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Post-quantum Cryptography: A Solution to the Challenges of Classical Encryption Algorithms 1

International Conference on Modern Electronics Devices and Communication Systems, MEDCOM 2021

作者： Sharma, Shagun Ramkumar, K.R. Kaur, Amanpreet Hasija, Taniya Mittal, Sudesh Singh, Bhupendra Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Centre for Artificial Intelligence & Robotics DRDO Bengaluru India

ISBN: (数字)9789811963834

ISBN: (纸本)9789811963827

With the rise in quantum computing, there is an increase in the security concerns of existing cryptographic algorithms such as Rivest Shamir Adleman (RSA) and advanced encryption standard (AES) along with elliptic curve cryptography (ECC). The problem with RSA and ECC is that they depend on the challenges of integer factorization and discrete logarithms, respectively, whereas AES can be the target of brute force attack. In the recent years, there is a huge advancement in quantum computing due to which the existing encryption standards found vulnerable as they are weak to handle quantum attacks. Hence, to solve the problems with quantum computing, a new set of algorithms came into existence called as post-quantum cryptography (PQC) which is safe and secure from quantum attacks. In this review, firstly, the quantum computers and number of classical algorithms are introduced, afterward the classification of PQC algorithms has been shown. The aim of this review is to present the technical overview about PQC algorithms and their working procedures to protect classical cryptographic-based encrypted information from quantum attacks during online transactions and communications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quantum computers

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A Survey on Performance Analysis of Different Architectures of AES Algorithm on FPGA 1

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International Conference on Modern Electronics Devices and Communication Systems, MEDCOM 2021

作者： Hasija, Taniya Kaur, Amanpreet Ramkumar, K.R. Sharma, Shagun Mittal, Sudesh Singh, Bhupendra Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Centre for Artificial Intelligence & Robotics DRDO Bangalore India

ISBN: (数字)9789811963834

ISBN: (纸本)9789811963827

Encryption is the primary way for ensuring communication security. The symmetric key method, often known as Advanced Encryption Standard (AES), is a well-known technique in the field of security. AES can be implemented in either software or hardware. In the current study, Field Programmable Gate Arrays (FPGAs) are utilized to implement AES. Number of studies have been done on experiments of AES using FPGAs. Till date, no study has been done on the architectures that are being utilized to implement AES on FPGA. This paper provides an in-depth examination of several hardware implementation of AES on FPGA in terms of through put and performance. This survey article enables the engineers to choose the best architecture of FPGAs to implement AES algorithm in terms of design as per the requirement. The surveyed architectures are sequential, pipelined, iterative, and parallel. Parallel architectures with pipelining between rounds have shown an excellent throughput. Certain improved S-box and key expansion approaches have also been employed by the researchers to reduce the hardware areas. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Parallel architectures

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Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation

arXiv

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arXiv 2024年

作者： Dou, Bin Zhang, Tianyu Wang, Zhaohui Ma, Yongjia Yuan, Zejian Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

Zero-shot neural scene segmentation, which reconstructs 3D neural segmentation field without manual annotations, serves as an effective way for scene understanding. However, existing models, especially the efficient 3D Gaussian-based methods, struggle to produce compact segmentation results. This issue stems primarily from their redundant learnable attributes assigned on individual Gaussians, leading to a lack of robustness against the 3D-inconsistencies in zero-shot generated raw labels. To address this problem, our work, named Compact Segmented 3D Gaussians (CoSegGaussians), proposes the Feature Unprojection and Fusion module as the segmentation field, which utilizes a shallow decoder generalizable for all Gaussians based on high-level features. Specifically, leveraging the learned Gaussian geometric parameters, semantic-aware image-based features are introduced into the scene via our unprojection technique. The lifted features, together with spatial information, are fed into the multi-scale aggregation decoder to generate segmentation identities for all Gaussians. Furthermore, we design CoSeg Loss to boost model robustness against 3D-inconsistent noises. Experimental results show that our model surpasses baselines on zero-shot semantic segmentation task, improving by ∼ 10% mIoU over the best baseline. Code and more results will be available at https://***/CoSegGaussians. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Drift Modeling and Compensation for Force Sensor Signal Using a Dual-Stage Attention-Based Transformation-Gated LSTM Network 42

Drift Modeling and Compensation for Force Sensor Signal Usin...

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42nd Chinese Control Conference, CCC 2023

作者： Zhang, Yiwen Dai, Yu Yao, Bin Zhou, Lu Collage of Artificial Intelligence Nankai University Tianjin300350 China Institute of Robotics and Information Automation Nankai University Tianjin300350 China

ISBN: (纸本)9789887581543

In minimally invasive surgical robot system, high-precision force perception will improve the efficiency of surgery. The sampling signal of multidimensional force sensor usually appears baseline drift and noise. In this article we propose a method that tries to solve this issue. The method combines wavelet decomposition and neural network to model and compensate the drift. This paper uses wavelet decomposition to construct features, and uses a dual-stage attention-based transformation-gated LSTM (DA-TGLSTM) network to fit the drift. Then this paper uses the signal acquired subtract the drift predicted by the network to correct the drift. The verification experiments based on the actual drift data show that this method can track the drift over better than several other basic methods on multiple indicator criteria. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Wavelet decomposition

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Cervical Cytology Classification with Coarse Labels Based on Two-Stage Weakly Supervised Contrastive Learning Framework

Cervical Cytology Classification with Coarse Labels Based on...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chai, Siyi Xin, Jingmin Wu, Jiayi Yu, Hongxuan Liang, Zhaohai Ma, Yong Zheng, Nanning Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an China Shaanxi Yunying Panorama Information Technology Company Xi'an China

ISBN: (纸本)9798350337488

Deep learning methods have achieved remarkable success in various tasks from cervical cytology images. However, for the gigapixel whole slide images (WSIs), the acquisition of annotations is a time-consuming and labor-intensive task requiring a high level of expertise. While the sparse distribution of malignant cells and the factors above pose great difficulties to label thousands of patches divided from the WSI, it is much easier to obtain the coarse labels at the WSI level. In this paper, we propose a novel weakly supervised contrastive learning framework, which utilizes only coarse labels from the WSIs for cervical cytology patch classification. The proposed framework consists of two stages, including the representation learning stage and the classifier finetuning stage. In the first stage, to effectively exploit useful information of coarse labels, we devise a re-weight cross-entropy loss, which can fast warm up the training and reduce the inexact supervision from the coarse labels simultaneously. To further excavate features bypassing the coarse labels, we propose a self-supervised contrastive loss, where the random augmentation and the mean teacher architecture enrich the external variations, and help better extract representations through patch similarities. In the second stage, based on ensemble predictions and uncertainty selections, reliable pseudo labels are generated for the inaccurate labels to finetune the classifier, with better performance achieved. Extensive experiments on the in-house dataset demonstrate that the proposed method is more efficient than other state-of-the-art methods. Our code is available on https://***/chaisiyii/WSCL. © 2023 IEEE.

关键词： Cervical Cytology Contrastive Learning Patch Classification Pseudo Labels Weakly Supervised Learning

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PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization

PromptTA: Prompt-driven Text Adapter for Source-free Domain ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haoran Zhang Shuanghao Bai Wanqi Zhou Jingwen Fu Badong Chen Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Source-free domain generalization (SFDG) tackles the challenge of adapting models to unseen target domains without access to source domain data. To deal with this challenging task, recent advances in SFDG have primarily focused on leveraging the text modality of vision-language models such as CLIP. These methods involve developing a transferable linear classifier based on diverse style features extracted from the text and learned prompts or deriving domain-unified text representations from domain banks. However, both style features and domain banks have limitations in capturing comprehensive domain knowledge. In this work, we propose Prompt-Driven Text Adapter (PromptTA) method, which is designed to better capture the distribution of style features and employ resampling to ensure thorough coverage of domain knowledge. To further leverage this rich domain information, we introduce a text adapter that learns from these style features for efficient domain information storage. Extensive experiments conducted on four benchmark datasets demonstrate that PromptTA achieves state-of-the-art performance. The code is available at https://***/zhanghr2001/PromptTA.

关键词： Adaptation models Codes Benchmark testing Signal processing Feature extraction Data models Acoustics Speech processing

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Is Segment Anything Model Ready for X-Ray Object Segmentation?

Is Segment Anything Model Ready for X-Ray Object Segmentatio...

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2023 China Automation Congress, CAC 2023

作者： Hu, Jingxue Liu, Yuehu Zhang, Chi Cui, Zhichao Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Electronics and Control Engineering Chan'an University Xi'an China

ISBN: (纸本)9798350303759

Since public transportation scenarios have high requirements for baggage security, accurate X-Ray Object Segmentation for baggage security inspection is vital to help maintain social security. Since X-Ray images generated by different X-ray security machines differ in terms of color schemes, pixels and geometric deformation, detection and segmentation methods trained on certain datasets generalize poorly in out-of-distribution X-ray images. Recently, the Segment Anything Model (SAM) has been published as a foundation model for image segmentation, which achieves great generalization ability on natural images based on its 1B mask training dataset. In this paper, we propose to explore whether such huge foundation model exist zero-shot segmentation capabilities on X-ray images like humans do. Compared with the simple experimental protocol (only positive point prompt mode involved) in the original SAM paper, we test its performance sufficiently on two X-ray prohibited datasets with a total of 37524 images, including 58304 instances in 16 categories, when categories, occlusion conditions, prompt modes and checkpoints vary. Moreover, we compared SAM's performance with other SOTA detection models and segmentation models. Our empirical study shows that though SAM has certain segmentation ability on X-ray images especially with prompts, the performance on irregular instances and complex overlapping conditions still wait to be improved due to the overlapping characteristics. © 2023 IEEE.

关键词： Image enhancement

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Multi-Robot Autonomous Exploration in Unknown Environment: A Review

Multi-Robot Autonomous Exploration in Unknown Environment: A...

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2023 China Automation Congress, CAC 2023

作者： Zhu, Lina Cheng, Jiyu Liu, Yuehu Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University Xi'an China School of Control Science and Engineering Shandong University Shandong China

ISBN: (纸本)9798350303759

Multi-robot collaborative exploration in unknown environments has drawn more and more attention, and the exploration algorithms can be well applied in many real-worlds applications such as search and rescue, underwater surveillance, space monitoring, etc. Despite much effort devoted to this field, the gap in data acquisition between research and real-world applications still poses big challenges that may hinder the development of this field. In this paper, we survey the current state of multi-robot exploration algorithms. We start by presenting the problem of multi-robot exploration. Then we review related work, covering a broad set of topics including policy selection, cooperation scheme, and communication, analyze the pros and cons of them, and point out some research directions to make them more applicable in real applications. © 2023 IEEE.

关键词： Multipurpose robots

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A Novel Sensor Scheduling Algorithm Based on Deep Reinforcement Learning for Bearing-Only Target Tracking in UWSNs

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1077-1079页

作者： Linyao Zheng Meiqin Liu Senlin Zhang Jian Lan Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’an 710049 State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China College of Electrical Engineering Zhejiang UniversityHangzhou 310027China

Dear Editor,This letter is concerned with the energy-aware multiple sensor coscheduling for bearing-only target tracking in the underwater wireless sensor networks(UWSNs).Considering the traditional methods facing with the problems of strong environment dependence and lack flexibility,a novel sensor scheduling algorithm based on the deep reinforcement learning is ***,the sensors’co-scheduling strategy in UWSNs is formulated as Markov decision process(MDP).

关键词： Deep networks underwater

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Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning 22

Model-based Dynamic Shielding for Safe and Efficient Multi-A...

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22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Xiao, Wenli Lyu, Yiwei Dolan, John The Chinese University of HongKong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Carnegie Mellon University United States

Multi-Agent Reinforcement Learning (MARL) discovers policies that maximize reward but do not have safety guarantees during the learning and deployment phases. Although shielding with Linear Temporal Logic (LTL) is a promising formal method to ensure safety in single-agent Reinforcement Learning (RL), it results in conservative behaviors when scaling to multi-agent scenarios. Additionally, it poses computational challenges for synthesizing shields in complex multi-agent environments. This work introduces Model-based Dynamic Shielding (MBDS) to support MARL algorithm design. Our algorithm synthesizes distributive shields, which are reactive systems running in parallel with each MARL agent, to monitor and rectify unsafe behaviors. The shields can dynamically split, merge, and recompute based on agents' states. This design enables efficient synthesis of shields to monitor agents in complex environments without coordination overheads. We also propose an algorithm to synthesize shields without prior knowledge of the dynamics model. The proposed algorithm obtains an approximate world model by interacting with the environment during the early stage of exploration, making our MBDS enjoy formal safety guarantees with high probability. We demonstrate in simulations that our framework can surpass existing baselines in terms of safety guarantees and learning performance. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Reinforcement learning

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