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arXiv 2021年

作者： Chen, Chen Qin, Chen Ouyang, Cheng Li, Zeju Wang, Shuo Qiu, Huaqi Chen, Liang Tarroni, Giacomo Bai, Wenjia Rueckert, Daniel Department of Computing Imperial College London United Kingdom Institute for Digital Communications School of Engineering University of Edinburgh United Kingdom Digital Medicine Research Centre School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of MICCAI Shanghai China CitAI Research Centre Department of Computer Science City University of London United Kingdom Department of Brain Sciences Imperial College London United Kingdom Data Science Institute Imperial College London United Kingdom Klinikum Rechts der Isar Technical University of Munich Germany

The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large medical image set is resource-intensive, expensive, and sometimes impractical due to data sharing and privacy issues. To address this challenge, we propose AdvChain, a generic adversarial data augmentation framework, aiming at improving both the diversity and effectiveness of training data for medical image segmentation tasks. AdvChain augments data with dynamic data augmentation, generating randomly chained photo-metric and geometric transformations to resemble realistic yet challenging imaging variations to expand training data. By jointly optimizing the data augmentation model and a segmentation network during training, challenging examples are generated to enhance network generalizability for the downstream task. The proposed adversarial data augmentation does not rely on generative networks and can be used as a plug-in module in general segmentation networks. It is computationally efficient and applicable for both low-shot supervised and semi-supervised learning. We analyze and evaluate the method on two MR image segmentation tasks: cardiac segmentation and prostate segmentation with limited labeled data. Results show that the proposed approach can alleviate the need for labeled data while improving model generalization ability, indicating its practical value in medical imaging applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Image segmentation

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FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizers by Exploiting Strong Convexity

arXiv

引用

arXiv 2021年

作者： Zhou, Yangfan Huang, Kaizhu Cheng, Cheng Wang, Xuguang Hussain, Amir Liu, Xin School of Nano-Tech and Nano-Bionics University of Science and Technology of China 96 Jinzhai Road Anhui Province Hefei230026 China Chinese Academy of Sciences 398 Ruoshui Road Suzhou Industrial Park Jiangsu Province Suzhou215123 China Data Science Research Center Duke Kunshan University No. 8 Duke Avenue Kunshan215316 China The School of Computing Edinburgh Napier University EdinburghEH11 4BN United Kingdom Gusu Laboratory of Materials 388 Ruoshui Road Jiangsu Province Suzhou215123 China

AdaBelief, one of the current best optimizers, demonstrates superior generalization ability over the popular Adam algorithm by viewing the exponential moving average of observed gradients. AdaBelief is theoretically appealing in that it has a data-dependent O(√T) regret bound when objective functions are convex, where T is a time horizon. It remains however an open problem whether the convergence rate can be further improved without sacrificing its generalization ability. To this end, we make a first attempt in this work and design a novel optimization algorithm called FastAdaBelief that aims to exploit its strong convexity in order to achieve an even faster convergence rate. In particular, by adjusting the step size that better considers strong convexity and prevents fluctuation, our proposed FastAdaBelief demonstrates excellent generalization ability as well as superior convergence. As an important theoretical contribution, we prove that FastAdaBelief attains a data-dependant O(log T) regret bound, which is substantially lower than AdaBelief in strongly convex cases. On the empirical side, we validate our theoretical analysis with extensive experiments in scenarios of strong convexity and non-convexity using three popular baseline models. Experimental results are very encouraging: FastAdaBelief converges the quickest in comparison to all mainstream algorithms while maintaining an excellent generalization ability, in cases of both strong convexity or non-convexity. FastAdaBelief is thus posited as a new benchmark model for the research community. © 2021, CC BY.

关键词： Image classification

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Addressing research software sustainability via institutes

arXiv

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arXiv 2021年

作者： Katz, Daniel S. Carver, Jeffrey C. Chue Hong, Neil P. Gesing, Sandra Hettrick, Simon Honeyman, Tom Ram, Karthik Weber, Nicholas NCSA & CS & ECE & iSchool University of Illinois UrbanaIL United States Department of Computer Science University of Alabama TuscaloosaAL United States Software Sustainability Institute EPCC University of Edinburgh Edinburgh United Kingdom Center for Research Computing University of Notre Dame Notre DameIN United States Software Sustainability Institute ECS University of Southampton Southampton United Kingdom Software Program Australian Research Data Commons Univ. of Technology Sydney Australia Berkeley Institute for Data Science University of California BerkeleyCA United States Information School University of Washington SeattleWA United States

Research software is essential to modern research, but it requires ongoing human effort to sustain: to continually adapt to changes in dependencies, to fix bugs, and to add new features. Software sustainability institutes, amongst others, develop, maintain, and disseminate best practices for research software sustainability, and build community around them. These practices can both reduce the amount of effort that is needed and create an environment where the effort is appreciated and rewarded. The UK SSI is such an institute, and the US URSSI and the Australian AuSSI are planning to become institutes, and this extended abstract discusses them and the strengths and weaknesses of this approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Sustainable development

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STAN: Towards Describing Bytecodes of Smart Contract

arXiv

引用

arXiv 2020年

作者： Li, Xiaoqi Chen, Ting Luo, Xiapu Zhang, Tao Yu, Le Xu, Zhou Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Center for Cybersecurity University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology Macau University of Science and Technology China School of Big Data and Software Engineering Chongqing University Chongqing China

More than eight million smart contracts have been deployed into Ethereum, which is the most popular blockchain that supports smart contract. However, less than 1% of deployed smart contracts are open-source, and it is difficult for users to understand the functionality and internal mechanism of those closed-source contracts. Although a few decompilers for smart contracts have been recently proposed, it is still not easy for users to grasp the semantic information of the contract, not to mention the potential misleading due to decompilation errors. In this paper, we propose the first system named STAN to generate descriptions for the bytecodes of smart contracts to help users comprehend them. In particular, for each interface in a smart contract, STAN can generate four categories of descriptions, including functionality description, usage description, behavior description, and payment description, by leveraging symbolic execution and NLP (Natural Language Processing) techniques. Extensive experiments show that STAN can generate adequate, accurate and readable descriptions for contract’s bytecodes, which have practical value for users. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

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An IGS algorithm applied to DNA sequence design 17

An IGS algorithm applied to DNA sequence design

引用

17th IEEE International Conference on Dependable, Autonomic and Secure computing, IEEE 17th International Conference on Pervasive Intelligence and computing, IEEE 5th International Conference on Cloud and Big data computing, 4th Cyber science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

作者： Liu, Kaiqiang Wang, Bin Liu, Yuan Wu, Ranfeng Zhang, Xun Liu, Xin Zhang, Qiang Yin, Zhixiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China School of Mathematics and Big Data Anhui University of Science and Technology Dalian China

ISBN: (纸本)9781728130255;9781728130248

American scientist Adelman first used DNA molecules to solve the NP-complete problem, thus creating a new research field. As an advanced interdisciplinary subject, it has attracted many scholars because of its high parallelism, great storage density and low energy consumption. DNA sequence design is critical in the field of DNA computing. The success of DNA computing depends on the quality of DNA sequence. In this paper, we proposed an improved gravitational search algorithm for DNA sequence design. We improved the gravitational coefficient and introduced chaotic system and Gaussian mutation to the position update of the particles. We compared the result from our improved algorithm with the result obtained by other algorithms. The result shows that our proposed algorithm can generate high quality DNA sequence for DNA computing. © 2019 IEEE.

关键词： DNA computing DNA sequence design Gravitational search algorithm NP-complete problem

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A block-based inter-band predictor using multilayer propagation neural network for hyperspectral image compression

arXiv

引用

arXiv 2019年

作者： Dusselaar, Rui Paul, Manoranjan Data Science Research Centre School of computing and Mathematics Charles Sturt University BathurstNSW2795 Australia

In this paper, a block-based inter-band predictor (BIP) with multilayer propagation neural network model (MLPNN) is presented by a completely new framework. This predictor can combine with diversity entropy coding methods. Hyperspectral (HS) images are composed by a series high similarity spectral bands. Our assumption is to use trained MLPNN predict the succeeding bands based on current band information. The purpose is to explore whether BIP-MLPNN can provide better image predictive results with high efficiency. The algorithm also changed from the traditional compression methods encoding images pixel by pixel, the compression process only encodes the weights and the biases vectors of BIP-MLPNN which require few bits to transfer. The decoder will reconstruct a band by using the same structure of the network at the encoder side. The BIP-MLPNN decoder does not need to be trained as the weights and biases have already been transmitted. We can easily reconstruct the succeeding bands by using the BIP-MLPNN decoder. The experimental results indicate that BIP-MLPNN predictor outperforms the CCSDS-123 HS image coding standard. Due to a good approximation of the target band, the proposed method outperforms the CCSDS-123 by more than 2.0dB PSNR image quality in the predicted bands. Moreover, the proposed method provides high quality image e.g., 30 to 40dB PSNR at very low bit rate (less than 0.1 bpppb) and outperforms the existing methods e.g., JPEG, 3DSPECK, 3DSPIHT and in terms of rate-distortion performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Multilayers

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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On the Detection of Shilling Attacks in Federated Collaborative Filtering

On the Detection of Shilling Attacks in Federated Collaborat...

引用

Reliable Distributed Systems

作者： Yangfan Jiang Yipeng Zhou Di Wu Chao Li Yan Wang School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University) Ministry of Education China Macquarie University Sydney NSW Australia Tencent Inc Shenzhen China

ISBN: (数字)9781728176260

ISBN: (纸本)9781728176277

Federated collaborative filtering (Fed-CF) is a variant of federated learning (FL) models, which can protect user privacy in recommender systems. In Fed-CF, the recommendation model is collectively trained across multiple decentralized clients by exchanging gradients only. However, the decentralized nature of Fed-CF makes it vulnerable to shilling attacks, which can be realized by inserting fake ratings of target items to distort recommendation results. Unfortunately, previous detection algorithms cannot work well in the FL framework, as all original data samples are not disclosed at all. In this paper, we are the first to systematically study the problem of shilling attacks in the context of federated learning, and propose an effective detection method called Federated Shilling Attack Detector (FSAD) to detect shilling attackers in Fed-CF. We first show the feasibility of shilling attacks in Fed-CF. Next, we dedicatedly design four novel features based on exchanged gradients among clients. By incorporating these gradient-based features, we train a semi-supervised Bayes classifier to identify shilling attackers effectively. Finally, we conduct extensive experiments based on real-world datasets to evaluate the performance of our proposed FSAD method. The experimental results show that FSAD can detect shilling attackers in Fed-CF with high accuracy, with the F 1 value as high as 0.90 on the Netflix dataset, which approaches the performance of the optimal detector that utilizes complete private user information for detection.

关键词： Privacy Collaborative filtering Detectors Collaborative work Information filters Reliability Recommender systems

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Puncturable Encryption: A Generic Construction from Delegatable Fully Key-Homomorphic Encryption

arXiv

引用

arXiv 2020年

作者： Susilo, Willy Duong, Dung Hoang Le, Huy Quoc Pieprzyk, Josef Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Northfields Avenue WollongongNSW2522 Australia CSIRO Data61 SydneyNSW2015 Australia Institute of Computer Science Polish Academy of Sciences Warsaw Poland

Puncturable encryption (PE), proposed by Green and Miers at IEEE S&P 2015, is a kind of public key encryption that allows recipients to revoke individual messages by repeatedly updating decryption keys without communicating with senders. PE is an essential tool for constructing many interesting applications, such as asynchronous messaging systems, forward-secret zero round-trip time protocols, public-key watermarking schemes and forward-secret proxy re-encryptions. This paper revisits PEs from the observation that the puncturing property can be implemented as efficiently computable functions. From this view, we propose a generic PE construction from the fully key-homomorphic encryption, augmented with a key delegation mechanism (DFKHE) from Boneh et al. at Eurocrypt 2014. We show that our PE construction enjoys the selective security under chosen plaintext attacks (that can be converted into the adaptive security with some efficiency loss) from that of DFKHE in the standard model. Basing on the framework, we obtain the first post-quantum secure PE instantiation that is based on the learning with errors problem, selective secure under chosen plaintext attacks (CPA) in the standard model. We also discuss about the ability of modification our framework to support the unbounded number of ciphertext tags inspired from the work of Brakerski and Vaikuntanathan at CRYPTO 2016. Copyright © 2020, The Authors. All rights reserved.

关键词： Public key cryptography

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Hierarchical bi-directional self-attention networks for paper review rating recommendation

arXiv

引用

arXiv 2020年

作者： Deng, Zhongfen Peng, Hao Xia, Congying Li, Jianxin He, Lifang Yu, Philip S. Department of Computer Science University of Illinois at Chicago ChicagoIL United States Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing. However, most existing methods either use handcrafted features or learn features using deep learning with simple text corpus as input for review rating prediction, ignoring the hierarchies among data. In this paper, we propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation, which can serve as an effective decision-making tool for the academic paper review process. Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and inter-review encoder (level three). Each encoder first derives contextual representation of each level, then generates a higher-level representation, and after the learning process, we are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers. Furthermore, we introduce two new metrics to evaluate models in data imbalance situations. Extensive experiments on a publicly available dataset (PeerRead) and our own collected dataset (OpenReview) demonstrate the superiority of the proposed approach compared with state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

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