by the notorious StuxNet malware, this paper presents a unique and resilient architecture to address the imminent issue of identifying advanced threats within critical infrastructure networks. We use adversarial machine learning techniques to keep ahead of ever-changing threats. Our defense system's fundamental component is a Multi-Layer Perceptron (MLP) architecture that we've dubbed 'StuxNet.' This innovative method combines threat intelligence, anomaly detection, and deep learning to forecast and counter sophisticated attacks. The effectiveness of our method in bolstering the security of critical infrastructure networks is supported by empirical evaluations and real-world case studies. Importantly, the StuxNet-MLP architecture has a 98 percent detection accuracy for sophisticated threats. These results highlight the paramount significance of preventative cybersecurity measures in protecting mission-critical systems and provide useful insights for fending off sophisticated attacks. To ensure a safer and more resilient digital future, this study highlights the need to stay ahead in the continuous battle to secure key infrastructure. © 2024 IEEE.

关键词： Critical infrastructures

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Driving Scene Understanding with Traffic Scene-Assisted Topology Graph Transformer 24

Driving Scene Understanding with Traffic Scene-Assisted Topo...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Rong, Fu Peng, Wenjin Lan, Meng Zhang, Qian Zhang, Lefei National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China Hong Kong University of Science and Technology Hong Kong Hong Kong Horizon Robotics Beijing China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)9798400706868

Driving scene topology reasoning aims to understand the objects present in the current road scene and model their topology relationships to provide guidance information for downstream tasks. Previous approaches fail to adequately facilitate interactions among traffic objects and neglect to incorporate scene information into topology reasoning, thus limiting the comprehensive exploration of potential correlations among objects and diminishing the practical significance of the reasoning results. Besides, the lack of constraints on lane direction may introduce erroneous guidance information and lead to a decrease in topology prediction accuracy. In this paper, we propose a novel topology reasoning framework, dubbed TSTGT, to address these issues. Specifically, we design a divide-and-conquer topology graph Transformer to respectively infer the lane-lane and lane-traffic topology relationships, which can effectively aggregate the local and global object information in the driving scene and facilitate the topology relationship learning. Additionally, a traffic scene-assisted reasoning module is devised and combined with the topology graph Transformer to enhance the practical significance of lane-traffic topology. In terms of lane detection, we develop a point-wise matching strategy to infer lane centerlines with correct directions, thereby improving the topology reasoning accuracy. Extensive experimental results on Openlane-V2 benchmark validate the superiority of our TSTGT over state-of-the-art methods and the effectiveness of our proposed modules. The code is available at https://***/rongfu-dsb/TSTGT. © 2024 ACM.

关键词： Graph theory

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Enhancing Protein Solubility Prediction through Pre-trained Language Models and Graph Convolutional Neural Networks

Enhancing Protein Solubility Prediction through Pre-trained ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Qian, Yurong Huang, Zhijian Xiao, Xiaojun Deng, Lei Xinjiang University School of Software Urumqi China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Achieving optimal protein solubility is pivotal for efficient high-throughput purification, especially in industrial settings. However, conventional experimental techniques for assessing protein solubility in such contexts are not only costly but also time-intensive. Currently, numerous methods are available for predicting protein solubility, yet their effectiveness remains limited. Most of these approaches are predominantly sequence-based, failing to harness the invaluable structural insights inherent in proteins. Addressing these limitations, we introduce PPSol, an innovative protein solubility prediction methodology. Operating on protein sequences, PPSol employs ESM2 to predict protein contact maps, forming the basis for constructing protein graphs. Subsequently, well-established techniques are employed to predict protein feature representations as node features, including the utilization of the Position-Specific Scoring Matrix (PSSM). The resulting graph is fed into a graph convolutional neural network (GCN), enabling the acquisition of spatial structural information from proteins. Concurrently, ESM2-generated features undergo dimensional reduction via fully connected layers, integrating into every layer of the GCN for precise protein solubility prediction. Our approach excels through the fusion of pre-trained protein language models and GCNs, surpassing existing methodologies. Notably, PPSol attains state-of-the-art performance, showcasing a remarkable 2.8% enhancement in AUROC performance com-pared to prior strategies. © 2023 IEEE.

关键词： ESM2 GCN Protein contact map Protein solubility

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CAPS: An Efficient Whole-Program Critical Paths Search Framework for Large-Scale software 35

CAPS: An Efficient Whole-Program Critical Paths Search Frame...

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35th International Conference on software engineering and Knowledge engineering, SEKE 2023

作者： Li, Peiyang Liu, Zixin Su, Yuening Wang, Hao Jiang, Bo State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University China

Tracking the flow of external inputs in a program with taint-analysis techniques can help developers better identify potential security vulnerabilities in the software. However, directly using the static taint analysis provided by Clang Static Analyzer is inefficient for large-scale software due to the huge but redundant ExplodedGraph generated. Therefore, we propose an efficient Whole-Program Critical Paths Search (CAPS) framework. It first performs a set of optimizations to reduce the ExplodedGraph of each function. Then, it constructs a global exploded graph by inserting call edges among the reduced ExplodedGraphs for each function within the Neo4j graph database. Finally, it proposes loop removal and graph segmentation to optimize the search process for critical paths on the global exploded graph. Our experiments on 3 large-scale software show that CAPS can significantly improve the efficiency of critical path search for large-scale software. © 2023 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Static analysis

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Predicting Associations between circRNAs and Drug Sensitivity using Heterogeneous Graphs and Graph Attention Networks

Predicting Associations between circRNAs and Drug Sensitivit...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xiao, Xiaojun Qian, Yurong Huang, Zhijian Zheng, Rongtao Deng, Lei Xinjiang University School of Software Urumqi China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Discovering associations between circular RNAs (circRNAs) and cellular drug sensitivity is essential for understanding drug efficacy and therapeutic resistance. Traditional experimental methods to verify such associations are costly and time-consuming. Thus, the development of efficient computational methods for predicting circRNA-drug associations is crucial. In this study, we introduce a novel computational predictor called HETACDA, aimed at predicting potential circRNA-drug sensitivity associations. HETACDA constructs a heterogeneous graph network, incorporating the characteristic structure graph of drugs and circRNAs, along with the circRNA-drug sensitivity topology graph. By employing a graph convolutional network, the drug embedding vector is computed from the molecular structure of the drug. Through a graph attention mechanism, HETACDA assigns distinct attention weights to nodes in order to emphasize the contribution of various neighborhood nodes to the central node. Subsequently, an association score between circRNA-drug sensitivity is predicted using a three-layer fully connected neural network. Extensive experimental comparisons against several state-of-the-art methods highlight the effectiveness of our proposed framework. The availability of our source code and datasets on GitHub (https://***/xiaoxiaojun131421/HETACDA) facilitates replication and further research in this area. © 2023 IEEE.

关键词： circRNA-drug sensitivity associations graph attention mechanism heterogeneous graph network

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Hyperspectral Image Classification Using Multi-feature Fusion Residual Hypergraph Convolution Network 8

Hyperspectral Image Classification Using Multi-feature Fusio...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Zhu, Tianxing Liu, Qin Zhang, Lixiang Tongji University School of Software Engineering Shanghai China Northwestern Polytechnical University School of Computer Science Xi'an China

ISBN: (纸本)9798350397932

Convolutional neural network (CNN) has been broadly adopted on hyperspectral image (HSI) processing due to its impressive feature extraction capabilities. Nevertheless, it is still a challenge for CNN-based hyperspectral image classification methods to extract more effective spectral-spatial features considering all spectral bands. The recent advances of graph convolutional neural networks (GCN), a combination of CNN and graph representation learning, have shown great potential in the HSI. However, the existing GCN-based methods have lower accuracy than the mature CNN methods due problems such as overdependence on the adjacency matrix, usage of a single modal feature. In this paper, we propose the Multi-Feature Fusion Residual Hypergraph Convolution Network (MF-RHCN) for HSI classification. We present a novel Hypergraph Convolution Network that fuses multimodal features effectively, and automatically updates the hyperedge weights, which solves the difficulties encountered by the previous GCN-based hyperspectral classification methods. MF-RHCN first generates hyperedges from features of different modalities to construct a hypergraph representing multimodal features in HSI. Then, the HSI and the extracted hypergraph are input into the hypergraph convolutional neural network for learning. We enhance the hypergraph convolutions with residual connection and input mapping, which helps reduce the over-smoothing issue associated with deep hypergraph models. Sufficient experiments on three datasets have proved the effectiveness of MF-RHCN. © 2023 IEEE.

关键词： Convolution

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Improving efficiency in rationale discovery for Out-of-Distribution molecular representations

Improving efficiency in rationale discovery for Out-of-Distr...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Jiahui Li, Jiahe Du, Wenjie Zhang, Shuai Wu, Di Wang, Yang University of Science and Technology of China School of Software Engineering Suzhou China University of Science and Technology of China School of Computer Science and Technology Suzhou China

ISBN: (纸本)9798350337488

Graph Neural Networks (GNNs), as the dominant approach in Molecular Representation Learning (MRL), have exhibited remarkable efficacy in diverse tasks, including molecular property prediction and drug discovery. Considering the dynamic and diverse nature of test molecules in real-world contexts, the previous independently and identically distributed (i.i.d.) assumption for training and test molecules is not align with the requirements of practical applications in chemistry. Rationalization has been proposed to enhance the Out-of-Distribution (OOD) generalization of GNN, but a critical concern remains unexplored: low efficiency in rationale discovery. We consider this bottleneck is due to large search space and overly flexible modeling. Here, we introduce a framework called Molecule Stratifier and Invariant Rational Improver (MSIRI) to overcome the challenge. Specifically, MSIRI adopt vector quantization to obtain invariant rationales and the search space is narrowed by substructure and two assumptions based on subgraph matching. Experimental results on ten datasets demonstrate that our approach both achieves significant improvements across various GNN backbones and outperforms other six Out-of-Distribution method, clearly demonstrating its effectiveness in addressing the OOD challenges in MRL. © 2023 IEEE.

关键词： GNN invariant learning molecule representation learning Out-of-Distribution

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Competition for roadside camera monocular 3D object detection

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National science Review 2023年第6期10卷 34-37页

作者： Jinrang Jia Yifeng Shi Yuli Qu Rui Wang Xing Xu Hai Zhang Baidu Inc. Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University College of Software Jilin University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Accurate environment perception is a critical topic in autonomous driving and intelligent *** environmental perception methods mostly rely on on-board ***,limited by the installation height,thereare problems such as blind spots and unstable long-range perception in vehicle perceptual ***,with the rapid improvement of intelligent infrastructure,it has become possible to use roadside cameras for traffic environment *** from the increased height when compared with on-boardsensors,roadside cameras can obtain a larger perceptual field of view and realize long-range observation.

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Fix the Tests: Augmenting LLMs to Repair Test Cases with Static Collector and Neural Reranker 35

Fix the Tests: Augmenting LLMs to Repair Test Cases with Sta...

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

作者： Liu, Jun Yan, Jiwei Xie, Yuanyuan Yan, Jun Zhang, Jian State Key Laboratory of Computer Science Institute of Software China Chinese Academy of Sciences Technology Center of Software Engineering Institute of Software China China UCAS School of Intelligent Science and Technology Hangzhou Institute for Advanced Study China

ISBN: (纸本)9798350353884

During software evolution, it is advocated that test code should co-evolve with production code. In real development scenarios, test updating may lag behind production code changing, which may cause compilation failure or bring other troubles. Existing techniques based on pre-trained language models can be directly adopted to repair obsolete tests caused by such unsynchronized code changes, especially syntactic-related ones. However, the lack of task-oriented contextual information affects the repair accuracy on large-scale projects. Starting from an obsolete test, the key challenging task is precisely identifying and constructing Test-Repair-Oriented Contexts (TROCtxs) from the whole repository within a limited token *** this paper, we propose Synter (SYNtactic-breaking- changes-induced TEst Repair), a novel approach based on LLMs to automatically repair obsolete test cases via precise and concise TROCtxs construction. Inspired by developers' programming practices, we design three types of TROCtx: class context, usage context, and environment context. Given an obsolete test case to repair, Synter firstly collects the related code information for each type of TROCtx through static analysis techniques automatically. Then, it generates reranking queries to identify the most relevant TROCtxs, which will be taken as the repair-required key contexts and be input to the large language model for the final test *** evaluate the effectiveness of Synter, we construct a benchmark dataset that contains a set of obsolete tests caused by syntactic breaking changes. The experimental results show that Synter outperforms baseline approaches both on textual- and intent-matching metrics. With the augmentation of constructed TROCtxs, hallucinations are reduced by 57.1%. © 2024 IEEE.

关键词： Syntactics

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