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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhenliang Li Liming Yuan Haixia Xu Rui Cheng Xianbin Wen School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Existing Multi-Instance learning (MIL) methods for medical image classification typically segment an image (bag) into small patches (instances) and learn a classifier to predict the label of an unknown bag. Most of such methods assume that instances within a bag are independently and identically distributed. However, instances in the same bag often interact with each other. In this paper, we propose an Induced SelfAttention based deep MIL method that uses the self-attention mechanism for learning the global structure information within a bag. To alleviate the computational complexity of the naive implementation of self-attention, we introduce an inducing point based scheme into the self-attention block. We show empirically that the proposed method is superior to other deep MIL methods in terms of performance and interpretability on three medical image data sets. We also employ a synthetic MIL data set to provide an intensive analysis of the effectiveness of our method. The experimental results reveal that the induced self-attention mechanism can learn very discriminative and different features for target and non-target instances within a bag, and thus fits more generalized MIL problems.

关键词： Feature extraction Medical diagnostic imaging Cancer Breast Logic gates Task analysis Standards

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A human vision system-based approach to improve computing efficiency in information systems

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International Journal of Information Systems and Change Management 2019年第1期11卷 44-55页

作者： Li, Jianxun Chen, Jim Q. Li, Weiqian Department of Management Science and Engineering School of Economics and Management Xi'an University of Technology Xi'an China Department of Information Systems Herberger Business School St. Cloud State University St. CloudMN United States Department of Software Engineering School of Computer Science Xi'an Polytechnic University Xi'an China

While a lot of research has been done in system design, reducing computational complexity is still a core problem in information systems. In order to break through the bottleneck of computing efficiency, this paper draws on the powerful processing capability of the human vision system (HVS) to propose a pixel domain computing framework. It converts the original problem into pixel operations, and pixels are taken as the basic unit to change the construction process of data analysis. The framework significantly decreases computational complexity, simplifies programming logic, and enhances the automaticity of information system. Our results demonstrate that the efficiency of the containment test is improved by 30 times, the logic complexity of the intersection area measurement is reduced by a factor of five, and the automaticity of root finding is significantly heightened. © 2019 Inderscience Enterprises Ltd.

关键词： Information systems

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Learnable Bag Similarity Based Deep Multi-Instance Network for Breast Cancer Diagnosis

Learnable Bag Similarity Based Deep Multi-Instance Network f...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rui Cheng Liming Yuan Haixia Xu Zhenliang Li Xianbin Wen School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

computer-aided diagnosis for breast cancer is an important and challenging research problem in medical image analysis. The main difficulty lies in that only image-level labels rather than fine-grained patch-level labels can be gotten for medical images in general. This situation fits well with the settings of Multi-Instance Learning (MIL). Following this line of research, Bag Similarity Network (BSN) uses the inter-bag similarities to learn the relationship between bags (images) and achieves a good performance in automatic diagnosis of breast cancer. Nevertheless, the inter-bag similarities are pre-defined rather than learnable. In this paper, we propose a Learnable Bag Similarity Network for deep MIL, called LBSN, to aid breast cancer diagnosis. To implement automatic similarity learning, LBSN first extracts fixed numbers of global representations of each bag using the attention mechanism, and then employs channel and spatial attention on similarity matrices for learning the relation of bags and that of instances, respectively. The experimental results on a publicly available breast cancer data set demonstrate that the proposed LBSN outperforms BSN by a large margin in terms of classification accuracy.

关键词： Feature extraction Training Supervised learning Neural networks Breast cancer Medical diagnostic imaging Data mining

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Ensemble Learning Based Sleeping Cell Detection in Cloud Radio Access Networks

Ensemble Learning Based Sleeping Cell Detection in Cloud Rad...

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IEEE Symposium on computers and Communications (ISCC)

作者： Zhao Ming Chuan Sun Xiuhua Li Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728180861

ISBN: (纸本)9781728180878

Sleeping cell problem refers to the degradation or unavailability of network services without triggered alarm, which is one of the most critical issues in current mobile networks. This problem is generally not detectable by the operators but only revealed after users' complaints occur. Therefore, it leads to the degradations of network performance in the service provision in the long run. To address this problem, we introduce a cloud-based sleeping cell detection platform into radio access networks (RANs) to detect the sleeping cells and deal with them automatically. In the cloud RANs (C-RANs), we combine and improve different methods employed in the pioneering studies in this field, and creatively use labeled training data and ensemble learning method for improving the accuracy. Particularly, we utilize expert optimization experience for further improving the detection framework. To evaluate the proposed ensemble learning based sleeping cell detection framework, we use a time-series dataset of Key Performance Indicator (KPI) in a real-world network. Trace-driven evaluation results show that the proposed framework can achieve up to 14.38% and 20.50% improvements compared with two existing schemes, respectively.

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Neighborhood-based uncertain QoS prediction of web services via matrix factorization 14th

Neighborhood-based uncertain QoS prediction of web services ...

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14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Zou, Guobing Pang, Shengye Wang, Pengwei Miao, Huaikou Niu, Sen Gan, Yanglan Zhang, Bofeng School of Computer Engineering and Science Shanghai University Shanghai China School of Computer Science and Technology Donghua University Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China

ISBN: (纸本)9783030129804

With the rapidly overwhelming number of services on the internet, QoS-based web service recommendation has become an urgent demand on service-oriented applications. Since there are a large number of missing QoS values in the user historical invocation records, accurately predicting these missing QoS values becomes a hot research issue. However, most existing service QoS prediction research assumes that the transactional process of the service was stable, and its QoS doesn’t change as time goes. In fact, service invocation process is usually affected by many factors (e.g., geographical location, network environment), leading to service invocations with QoS uncertainty. Therefore, QoS prediction based on traditional methods can not exactly adapt to the scenarios in real-world applications. To solve the issue, combined with the collaborative filtering and matrix factorization theory, we propose a novel approach for prediction of uncertain service QoS under the dynamic Internet environment. Extensive experiments have been conducted on a real-world data set and the results demonstrate the effectiveness and applicability of our approach for QoS prediction. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Forecasting

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RETRACTED: Distributed vision navigation technology for mobile robots based on jitter privacy ant colony algorithm

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International Journal of Electrical engineering & Education 2019年第1_SUPPL期60卷 1379 - 1389页

作者： Fanlei Yan Key Laboratory of Advanced Electrical Engineering and Energy Technology Tianjin Polytechnic University Tianjin ChinaSchool of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin China

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MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

arXiv

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arXiv 2024年

作者： Dai, Yuekun Zhang, Dafeng Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Yang, Peiqing Jin, Zhezhu Liu, Guanqun Loy, Chen Change Zhang, Lize Liu, Shuai Feng, Chaoyu Wang, Luyang Chen, Shuan Shao, Guangqi Wang, Xiaotao Lei, Lei Yang, Qirui Cheng, Qihua Xu, Zhiqiang Liu, Yihao Yue, Huanjing Yang, Jingyu Vasluianu, Florin-Alexandru Wu, Zongwei Ciubotariu, George Timofte, Radu Zhang, Zhao Zhao, Suiyi Wang, Bo Zuo, Zhichao Wei, Yanyan Teja, Kuppa Sai Sri Jayakar Reddy, A. Rongali, Girish Mitra, Kaushik Ma, Zhihao Liu, Yongxu Zhang, Wanying Shang, Wei He, Yuhong Peng, Long Yu, Zhongxin Luo, Shaofei Wang, Jian Miao, Yuqi Li, Baiang Wei, Gang Verma, Rakshank Maheshwari, Ritik Tekchandani, Rahul Hambarde, Praful Tazi, Satya Narayan Vipparthi, Santosh Kumar Murala, Subrahmanyam Zhang, Haopeng Hou, Yingli Yao, Mingde Levin, M.S. Sundararajan, Aniruth Hari Kumar, A. Xiaomi Inc. China School of Electrical and Information Engineering Tianjin University China Shenzhen MicroBT Electronics Technology Co. Ltd China Shanghai Artificial Intelligence Laboratory China Computer Vision Lab CAIDAS IFI University of Würzburg Germany Laboratory for Multimedia Computing Hefei University of Technology China Detect Technologies Pvt Ltd India Indian Institute of Technology Madras India Xidian University China Harbin Institute of Technology China Northeastern University China University of Science and Technology of China China Fujian Normal University China Snap Inc. United States Tongji University China Hefei University of Technology China GEC Ajmer India CVPR Lab IIT Ropar India SCSS Trinity College Dublin Ireland Faculty of Robot Science and Engineering Northeastern University China Software College Northeastern University China The Chinese University of Hong Kong China Shiv Nadar University India

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024. Copyright © 2024, The Authors. All rights reserved.

关键词： Image sensors

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SG-PBFT: A secure and highly efficient blockchain PBFT Consensus Algorithm for internet of vehicles

arXiv

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arXiv 2021年

作者： Xu, Guangquan Liu, Yihua Xing, Jun Luo, Tao Gu, Yonghao Liu, Shaoying Zheng, Xi Vasilakos, Athanasios V. The Big Data School Qingdao Huanghai University Qingdao266427 China College of Intelligence and Computing Tianjin University Tianjin300350 China The International Engineering Institute Tianjin University Tianjin300350 China The College of Intelligence and Computing Tianjin University Tianjin Tianjin300350 China The school of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China The Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China School of Informatics and Data Science Hiroshima University Japan The Department of Computing Macquarie University SydneyNSW2109 Australia The University of Technology Sydney Australia The Fuzhou University Fuzhou China Lulea University of Technology Lulea Sweden

The Internet of Vehicles (IoV) is an application of the Internet of things (IoT). It faces two main security problems: (1) the central server of the IoV may not be powerful enough to support the centralized authentication of the rapidly increasing connected vehicles, (2) the IoV itself may not be robust enough to single-node attacks. To solve these problems, this paper proposes SG-PBFT: a secure and highly efficient PBFT consensus algorithm for Internet of Vehicles, which is based on a distributed blockchain structure. The distributed structure can reduce the pressure on the central server and decrease the risk of single-node attacks. The SG-PBFT consensus algorithm improves the traditional PBFT consensus algorithm by using a score grouping mechanism to achieve a higher consensus efficiency. The experimental result shows that our method can greatly improve the consensus efficiency and prevent single-node attacks. Specifically, when the number of consensus nodes reaches 1000, the consensus time of our algorithm is only about 27% of what is required for the state-of-the-art consensus algorithm (PBFT). Our proposed SG-PBFT is versatile and can be used in other application scenarios which require high consensus efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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ATVHunter: Reliable Version Detection of Third-Party Libraries for Vulnerability Identification in Android Applications

ATVHunter: Reliable Version Detection of Third-Party Librari...

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International Conference on software engineering (ICSE)

作者： Xian Zhan Lingling Fan Sen Chen Feng We Tianming Liu Xiapu Luo Yang Liu Department of Computing The Hong Kong Polytechnic University Hong Kong China College of Cyber Science Nankai University China College of Intelligence and Computing Tianjin University China Nanyang Technological University Singapore Singapore Faculty of Information Technology Monash University Australia School of Computer Science and Engineering Nanyang Technological University Singapore

Third-party libraries (TPLs) as essential parts in the mobile ecosystem have become one of the most significant contributors to the huge success of Android, which facilitate the fast development of Android applications. Detecting TPLs in Android apps is also important for downstream tasks, such as malware and repackaged apps identification. To identify in-app TPLs, we need to solve several challenges, such as TPL dependency, code obfuscation, precise version representation. Unfortunately, existing TPL detection tools have been proved that they have not solved these challenges very well, let alone specify the exact TPL versions. To this end, we propose a system, named ATVHunter, which can pinpoint the precise vulnerable in-app TPL versions and provide detailed information about the vulnerabilities and TPLs. We propose a two-phase detection approach to identify specific TPL versions. Specifically, we extract the Control Flow Graphs as the coarse-grained feature to match potential TPLs in the pre-defined TPL database, and then extract opcode in each basic block of CFG as the fine-grained feature to identify the exact TPL versions. We build a comprehensive TPL database (189,545 unique TPLs with 3,006,676 versions) as the reference database. Meanwhile, to identify the vulnerable in-app TPL versions, we also construct a comprehensive and known vulnerable TPL database containing 1,180 CVEs and 224 security bugs. Experimental results show AtVHunter outperforms state-of-the-art TPL detection tools, achieving 90.55% precision and 88.79% recall with high efficiency, and is also resilient to widely-used obfuscation techniques and scalable for large-scale TPL detection. Furthermore, to investigate the ecosystem of the vulnerable TPLs used by apps, we exploit newtool to conduct a large-scale analysis on 104,446 apps and find that 9,050 apps include vulnerable TPL versions with 53,337 vulnerabilities and 7,480 security bugs, most of which are with high risks and are not recognized

关键词： Databases Ecosystems computer bugs Tools Feature extraction Libraries Security

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ID-Based Public Auditing Protocol for Cloud Storage Data Integrity Checking with Strengthened Authentication and Security

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Wuhan university Journal of Natural sciences 2018年第4期23卷 362-368页

作者： JIANG Hong XIE Mingming KANG Baoyuan LI Chunqing SI Lin School of Management Tianjin Polytechnic UniversityTianjin 300387 China School of Computer Science and Software Tianjin Polytechnic University Tianjin 300387 China

Cloud storage service reduces the burden of data users by storing users＇ data files in the cloud. But, the files might be modified in the cloud. So, data users hope to check data files integrity periodically. In a public auditing protocol, there is a trusted auditor who has certain ability to help users to check the integrity of data files. With the advantages of no public key management and verification, researchers focus on public auditing protocol in ID-based cryptography recently. However, some existing protocols are vulnerable to forgery attack. In this paper, based on ID-based signature technology, by strengthening information authentication and the computing power of the auditor, we propose an ID-based public auditing protocol for cloud data integrity checking. We also prove that the proposed protocol is secure in the random oracle model under the assumption that the Diffie-Hellman problem is hard. Furthermore, we compare the proposed protocol with other two ID-based auditing protocols in security features, communication efficiency and computation cost. The comparisons show that the proposed protocol satisfies more security features with lower computation cost.

关键词： ID-based auditing data integrity checking digital signature security bilinear map

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