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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Zhongbin Li Liang Duan Kun Yue School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

Link prediction has achieved great success on ubiquitous graph-based applications, which usually contain multiple types of connections. The heterogeneity of networks introduces complexities in two aspects: representation of multiple types of links, and incorporation of domain knowledge. In this paper, we construct Markov logic network (MLN) to preserve he logical information of graphs for link prediction tasks. By using the graph neural network, we propose a novel inference method in MLNs to guarantee the scalability of link prediction. Experimental results on various datasets show that our proposed method outperforms the state-of-the-art competitors on precision and efficiency.

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Delegatable Multi-Authority Attribute-based Anonymous Credentials

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IEEE Transactions on cloud computing 2025年

作者： Sun, Meng Lai, Junzuo Mo, Xiaohan Wu, Chi Li, Peng Chu, Cheng-Kang Deng, Robert H. Jinan University College of Cyber Security Guangzhou510632 China Jinan University College of Information Science and Technology Guangzhou510632 China Tsinghua University Institute of Trustworthy Networks and Systems School of Software Beijing100084 China Digital Identity and Trustworthiness Laboratory Huawei Singapore 138588 Singapore Singapore Management University School of Computing and Information Systems 188065 Singapore

In cloud computing, users need to authenticate to access various resources. Attribute-based anonymous credentials (ABCs) provide a tool for privacy-preserving authentication, allowing users to prove possession of a set of attributes to cloud service providers anonymously. Most existing works on ABC deal with credentials on attributes issued by a single authority (issuer). In reality, it is more practical for users to obtain credentials on attributes from multiple authorities. There are a few works on multi-authority ABC, which do not support delegation needed in real deployments. In this paper, we present the first delegatable multi-authority attribute-based anonymous credential system, which simultaneously achieves revocation and traceability. We also give the security analysis of our construction. Finally, we implement our system, and the experimental results show its efficiency. © 2013 IEEE.

关键词： Anonymity

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TIME-INDEPENDENT SPIKING NEURON VIA MEMBRANE POTENTIAL ESTIMATION FOR EFFICIENT SPIKING NEURAL NETWORKS

arXiv

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arXiv 2024年

作者： Chen, Hanqi Yu, Lixing Zhan, Shaojie Yao, Penghui Shao, Jiankun School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing China

The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at https://***/chrazqee/MPE-PSN. © 2024, CC BY.

关键词： Neurons

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VGRISys: A Vision-Guided Robotic Intelligent System for Autonomous Instrument Calibration

VGRISys: A Vision-Guided Robotic Intelligent System for Auto...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Li, Zhenqi Wang, Hewei Li, Yijie Dev, Soumyabrata Zuo, Guoyu Beijing University of Technology Beijing-Dublin International College Beijing100124 China University College Dublin School of Computer Science Dublin Ireland Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China

ISBN: (纸本)9798350325706

With the increasing sophistication of musical instruments and the importance of precise tuning, a novel and efficient approach to instrument calibration is of significant necessity. This paper presents VGRISys, an innovative system leveraging robotics and artificial intelligence to facilitate the intricate process of instrument tuning. VGRISys incorporates a multi-axis robotic arm, meticulously engineered for fine-tuned physical interaction with various musical instruments, alongside an intelligent system that guides the robotic arm using real-time audio feedback analysis. In addition, it employs advanced signal processing and machine learning algorithms, allowing the system to identify pitch discrepancies and respond with appropriate corrective action. VGRISys remarkably reduces the time required for instrument tuning, thus improving efficiency and democratizing access to high-quality tuning. Furthermore, the system's machine learning capabilities allow it to refine its tuning process over repeated operations, providing a scalable solution to the ever-evolving complexities of instrument tuning. The exploration and development of VGRISys contribute significantly to the wider fields of robotics, artificial intelligence, and music technology. © 2023 IEEE.

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Learning Transferable Negative Prompts for Out-of-Distribution Detection

arXiv

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arXiv 2024年

作者： Li, Tianqi Pang, Guansong Bai, Xiao Miao, Wenjun Zheng, Jin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University China School of Computing and Information Systems Singapore Management University Singapore

Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate. To address this issue, we introduce a novel OOD detection method, named ‘NegPrompt’, to learn a set of negative prompts, each representing a negative connotation of a given class label, for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only, without any reliance on external outlier data. Further, current methods assume the availability of samples of all ID classes, rendering them ineffective in open-vocabulary learning scenarios where the inference stage can contain novel ID classes not present during training. In contrast, our learned negative prompts are transferable to novel class labels. Experiments on various ImageNet benchmarks show that NegPrompt surpasses state-of-the-art prompt-learning-based OOD detection methods and maintains a consistent lead in hard OOD detection in closed- and open-vocabulary classification scenarios. Code is available at https://***/mala-lab/negprompt. © 2024, CC BY.

关键词： Chemical detection

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Conditional diffusion guided by part-level latent for dental crown point cloud generation

Conditional diffusion guided by part-level latent for dental...

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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Zhang, Ao Shen, Zhen Yang, Jian Fang, Qihang Xiong, Gang Dong, Xisong State Key Laboratory of Multimodal Artificial Intelligence System Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China

ISBN: (纸本)9781510680425

Due to the scarcity of point cloud datasets in a specific domain, utilizing generative model approaches becomes essential for data augmentation. Diffusion models have demonstrated impressive capabilities in data generation through a guided reverse process. In this work, we employ a reverse process of a Markov chain conditioned on shape latent to progressively generate dental crown point cloud from a noise distribution. We propose to map the global shape latent to a set of part-level implicit representations and introduce a cross-attention block to provide geometric structural information for point cloud generation. We conduct a series of experiments on a real dental crown dataset, and the experimental results show certain improvement compared to the baselines, demonstrating the efficacy of our approach. In experiments, we present the capability of our method to generate large-scale dental crown models through unsupervised learning, effectively enriching the existing dental crown dataset. © 2024 SPIE.

关键词： Markov chains

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom Hartpury University Animal and Agriculture Department Gloucester United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

ISBN: (纸本)9798350351255

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model's performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis. © 2024 IEEE.

关键词： Electronic health record

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FlexFL: Flexible and Effective Fault Localization with Open-Source Large Language Models

arXiv

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arXiv 2024年

作者： Xu, Chuyang Liu, Zhongxin Ren, Xiaoxue Zhang, Gehao Liang, Ming Lo, David State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou310027 China Ant Group China School of Computing and Information Systems Singapore Management University 188065 Singapore

Fault localization (FL) targets identifying bug locations within a software system, which can enhance debugging efficiency and improve software quality. Due to the impressive code comprehension ability of Large Language Models (LLMs), a few studies have proposed to leverage LLMs to locate bugs, i.e., LLM-based FL, and demonstrated promising performance. However, first, these methods are limited in flexibility. They rely on bug-triggering test cases to perform FL and cannot make use of other available bug-related information, e.g., bug reports. Second, they are built upon proprietary LLMs, which are, although powerful, confronted with risks in data privacy. To address these limitations, we propose a novel LLM-based FL framework named FlexFL, which can flexibly leverage different types of bug-related information and effectively work with open-source LLMs. FlexFL is composed of two stages. In the first stage, FlexFL reduces the search space of buggy code using state-of-the-art FL techniques of different families and provides a candidate list of bug-related methods. In the second stage, FlexFL leverages LLMs to delve deeper to double-check the code snippets of methods suggested by the first stage and refine fault localization results. In each stage, FlexFL constructs agents based on open-source LLMs, which share the same pipeline that does not postulate any type of bug-related information and can interact with function calls without the out-of-the-box capability. Extensive experimental results on Defects4J demonstrate that FlexFL outperforms the baselines and can work with different open-source LLMs. Specifically, FlexFL with a lightweight open-source LLM Llama3-8B can locate 42 and 63 more bugs than two state-of-the-art LLM-based FL approaches AutoFL and AgentFL that both use GPT-3.5. In addition, FlexFL can localize 93 bugs that cannot be localized by non-LLM-based FL techniques at the top 1. Furthermore, to mitigate potential data contamination, we conduct experiment

关键词： Program debugging

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tSS-BO: Scalable Bayesian Optimization for Analog Circuit Sizing via Truncated Subspace Sampling

tSS-BO: Scalable Bayesian Optimization for Analog Circuit Si...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Tianchen Gu Jiaqi Wang Zhaori Bi Changhao Yan Fan Yang Yajie Qin Tao Cui Xuan Zeng State Key Laboratory of Integrated Chips and Systems School of Microelectronics Fudan University Shanghai China State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences & University of Chinese Academy of Sciences Beijing China State Key Laboratory of Integrated Chips and Systems School of Information Science and Technology Fudan University Shanghai China

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

We propose a novel scalable Bayesian optimization method with truncated subspace sampling (tSS-BO) to tackle high-dimensional optimization challenges for large-scale analog circuit sizing. To address the high-dimensional challenges, we propose subspace sampling subject to a truncated Gaussian distribution. This approach limits the effective sampling dimensionality down to a constant upper bound, independent of the original dimensionality, leading to a significant reduction in complexity associated with the curse of dimensionality. The distribution covariance is iteratively updated using a truncated flow, where approximate gradients and center steps are integrated with decaying prior subspace features. We introduce gradient sketching and local Gaussian process (GP) models to approximate gradients without additional simulations to mitigate systematic errors. To enhance efficiency and ensure compatibility with constraints, we utilize local GP models for the selection of promising candidates, avoiding the cost of acquisition function optimization. The proposed tSS-BO method exhibits clear advantages over state-of-the-art methods in experimental comparisons. In synthetic benchmark functions, the tSS-BO method achieves up to $4.93\times$ evaluation speedups and a remarkable over $30\times$ algorithm complexity reduction compared to the Bayesian baseline. In real-world analog circuits, our method achieves up to $2\times$ speedups in simulation number and runtime.

关键词： Adaptation models Upper bound Runtime Systematics Optimization methods Gaussian processes Analog circuits

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Improving accuracy-robustness trade-off via pixel reweighted adversarial training 24

Improving accuracy-robustness trade-off via pixel reweighted...

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Proceedings of the 41st International Conference on Machine Learning

作者： Jiacheng Zhang Feng Liu Dawei Zhou Jingfeng Zhang Tongliang Liu School of Computing and Information Systems The University of Melbourne State Key Laboratory of Integrated Services Networks Xidian University School of Computer Science The University of Auckland / RIKEN AIP Sydney AI Centre The University of Sydney

Adversarial training (AT) trains models using adversarial examples (AEs), which are natural images modified with specific perturbations to mislead the model. These perturbations are constrained by a predefined perturbation budget ε and are equally applied to each pixel within an image. However, in this paper, we discover that not all pixels contribute equally to the accuracy on AEs (i.e., robustness) and accuracy on natural images (i.e., accuracy). Motivated by this finding, we propose Pixel-reweighted AdveRsarial Training (PART), a new framework that partially reduces ε for less influential pixels, guiding the model to focus more on key regions that affect its outputs. Specifically, we first use class activation mapping (CAM) methods to identify important pixel regions, then we keep the perturbation budget for these regions while lowering it for the remaining regions when generating AEs. In the end, we use these pixel-reweighted AEs to train a model. PART achieves a notable improvement in accuracy without compromising robustness on CIFAR-10, SVHN and TinyImagenet-200, justifying the necessity to allocate distinct weights to different pixel regions in robust classification.

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