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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of Control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic Control Society.

关键词： Trucks

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A New Perspective of Deep Learning Testing Framework: Human-computer Interaction Based Neural Network Testing

A New Perspective of Deep Learning Testing Framework: Human-...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wei Kong Hu Li Qianjin Du Huayang Cao Xiaohui Kuang National Key Laboratory of Science and Technology on Information System Security Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Deep learning models have revolutionized various domains but have also raised concerns regarding their security and reliability. Adversarial attacks and coverage-based testing have been extensively studied to assess and enhance the dependability of deep neural networks. However, current research in this area has reached a state of stagnation. Adversarial attacks focus on exploiting vulnerabilities in models, while coverage-based testing aims to achieve comprehensive testing but overlooks application scenarios. Moreover, evaluating test cases solely based on their fault-revealing capability is insufficient. To address these limitations, we propose an innovative interdisciplinary framework that incorporates human-computer interaction methods in deep learning security testing. By considering the attributes of model application scenarios, we can design more effective test suites that intend to reveal the model's behavior across various scenarios, aiding in the identification of potential defects. Consequently, the test suite plays a crucial role in the testing process of deep learning models, contributing to the assurance of model robustness and reliability. Additionally, we establish a comprehensive evaluation metric for test suite quality, considering factors such as diversity and naturalness. This framework promotes reliable and secure deployment of deep learning models, fostering interdisciplinary collaboration between artificial intelligence and human-computer interaction.

关键词： Deep learning Human computer interaction Measurement Diversity reception Collaboration Robustness Security

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Drug-Target Interaction Prediction Based on Drug Subgraph Fingerprint Extraction Strategy and Subgraph Attention Mechanism 9th

Drug-Target Interaction Prediction Based on Drug Subgraph Fi...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Wang, Lizhi Zhang, Xiaolong Lin, Xiaoli Hu, Jing College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System Hubei Wuhan China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9783031466700

Drug discovery is a major focus of modern research, and predicting drug-target interactions is one of the strategies to facilitate this research process. Traditional laboratory methods have long time cycles and are relatively costly, so the use of high-precision virtual screening is essential. Previous virtual screens have only considered the sequence structure of the drug or the graph structure of the drug, without considering the role of the drug subgraph structure. Therefore, this paper adopts a method for extracting drug subgraphs and designs a strategy for extracting drug subgraph fingerprints. Then, the fingerprint vector data of the drugs are trained by the graph neural network to produce drug information vectors containing the spatial structure of the drug subgraph. Finally, the subgraph attention mechanism is used to update the information on protein targets, facilitating the model to extract more information about the target site. The experimental results show that the model can predict drug-target interactions well, outperforming the state-of-the-art model in all metrics. And it is also effective in both classification prediction and affinity regression prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Graph neural networks

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Fuzzing DBMS via NNLM 7

Fuzzing DBMS via NNLM

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Li, Yabin Nie, Yuanping Kuang, Xiaohui Beijing University of Posts and Telecommunications School of Computer Science Beijing China National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

Vulnerabilities in database management system (DBMS) can cause serious security problems affecting hundreds of millions of software systems. Fuzzing is an effective vulnerability mining technology, but few studies utilize neural network language model (NNLM) to fuzz DBMS. In this paper, we explore this technical roadmap and use the sequence model to automatically generate test cases to fuzz DBMS. One of the advantages of the method is that it can effectively test the DBMS in a black-box situation. We implemented our tool NNFuzz and evaluated it on SQLite. Experimental results show that NNFuzz can generate valid test cases and achieve higher code coverage than the initial training set. © 2022 IEEE.

关键词： Database systems

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A new class of generalized quasi-variational inequalities with applications to Oseen problems under nonsmooth boundary conditions

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Science China Mathematics 2024年第2期67卷 315-338页

作者： Shengda Zeng Akhtar A.Khan Stanislaw Migórski Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal UniversityYulin 537000China Faculty of Mathematics and Computer Science Jagiellonian University in Krak´owKrak´ow 30348Poland Center for Applied and Computational Mathematics School of Mathematical SciencesRochester Institute of TechnologyRochesterNY 14623USA College of Sciences Beibu Gulf UniversityQinzhou 535000China

In this paper, we study a generalized quasi-variational inequality (GQVI for short) with twomultivalued operators and two bifunctions in a Banach space setting. A coupling of the Tychonov fixedpoint principle and the Katutani-Ky Fan theorem for multivalued maps is employed to prove a new existencetheorem for the GQVI. We also study a nonlinear optimal control problem driven by the GQVI and givesufficient conditions ensuring the existence of an optimal control. Finally, we illustrate the applicability of thetheoretical results in the study of a complicated Oseen problem for non-Newtonian fluids with a nonmonotone andmultivalued slip boundary condition (i.e., a generalized friction constitutive law), a generalized leak boundarycondition, a unilateral contact condition of Signorini’s type and an implicit obstacle effect, in which themultivalued slip boundary condition is described by the generalized Clarke subgradient, and the leak boundarycondition is formulated by the convex subdifferential operator for a convex superpotential.

关键词： generalized quasi-variational inequality existence theorem optimal control Kakutani-Ky Fan theorem Oseen problem non-Newtonian fluid nonmonotone slip boundary condition

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Opnet: Deep Occlusion Perception Network with Boundary Awareness for Amodal Instance Segmentation

Opnet: Deep Occlusion Perception Network with Boundary Aware...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shihui Zhang Ziteng Xue Yuhong Jiang Houlin Wang School of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

The Amodal Instance Segmentation (AIS) task aims to infer the visible and occluded regions of an object instance. Existing AIS methods typically focus on directly predicting visible and occluded regions or leveraging prior knowledge to guide predictions. However, these methods often ignore the perception of occluded views, leading to inaccurate results. To address this issue and achieve high-quality AIS, we propose a boundary-aware Occlusion Perception Network (OPNet). OPNet consists of three main components: the Dynamic Feature Augmentation Pyramid (DFAP), the Dual-path Boundary Aware Module (DBAM), and the Shape-guide Refinement Module (SRM). Specifically, DBAM employs an occlusion-perception strategy to learn discriminative features with boundary information, enabling it to distinguish occlusion from multiple views. Additionally, DFAP and SRM optimize the results by enhancing feature aggregation and imposing geometric constraints. Experiments on the D2SA, KINS, and CWALT datasets show that OPNet significantly outperforms state-of-the-art AIS methods that without prior knowledge. Code is available at https://***/ZitengXue/OPNet.

关键词：

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Multimodal Aspect-Based Sentiment Analysis under Conditional Relation 31

Multimodal Aspect-Based Sentiment Analysis under Conditional...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Xinjing Li, Ruifan Ye, Shuqin Zhang, Guangwei Wang, Xiaojie School of Artificial Intelligence Beijing University of Posts and Telecommunications China School of Computer Science Beijing University of Posts and Telecommunications China Engineering Research Center of Information Networks Ministry of Education China Key Laboratory of Interactive Technology and Experience System Ministry of Culture and Tourism China

ISBN: (纸本)9798891761964

Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to extract aspect terms from text-image pairs and identify their sentiments. Previous methods are based on the premise that the image contains the objects referred by the aspects within the text. However, this condition cannot always be met, resulting in a suboptimal performance. In this paper, we propose COnditional Relation based Sentiment Analysis framework (CORSA). Specifically, we design a conditional relation detector (CRD) to mitigate the impact of the unmet conditional image. Moreover, we design a visual object localizer (VOL) to locate the exact condition-related visual regions associated with the aspects. With CRD and VOL, our CORSA framework takes a multi-task form. In addition, to effectively learn CORSA we conduct two types of annotations. One is the conditional relation using a pretrained referring expression comprehension model;the other is the bounding boxes of visual objects by a pretrained object detection model. Experiments on our built C-MABSA dataset show that CORSA consistently outperforms existing methods. The code and data are available at https://***/Liuxj-Anya/CORSA. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Optimized Design of a Haptic Unit for Vibrotactile Amplitude Modulation

Optimized Design of a Haptic Unit for Vibrotactile Amplitude...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Huang, Jingchen Fang, Yun Guo, Weichao Qiao, Zhijian Sheng, Xinjun School of Mechanical Engineering Meta Robotics Institute Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration Shanghai200240 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665481090

Communicating information to users is a crucial aspect of human-machine interaction. Vibrotactile feedback encodes information into spatiotemporal vibrations, enabling users to perceive tactile sensations. It offers advantages such as lightweight, wearability, and high stability, with broad applications in sensory substitution, virtual reality, education, and healthcare. However, existing haptic unit designs lack amplitude modulation capabilities, which limits their applications. This paper proposed an optimized design of the haptic unit from the perspective of vibration amplitude modulation. A modified elastic model was developed to describe the propagation and attenuation mechanisms of vibration in the skin. Based on the model, two types of hierarchical architectural design were proposed. The design incorporated various materials arranged in multiple layers to amplify or attenuate the vibration amplitude as it traveled through the structure. An experimental platform was built to evaluate the performance of the optimized design. © 2024 IEEE.

关键词： Virtual reality

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Research on Task Scheduling Algorithms for Cloud-Edge Collaboration 19th

Research on Task Scheduling Algorithms for Cloud-Edge Collab...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Lu, Shuai Zhou, Haibo Zhao, Shuaishuai Xu, Wangbei Fang, Kai Tianjin300384 China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China College of Mathematics and Computer Sciences Zhejiang A&F University Hangzhou311300 China

ISBN: (纸本)9783031651229

With the advent of the 5G era, the development of IoT technology has been accelerated. Due to the continuous increase in the amount of data waiting to be processed from the edge, edge nodes may struggle to handle such a vast amount of data. Therefore, the technology of cloud-edge collaboration has emerged, and how to achieve cloud-edge collaborative task scheduling has become a current research hotspot. This article provides a detailed exposition of the relevant work on task scheduling in the cloud-edge environment, and outlines the common optimization objectives in the cloud-edge collaboration scenario. The methods used to solve task scheduling problems are classified and summarized, including heuristic, heuristic algorithm based on linear programming, and meta-heuristic algorithms. The advantages and disadvantages of each algorithm are analyzed. Finally, the development trends of large-scale task scheduling in the cloud-edge environment are discussed, providing valuable insights for achieving real-time performance, efficiency, and energy conservation in the Internet of Things. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Scheduling algorithms

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Exploring the usefulness of light field super-resolution for object detection

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The Journal of China Universities of Posts and Telecommunications 2021年第5期28卷 68-81页

作者： Zhang Wenzhe Shi Fan Zhao Meng Chen Shengyong Engineering Research Center of Learning-Based Intelligent System of Ministry of Education Tianjin University of TechnologyTianjin 300384China The Key Laboratory of Computer Vision and System of Ministry of Education Tianjin University of TechnologyTianjin 300384China School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China The MOE Key Laboratory of Weak-Light Nonlinear Photonics Nankai UniversityTianjin 300457China

In order to solve the impact of image degradation on object detection, an object detection method based on light field super-resolution(LFSR) is proposed. This method takes LFSR as an image enhancement step to provide high-quality images for object detection without using expensive imaging equipment. To evaluate this method, three types of objects: person, bicycle, and car, are chosen and the results are compared from 5 parts: detected object quantity, mean confidence score, detection results in different scenes, error detection, and detection results from different images sizes and detection speed. Experimental results based on the common object in context(COCO) dataset show that the method incorporated LFSR improves performance of object detection models.

关键词： light-field(LF) super-resolution(SR) object detection RetinaNet YOLOv3 TinyYOLOv3

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