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arXiv 2025年

作者： Carrica, Vicki Onyango, Maxwell Alomairy, Rabab Ringoot, Evelyne Schloss, James Edelman, Alan Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

This paper presents a performant and portable recursive implementation of triangular matrix-matrix multiplication (TRMM) and triangular solve (TRSM) operations in Julia for GPUs, which form the backbone of many other linear algebra algorithms. This work is based on an existing recursive implementation for TRMM and TRSM, which restructures the operations to include general matrix-matrix multiplication (GEMM) calls, facilitating better utilization of the GPU memory hierarchy, and reducing latency overhead. The unified implementation in Julia harnesses the language’s multiple-dispatch and metaprogramming capabilities through the existing GPUArrays and KernelAbstractions frameworks, enabling performant hardware-agnostic execution across different GPU architectures. By supporting a consistent API, this implementation allows users to seamlessly switch between different GPU backends. The recursive hardware-agnostic implementation we present achieves performance comparable to vendor-optimized (cuBLAS/rocBLAS) libraries for larger matrix sizes and provides such methods for the first time to Apple Silicion hardware with only a few hundred lines of code, demonstrating the power of unified implementations. Copyright © 2025, The Authors. All rights reserved.

关键词： computer hardware description languages

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A Password-Based Mutual Authenticated Key Exchange Scheme by Blockchain for WBAN 1

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5th International Conference on Machine Learning for Cyber Security, ML4CS 2023

作者： Huang, Pei He, Yaorui Liang, Ting Xia, Zhe School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology Wuhan430070 China

ISBN: (数字)9789819724581

ISBN: (纸本)9789819724574

With the continuous development of Internet of Things technologies, Wireless Body Area Networks (WBAN) have shown great application potentials in the healthcare industry. However, adversaries may masquerade as legitimate users and sensitive medical data may be intercepted during transmission. Therefore, proper authentication and secure communications are required in WBAN. Password-based authenticated key exchange (PAKE) is an attractive solution for this problem due to its simplicity and low costs, i.e. the user and the server can use their shared password to perform authentication and to establish a session key for secure information exchange. However, many existing PAKE protocols are suffering some limitations. First, some schemes only consider one-way authentication, while masquerade is still possible on the server side. Second, some schemes are vulnerable to the offline dictionary attack, and the consequence is that the user’s password with limited entropy can be leaked. Third, some schemes need to employ secure channels, making them less practical in real-world applications. In this paper, we propose a password-based mutual authenticated key exchange scheme by Blockchain for WBAN, in which all these issues are addressed. In particular, mutual authentication is realized, and the adversary cannot launch the offline dictionary attack. Moreover, these features are achieved without employing a secure channel. Therefore, it achieves a good balance between usability and security. Security and performance analyses demonstrate that it satisfies the desirable security requirements and is very efficient for practical applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Authentication

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Sequential Kernel Goodness-of-fit Testing 41

Sequential Kernel Goodness-of-fit Testing

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41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Zhengyu Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Goodness-of-fit testing, a classical statistical tool, has been extensively explored in the batch setting, where the sample size is ***, practitioners often prefer methods that adapt to the complexity of a problem rather than fixing the sample size *** batch tests are generally unsuitable for streaming data, as valid inference after data peeking requires multiple testing corrections, resulting in reduced statistical *** address this issue, we delve into the design of consistent sequential goodness-of-fit *** the principle of testing by betting, we reframe this task as selecting a sequence of payoff functions that maximize the wealth of a fictitious bettor, betting against the null in a repeated *** conduct experiments to demonstrate the adaptability of our sequential test across varying difficulty levels of problems while maintaining control over type-I errors. Copyright 2024 by the author(s)

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Verifiable Attribute-Based Proxy Re-encryption with Non-repudiation Based on Blockchain 3rd

Verifiable Attribute-Based Proxy Re-encryption with Non-rep...

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3rd EAI International Conference on Blockchain Technology and Emerging Applications, BlockTEA 2023

作者： He, Yaorui Liang, Ting Huang, Pei Xia, Zhe School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology Wuhan430071 China

ISBN: (纸本)9783031600364

With the development of Blockchain, cloud computing, and artificial intelligence, smart transportation is highly likely to drive urban transportation in the direction of intelligence and digitalization. However, when connected vehicle devices share data in edge networks, there are security and privacy issues, e.g. leakage of sensitive information will cause serious threats. The separation of user data ownership and physical control requires users to impose access control on the outsourced data. Existing privacy protection schemes still suffer from problems such as a lack of supervision of centralized third-party cloud servers, which face the challenge of data leakage. In this paper, Blockchain and proxy re-encryption techniques are used to address these challenges. To enable sharing of the outsourced data in a secure way, Attribute-Based Proxy Re-encryption (ABPRE) is a popular technique. At the same time, verifiability enables the shared user to verify that the re-encrypted ciphertext returned by the server is correct. In addition, the introduction of a decentralized Blockchain provides distributed storage and it is tamper-proof for enhancing the trustworthiness of the connected vehicle devices as well as the security of the communication between the entities. The security and performance analyses demonstrate that our proposed scheme can achieve the desirable security features and it is efficient for protecting data privacy. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Network security

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PM2.5 prediction model based on multivariate feature and frequency information enhancement 4

PM2.5 prediction model based on multivariate feature and fre...

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4th International Conference on Electronic Information Engineering and computer Communication, EIECC 2024

作者： Yuan, Jiajing Liu, Lizhi Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan430205 China Wuhan Institute of Technology School of Computer Science & Engineering Artificial Intelligence Wuhan430205 China

ISBN: (纸本)9798331534622

In order to improve the prediction accuracy of PM2.5, a combined prediction model based on empirical mode decomposition, temporal convolutional network and frequency-enhanced gated attention was proposed. The eigenmode function of PM2.5 time series is obtained by empirical mode decomposition, and the sample entropy is calculated to distinguish the components of high-frequency, medium-frequency and low-frequency components. The component components are fed into the temporal convolutional network together with other relevant meteorological data to capture the local characteristics and long-term dependencies of the time series. The discrete cosine transform is used to convert the time-domain features extracted by the temporal convolutional network into the frequency domain, and the importance of different frequency components is dynamically evaluated through the attention mechanism. The experimental results show that the EMD-TCN-FEGA combined model is superior to the traditional model in PM2.5 concentration prediction, and has higher accuracy and generalization ability. © 2024 IEEE.

关键词： Empirical mode decomposition

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ULAREF: A Unified Label Refinement Framework for Learning with Inaccurate Supervision 41

ULAREF: A Unified Label Refinement Framework for Learning wi...

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41st International Conference on Machine Learning, ICML 2024

作者： Qiao, Congyu Xu, Ning Hu, Yihao Geng, Xin School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China

Learning with inaccurate supervision is often encountered in weakly supervised learning, and researchers have invested a considerable amount of time and effort in designing specialized algorithms for different forms of annotations in inaccurate supervision. In fact, different forms of these annotations share the fundamental characteristic that they all still incorporate some portion of correct labeling information. This commonality can serve as a lever, enabling the creation of a cohesive framework designed to tackle the challenges associated with various forms of annotations in learning with inaccurate supervision. In this paper, we propose a unified label refinement framework named ULAREF, i.e., a Unified LAbel REfinement Framework for learning with inaccurate supervision, which is capable of leveraging label refinement to handle inaccurate supervision. Specifically, our framework trains the predictive model with refined labels through global detection of reliability and local enhancement using an enhanced model fine-tuned by a proposed consistency loss. Also, we theoretically justify that the enhanced model in local enhancement can achieve higher accuracy than the predictive model on the detected unreliable set under mild assumptions. Copyright 2024 by the author(s)

关键词： Self-supervised learning

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Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation 24

Devil is in Details: Locality-Aware 3D Abdominal CT Volume G...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yuran Wan, Zhijing Qiu, Yansheng Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

In the realm of medical image analysis, self-supervised learning (SSL) techniques have emerged to alleviate labeling demands, while still facing the challenge of training data scarcity owing to escalating resource requirements and privacy constraints. Numerous efforts employ generative models to generate high-fidelity, unlabeled 3D volumes across diverse modalities and anatomical regions. However, the intricate and indistinguishable anatomical structures within the abdomen pose a unique challenge to abdominal CT volume generation compared to other anatomical regions. To address the overlooked challenge, we introduce the Locality-Aware Diffusion (Lad), a novel method tailored for exquisite 3D abdominal CT volume generation. We design a locality loss to refine crucial anatomical regions and devise a condition extractor to integrate abdominal priori into generation, thereby enabling the generation of large quantities of high-quality abdominal CT volumes essential for SSL tasks without the need for additional data such as labels or radiology reports. Volumes generated through our method demonstrate remarkable fidelity in reproducing abdominal structures, achieving a decrease in FID score from 0.0034 to 0.0002 on AbdomenCT-1K dataset, closely mirroring authentic data and surpassing current methods. Extensive experiments demonstrate the effectiveness of our method in self-supervised organ segmentation tasks, resulting in an improvement in mean Dice scores on two abdominal datasets effectively. These results underscore the potential of synthetic data to advance self-supervised learning in medical image analysis. © 2024 ACM.

关键词： Self-supervised learning

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Aligned Objective for Soft-Pseudo-Label Generation in Supervised Learning 41

Aligned Objective for Soft-Pseudo-Label Generation in Superv...

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41st International Conference on Machine Learning, ICML 2024

作者： Xu, Ning Hu, Yihao Qiao, Congyu Geng, Xin School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China

Soft pseudo-labels, generated by the softmax predictions of the trained networks, offer a probabilistic rather than binary form, and have been shown to improve the performance of deep neural networks in supervised learning. Most previous methods adopt classification loss to train a classifier as the soft-pseudo-label generator and fail to fully exploit their potential due to the misalignment with the target of soft-pseudo-label generation, aimed at capturing the knowledge in the data rather than making definitive classifications. Nevertheless, manually designing an effective objective function for a soft-pseudo-label generator is challenging, primarily because datasets typically lack ground-truth soft labels, complicating the evaluation of the soft pseudo-label accuracy. To deal with this problem, we propose a novel framework that alternately trains the predictive model and the soft-pseudo-label generator guided by a meta-network-parameterized label enhancement objective. The parameters of the objective function are optimized based on the feedback from both the performance of the predictive model and the soft-pseudo-label generator in the learning task. Additionally, the framework offers versatility across different learning tasks by allowing direct modifications to the task loss. Experiments on the benchmark datasets validate the effectiveness of the proposed framework. Source code is available at https://***/palm-ml/SEAL. Copyright 2024 by the author(s)

关键词： Self-supervised learning

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Predict Malware Using Machine Learning

Predict Malware Using Machine Learning

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Domingos Araújo Joaquim Silva Patrícia Leite Óscar Ribeiro Paulo Teixeira School of Technology Polythecnic Institute of Cavado and Ave Barcelos Portugal IPCA 2Ai – School of Technology Barcelos Portugal LASI – Associate Laboratory of Intelligent Systems LIACC - Artificial Intelligence and Computer Science Laboratory Porto Portugal Polythecnic Institute of Cavado and Ave Barcelos Portugal LIACC- Artificial Intelligence and Computer Science Laboratory

This paper has as main goal to demonstrate how artificial intelligence models can be used to detect malicious files, executables, etc. The detection of malware allows preventing the loss of information, software, and hardware. Throughout this paper the models that were chosen and the results obtained by those models will be presented. The pipeline (application) that was created to help in the flow of information extraction, dataset creation, dataset processing, dataset training and testing for several models and the results will be presented. It will be shown how the pipeline helps in "fine-tuning" the models and datasets. It will be presented how the user can reproduce these results or obtain new results. In summary, the main objective of this paper is to demonstrate in a simple way which are the best Malware prediction models.

关键词： Training Analytical models Pipelines Machine learning Predictive models Malware Data models

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Red Alarm for Pre-trained Models:Universal Vulnerability to Neuron-level Backdoor Attacks

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Machine intelligence Research 2023年第2期20卷 180-193页

作者： Zhengyan Zhang Guangxuan Xiao Yongwei Li Tian Lv Fanchao Qi Zhiyuan Liu Yasheng Wang Xin Jiang Maosong Sun Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Institute for Artificial Intelligence Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Beijing 100084China Huawei Noah′s Ark Laboratory Hong Kong 999077China

The pre-training-then-fine-tuning paradigm has been widely used in deep *** to the huge computation cost for pre-training,practitioners usually download pre-trained models from the Internet and fine-tune them on downstream datasets,while the downloaded models may suffer backdoor *** from previous attacks aiming at a target task,we show that a backdoored pre-trained model can behave maliciously in various downstream tasks without foreknowing task *** can restrict the output representations(the values of output neurons)of trigger-embedded samples to arbitrary predefined values through additional training,namely neuron-level backdoor attack(NeuBA).Since fine-tuning has little effect on model parameters,the fine-tuned model will retain the backdoor functionality and predict a specific label for the samples embedded with the same *** provoke multiple labels in a specific task,attackers can introduce several triggers with predefined contrastive *** the experiments of both natural language processing(NLP)and computer vision(CV),we show that NeuBA can well control the predictions for trigger-embedded instances with different trigger *** findings sound a red alarm for the wide use of pre-trained ***,we apply several defense methods to NeuBA and find that model pruning is a promising technique to resist NeuBA by omitting backdoored neurons.

关键词： Pre-trained language models backdoor attacks transformers natural language processing(NLP) computer vision(CV)

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