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Training, testing and benchmarking medical AI models using Clinical AIBench

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BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2022年第1期2卷 100037-100037页

作者： Huang, Yunyou Miao, Xiuxia Zhang, Ruchang Ma, Li Liu, Wenjing Zhang, Fan Guan, Xianglong Liang, Xiaoshuang Lu, Xiangjiang Tang, Suqing Zhang, Zhifei Guangxi Key Lab of Multi-Source Information Mining & Security School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Guilin Medical University Guilin China Department of Physiology and Pathophysiology Capital Medical University Beijing China Faculty of Education Guangxi Normal University Guilin China International Open Benchmark Council Beijing China

AI technology has been used in many clinical research fields, but most AI technologies are difficult to land in real-world clinical settings. In most current clinical AI research settings, the diagnosis task is to identify different types of diseases among the given ones. However, the diagnosis in real-world settings needs dynamically developing inspection strategies based on the existing resources of medical institutions and identifying different kinds of diseases out of many possibilities. To promote the development of different clinical AI technologies and the implementation of clinical applications, we propose a benchmark named Clinical AIBench for developing, verifying, and evaluating clinical AI technologies in real-world clinical settings. Specifically, Clinical AIBench can be used for: (1) Model training and testing: Researchers can use the data to train and test their models. (2)Model evaluation: Researchers can use Clinical AIBench to objectively, fairly, and comparably evaluate various models of different researchers. (3) Clinical value evaluation: Researchers can use the clinical indicators provided by Clinical AIBench to evaluate the clinical value of models, which will be applied in real-world clinical settings. For convenience, Clinical AIBench provides three different levels of clinical settings: restricted clinical setting, which is named closed clinical setting, data island clinical setting, and real-world clinical setting, which is called open clinical setting. In addition, Clinical AIBench covers three diseases: Alzheimer's disease, COVID-19, and dental. Clinical AIBench provides python APIs to researchers. The data and source code are publicly available from the project website https://***/clinical_aibench/. © 2022 The Authors

关键词： COVID-19

MrTF: Model Refinery for Transductive Federated Learning

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arXiv 2023年

作者： Li, Xin-Chun Yang, Yang Zhan, De-Chuan State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210023 China School of Artificial Intelligence Nanjing University Jiangsu Nanjing210023 China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China Department of Computing The Hong Kong Polytechnic University 100872 Hong Kong

We consider a real-world scenario in which a newly-established pilot project needs to make inferences for newly-collected data with the help of other parties under privacy protection policies. Current federated learning (FL) paradigms are devoted to solving the data heterogeneity problem without considering the to-be-inferred data. We propose a novel learning paradigm named transductive federated learning (TFL) to simultaneously consider the structural information of the to-be-inferred data. On the one hand, the server could use the pre-available test samples to refine the aggregated models for robust model fusion, which tackles the data heterogeneity problem in FL. On the other hand, the refinery process incorporates test samples into training and could generate better predictions in a transductive manner. We propose several techniques including stabilized teachers, rectified distillation, and clustered label refinery to facilitate the model refinery process. Abundant experimental studies verify the superiorities of the proposed Model refinery framework for Transductive Federated learning (MrTF). The source code is available at https://***/lxcnju/MrTF. © 2023, CC BY-NC-ND.

关键词： Distillation

GeneDroid Fuzz: An Android Intent Fuzzing Method Based on Gene Mutation

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GeneDroid Fuzz: An Android Intent Fuzzing Method Based on Ge...

IEEE Conference on Global Communications (GLOBECOM)

作者： Runfeng Lu Yuzhu Sun Haofeng Sun Xiao Fu Bin Luo Xiaojiang Du Jin Shi Nadjib Aitsaadi Mohsen Guizani State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA School of Information Management Nanjing University Nanjing China DAVID UVSQ Université Paris-Saclay France Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

With the rapid expansion of mobile internet usage, the prevalence of the Android operating system on smartphones is steadily growing. However, improper utilization of the Intent mechanism within Android applications can result in security vulnerabilities. Presently, the majority of Android security testing methods, which rely heavily on fuzzing, are predominantly focused on UI interactions, lacking sufficient testing capabilities for Intents. The motivation of this paper is to find a more effective testing method to improve the security detection capabilities of Intents. This paper introduces an Intent fuzzing method based on genetic mutation principles. Initially, we establish an Intent seed library using a text classification model, followed by employing Jaccard distance and minimum edit distance to refine high-quality seeds. Subsequently, we augment the seeds through extensive mutation using genetic algorithms, generating numerous test cases that exhibit structural similarity but contain varied content. During testing, we compare the state before and after Intent testing using image similarity to detect anomalies. Experimental results demonstrate that this method effectively enhances test coverage and identifies potential issues in edge cases. This approach offers an efficient means of conducting Intent security testing and enhances Android app robustness and security.

关键词： Operating systems Image edge detection Genetic mutations Text categorization Fuzzing Libraries Robustness Security Smart phones Testing

Fundamentals of Delay-Doppler Communications: Practical Implementation and Extensions to OTFS

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arXiv 2024年

作者： Li, Shuangyang Jung, Peter Yuan, Weijie Wei, Zhiqiang Yuan, Jinhong Bai, Baoming Caire, Giuseppe Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin10587 Germany Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China School of Mathematics and Statistics Xi'an Jiaotong University Xi'An710049 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia State Key Lab. of ISN Xidian University Xi'An710071 China

The recently proposed orthogonal time frequency space (OTFS) modulation, which is a typical Delay-Doppler (DD) communication scheme, has attracted significant attention thanks to its appealing performance over doublyselective channels. In this paper, we present the fundamentals of general DD communications from the viewpoint of the Zak transform. We start our study by constructing DD domain basis functions aligning with the time-frequency (TF)- consistency condition, which are globally quasi-periodic and locally twisted-shifted. We unveil that these features are translated to unique signal structures in both time and frequency, which are beneficial for communication purposes. Then, we focus on the practical implementations of DD Nyquist communications, where we show that rectangular windows achieve perfect DD orthogonality, while truncated periodic signals can obtain sufficient DD orthogonality. Particularly, smoothed rectangular window with excess bandwidth can result in a slightly worse orthogonality but better pulse localization in the DD domain. Furthermore, we present a practical pulse shaping framework for general DD communications and derive the corresponding input-output relation under various shaping pulses. Our numerical results agree with our derivations and also demonstrate advantages of DD communications over conventional orthogonal frequency-division multiplexing (OFDM). © 2024, CC BY-NC-SA.

关键词： Pulse shaping

Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing

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arXiv 2022年

作者： Li, Kaicheng Yang, Hongyu Chen, Binghui Li, Pengyu Wang, Biao Huang, Di State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Institute of Artificial Intelligence Beihang University China Hangzhou Innovation Institute Beihang University China

Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace disentanglement framework has shown great potential for coping with both seen and unseen spoof scenarios, but the performance is largely restricted by the single-modal input. This paper focuses on this issue and presents a multi-modal disentanglement model which targetedly learns polysemantic spoof traces for more accurate and robust generic attack detection. In particular, based on the adversarial learning mechanism, a two-stream disentangling network is designed to estimate spoof patterns from the RGB and depth inputs, respectively. In this case, it captures complementary spoofing clues inhering in different attacks. Furthermore, a fusion module is exploited, which recalibrates both representations at multiple stages to promote the disentanglement in each individual modality. It then performs cross-modality aggregation to deliver a more comprehensive spoof trace representation for prediction. Extensive evaluations are conducted on multiple benchmarks, demonstrating that learning polysemantic spoof traces favorably contributes to anti-spoofing with more perceptible and interpretable results. © 2022, CC BY-NC-SA.

关键词： Face recognition

Quantum Secret Sharing Enhanced: Utilizing W states for Anonymous and Secure Communication

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arXiv 2024年

作者： Li, Guo-Dong Cheng, Wen-Chuan Wang, Qing-Le Cheng, Long Mao, Ying Jia, Heng-Yue School of Control and Computer Engineering North China Electric Power University Beijing102206 China Key Lab of Information Network Security Ministry of Public Security Shanghai200031 China Department of Computer and Information Science Fordham University New York City10458 United States School of Information Central University of Finance and Economics Beijing102206 China Engineering Research Center of State Financial Security Ministry of Education Central University of Finance and Economics Beijing102206 China

Quantum secret sharing (QSS) is the result of merging the principles of quantum mechanics with secret information sharing. It enables a sender to share a secret among receivers, and the receivers can then collectively recover the secret when the need arises. To enhance the practicality of these quantum protocols, an innovative concept of quantum anonymous secret sharing (QASS) is advanced. In this paper, we propose a QASS protocol via W states, which can share secrets while ensuring recover-ability, recover-security, and recover-anonymity. We have rigorously evaluated our protocols, verifying their accuracy and fortifying their security against scenarios involving the active adversary. This includes considerations for dishonest receivers and non-receivers. Moreover, acknowledging the imperfections inherent in real-world communication channels, we have also undertaken an exhaustive analysis of our protocol’s security and effectiveness in a quantum network where some form of noise is present. Our investigations reveal that W states exhibit good performance in mitigating noise interference, making them apt for practical applications. © 2024, CC BY-NC-ND.

关键词： Quantum theory

MISA: UNVEILING THE VULNERABILITIES IN SPLIT FEDERATED LEARNING

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arXiv 2023年

作者： Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Li, Minghui Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of robustness. In this paper, we present a novel poisoning attack known as MISA. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention. Copyright © 2023, The Authors. All rights reserved.

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A simple way to verify linearizability of concurrent stacks

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arXiv 2021年

作者： Wen, Tangliu Song, Lan Gannan University of Science and Technology Jiangxi University of Science and Technology Ganzhou China State Key Lab of Software Engineering School of Computer Wuhan University Wuhan430072 China

Linearizability is a commonly accepted correctness criterion for concurrent data structures. However, verifying linearizability of highly concurrent data structures is still a challenging task. In this paper, we present a simple and complete proof technique for verifying linearizability of concurrent stacks. Our proof technique reduces linearizability of concurrent stacks to establishing a set of conditions. The conditions are based on happened-before order of operations, intuitively express the "LIFO" semantics and can be proved by simple arguments. Verifiers of concurrent data structures can easily and quickly learn to use the proof technique. We have successfully applied the method to several challenging concurrent stacks: the TS stack, the HSY stack, and the FA stack. © 2021, CC BY.

关键词： Semantics

Attention-Based Knowledge-aware Multi-Interest Intelligent Model for Sequential Recommendation

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Attention-Based Knowledge-aware Multi-Interest Intelligent M...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yang Li Qianmu Li Shunmei Meng Jun Hou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Intelligent Manufacturing Department Wuyi University Jiangmen China State Key Lab. for Novel Software Technology Nanjing University Nanjing China School of Social Science Nanjing Vocational University of Industry Technology Nanjing China

As a fundamental means of intelligent service, recommender systems have shown a trend of diversification. Sequential recommendation, a class of time-aware methods, aims to predict the user’s next click. Multi-interest representation is a hot topic in recommender systems, but the research on how to apply multi-interest into sequences is still in the minority. And many methods will introduce knowledge graph to improve the representation of items, but how to consider both knowledge graph and user’s multi-interest in sequential recommendation has become a temporary blank. In this paper, we propose an attentive sequential recommendation with knowledge-aware multi-interest module (AKAMI). Items are embedded from the sequence’s structure and user’s interests. Specifically, we represent items by stacking self-attention layers to mine the internal correlation of the sequence. Meanwhile, we segment the sequence via the common neighbors in the knowledge graph and cast the user’s multi-interest based on the aggregation of subsequences. Multi-interests are integrated into item representation by the gating network adaptively. Comprehensive experiments on two real-world datasets show that the proposed model performs better than various representative methods under different evaluation metrics. Additionally, we also visually explain the model through a case study.

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