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SSRN 2023年

作者： Zhao, Zhen Susilo, Willy Guo, Fuchun Lai, Jianchang Wang, Baocang State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Wollongong2522 Australia School of Cyber Science and Engineering Southeast University Nanjing211189 China

Accountable identity-based encryption (A-IBE) was proposed to relieve the key escrow problem caused by the fully trustworthy private-key generator (PKG) in the IBE system, where the true generator of private keys or decoder boxes can be traced back to the PKG or related users. Retrievable A-IBE (RA-IBE) enhances the security of A-IBE by providing retrievability to the master secret key of the PKG when more than one private key of the same user are released. RA-IBE strengthens the deterrent effect of A-IBE against the PKG since disclosure of the master secret key could lead to the breakdown of the entire IBE system. However, current RA-IBE schemes only provide retrievability in a white-box model, which limits the ability to support traceability and retrievability on well-formed private keys only. This overlooks the fact that a malicious PKG can easily conceal a private key within a decoder box, making the inserted private key inaccessible. To overcome this limitation, we propose a full black-box RA-IBE scheme, where traceability and retrievability of decoder boxes are provided while the malicious PKG is allowed to access the decryption oracle in the security model simultaneously. We first give the formal definition and security models of full black-box RA-IBE and then present a concrete construction. In our construction, a user interacts with the PKG to obtain its private key and an additional commitment tuple with which the user can retrieve the master secret key of the PKG using a related decoder box generated by the PKG. Finally, we show that the proposed full black-box RA-IBE scheme is secure in the random oracle model. © 2023, The Authors. All rights reserved.

关键词： Decoding

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TS-ENAS:Two-Stage Evolution for Cell-based Network Architecture Search

arXiv

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arXiv 2023年

作者： Zou, Juan Wu, Shenghong Xia, Yizhang Jiang, Weiwei Wu, Zeping Zheng, Jinhua Engineering Research Center of Hunan Province for Optimization and Security of Intelligent System Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China The College of Aerospace Science and Engineering National University of Defense Technology Hunan Changsha410073 China

Neural network architecture search provides a solution to the automatic design of network structures. However, it is difficult to search the whole network architecture directly. Although using stacked cells to search neural network architectures is an effective way to reduce the complexity of searching, these methods do not able find the global optimal neural network structure since the number of layers, cells and connection methods is fixed. In this paper, we propose a Two-Stage Evolution for cell-based Network Architecture Search(TS-ENAS), including one-stage searching based on stacked cells and second-stage adjusting these cells. In our algorithm, a new cell-based search space and an effective two-stage encoding method are designed to represent cells and neural network structures. In addition, a cell-based weight inheritance strategy is designed to initialize the weight of the network, which significantly reduces the running time of the algorithm. The proposed methods are extensively tested and compared on four image classification dataset, Fashion-MNIST, CIFAR10, CIFAR100 and ImageNet and compared with 22 state-of-the-art algorithms including hand-designed networks and NAS networks. The experimental results show that TS-ENAS can more effectively find the neural network architecture with comparative performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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The behavioral sign of account theft: Realizing online payment fraud alert 29

The behavioral sign of account theft: Realizing online payme...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wang, Cheng Department of Computer Science Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University China

ISBN: (纸本)9780999241165

As a matter of fact, it is usually taken for granted that the occurrence of unauthorized behaviors is necessary for the fraud detection in online payment services. However, we seek to break this stereotype in this work. We strive to design an ex-ante anti-fraud method that can work before unauthorized behaviors occur. The feasibility of our solution is supported by the cooperation of a characteristic and a finding in online payment fraud scenarios: The well-recognized characteristic is that online payment frauds are mostly caused by account compromise. Our finding is that account theft is indeed predictable based on users' high-risk behaviors, without relying on the behaviors of thieves. Accordingly, we propose an account risk prediction scheme to realize the ex-ante fraud detection. It takes in an account's historical transaction sequence, and outputs its risk score. The risk score is then used as an early evidence of whether a new transaction is fraudulent or not, before the occurrence of the new transaction. We examine our method on a real-world B2C transaction dataset from a commercial bank. Experimental results show that the ex-ante detection method can prevent more than 80% of the fraudulent transactions before they actually occur. When the proposed method is combined with an interim detection to form a real-time anti-fraud system, it can detect more than 94% of fraudulent transactions while maintaining a very low false alarm rate (less than 0.1%). © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Crime

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Linear uncertainty quantification of graphical model inference 24

Linear uncertainty quantification of graphical model inferen...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Chenghua Guo Han Yu Jiaxin Liu Chao Chen Qi Li Sihong Xie Xi Zhang Key Laboratory of Trustworthy Distributed Computing and Service (MoE) Beijing University of Posts and Telecommunications China College of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Computer Science and Engineering Lehigh University School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) China Department of Computer Science Iowa State University AI Thrust The Hong Kong University of Science and Technology (Guangzhou) China

ISBN: (纸本)9798331314385

Uncertainty Quantification (UQ) is vital for decision makers as it offers insights into the potential reliability of data and model, enabling more informed and risk-aware decision-making. Graphical models, capable of representing data with complex dependencies, are widely used across domains. Existing sampling-based UQ methods are unbiased but cannot guarantee convergence and are time-consuming on large-scale graphs. There are fast UQ methods for graphical models with closed-form solutions and convergence guarantee but with uncertainty underestimation. We propose LinUProp, a UQ method that utilizes a novel linear propagation of uncertainty to model uncertainty among related nodes additively instead of multiplicatively, to offer linear scalability, guaranteed convergence, and closed-form solutions without underestimating uncertainty. Theoretically, we decompose the expected prediction error of the graphical model and prove that the uncertainty computed by LinUProp is the generalized variance component of the decomposition. Experimentally, we demonstrate that LinUProp is consistent with the sampling-based method but with linear scalability and fast convergence. Moreover, LinUProp outperforms competitors in uncertainty-based active learning on four real-world graph datasets, achieving higher accuracy with a lower labeling budget.

关键词：

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Deep Multi-Instance Learning with Adaptive Recurrent Pooling for Medical Image Classification

Deep Multi-Instance Learning with Adaptive Recurrent Pooling...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yi Ding Lu Zhao Liming Yuan 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 School of Computer and Information Engineering Tianjin Chengjian University Tianjin China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Recently, deep multi-instance neural networks have been successfully applied for medical image classification, where only image-level labels rather than fine-grained patch-level labels are available for use. One key issue for these multi-instance neural networks is how to aggregate all patch (instance) features into an entire image (bag) representation, referred to as multi-instance pooling, e.g., max, mean, and attention based pooling. Nevertheless, these multi-instance pooling operations do not take the structural information within an image into account. This is obviously inappropriate for medical image classification since there often exist certain dependencies among regional patches/lesions. We propose an adaptive recurrent pooling based deep multi-instance neural network in this paper. In this network, we first extract multi-view global structural features from every bag using the self-attention mechanism, and then aggregate these multi-view features into a whole bag representation based on the adaptive recurrent pooling operation in order to further capture the contextual information within the bag. Moreover, we introduce the cross-normalization operation used in the Unit Force Operated Vision Transformer into the self-attention module to reduce its computational complexity. We have experimentally evaluated the performance of the proposed network on three medical image datasets, namely UCSB breast, Messidor, and Colon cancer. The results demonstrate the advantage of our network over current state-of-the-art deep multi-instance networks in terms of classification accuracy and interpretability.

关键词： Technological innovation Adaptive systems Aggregates Neural networks Force Feature extraction Transformers

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Multi-View Representation Learning for Multi-Instance Learning with Applications to Medical Image Classification

Multi-View Representation Learning for Multi-Instance Learni...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lu Zhao Liming Yuan Zhenliang Li Xianbin Wen School of Computer and Information Engineering Tianjin Chengjian University Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Multi-Instance Learning (MIL) is a weakly supervised learning paradigm, in which every training example is a labeled bag of unlabeled instances. In typical MIL applications, instances are often used for describing the features of regions/parts in a whole object, e.g., regional patches/lesions in an eye-fundus image. However, for a (semantically) complex part the standard MIL formulation puts a heavy burden on the representation ability of the corresponding instance. To alleviate this pressure, we still adopt a bag-of-instances as an example in this paper, but extract from each instance a set of representations using $1 \times1$ convolutions. The advantages of this tactic are two-fold: i) This set of representations can be regarded as multi-view representations for an instance; ii) Compared to building multi-view representations directly from scratch, extracting them automatically using $1 \times1$ convolutions is more economical, and may be more effective since $1 \times1$ convolutions can be embedded into the whole network. Furthermore, we apply two consecutive multi-instance pooling operations on the reconstituted bag that has actually become a bag of sets of multi-view representations. We have conducted extensive experiments on several canonical MIL data sets from different application domains. The experimental results show that the proposed framework outperforms the standard MIL formulation in terms of classification performance and has good interpretability.

关键词： Training Representation learning Retinopathy Supervised learning Buildings Diabetes Bioinformatics

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Blockchain-based Distributed Storage system for Task Offloading in Vehicular Edge computing

Blockchain-based Distributed Storage System for Task Offload...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yutian Zhang Bing Tang Qing Yang Xiaoyuan Zhang Jincheng Luo Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou China

With the deepening requirements of development for the Internet of Vehicles technology, intelligent vehicles need to complete the complex task in a fast response time to meet the needs, which brings great challenges to reliable vehicle service provision. In order to solve this problem, we propose a task offloading scheme into the Internet of Vehicles, which can reduce the execution time of tasks. In this paper, we introduce a location-aware distributed storage system called LDSS to support task offloading. Data can be written, stored, computed and read out through different mobile edge servers. Firstly, the identity of the vehicle is stored on the blockchain, and then the vehicle needs to offload the data to the nearest mobile edge server. Then, the storage node and computing node are selected based on the time optimization strategy. Finally, by predicting the location of the vehicle, the mobile edge server nearest to the predicted vehicle location is selected as the access node for result downloading. After validating the identity of vehicle based on blockchain technology, the output result is returned to the vehicle. The experiments show that our proposed task offloading scheme based on blockchain can effectively achieve the effect of low latency and faster task response, as well as ensure data security.

关键词： Blockchains Servers Reliability Time factors Task analysis Low latency communication Optimization

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Controls on the Northward Movement of the ITCZ over the South China Sea in Autumn:A Heavy Rain Case Study

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Advances in Atmospheric Sciences 2021年第10期38卷 1651-1664页

作者： Fei WANG Lifang SHENG Xiadong AN Qian LIU Haixia ZHOU Yingying ZHANG Jianping LI Department of Marine Meteorology College of Oceanic and Atmospheric SciencesOcean University of ChinaQingdao 266100China Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province Haikou 570000China Ocean-Atmosphere Interaction and Climate Laboratory Key Laboratory of Physical OceanographyOcean University of ChinaQingdao 266100China Department of Marine Meteorology School of Atmospheric SciencesSun Yat-Sen UniversityZhuhai 519000China Sansha Meteorological Bureau of Hainan Province Sansha 573199China Professional Meteorological Science and Technology Service Center of Hainan province Haikou 570000China Frontiers Science Center for Deep Ocean Multispheres and Earth System(FDOMES)/Key Laboratory of Physical Oceanography/Institute for Advanced Ocean Studies Ocean University of ChinaQingdao 266100China Laboratory for Ocean Dynamics and Climate Pilot Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China

The autumn Intertropical Convergence Zone(ITCZ)over the South China Sea(SCS)is typically held south of 10°N by prevailing northeasterly and weakening southwesterly ***,the ITCZ can move north,resulting in heavy rainfall in the northern SCS(NSCS).We investigate the mechanisms that drove the northward movement of the ITCZ and led to heavy non-tropical-cyclone rainfall over the NSCS in autumn of *** results show that the rapid northward movement of the ITCZ on 1 and 2 October was caused by the joint influence of the equatorial easterlies(EE),southwesterly winds,and the easterly jet(EJ)in the NSCS.A high pressure center on the east side of Australia,strengthened by the quasi-biweekly oscillation and strong Walker circulation,was responsible for the EE to intensify and reach the *** EE finally turned southeast and together with enhanced southwesterly winds associated with an anticyclone,pushed the ITCZ ***,the continental high moved east,which reduced the area of the EJ in the NSCS and made room for the *** regression analysis showed that the reduced area of the EJ and increased strength of the EE contributed significantly to the northward movement of the *** enhancement of the EE preceded the northward movement of the ITCZ by six hours and pushed the ITCZ continually *** the ITCZ approached 12°N,it not only transported warm moist air but also strengthened the dynamic field by transporting the positive vorticity horizontally and vertically which further contributed to the heavy rainfall.

关键词： ITCZ rainfall northern South China Sea equatorial easterly easterly jet

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EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events

arXiv

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arXiv 2023年

作者： Ren, Ying Shen, Kailai Ye, Zhe Yan, Diqun Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Shandong China Juphoon System Software Co. Ltd Ningbo China

Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting speech’s mean opinion score (MOS) without requiring the reference speech. Researchers have gradually started to apply NISQA to various practical scenarios. However, little attention has been paid to the security of NISQA models. Backdoor attacks represent the most serious threat to deep neural networks (DNNs) due to the fact that backdoors possess a very high attack success rate once embedded. However, existing backdoor attacks assume that the attacker actively feeds samples containing triggers into the model during the inference phase. This is not adapted to the specific scenario of NISQA. And current backdoor attacks on regression tasks lack an objective metric to measure the attack performance. To address these issues, we propose a novel backdoor triggering approach (EventTrojan) that utilizes an event during the usage of the NISQA model as a trigger. Moreover, we innovatively provide an objective metric for backdoor attacks on regression tasks. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the EventTrojan attack. Besides, it also has good resistance to several defense methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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