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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Kumar, Pramod Mohanraj, B. Munuswamy, E. Soni, Mukesh Maranan, Ramya Byeon, Haewon Ganga Institute of Technology and Management KablanaHaryana Jhaajji124104 India Sona College of Technology Department of Information Technology SALEM India Computer Science and Engineering Kings Engineering College Sriperumbudur Kanchipuram India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India Inje University Department of Digital Anti-Aging Healthcare Gimhae50834 Korea Republic of

ISBN: (纸本)9798350317060

The proposed authentication and key exchange protocol for unmanned aerial vehicles (UAVs)are based on a combination of elliptic curve cryptography, symmetric cryptography, and blockchain technology. The protocol, called Blockchain-based Lightweight Authentication and Key exchange Protocol, aims to provide secure and efficient communication between the UAV and the ground station while minimizing computational costs and reducing the risk of cyber attacks. The elliptic curve cryptography is used for key exchange, symmetric cryptography is used for message encryption and decryption, and blockchain technology is used to store and distribute the public keys. The proposed protocol is evaluated through simulations and equated with current protocols. The outcomes show that the protocol has lower computational overhead, better security, and is highly resistant to attacks due to the use of blockchain technology. This makes the proposed protocol a suitable solution for secure communication in UAV applications. The paper builds on previous work such as DroneSecand DroneSec - lite, which is also based on elliptic curve cryptography and symmetric cryptography, but with a focus on lightweight authentication and key exchange protocols for UAV systems with varying computational capabilities. © 2023 IEEE.

关键词： Authentication

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Quantum Quest: Resource Requirement of RC4 Cryptanalysis under Grover’s Lens

Quantum Quest: Resource Requirement of RC4 Cryptanalysis und...

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IEEE Calcutta Conference (CALCON)

作者： Sarbani Sen Soumen Bajpayee Imon Mukherjee Goutam Paul Bhupendra Singh Computer Science & Engineering Indian Institute of Information Technology Kalyani India Cryptology & Security Research Unit Indian Statistical Institute Kolkata India Centre for Artificial Intelligence and Robotics DRDO Bengaluru India

ISBN: (数字)9798331516666

ISBN: (纸本)9798331516673

The RC4 cipher is a widely used software stream cipher which has long stood as a cornerstone in cryptographic applications due to its design simplicity, speed and efficiency. In this paper, we present an approach to decrypt RC4-encrypted data by leveraging Grover’s quantum search algorithm. To the best of our knowledge, this is the first documented attempt to employ Grover’s algorithm in breaking RC4 encryption. We enumerate the quantum resources required to perform exhaustive key search attack on RC4. Our approach demonstrates the potential of quantum computing to revolutionize cryptanalysis.

关键词： Ciphers Costs Quantum computing Software algorithms NIST Software Encryption Optimization Faces Lenses

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Path optimization method for the sign problem caused by fermion determinant

arXiv

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arXiv 2025年

作者： Hisayoshi, Kazuki Kashiwa, Kouji Namekawa, Yusuke Takase, Hayato Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka811-0295 Japan Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima730-0053 Japan

The path optimization method with machine learning is applied to the one-dimensional massive lattice Thirring model, which has the sign problem caused by the fermion determinant. This study aims to investigate how the path optimization method works for the sign problem. We show that the path optimization method successfully reduces statistical errors and reproduces the analytic results. We also examine an approximation of the Jacobian calculation in the learning process and show that it gives consistent results with those without an approximation. Copyright © 2025, The Authors. All rights reserved.

关键词： Fermions

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M3CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought

arXiv

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arXiv 2024年

作者： Chen, Qiguang Qin, Libo Zhang, Jin Chen, Zhi Xu, Xiao Che, Wanxiang Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China School of Computer Science and Engineering Central South University China Shanghai AI Laboratory China

Multi-modal Chain-of-Thought (MCoT) requires models to leverage knowledge from both textual and visual modalities for step-by-step reasoning, which gains increasing attention. Nevertheless, the current MCoT benchmark still faces some challenges: (1) absence of visual modal reasoning, (2) single-step visual modal reasoning, and (3) Domain missing, thereby hindering the development of MCoT. Motivated by this, we introduce a novel benchmark (M3CoT) to address the above challenges, advancing the multi-domain, multi-step, and multi-modal CoT. Additionally, we conduct a thorough evaluation involving abundant MCoT approaches on Vision Large Language Models (VLLMs). In addition, we highlight that the current VLLMs still struggle to correctly reason in M3CoT and there remains a large gap between existing VLLMs and human performance in M3CoT, despite their superior results on previous MCoT benchmarks. To our knowledge, we take the first meaningful step toward the multi-domain, multi-step, and multi-modal scenario in MCoT. We hope that M3CoT can serve as a valuable resource, providing a pioneering foundation in multi-domain, multi-step, multi-modal chain-of-thought research. © 2024, CC BY-NC-SA.

关键词： Benchmarking

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

arXiv

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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation

arXiv

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arXiv 2024年

作者： Wang, Tianfu Fan, Qilin Wang, Chao Yang, Long Ding, Leilei Yuan, Nicholas Jing Xiong, Hui School of Computer Science and Technology University of Science and Technology of China China School of Big Data and Software Engineering Chongqing University China Guangzhou HKUST Fok Ying Tung Research Institute China School of Artificial Intelligence Peking University China Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong

Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Generalizable RL framework for VNE, named FlagVNE. Specifically, we design a bidirectional action-based Markov decision process model that enables the joint selection of virtual and physical nodes, thus improving the exploration flexibility of solution space. To tackle the expansive and dynamic action space, we design a hierarchical decoder to generate adaptive action probability distributions and ensure high training efficiency. Furthermore, to overcome the generalization issue for varying VNR sizes, we propose a meta-RL-based training method with a curriculum scheduling strategy, facilitating specialized policy training for each VNR size. Finally, extensive experimental results show the effectiveness of FlagVNE across multiple key metrics. Our code is available at GitHub1 Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Cyclic Sensing: An Orbital Spectrum Sensing Method for Uplink NOMA IoT Systems

Cyclic Sensing: An Orbital Spectrum Sensing Method for Uplin...

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IEEE International Conference on Communications (ICC)

作者： Tianheng Xu Yinjun Xu Jingyi Wu Haijun Zhang Ting Zhou Honglin Hu Victor C.M. Leung Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China Shanghai Frontier lnnovation Research Institute Shanghai China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Microelectronics Shanghai University Shanghai China Department of Electrical and Computer Engineering The University of British Columbia Canada College of Computer and Software Engineering Shenzhen University China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The upcoming sixth-generation (6G) of ubiquitous connectivity communications systems is driven by the next-generation of Internet of Things (IoT) technology. Accordingly, the demand for spectrum resources is growing exponentially. Spectrum sensing, which dynamically explores spectrum holes, is expected to be crucial in the 6G era. In the meantime, Non-Orthogonal Multi-Access (NOMA), an efficient means of reusing resources, can enable multiple users to share the same frequency band stably. The combination of both technologies holds promise for more effective use of spectrum resources in future commu-nications. In this paper, we present a spectrum sensing method for the uplink communication scenario in NOMA with multi-user interference, which aims to make effective use of both static and dynamic gain on spectrum efficiency. Firstly, the sensing criterion and workflow are outlined. The closed-form solution for the sensing threshold configuration is derived thereafter. The simulation results demonstrate the feasibility of the proposed approach, which can increase the system throughput up to 19.7%, when compared to NOMA without spectrum sensing.

关键词： 6G mobile communication NOMA Closed-form solutions Simulation Throughput Orbits Sensors

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A Method for Absolute Pose Regression Based on Cascaded Attention Modules

SSRN

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SSRN 2024年

作者： Song, Xiaogang Tang, Junjie Guo, Weixuan Lu, Xiaofeng Hei, Xinhong Xi’an University of Technology School of Computer Science and Engineering Xi’an710048 China Engineering Research Center of Human-Machine Integration Intelligent Robot Universities of Shaanxi Province Xi’an710048 China Xi’an Microelectronics Technology Institute China

The absolute camera pose regressor estimates the position and orientation of the camera solely based on captured RGB images. However, current single-image techniques often lack robustness, resulting in significant outliers. To address the issues of pose regressors in repetitive textures and dynamic blur scenarios, this paper proposes an absolute pose regression method based on cascaded attention modules. This network integrates global and local information through cascaded attention modules and then employs a dual-stream attention module to reduce the impact of dynamic objects and lighting changes on localization performance by constructing dual-channel dependencies. Specifically, the cascaded attention modules guide the model to focus on the relationships between global and local features and establish long-range channel dependencies, enabling the network to learn richer multi-scale feature representations. Additionally, a dual-stream attention module is introduced to further enhance feature representation by closely associating spatial and channel dimensions. This method is evaluated and analyzed on various indoor and outdoor datasets, and experimental results demonstrate that the proposed method can significantly improve localization performance. Ablation studies on multiple categories further verify the effectiveness of the proposed modules. © 2024, The Authors. All rights reserved.

关键词： Cameras

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Continual Self-Supervised Learning: Towards Universal Multi-Modal Medical Data Representation Learning

Continual Self-Supervised Learning: Towards Universal Multi-...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Qi Wu Yong Xia School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning (AIML) The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Research & Development Institute of Northwestern Polytechnical University in Shenzhen China Ningbo Institute of Northwestern Polytechnical University China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Self-supervised learning (SSL) is an efficient pre-training method for medical image analysis. However, current research is mostly confined to certain modalities, consuming considerable time and resources without achieving universality across different modalities. A straightforward solution is combining all modality data for joint SSL, which poses practical challenges. Firstly, our experiments reveal conflicts in representation learning as the number of modalities increases. Secondly, multi-modal data collected in advance cannot cover all real-world scenarios. In this paper, we reconsider versatile SSL from the perspective of continual learning and propose MedCoSS, a continuous SSL approach for multi-modal medical data. Different from joint representation learning, MedCoSS assigns varying data modalities to separate training stages, creating a multi-stage pre-training process. We propose a rehearsal- based continual learning approach to manage modal conflicts and prevent catastrophic forgetting. Specifically, we use the k-means sampling to retain and rehearse previous modality data during new modality learning. Moreover, we apply feature distillation and intra-modal mixup on buffer data for knowledge retention, bypassing pretext tasks. We conduct experiments on a large-scale multi-modal unlabeled dataset, including clinical reports, X-rays, CT, MRI, and pathological images. Experimental results demonstrate MedCoSS's exceptional generalization ability across 9 downstream datasets and its significant scalability in inte- grating new modality data. The code and pre-trained model are available at https://***/yeerwen/MedCoSS.

关键词： Representation learning Continuing education Training Pathology Scalability Magnetic resonance imaging X-rays

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Cancer Cell Detection and Classification from Digital Whole Slide Image 5th

Cancer Cell Detection and Classification from Digital Whole ...

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5th International Conference on Smart Technologies in Data science and Communication, SMART-DSC 2022

作者： Gavade, Anil B. Nerli, Rajendra B. Ghagane, Shridhar Gavade, Priyanka A. Bhagavatula, Venkata Siva Prasad Department of E&C KLS Gogte Institute of Technology Karnataka Belagavi India Karnataka Belagavi India Department of Biotechnology KAHER’s Dr. Prabhakar Kore Basic Science Research Center V. K. Institute of Dental Sciences Campus Karnataka Belagavi India Department of Computer Science and Engineering KLE Tech University Dr. M. S. Sheshgiri College of Engineering and Technology Karnataka Belagavi India Hyderabad Medtronic India

ISBN: (纸本)9789811968792

The World Health Organisation has identified cancer as one of the foremost causes of death globally which reports that nearly one in six deaths is due to cancer. Hence, an early and correct diagnosis is required to assist doctors in selecting the accurate and best treatment option for the patient. Pathological data have huge tumour information that can be used to diagnose cancer. Digitizing pathological data into images and its analysis using Deep learning applications will be a significant contribution to clinical testing. Due to advancements in technology, artificial intelligence (AI) and digital pathology can now be combined allowing for image-based diagnosis. This study uses residual networks (ResNet-50) and convolutional neural network (CNN), which is pre-trained on ImageNet dataset to train and categories lung histopathology images into non-cancerous, lung adenocarcinoma, and lung squamous cell carcinoma delivering an accuracy of 98.9%. Experimentation results show that the ResNet-50 model delivers finer classification results when compared to state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence Classification Convolutional neural network Deep learning Graphic processing unit Histopathological image Residual networks

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