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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Wang Lin Zhengfeng Yang Xin Chen Qingye Zhao Xiangkun Li Zhiming Liu Jifeng He School of Information Science and Technology Zhejiang Sci-Tech University Shanghai Key Lab of Trustworthy Computing East China Normal University State Key Laboratory for Novel Software Technology Nanjing University Center for Research and Innovation in Software Engineering Southwest University

ISBN: (纸本)9781728132945

There is a pressing need to verify robustness of classification deep neural networks (CDNNs) as they are embedded in many safety-critical applications. Existing robustness verification approaches rely on computing the over-approximation of the output set, and can hardly scale up to practical CDNNs, as the result of error accumulation accompanied with approximation. In this paper, we develop a novel method for robustness verification of CDNNs with sigmoid activation functions. It converts the robustness verification problem into an equivalent problem of inspecting the most suspected point in the input region which constitutes a nonlinear optimization problem. To make it amenable, by relaxing the nonlinear constraints into the linear inclusions, it is further refined as a linear programming problem. We conduct comparison experiments on a few CDNNs trained for classifying images in some state-of-the-art benchmark-s, showing our advantages of precision and scalability that enable effective verification of practical CDNNs.

关键词： linear programming Verification Robustness Neural network Scalability

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Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends

MedComm

引用

MedComm 2025年第6期6卷 e70247页

作者： Ruoyun Wang Fanxuan Chen Haoman Chen Chenxing Lin Jincen Shuai Yutong Wu Lixiang Ma Xiaoqu Hu Min Wu Jin Wang Qi Zhao Jianwei Shuai Jingye Pan Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China. Wenzhou Medical University Wenzhou China. UCSC Baskin School of Engineering University of California Santa Cruz California USA. Department of Anatomy Histology & Embryology School of Basic Medical Sciences Fudan University Shanghai China. Department of Medicine Harvard Medical School and Brigham and Women's Hospital Boston Massachusetts USA. Joint Research Centre on Medicine The Affiliated Xiangshan Hospital of Wenzhou Medical University Ningbo China. Stony Brook University Stony Brook New York USA. School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China. Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province Wenzhou China. Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou China. Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization Wenzhou China.

The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possibilities for early disease diagnosis. Early detection is especially important as the incidence of breast cancer increases. However, DBT also presents challenges in terms of poorer results for dense breasts, increased false positive rates, slightly higher radiation doses, and increased reading times. Deep learning (DL) has been shown to effectively increase the processing efficiency and diagnostic accuracy of DBT images. This article reviews the application and outlook of DL in DBT-based breast cancer screening. First, the fundamentals and challenges of DBT technology are introduced. The applications of DL in DBT are then grouped into three categories: diagnostic classification of breast diseases, lesion segmentation and detection, and medical image generation. Additionally, the current public databases for mammography are summarized in detail. Finally, this paper analyzes the main challenges in the application of DL techniques in DBT, such as the lack of public datasets and model training issues, and proposes possible directions for future research, including large language models, multisource domain transfer, and data augmentation, to encourage innovative applications of DL in medical imaging.

关键词： deep learning digital breast tomosynthesis early breast cancer screening medical image analysis public database

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Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks

Deep Reinforcement Learning for Fresh Data Collection in UAV...

引用

IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Mengjie Yi Xijun Wang Juan Liu Yan Zhang Bo Bai Science and Technology on Communication Network Laboratory Shijiazhuang Hebei China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai China School of Electrical Engineering and Computer Science Ningbo University Zhejiang China Theory Lab (FKA Future Network Theory Lab) 2012 Labs Huawei Technologies Co. Ltd. Hong Kong

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical applications. How to maintain the information freshness is a challenging issue. In this paper, we investigate the fresh data collection problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the sensors to collect status update packets within a given duration while maintaining a non-negative residual energy. We formulate a Markov Decision Process (MDP) to find the optimal flight trajectory of the UAV and transmission scheduling of the sensors that minimizes the weighted sum of the age of information (AoI). A UAV-assisted data collection algorithm based on deep reinforcement learning (DRL) is further proposed to overcome the curse of dimensionality. Extensive simulation results demonstrate that the proposed DRL-based algorithm can significantly reduce the weighted sum of the AoI compared to other baseline algorithms.

关键词： Sensors Trajectory Data collection Unmanned aerial vehicles Internet of Things Wireless sensor networks Energy consumption

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autoC: an efficient translator for model checking deterministic scheduler based OSEK/VDX applications

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Science China(information Sciences) 2018年第5期61卷 141-155页

作者： Haitao ZHANG Zhuo CHENG Guoqiang LI Shaoying LIU School of Information Science and Engineering Lanzhou University School of Information Science Japan Advanced Institute of Science and Technology School of Software Shanghai Jiao Tong University Faculty of Computer and Information Sciences Hosei University

The OSEK/VDX automotive OS standard has been widely adopted by many automobile manufacturers, such as BMW and TOYOTA, as the basis for designing and implementing a vehicle-mounted OS. With the increasing functionalities in vehicles, more and more multi-task applications are developed based on the OSEK/VDX OS. Currently, ensuring the reliability of the developed applications is becoming a challenge for developers. As to ensure the reliability of OSEK/VDX applications, model checking as a potential solution has attracted great attention in the automotive industry. However, existing model checkers are often unable to verify a large-scale OSEK/VDX application that consists of many tasks, since the corresponding application model too complex. To make existing model checkers more scalable in verifying large-scale OSEK/VDX applications, we describe a software tool named auto C to tackle this problem by automatically translating a multi-task OSEK/VDX application into an equivalent sequential model. We conducted a series of experiments to evaluate the efficiency of auto C. The experimental results show that auto C is not only capable of efficiently sequentializing OSEK/VDX applications, but also of improving the scalability and efficiency of existing model checkers in verifying large-scale OSEK/VDX applications.

关键词： OSEK/VDX application deterministic scheduler software formal method model checking sequentialization

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Priority-Based Optimization of I/O Isolation for Hybrid Deployed Services 15th

Priority-Based Optimization of I/O Isolation for Hybrid Depl...

引用

15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Jiancheng Li, Youhuizi Zhou, Li Ren, Zujie Wan, Jian Wang, Yuan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Lab Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co.Ltd Hangzhou China

ISBN: (纸本)9783030301453

With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high efficiency. To improve the resources utilization, virtualization technology like container is used and it enables multiple services running in the same physical machine. However, since the host physical machine is shared by several services, the resource competition is inevitable. Isolation is an effective solution, but the weak isolation mechanisms of container cannot handle such complicated scenarios. In the worst situation, the performance of services cannot meet the requirements and the system may crash. In order to solve this problem, we propose a priority-based optimization mechanism for I/O isolation after analyzing the characteristics of typical service workloads. Based on the real-time performance data, priority is automatically assigned to each service and corresponding optimization methods are applied. We evaluate the optimization effects of the priority-based mechanism in both static and dynamic workload cases, besides, the influence of different priority order is also analyzed. The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Containers

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An incremental turn-taking model for task-oriented dialog systems

arXiv

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arXiv 2019年

作者： Coman, Andrei C. Yoshino, Koichiro Murase, Yukitoshi Nakamura, Satoshi Riccardi, Giuseppe Signals and Interactive Systems Lab Department of Information Engineering and Computer Science University of Trento Italy Augmented Human Communication Laboratory Graduate School of Information Science Nara Institute of Science and Technology Japan

In a human-machine dialog scenario, deciding the appropriate time for the machine to take the turn is an open research problem. In contrast, humans engaged in conversations are able to timely decide when to interrupt the speaker for competitive or non-competitive reasons. In state-of-the-art turn-by-turn dialog systems the decision on the next dialog action is taken at the end of the utterance. In this paper, we propose a token-by-token prediction of the dialog state from incremental transcriptions of the user utterance. To identify the point of maximal understanding in an ongoing utterance, we a) implement an incremental Dialog State Tracker which is updated on a token basis (iDST) b) re-label the Dialog State Tracking Challenge 2 (DSTC2) dataset and c) adapt it to the incremental turn-taking experimental scenario. The re-labeling consists of assigning a binary value to each token in the user utterance that allows to identify the appropriate point for taking the turn. Finally, we implement an incremental Turn Taking Decider (iTTD) that is trained on these new labels for the turn-taking decision. We show that the proposed model can achieve a better performance compared to a deterministic handcrafted turn-taking algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Joint Bayesian guided metric learning for end-to-end face verification

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Neurocomputing 2018年 275卷 560-567页

作者： Di Chen Chunyan Xu Jian Yang Jianjun Qian Yuhui Zheng Linlin Shen School of Computer Science and Engineering Nanjing University of Science and Technology 210094 China Jiangsu Engineering Center of Network Monitoring School of computer and software Nanjing University of Information Science and Technology 210044 China School of Computer Science and Software Engineering Shenzhen University 518060 China

In this work, we address the problem of face verification, namely determining whether a pair of face images belongs to the same or different subjects. Previous works often consider solving the problem of face verification in two steps: feature extraction and face recognition, resulting in a fragmented procedure. We argue that these techniques, although working well, fail to explicitly exploit a full end-to-end framework for face verification, which has received much attention and achieved significant improvements recently. In this paper, we propose a novel Joint Bayesian guided metric learning technique for dealing with the face verification task, which well integrates the above two steps of face verification into an end-to-end convolutional neural network (CNN) architecture. In the training stage, an initial neural network, which has the similar architecture with GoogLeNet CNN model, is firstly pre-trained by optimizing classification-based objective functions on the publicly available CASIA WebFace database. Based on constructed face pairs dataset from CASIA WebFace and LFW datasets, we then fine-tune the whole network parameters under the guide of the learned knowledge, which is obtained from the highly successful Joint Bayesian model. This guided learning procedure, which can also be seen as a metric learning technique, can further update network parameters for discriminating face pairs. In the testing process, the outputs by this unified network are discriminated with a threshold value to produce the ultimate prediction for the face verification task. Comprehensive evaluations over the LFW dataset well demonstrate the encouraging face verification performance of our proposed framework.

关键词： Face verification Convolutional neural network Joint Bayesian model

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Handling preferences in LPMLN: A preliminary report 8

Handling preferences in LPMLN: A preliminary report

引用

8th International Workshop on Combinations of Intelligent Methods and Applications, CIMA 2018

作者： Wang, Bin Zhang, Shutao Xu, Hongxiang Zhang, Zhizheng Wu, Wei Li, Xiaodong School of Computer Science and Engineering Southeast University Nanjing China Science and Technology on Information System Engineering Lab 28th Research Institute of China Electronics Technology Group Corparation Nanjing China

In this paper, we present a new knowledge representation and reasoning tool to handle uncertainty, inconsistencies, and preferences by combining the ideas of LPMLN and logic programming with ordered disjunction (LPOD). The former is an extension of Answer Set Programming (ASP) that is designed for handling uncertainty and inconsistencies in knowledge representation by incorporating the method in Markov Logic Networks (MLN). The latter is a logic formalism to represent preferences in ASP, which is a simple yet effective way to handle preferences. Firstly, we present a new knowledge representation language o-LPMLN that extends LPMLN by introducing ordered disjunctions. Then, we present an example to show the use of the language. Finally, we present an approach to computing o-LPMLN programs by translating them into regular LPMLN programs. Results obtained in this paper provide an alternative tool to handle inconsistencies, uncertainty, and preferences in a unified framework. Moreover, a by-product of the paper is that we present an approach to implementing LPOD via using LPMLN solvers. © 2018 CEUR-WS. All Rights Reserved.

关键词： Logic programming

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Multi-scale dynamic feature encoding network for image demoiŕeing

arXiv

引用

arXiv 2019年

作者： Cheng, Xi Fu, Zhenyong Yang, Jian PCA Lab Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

The prevalence of digital sensors, such as digital cameras and mobile phones, simplifies the acquisition of photos. Digital sensors, however, suffer from producing Moiŕe when photographing objects having complex textures, which deteriorates the quality of photos. Moiŕe spreads across various frequency bands of images and is a dynamic texture with varying colors and shapes, which pose two main challenges in demoiŕeingan important task in image restoration. In this paper, towards addressing the first challenge, we design a multi-scale network to process images at different spatial resolutions, obtaining features in different frequency bands, and thus our method can jointly remove moiŕe in different frequency bands. Towards solving the second challenge, we propose a dynamic feature encoding module (DFE), embedded in each scale, for dynamic texture. Moiŕe pattern can be eliminated more effectively via DFE. Our proposed method, termed Multi-scale convolutional network with Dynamic feature encoding for image DeMoiŕeing (MDDM), can outperform the state of the arts in fidelity as well as perceptual on benchmarks. Copyright © 2019, The Authors. All rights reserved.

关键词： Textures

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An overview of attacks and defences on intelligent connected vehicles

arXiv

引用

arXiv 2019年

作者： Dibaei, Mahdi Zheng, Xi Jiang, Kun Maric, Sasa Abbas, Robert Liu, Shigang Zhang, Yuexin Deng, Yao Wen, Sheng Zhang, Jun Xiang, Yang Yu, Shui Department of Computing Macquarie University SydneyNSW Australia China State Key Lab of Automotive Safety and Energy Tsinghua University Beijing China School of Engineering Macquarie University SydneyNSW Australia School of Software and Electrical Engineering Swinburne University of Technology HawthornVIC Australia Faculty of Engineering & Information Technology University of Technology Sydney SydneyNSW Australia

Cyber security is one of the most significant challenges in connected vehicular systems and connected vehicles are prone to different cybersecurity attacks that endanger passengers’ safety. Cyber security in intelligent connected vehicles is composed of in-vehicle security and security of inter-vehicle communications. Security of Electronic Control Units (ECUs) and the Control Area Network (CAN) bus are the most significant parts of in-vehicle security. Besides, with the development of 4G LTE and 5G remote communication technologies for vehicle-to-everything (V2X) communications, the security of inter-vehicle communications is another potential problem. After giving a short introduction to the architecture of next-generation vehicles including driverless and intelligent vehicles, this review paper identifies a few major security attacks on the intelligent connected vehicles. Based on these attacks, we provide a comprehensive survey of available defences against these attacks and classify them into four categories, i.e. cryptography, network security, software vulnerability detection, and malware detection. We also explore the future directions for preventing attacks on intelligent vehicle systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Cybersecurity

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