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Capability-based IoT access control using blockchain

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Digital Communications and Networks 2021年第4期7卷 463-469页

作者： Yue Liu Qinghua Lu Shiping Chen Qiang Qu Hugo’Connor Kim-Kwang Raymond Choo He Zhang School of Computer Science and Engineering University of New South WalesAustralia Data61 CSIROAustralia Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesChina Department of Information Systems and Cyber Security University of Texas at San AntonioUSA Software Institute Nanjing UniversityChina

Internet of Things(IoT)devices facilitate intelligent service delivery in a broad range of settings,such as smart offices,homes and ***,the existing IoT access control solutions are mainly based on conventional identity management schemes and use centralized *** are knowm security and privacy limitations with such schemes and architectures,such as the single-point failure or surveillance(e.g.,device tracking).Hence,in this paper,we present an architecture for capability-based IoT access control utilizing the blockchain and decentralized identifiers to manage the identity and access control for IoT ***,we propose a protocol to provide a systematic view of system interactions,to improve *** also implement a proof-of-concept prototype of the proposed approach and evaluate the prototype using a real-world use *** evaluation results show that the proposed solution is feasible,secure,and scalable.

关键词： Blockchain Internet of things Capability-based access control Identity management Architecture design

No Free Lunch Theorem for Privacy-Preserving LLM Inference

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arXiv 2024年

作者： Zhang, Xiaojin Fei, Yulin Kang, Yan Chen, Wei Fan, Lixin Jin, Hai Yang, Qiang Huazhong University of Science and Technology China WeBank China Hong Kong University of Science and Technology 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 Technology Huazhong University of Science and Technology Wuhan430074 China

Individuals and businesses have been significantly benefited by Large Language Models (LLMs) including PaLM, Gemini and ChatGPT in various ways. For example, LLMs enhance productivity, reduce costs, and enable us to focus on more valuable tasks. Furthermore, LLMs possess the capacity to sift through extensive datasets, uncover underlying patterns, and furnish critical insights that propel the frontiers of technology and science. However, LLMs also pose privacy concerns. Users’ interactions with LLMs may expose their sensitive personal or company information. A lack of robust privacy safeguards and legal frameworks could permit the unwarranted intrusion or improper handling of individual data, thereby risking infringements of privacy and the theft of personal identities. To ensure privacy, it is essential to minimize the dependency between shared prompts and private information. Various randomization approaches have been proposed to protect prompts’ privacy, but they may incur utility loss compared to unprotected LLMs prompting. Therefore, it is essential to evaluate the balance between the risk of privacy leakage and loss of utility when conducting effective protection mechanisms. The current study develops a framework for inferring privacy-protected Large Language Models (LLMs) and lays down a solid theoretical basis for examining the interplay between privacy preservation and utility. The core insight is encapsulated within a theorem that is called as the NFL (abbreviation of the word No-Free-Lunch) Theorem. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

VidEgoThink: ASSESSING EGOCENTRIC VIDEO UNDERSTANDING CAPABILITIES FOR EMBODIED AI

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arXiv 2024年

作者： Cheng, Sijie Fang, Kechen Yu, YangYang Zhou, Sicheng Li, Bohao Tian, Ye Li, Tingguang Han, Lei Liu, Yang Department of Computer Science and Technology Tsinghua University China Tsinghua University China Department of Mechanical and Industrial Engineering University of Toronto Canada School of Data Science The Chinese University of HongKong Hong Kong Zhili College Tsinghua University China Tencent Robotics X China

Recent advancements in Multi-modal Large Language Models (MLLMs) have opened new avenues for applications in Embodied AI. Building on previous work, EgoThink, we introduce VidEgoThink, a comprehensive benchmark for evaluating egocentric video understanding capabilities. To bridge the gap between MLLMs and low-level control in Embodied AI, we design four key interrelated tasks: video question-answering, hierarchy planning, visual grounding and reward modeling. To minimize manual annotation costs, we develop an automatic data generation pipeline based on the Ego4D dataset, leveraging the prior knowledge and multimodal capabilities of GPT-4o. Three human annotators then filter the generated data to ensure diversity and quality, resulting in the VidEgoThink benchmark. We conduct extensive experiments with three types of models: API-based MLLMs, open-source image-based MLLMs, and open-source video-based MLLMs. Experimental results indicate that all MLLMs, including GPT-4o, perform poorly across all tasks related to egocentric video understanding. These findings suggest that foundation models still require significant advancements to be effectively applied to first-person scenarios in Embodied AI. In conclusion, VidEgoThink reflects a research trend towards employing MLLMs for egocentric vision, akin to human capabilities, enabling active observation and interaction in the complex real-world environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Video analysis

Temporal Based Instance-Level Fusion for Video Object Detection 17th

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Temporal Based Instance-Level Fusion for Video Object Detect...

17th Chinese Intelligent Systems Conference, CISC 2021

作者： Cai, Qiang Kang, Nan Li, Haisheng Cao, Jian Liu, Wenqing Wan, Ruyi School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China National Engineering Laboratory For Agri-product Quality Traceability Beijing China Beijing zhiyunda Technology Co. Ltd. Beijing100085 China

ISBN: (纸本)9789811663277

Static image object detection is naively applied to videos in a frame-by-frame approach, and the prediction is often temporally inconsistent. Enhancement of every selected frame becomes a typical solution by aggregating temporal nearby frames features. But the solutions above just use features in pixel-level. In this work, we emphasize the importance of the temporal sequential relationship, which considers calibrating instance-level features on time line, and combining the pixel-level features with the calibrated instance-level features finally. In order to prevent mismatch in video boundary, pixel-level re-fusion is remained to amend above issue. Although the instance-level fusion gains more global motion cues for the pixel-level calibration’s sake of robustness to occlusion, using temporal information can further improve the robustness. Experiments on the large-scale ImageNet VID dataset state the improvement of the our work for object detection in videos. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation

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arXiv 2022年

作者： Li, Lin Xiao, Jun Shi, Hanrong Zhang, Hanwang Yang, Yi Liu, Wei Chen, Long The College of Computer Science Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The Data Platform Tencent Shenzhen518000 China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology 999077 Hong Kong

Nearly all existing scene graph generation (SGG) models have overlooked the ground-truth annotation qualities of mainstream SGG datasets, i.e., they assume: 1) all the manually annotated positive samples are equally correct;2) all the un-annotated negative samples are absolutely background. In this paper, we argue that neither of the assumptions applies to SGG: there are numerous "noisy" ground-truth predicate labels that break these two assumptions and harm the training of unbiased SGG models. To this end, we propose a novel NoIsy label CorrEction and Sample Training strategy for SGG: NICEST, which rules out these noisy label issues by generating high-quality samples and designing an effective training strategy. Specifically, it consists of: 1) NICE: it detects noisy samples and then reassigns higher-quality soft predicate labels to them. To achieve this goal, NICE contains three main steps: negative Noisy Sample Detection (Neg-NSD), positive NSD (Pos-NSD), and Noisy Sample Correction (NSC). Firstly, in Neg-NSD, it is treated as an out-of-distribution detection problem, and the pseudo labels are assigned to all detected noisy negative samples. Then, in Pos-NSD, we use a density-based clustering algorithm to detect noisy positive samples. Lastly, in NSC, we use weighted KNN to reassign more robust soft predicate labels rather than hard labels to all noisy positive samples. 2) NIST: it is a multi-teacher knowledge distillation based training strategy, which enables the model to learn unbiased fusion knowledge. A dynamic trade-off weighting strategy in NIST is designed to penalize the bias of different teachers. Due to the model-agnostic nature of both NICE and NIST, NICEST can be seamlessly incorporated into any SGG architecture to boost its performance on different predicate categories. In addition, to better assess the generalization ability of SGG models, we propose a new benchmark, VG-OOD, by reorganizing the prevalent VG dataset. This reorganization deliberately

关键词： Distillation

Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness

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arXiv 2023年

作者： Chen, Huajie Zhu, Tianqing Zhao, Yuan Liu, Bo Yu, Xin Zhou, Wanlei Centre for Cyber Security and Privacy The School of Computer Science University of Technology Sydney SydneyNSW2007 Australia School of Information Technology and Electrical Engineering University of Queensland BrisbaneQLD4067 Australia Institute of Data Science City University of Macau China

Image deep steganography (IDS) is a technique that utilizes deep learning to embed a secret image invisibly into a cover image to generate a container image. However, the container images generated by convolutional neural networks (CNNs) are vulnerable to attacks that distort their high-frequency components. To address this problem, we propose a novel method called Low-frequency Image Deep Steganography (LIDS) that allows frequency distribution manipulation in the embedding process. LIDS extracts a feature map from the secret image and adds it to the cover image to yield the container image. The container image is not directly output by the CNNs, and thus, it does not contain high-frequency artifacts. The extracted feature map is regulated by a frequency loss to ensure that its frequency distribution mainly concentrates on the low-frequency domain. To further enhance robustness, an attack layer is inserted to damage the container image. The retrieval network then retrieves a recovered secret image from a damaged container image. Our experiments demonstrate that LIDS outperforms state-of-the-art methods in terms of robustness, while maintaining high fidelity and specificity. By avoiding high-frequency artifacts and manipulating the frequency distribution of the embedded feature map, LIDS achieves improved robustness against attacks that distort the high-frequency components of container images. © 2023, CC BY-SA.

关键词： Steganography

Building Intelligent Automated Real-Time data Analysis Systems Through Machine Learning

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Building Intelligent Automated Real-Time Data Analysis Syste...

International Conference on Futuristic Technologies (INCOFT)

作者： Sahil Rathee M. Vigenesh Kala Priydarshini Vijaykumar R Shweta Singh Jyoti Seth Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

This paper explores the capacity of using gadget-get-to-know-based facts evaluation structures to build intelligent, automated real-time statistics analysis systems. It examines the demanding situations present points evaluation systems pose and proposes a unique remedy. The proposed method leverages supervised and unsupervised devices and learning strategies to enable deep information on complex datasets. It then makes use of predictive skills to make clever selections with the aid of comparing input facts about databases. ultimately, the paper describes a case to demonstrate the effectiveness of this proposed machine and outlines implications for comparable implementations in the enterprise. This work is precious for information scientists and commercial enterprise decision-makers, as it outlines an ability to deploy reliable and accurate records analysis structures.

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

ISBN: (纸本)9798350368741

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2025 IEEE.

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Efficient FPGA-based graph processing with hybrid pull-push computational model

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Frontiers of computer science 2020年第4期14卷 13-28页

作者： Chengbo YANG Long ZHENG Chuangyi GUI Hai JIN National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China

Hybrid pull-push computational model can provide compelling results over either of single one for processing real-world *** and pipeline parallelism of FPGAs make it potential to process different stages of graph ***,considering the limited on-chip resources and streamline pipeline computation,the efficiency of hybrid model on FPGAs often suffers due to well-known random access feature of graph *** this paper,we present a hybrid graph processing system on FPGAs,which can achieve the best of both *** approach on FPGAs is unique and novel as ***,we propose to use edge block(consisting of edges with the same destination vertex set),which allows to sequentially access edges at block granularity for locality while still preserving the *** to the independence of blocks in the sense that all edges in an inactive block are associated with inactive vertices,this also enables to skip invalid blocks for reducing redundant ***,we consider a large number of vertices and their associated edge-blocks to maintain a predictable execution *** also present to switch models in advance with few stalls using their state *** evaluation on a wide variety of graph algorithms for many real-world graphs shows that our approach achieves up to 3.69x speedup over state-of-the-art FPGA-based graph processing systems.

关键词： graph processing efficiency computational model FPGAs