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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Xiao, Tao Zhang, Zhenyong Shao, Kuan Li, Hao School of Computer Science and Engineering Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9798331507886

As convolutional neural networks (CNNs) have shown excellent performance in various inference tasks, it has become increasingly critical to enable Artificial Intelligence of Things (A IoT) systems to run CNN-based applications. However, using CNN-based applications in many AIoT scenarios raises privacy concerns. Protecting the inference phase can preserve the user's privacy, which is an urgent issue according to worldwide rules and policies. In this paper, we propose SeP2CNN, a simple and efficient fully homomorphic encryption (FHE) method to realize the privacy-preserving CNN inference in pervasive AIoT applications. By carefully combining cryptography and deep learning, we compute ReLU with a composite polynomial as the activation function over ciphertexts. To improve the efficiency, we transform the three-dimensional convolution into a two-dimensional convolution to simplify the calculation. Finally, through extensive experiments on multiple datasets, we validate and evaluate the performance of SeP2CNN. © 2024 IEEE.

关键词： Differential privacy

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(Information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Grasping by Parallel Shape Matching

Grasping by Parallel Shape Matching

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2024 Australasian Conference on Robotics and Automation, ACRA 2024

作者： Zhang, Wenzheng Maken, Fahira Afzal Lai, Tin Ramos, Fabio School of Computer Science The University of Sydney Australia Data61 CSIRO Australia NVIDIA United States

Grasping is essential in robotic manipulation, yet challenging due to object and gripper diversity and real-world complexities. Traditional analytic approaches often have long optimization times, while data-driven methods struggle with unseen objects. This paper formulates the problem as a rigid shape matching between gripper and object, which optimizes with Annealed Stein Iterative Closest Point (AS-ICP) and leverages GPU-based parallelization. By incorporating the gripper’s tool centre point and the object’s centre of mass into the cost function and using a signed distance field of the gripper for collision checking, our method achieves robust grasps with low computational time. Experiments with the Kinova KG3 gripper show an 87.3% success rate and 0.926s computation time across various objects and settings, highlighting its potential for real-world applications. © 2024 Australasian Robotics and Automation Association. All rights reserved.

关键词： Cost functions

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Late Breaking Results: Mixed-Cell-Height Detailed Placement under Multi-Cell Spacing Constraints 24

Late Breaking Results: Mixed-Cell-Height Detailed Placement ...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Zhu, Benchao Zeng, Zheng Chen, Jianli School of Microelectronics Fudan University Shanghai China College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798400706011

As technology scales down, multi-cell spacing constraints are imposed by modern circuit designs. Previous works compromise solution quality to address the problem by transforming it into two-cell spacing constraints. In this paper, we propose a detailed placement algorithm considering multi-cell spacing constraints. First, an SAT-based multi-cell spacing violation reduction method is presented to reduce the number of violations with minimum displacement. Then, a window-based violation elimination method is adopted to resolve all the remaining violations. Finally, we refine the placement result with ILP to reduce the cell displacement. Compared with the state-of-the-art work, experimental results show that our algorithm achieves a 19% improvement in displacement and a 39% reduction in runtime. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

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Group Signature with Time-Bound Keys for Secure E-health Record Sharing

Group Signature with Time-Bound Keys for Secure E-health Rec...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lu, Junze Hu, Chunqiang Ruan, Conghao Cai, Bin Xiang, Tao Chongqing University School of Big Data & Software Engineering China Chongqing University College of Computer Science China

ISBN: (纸本)9798350386226

With the advent of various mobile IoT devices, a large amount of e-health record (EHR) data has been generated. This data has great potential to improve medical research. However, there are many challenges regarding the sharing of medical data. Firstly, users are more inclined to interact anonymously. Secondly, verifying the validity of certificates in the case of anonymous interactions is challenging. In addition, it is necessary to uncover the identities of the actual interacting parties in the event of malicious behavior. Therefore, we address the above challenges and propose group signatures with time constraints to support anonymous and traceable EHR data sharing. First, we propose a group signature scheme that supports traceability. Second, to address the issue of validating anonymous certificates, we propose group signatures with time constraints that enable dynamic updates to validity. Through this, we can dynamically revoke group members. Lastly, security proofs and efficiency analyses demonstrate that our scheme is both secure and efficient. © 2024 IEEE.

关键词： Electronic health record

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Fractional-Order Image Moments and Applications 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Xu, Liyun Zhang, Min Institute of Big Data Science and Industry School of Computer and Information Technology Shanxi University Taiyuan030006 China

ISBN: (数字)9783031533112

ISBN: (纸本)9783031533105

Image moments, as a global feature descriptor for images, have become a powerful tool for pattern recognition and image analysis. Most of the currently existing fractional-order image moments are polynomial-based. Three novel moments, namely Zernike fractional Fourier moment, Merlin fractional Fourier moment, and exponential fractional Fourier moment, combined with classical moments and fractional Fourier transform, are introduced based on angular functions. Additionally, we propose a zero watermarking algorithm based on these moments. We present robustness and comparative analysis experiments, including the effects of noise, filtering, rotation, and scaling. The experimental results demonstrate that the zero watermarking algorithm utilizing fractional-order moments can effectively withstand image processing attacks and geometric attacks. In both cases, their performance surpasses that of their corresponding integer-order image moments. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Security of data

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A Prediction-Based Multi-Objective VM Consolidation Approach for Cloud data Centers

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computers, Materials & Continua 2024年第7期80卷 1601-1631页

作者： Xialin Liu Junsheng Wu Lijun Chen Jiyuan Hu School of Computer Science Northwestern Polytechnical UniversityXi’an710005China School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710021China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and TelecommunicationsXi’an710021China School of Software and Microelectronics Northwestern Polytechnical UniversityXi’an710005China School of Automation Xi’an University of Posts and TelecommunicationsXi’an710021China

Virtual machine(VM)consolidation aims to run VMs on the least number of physical machines(PMs).The optimal consolidation significantly reduces energy consumption(EC),quality of service(QoS)in applications,and resource *** paper proposes a prediction-basedmulti-objective VMconsolidation approach to search for the best mapping between VMs and PMs with good timeliness and practical *** use a hybrid model based on Auto-Regressive Integrated Moving Average(ARIMA)and Support Vector Regression(SVR)(HPAS)as a prediction model and consolidate VMs to PMs based on prediction results by HPAS,aiming at minimizing the total EC,performance degradation(PD),migration cost(MC)and resource wastage(RW)*** results usingMicrosoft Azure trace show the proposed approach has better prediction accuracy and overcomes the multi-objective consolidation approach without prediction(i.e.,Non-dominated sorting genetic algorithm 2,Nsga2)and the renowned Overload Host Detection(OHD)approaches without prediction,such as Linear Regression(LR),Median Absolute Deviation(MAD)and Inter-Quartile Range(IQR).

关键词： VM consolidation prediction multi-objective optimization machine learning

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English-to-Low-Resource Translation: A Multimodal Approach for Hindi, Malayalam, Bengali, and Hausa 9

English-to-Low-Resource Translation: A Multimodal Approach f...

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9th Conference on Machine Translation, WMT 2024

作者： Hatami, Ali Banerjee, Shubhanker Arcan, Mihael Buitelaar, Paul McCrae, John Philip Insight SFI Research Centre for Data Analytics Data Science Institute University of Galway Ireland ADAPT Centre Data Science Institute University of Galway Ireland Lua Health Galway Ireland School of Computer Science University of Galway Ireland

ISBN: (纸本)9798891761797

Multimodal machine translation leverages multiple data modalities to enhance translation quality, particularly for low-resourced languages. This paper uses a multimodal model that integrates visual information with textual data to improve translation accuracy from English to Hindi, Malayalam, Bengali, and Hausa. This approach employs a gated fusion mechanism to effectively combine the outputs of textual and visual encoders, enabling more nuanced translations that consider both language and contextual visual cues. The model’s performance was evaluated against the text-only machine translation model based on BLEU, ChrF2 and TER. Experimental results demonstrate that the multimodal approach consistently outperforms the text-only baseline, highlighting the potential of integrating visual information in low-resourced language translation tasks. ©2024 Association for Computational Linguistics.

关键词： Machine translation

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Routing with Massive Trajectory data 40

Routing with Massive Trajectory Data

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Jensen, Christian S. Yang, Bin Guo, Chenjuan Hu, Jilin Torp, Kristian Aalborg University Department of Computer Science Aalborg Denmark School of Data Science & Engineering East China Normal University Shanghai China

ISBN: (纸本)9798350317152

The unprecedented availability of new types of data coupled with the invention of new technologies combine to enable entirely new or higher-resolution services that in turn enable more rational and data-driven processes. We consider the overall process of vehicular transportation and, more specifically, the process of deciding which route to follow when having to reach a destination. Early solutions modeled a road work as a graph, used sparse in-road sensor data to assign weights to graph edges, and then applied improved versions of Dijkstra's algorithm to find routes with the lowest sums of edge weights. Since then, massive vehicle trajectory data has become available. When coupled with new technologies, this data enables entirely new and higher-resolution routing services that in turn enable better routing. For more than a decade, the authors have engaged in research aimed at exploiting trajectory data to enable better routing. The resulting technologies were developed outside a DBMS. Here, we cover aspects of this research. Further, we challenge the community to develop DBMS support for these and other aspects of routing. © 2024 IEEE.

关键词： database systems

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Identification and Grouping of Skin Sickness by Means of Deep Learning

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SN computer science 2023年第3期4卷 236页

作者： Udupa, Pradeep Naidu, Aishwarya V. K. Shettigar, Suraj Kumar Maurya, Jayender Bahadur School of Computer Science and Applications REVA University Bangalore India School of Computer Science and Applications Reva University Bangalore India School of CSA II MSc in Data Science Reva University Bangalore India

The individuals’ health is important than any other situations. Skin situations are significantly produced by fungoid contagion, microbes, or infections, etc. The spotlights progression and Photonics stranded medical technology is used in view of the skin conditions impatiently and directly. The medical accoutrements for alike opinion is limited and most valuable. Therefore, Deep learning customs assist in discovery of skin grievance at an exclusive phase. The point birth plays a vital part in bracket of skin circumstances. The process of Deep Knowledge procedures has condensed the need for mortal labour, alike as household point birth and data reconstruction for bracket purpose. A dataset of 938 images has been taken for the Bracket of Skin illnesses they include Melanoma, Nevus, and Sebborheic Keratosis. Using CNN procedures, 70 delicacy is attained in bracket of skin complaint. AlexNet gives 83% delicacy as in Table 1, in proposed research three skin disease images were taken into consideration, in proposed method features are extracted using AlexNet then classified using support vector machine and compared with images in the dermnet dataset from Kaggle as mentioned in Table 1 and Figs. 2 and 3. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence Artificial neural networks Civil production Expert systems Pattern recognition Structural engineering

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