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10th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2024

作者： Deng, Shaojun Liu, Wanwei Zhang, Miaomiao College of Computer Science and Technology National University of Defense Technology Changsha China School of Software Engineering Tongji Uniersity Shanghai China

ISBN: (纸本)9789819606016

Neural-symbolic systems (NSSs), which are typically cyber-physical systems integrated with artificial intelligence modules, have received much attention in both academic and industrial fields. However, thorough verification (such as model checking) of such systems in general leads to prohibitively high costs due to the scale of the state space, and this makes it barely accomplishable within desired time-span. Consequently, light-weight verification techniques are introduced to deal with such cases, e.g., runtime verification (RV, for short). In this paper, we investigate an RV framework for NSSs against signal temporal logic (STL) properties. To guarantee that the on-line monitoring could be accomplished in time, we utilize Euler-prediction to sample the system under scrutiny at discrete time-steps. Our framework supports both qualitative and quantitative on-line monitoring of STL specifications, and thus it terminates whenever a property is satisfied and/or violated by a finite prefix of the trajectory, and it can also provide the bounds of the quantitative satisfaction/violation w.r.t. the remaining execution. Notably, for the so-called piece-wise linear NSSs against affine STL formulas, our approach is exact. On top of it, a prototype tool is implemented, and is experimentally evaluated. The results demonstrate the feasibility of the presented runtime verification approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Model checking

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Budget-aware Scheduling Algorithm Using Negative Offset Mechanism for Snake Optimization in Heterogeneous Cloud 22

Budget-aware Scheduling Algorithm Using Negative Offset Mech...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Longxin Zhang, Yanfen Lu, Xiaotong Tan, Runti Huang, Xianming Chen, Jianguo Hunan University of Technology College of Computer Science Zhuzhou China Sun YatSen University School of Software Engineering Zhuhai519082 China

ISBN: (纸本)9798331509712

Cloud computing, as a cutting-edge computing paradigm, offers substantial data processing and storage capabilities. In a heterogeneous cloud environment, the diversity among cloud platforms results in varying task execution times, posing challenges in minimizing workflow makespan under budget constraints. On this basis, a novel meta-heuristic optimization algorithm, named snake optimizer (SO), is proposed for workflow scheduling in the cloud. Then, a negative offset mechanism is designed to dynamically guide the offset of individual positions during population update to prevent falling into local optimums, thereby optimizing the search for feasible solutions and improving the success rate. Finally, using the negative offset mechanism, a snake optimization budget-aware scheduling algorithm (NO-SO) is developed to schedule budget-constrained workflows in heterogeneous cloud computing environments and minimize the makespan. A series of comparative experiments conducted on real-world scientific workflows demonstrates that the NO-SO algorithm enhances the success rate in finding a feasible solution by 38.89% and 34.45% compared with the advanced MG-PRO algorithm and the original SO algorithm, respectively. Moreover, it achieves an average reduction in makespan of 30.30% and 32.19%. © 2024 IEEE.

关键词： Cloud platforms

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A Comparative Analysis of Query Generation Methods for IoT Middleware Evaluation 17

A Comparative Analysis of Query Generation Methods for IoT M...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： De Silva, Ravindi Zaslavsky, Arkady Loke, Seng W. Jayaraman, Prem Prakash Deakin University School of Information Technology Melbourne Australia Swinburne University of Technology Department of Computer Science and Software Engineering Melbourne Australia

ISBN: (纸本)9798350351637

The rapid expansion of Internet of Things (IoT) applications necessitates the demand for efficient IoT middleware platforms, especially Context Management Platforms (CMPs) for accessing real-time contextual information. Current IoT middleware evaluation methods lack accessibility, posing a significant challenge for standardized performance analysis of CMPs. This paper introduces a novel scenario-based context query generation approach for assessing the data retrieval performance of a CMP. The proposed approach enables scene creation using either IoT data or real-world images, employing both data-driven and image-driven scene generation methods. This paper presents the challenges, limitations, and applicability of each approach in scene generation through a qualitative evaluation. The scenes are transformed into scene graphs, enabling inference to identify situations. Both approaches generate queries reflecting dynamic real-world situations, allowing for random query loads to evaluate CMPs fairly. This paper validates the proposed approach using real-world IoT datasets and camera images with a focus on bicycle riding safety use cases. The evaluation provides insights into the contextual completeness of scene graph representations crucial for accurately capturing real-world situations. The proposed approach generates queries of varying complexity, with the only difference being the underlying mechanism of scenario generation, providing a streamlined and user-friendly method for evaluating the data retrieval performance of CMPs. © 2024 IEEE.

关键词： Middleware

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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Distributed Truss Computation in Dynamic Graphs

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Tsinghua science and Technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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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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Multimodal Relation Extraction via a Mixture of Hierarchical Visual Context Learners 24

Multimodal Relation Extraction via a Mixture of Hierarchical...

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33rd ACM Web Conference, WWW 2024

作者： Liu, Xiyang Hu, Chunming Zhang, Richong Sun, Kai Mensah, Samuel Mao, Yongyi SKLSDE School of Software Beihang University Beijing China SKLSDE School of Computer Science Beihang University Beijing China School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China Department of Computer Science University of Sheffield South Yorkshire Sheffield United Kingdom School of Electrical Engineering and Computer Science University of Ottawa OttawaON Canada

ISBN: (纸本)9798400701719

Multimodal relation extraction is a fundamental task of multimodal information extraction. Recent studies have shown promising results by integrating hierarchical visual features from local regions, like image patches, to the broader global regions that form the entire image. However, research to date has largely ignored the understanding of how hierarchical visual semantics are represented and the characteristics that can benefit relation extraction. To bridge this gap, we propose a novel two-stage hierarchical visual context fusion transformer incorporating the mixture of multimodal experts framework to effectively represent and integrate hierarchical visual features into textual semantic representations. In addition, we introduce the concept of hierarchical tracking maps to facilitate the understanding of the intrinsic mechanisms of image information processing involved in multimodal models. We thoroughly investigate the implications of hierarchical visual contexts through four dimensions: performance evaluation, the nature of auxiliary visual information, the patterns observed in the image encoding hierarchy, and the significance of various visual encoding levels. Empirical studies show that our approach achieves new state-of-the-art performance on the MNRE dataset. © 2024 ACM.

关键词： Encoding (symbols)

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3D Atrous Spatial Pyramid Pooling based Multi-Scale Feature Fusion Network for Hyperspectral Image Classification

3D Atrous Spatial Pyramid Pooling based Multi-Scale Feature ...

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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

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

ISBN: (纸本)9781510668898

The convolutional neural network (CNN) has been widely adopted in the processing of hyperspectral images (HSI) due to its remarkable capabilities in feature extraction. However, CNN-based methods for HSI classification still face the challenge of extracting spectral-spatial features that are more effective while considering all spectral bands. This paper reviews various spectral and spectral-spatial classifiers that have been developed to overcome the challenges of HSI classification, including the limitations of handcrafted features and the problems caused by spectral variability and nonlinearity. The paper proposes a novel method, the 3D ASPP Multi-Scale Feature Fusion Network (3A-MFFN), which simultaneously preserves high-resolution spatial detail information and high-level semantic information of HSI. The proposed network extracts and fuses multi-scale features for HSI classification using a multi-scale 3D ASPP convolution block to fuse different levels of feature output. The paper presents experiments and analysis to demonstrate the effectiveness of the proposed method. The results suggest that the 3A-MFFN algorithm can significantly enhance the classification accuracy of HSI with limited samples, making it a valuable contribution to the hyperspectral remote sensing community. © 2023 SPIE.

关键词： Convolution

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Prior Information Guided Coarse-to-fine Dual-branch Encoding Network for Fovea Localization and Optic Disc/Cup Segmentation

Prior Information Guided Coarse-to-fine Dual-branch Encoding...

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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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Research to Practice of Blended Learning in computer Programming Course 53

Research to Practice of Blended Learning in Computer Program...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Zhang, Liang Chen, Tianyi Zong, Yue Zhang, Lijun School of Computer Science and Engineering Beihang University Beijing China School of Aeronautic Science and Engineering Beihang University Beijing China School of Software Beihang University Beijing China

ISBN: (纸本)9798350336429

This research to practice WIP paper presented a Blended Learning (BL) approach to teaching Data Structures and Programming in Python (DSPP) course. This approach increased students' learning engagement, academic performance, and course satisfaction. There is an intense need to study the BL approach in higher education, especially during the COVID-19 epidemic. Although BL pedagogy has achieved great success in college, there needs to be more quantitative research and practice in the computer programming course. This paper takes the DSPP course as an example to introduce the BL approach in detail. First, we designed the BL process for the computer programming course, which includes specific pre-class, in-class, and after-class activities. Second, we divide a course's knowledge and BL activities into remember, understand, apply, analyze, evaluate, and create levels according to Bloom's Taxonomy. So the knowledge and activities can be corresponding according to the cognitive level. We take a chapter in the DSPP course as an example to introduce it in detail. Third, to support the efficient implementation of BL activities, we have designed and developed several online learning systems, including an Online Judge system (OJ), Question&Anwser website (Q&A), and Peer Review Studio (PRS). To assess the effectiveness of the BL approach, we conducted the study at the DSPP course for undergraduate students in 2021 and 2023. In 2021, we taught the DSPP course in traditional pedagogy. In 2023, we began to implement BL pedagogy. By analyzing students' learning data, we compared the effect of the two pedagogy. We use the nonparametric independent-samples Kruskal-Wallis test to measure the changes in learning engagement, course performance, and student satisfaction. The results show that they were significantly improved in 2022. The course questionnaire of 2022 shows that most students prefer the BL approach. © 2023 IEEE.

关键词： Active Learning Blended Learning Bloom's Taxonomy computer programming Online Learning System

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