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2023 IEEE International Conference on Communications, computing, Cybersecurity and Informatics, CCCI 2023

作者： Obaidat, Mohammad S. Meshram, Chandrashekhar Kartikey, Deepak Hsiao, Kuei-Fang Imoize, Agbotiname Lucky Barve, Sharad Kumar Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Computing Srm University Department of Computational Intelligence TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India Jayawanti Haksar Government Post Graduation College College of Barkatullah University Department of Post Graduation Studies and Research in Mathematics M.P. Betul460001 India S.S.P. Govt. College Waraseoni Department of Mathematics M.P. Balaghat India University of Texas-Permian Basin Computer Science Department OdessaTX79762 United States University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Lagos100213 Nigeria Institute of Digital Communication Ruhr University Department of Electrical Engineering and Information Technology Bochum44801 Germany Pandit S. N. Shukla University Shahdol Department of Mathematics Madhya Pradesh Shahdol484001 India

ISBN: (纸本)9798350302561

Smart cities are fast proliferating the global space in recent times. In order to achieve the envisioned next-generation cities, the smart city paradigm aspires to develop a completely integrated network-based infrastructure. With the development of innovative smart cities, ICT-based e-governance has made it possible for services to be delivered intelligently and interactively via online platforms. However, the communication between government agencies and other stakeholders in the smart city ecosystem is through open channels, which poses serious security concerns. In order to address this problem, this work proposes a robust remote client password authentication technique for e-governance applications in smart cities. The proposed authentication technique is lightweight, resistant to several attacks, and achieves low computational overhead. © 2023 IEEE.

关键词： Smart cards

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Soft-lock drawing of super-aligned carbon nanotube bundles for nanometre electrical contacts (vol 17, pg 278, 2022)

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NATURE NANOTECHNOLOGY 2022年第3期17卷 331-331页

作者： Guo, Yunfan Shi, Enzheng Zhu, Jiadi Shen, Pin-Chun Wang, Jiangtao Lin, Yuxuan Mao, Yunwei Deng, Shibin Li, Baini Park, Ji-Hoon Lu, Ang-Yu Zhang, Shuchen Ji, Qingqing Li, Zhe Qiu, Chenguang Qiu, Song Li, Qingwen Dou, Letian Wu, Yue Zhang, Jin Palacios, Tomas Cao, Anyuan Kong, Jing Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Research Institute of Frontier Technology College of Micro-Nano Electronics Zhejiang University Hangzhou China Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of Engineering Westlake University Hangzhou China School of Materials Science and Engineering Peking University Beijing China Davidson School of Chemical Engineering Purdue University West Lafayette IN USA Taiwan Semiconductor Manufacturing Company (TSMC) Hsinchu Taiwan Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Chemistry Purdue University West Lafayette IN USA College of Chemistry and Molecular Engineering Peking University Beijing China Department of Chemical and Biological Engineering Iowa State University Ames IA USA Key Laboratory for the Physics and Chemistry of Nanodevices and Department of Electronics Peking University Beijing China Division of Advanced Nano-Materials Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Suzhou China

The assembly of single-walled carbon nanotubes (CNTs) into high-density horizontal arrays is strongly desired for practical applications, but challenges remain despite myriads of research efforts. Herein, we developed a non-destructive soft-lock drawing method to achieve ultraclean single-walled CNT arrays with a very high degree of alignment (angle standard deviation of ~0.03°). These arrays contained a large portion of nanometre-sized CNT bundles, yielding a high packing density (~400 µm−1) and high current carrying capacity (∼1.8 × 108 A cm−2). This alignment strategy can be generally extended to diverse substrates or sources of raw single-walled CNTs. Significantly, the assembled CNT bundles were used as nanometre electrical contacts of high-density monolayer molybdenum disulfide (MoS2) transistors, exhibiting high current density (~38 µA µm−1), low contact resistance (~1.6 kΩ µm), excellent device-to-device uniformity and highly reduced device areas (0.06 µm2 per device), demonstrating their potential for future electronic devices and advanced integration technologies.

关键词： Carbon nanotubes and fullerenes Materials science general Nanotechnology Nanotechnology and Microengineering

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Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1955-2005页

作者： Zhou, Hao Hu, Chengming Yuan, Ye Cui, Yufei Jin, Yili Chen, Can Wu, Haolun Yuan, Dun Jiang, Li Wu, Di Liu, Xue Zhang, Charlie Wang, Xianbin Liu, Jiangchuan McGill University School of Computer Science MontrealQCH3A 0E9 Canada McGill University School of Electrical and Computer Engineering MontrealQCH3A 0E9 Canada Samsung Research America PlanoTX75023 United States Western University Department of Electrical and Computer Engineering LondonONN6A 3K7 Canada Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada

Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fields. The advancement of LLM techniques also offers promising opportunities to automate many tasks in the telecommunication (telecom) field. After pre-training and fine-tuning, LLMs can perform diverse downstream tasks based on human instructions, paving the way to artificial general intelligence (AGI)-enabled 6G. Given the great potential of LLM technologies, this work aims to provide a comprehensive overview of LLM-enabled telecom networks. In particular, we first present LLM fundamentals, including model architecture, pre-training, fine-tuning, inference and utilization, model evaluation, and telecom deployment. Then, we introduce LLM-enabled key techniques and telecom applications in terms of generation, classification, optimization, and prediction problems. Specifically, the LLM-enabled generation applications include telecom domain knowledge, code, and network configuration generation. After that, the LLM-based classification applications involve network security, text, image, and traffic classification problems. Moreover, multiple LLM-enabled optimization techniques are introduced, such as automated reward function design for reinforcement learning and verbal reinforcement learning. Furthermore, for LLM-aided prediction problems, we discussed time-series prediction models and multi-modality prediction problems for telecom. Finally, we highlight the challenges and identify the future directions of LLM-enabled telecom networks. © 1998-2012 IEEE.

关键词： Reinforcement learning

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Large Scale Multi-GPU Based Parallel Traffic Simulation for Accelerated Traffic Assignment and Propagation

arXiv

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arXiv 2024年

作者： Jiang, Xuan Sengupta, Raja Demmel, James Williams, Samuel Department of Civil and Environmental Engineering University of California BerkeleyCA94709 United States Department of Electrical Engineering and Computer Science University of California BerkeleyCA94709 United States Performance and Algorithms Research Group Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

Traffic simulation is a critical tool for congestion analysis, travel time estimation, and route optimization in urban planning, benefiting navigation apps, transportation network companies, and state agencies. Traditionally, traffic micro-simulation frameworks are based on road segments and can only support a limited number of main roads. Efficient traffic simulation on a regional scale remains a significant challenge due to the complexity of urban mobility and the large scale of spatiotemporal data. This paper introduces a Large Scale Multi-GPU Parallel computing based Regional Scale Traffic Simulation Framework (LPSim), which leverages graphical processing unit (GPU) parallel computing to address these challenges. LPSim utilizes a multi-GPU architecture to simulate extensive and dynamic traffic networks with high fidelity and reduced computation time. Using the parallel processing capabilities of GPUs, LPSim can perform tens of millions of individual vehicle dynamics simulations simultaneously, significantly outperforming traditional CPU-based approaches. The framework is designed to be scalable and can easily accommodate the increasing complexity of traffic simulations. We present the theory behind GPU-based traffic simulation, the architecture of single- and multi-GPU based simulations, and the graph partition strategies that enhance computation resource load balance. Our experimental results demonstrate the effectiveness of LPSim in simulating large-scale traffic scenarios. LPSim is capable of completing simulations of 2.82 million trips in just 6.28 minutes on a single GPU machine equipped with 5120 CUDA cores (Tesla V100-SXM2). Furthermore, utilizing a Google Cloud instance with two NVIDIA V100 GPUs, which collectively offer 10240 CUDA cores, LPSim successfully simulates 9.01 million trips within 21.16 minutes. We further tested our simulator with the same demand on dual NVIDIA A100-PCIE-40GB GPUs, which finished the simulation in 0.0398 hours, approximately

关键词： Travel time

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Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

arXiv

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arXiv 2024年

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Boureima, Ismael Matuszek, Cynthia Alexandrov, Boian S. Theoretical Division Los Alamos National Laboratory Los Alamos United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore CountyMD United States

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as non-negative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter k to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific k. The score is calculated for different trials over a range of k, and the ideal k is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good k value, visiting a range of k can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the k search space for these grid-search ML algorithms by truncating the target k values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal k and the accuracy of the Binary Bleed in identifying the correct k for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub. Copyright © 2024, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xuefei Li Mingzhe Chen Danpu Liu Zhilong Zhang Tony Q. S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs).

关键词： Base stations Correlation Network topology Simulation Collaboration Reinforcement learning Vectors Sensors Global communication Optimization

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Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

arXiv

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arXiv 2024年

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Wanna, Selma Solovyev, Nicholas Rasmussen, Kim Alexandrov, Boian S. Nicholas, Charles Matuszek, Cynthia Theoretical Division Los Alamos National Laboratory Los Alamos United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore CountyMD United States

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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CADEC: A Combinatorial Auction for Dynamic Distributed DNN Inference Scheduling in Edge-Cloud Networks

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IEEE Transactions on Mobile computing 2025年

作者： Xu, Xiaolong Hu, Yuhao Cui, Guangming Qi, Lianyong Dou, Wanchun Nanjing University of Information Science and Technology School of Software Nanjing210044 China Nanjing University of Information Science and Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China College of Computer science and technology Qingdao China Georgia State University Department of Computer Science United States

Deep Neural Network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 46% compared with state-of-the-art distributed DNN inference schemes. © 2002-2012 IEEE.

关键词： Convex optimization

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Intelligent Reflecting Surface Aided Activity Detection: A Covariance-Based Learning Approach

Intelligent Reflecting Surface Aided Activity Detection: A C...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Qingfeng Lin Yang Li Yik-Chung Wu Rui Zhang Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Shenzhen Research Institute of Big Data Shenzhen China Peng Cheng Laboratory Shenzhen China Pazhou Laboratory (Huangpu) Guangzhou China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore

This paper investigates a covariance-based learning approach for intelligent reflecting surface (IRS) aided activity detection in massive machine-type communications (mMTC). In the conventional scenario without IRS, the covariance-based approach has been demonstrated to outperform the compressed sensing approach, as the covariance-based approach can well exploit the probability density function (PDF) of the received signals at the base station (BS). However, when taking the impact of the IRS into account, due to the newly introduced cascaded channels, it is quite difficult to obtain the exact PDF of the received signals at the BS. To tackle this challenge, we propose an approximation for the intended PDF by modeling a correlation parameter in the covariance matrix of the received signals. Based on the covariance-based formulation, a learning approach is further proposed to automatically learn the correlation parameter. Simulation results demonstrate the performance of the covariance-based activity detection, and the superiority of the proposed covariance-based learning approach.

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Probabilistic Device Scheduling for Over-the-Air Federated Learning

Probabilistic Device Scheduling for Over-the-Air Federated L...

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International Conference on Communication Technology (ICCT)

作者： Yuchang Sun Zehong Lin Yuyi Mao Shi Jin Jun Zhang Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong National Mobile Communications Research Laboratory Southeast University Nanjing China

Federated learning (FL) is an emerging distributed training scheme where edge devices collaboratively train a model by uploading model updates instead of private data. To address the communication bottleneck, over-the-air (OTA) computation has been introduced to FL, which allows multiple edge devices to upload their gradient updates concurrently for aggregation. However, OTA computation is plagued by the communication error, which is critically affected by the device selection policy and impacts the performance of the output model. In this paper, we propose a probabilistic device selection scheme PO-FL, which effectively enhances the convergence performance of over-the-air FL. Specifically, each device is selected for OTA computation according to the predetermined probability, and its local update is scaled by this probability. By analyzing the convergence of PO-FL, we show that its convergence is determined by the device selection via the communication error and the variance of global update. Then, we propose a device selection algorithm that jointly considers the channel condition and gradient update importance of edge devices to optimize their selection probabilities. The experimental results on the MNIST dataset demonstrate that the proposed algorithm converges faster and learns better models than the baselines.

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