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arXiv 2023年

作者： Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Lam, Kwok-Yan Fang, Yuguang Li, Yonghui School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore National Centre for Research in Digital Trust School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science City University of Hong Kong Hong Kong School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia

Generative AI (GAI) models have been rapidly advancing, with a wide range of applications including intelligent networks and mobile AI-generated content (AIGC) services. Despite their numerous applications and potential, such models create opportunities for novel security challenges. In this paper, we examine the challenges and opportunities of GAI in the realm of the security of intelligent network AIGC services such as suggesting security policies, acting as both a "spear" for potential attacks and a "shield" as an integral part of various defense mechanisms. First, we present a comprehensive overview of the GAI landscape, highlighting its applications and the techniques underpinning these advancements, especially large language and diffusion models. Then, we investigate the dynamic interplay between GAI’s spear and shield roles, highlighting two primary categories of potential GAI-related attacks and their respective defense strategies within wireless networks. A case study illustrates the impact of GAI defense strategies on energy consumption in an image request scenario under data poisoning attack. Our results show that by employing an AI-optimized diffusion defense mechanism, energy can be reduced by 8.7%, and retransmission count can be decreased from 32 images, without defense, to just 6 images, showcasing the effectiveness of GAI in enhancing network security. © 2023, CC BY.

关键词： Network security

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CNN Wave Computing: Theory, Architectures, Implementations and Applicaions

CNN Wave Computing: Theory, Architectures, Implementations a...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： Marco Gilli Csaba Rekeczky Bertram Shi Department of Electronics Politecnico di Turino Torino Italy Analogic and Neural Computing Systems Laboratory of the Computer and Automation Information Technology Department Institute of the Hungarian Academy of Sciences Budapest Hungary Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong China

A cellular neural/nonlinear network (CNN) is any spatial arrangement of mainly locally coupled cells, where each cell is a dynamical system which has an input, an output and a state that evolves according to some prescribed dynamical laws. Since the CNN was first introduced in 1988, research in this field has developed rapidly. The goal of this tutorial is to provide participants with a snapshot of the current state of the art and research trends. It covers the broad multi-disciplinary areas of CNN research, from theoretical aspects to applications. The presenters have experience ranging from theoretical analysis of CNN dynamics, VLSI implementation and system level applications

关键词： Cellular neural networks computer architecture computer networks Very large scale integration Analog computers computer interfaces computer vision Biological system modeling Nonlinear dynamical systems computer industry

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Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints

arXiv

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arXiv 2024年

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Computer and Information Science Linköping University Linköping Sweden Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore

Diffusion models have been extensively utilized in AI-generated content (AIGC) in recent years, thanks to the superior generation capabilities. Combining with semantic communications, diffusion models are used for tasks such as denoising, data reconstruction, and content generation. However, existing diffusion-based generative models do not consider the stringent bandwidth limitation, which limits its application in wireless communication. This paper introduces a diffusion-driven semantic communication framework with advanced VAE-based compression for bandwidth-constrained generative model. Our designed architecture utilizes the diffusion model, where the signal transmission process through the wireless channel acts as the forward process in diffusion. To reduce bandwidth requirements, we incorporate a downsampling module and a paired upsampling module based on a variational auto-encoder with reparameterization at the receiver to ensure that the recovered features conform to the Gaussian distribution. Furthermore, we derive the loss function for our proposed system and evaluate its performance through comprehensive experiments. Our experimental results demonstrate significant improvements in pixel-level metrics such as peak signal to noise ratio (PSNR) and semantic metrics like learned perceptual image patch similarity (LPIPS). These enhancements are more profound regarding the compression rates and SNR compared to deep joint source-channel coding (DJSCC). Copyright © 2024, The Authors. All rights reserved.

关键词： Bandwidth

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The analysis of a simple k-means clustering algorithm 00

The analysis of a simple k-means clustering algorithm

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Proceedings of the sixteenth annual symposium on Computational geometry

作者： Tapas Kanungo David M. Mount Nathan S. Netanyahu Christine Piatko Ruth Silverman Angela Y. Wu Center for Automation Research University of Maryland College Park Maryland Department of Computer Science University of Maryland College Park Maryland Department of Mathematics and Computer Science Bar-Ilan University Ramat-Gan 52900 Israel and Center for Automation Research University of Maryland College Park Maryland The Johns Hopkins University Applied Physics Laboratory Laurel Maryland Department of Computer Science University of the District of Columbia Washington DC and Center for Automation Research University of Maryland College Park Maryland Department of Computer Science and Information Systems The American University Washington DC

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Approximate Knowledge Mass and Extended automation Reasoning System in Type II Topological Logic

Approximate Knowledge Mass and Extended Automation Reasoning...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Yalin Zheng Guang Yang Jing Zheng Peican Huang State Key Laboratory for Intelligent Technology and Systems Department of Automation Faculty of Information Science and Technology Tsinghua University Beijing China Research Center of Intelligent Information Technology Department of Computer Science and Technology Faculty of Software Dongguan University of Science and Technology Dongguan China Research Center of Dongguan Economy Department of Economy and Commerce Dongguan University of Science and Technology Dongguan China Research Center of Intelligent Information Technology Department of Computer Science and Technology Faculty of Software Dongguan University of Science and Technology Dongguan China

This paper introduces and investigate the type II true level k knowledge circle, extended type II knowledge base, extended type II automatic reasoning system and the level (k,j) perfectness of extended type II knowled... 详细信息

关键词： automation Automatic logic units Fuzzy logic Fuzzy systems information technology computer science Laboratories Intelligent systems information science Business

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Computing nearest neighbors for moving points and applications to clustering 99

Computing nearest neighbors for moving points and applicatio...

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Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms

作者： Tapas Kanungo David M. Mount Nathan S. Netanyahu Christine Piatko Ruth Silverman Angela Y. Wu Center for Automation Research University of Maryland College Park Maryland Department of Computer Science University of Maryland College Park Maryland Center for Automation Research University of Maryland College Park and CESDIS NASA Goddard Space Flight Center The Johns Hopkins University Applied Physics Laboratory Laurel Maryland Department of Computer Science University of the District of Columbia Washington DC and Center for Automation Research University of Maryland College Park Maryland Department of Computer Science and Information Systems The American University Washington DC

No abstract available.

ISBN: (纸本)9780898714340

No abstract available.

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Multi-Scale Recurrent Neural Network for Sound Event Detection

Multi-Scale Recurrent Neural Network for Sound Event Detecti...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Rui Lu Zhiyao Duan Changshui Zhang Department of Automation Tsinghua University State Key Lab of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Beijing P.R.China Department of Electrical and Computer Engineering University of Rochester Rochester NY USA

ISBN: (纸本)9781538646595

Sound event detection (SED) in real life is an interesting but challenging task due to the polyphonic and long-term dependent nature of sound events. Recently, multi-label recurrent neural networks (RNNs) have shown promises. However, even equipped with long short-term memory (LSTM) or gated recurrent unit (GRU) cells, RNNs are still limited to model the long-term dependency. In this paper, we propose a multiscale RNN to address this issue. By integrating information from different time resolutions, we can better capture both the fine-grained and long-term dependencies of sound events. We experiment on the development sets of Task3 of DCASE2016 and DCASE2017. Compared to our previously proposed single-scale RNN that won the third place among the 13 teams in Task3 of DCASE2017, the proposed multiscale model achieves statistically significantly better performance on the development datasets of both DECASE2016 and DCASE2017.

关键词： Multi-scale model deep learning recurrent neural network sound event detection recurrent neural nets multiscale models Event detection Machine Learning SOUND Vibration Hearing temporal resolution

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Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

arXiv

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arXiv 2023年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of AIGC and SemCom is developed. Particularly, a content generation level is introduced on top of the semantic level that provides a clear outline of how AIGC and SemCom interact with each other to produce meaningful and effective content. Moreover, a novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information, considering the joint optimization of semantic extraction and evaluation metrics tailored to AIGC services. The framework can adapt to different types of content generated, the required quality and the semantic information utilized. By employing a Deep Q Network (DQN), a case study is presented that provides useful insights into the feasibility of the optimization problem and its convergence characteristics. © 2023, CC BY.

关键词： Wireless networks

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Multi-Stage Multi-Task Feature Learning* 12

Multi-Stage Multi-Task Feature Learning*

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Annual Conference on Neural information Processing systems

作者： Pinghua Gong Jieping Ye Changshui Zhang State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Automation Tsinghua University Beijing 100084 China Computer Science and Engineering Center for Evolutionary Medicine and Informatics The Biodesign Institute Arizona State University Tempe AZ 85287 USA

ISBN: (纸本)9781627480031

Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to its looseness for approximating an ?_0o-type regularizer. In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel regularizer. To solve the non-convex optimization problem, we propose a Multistage Multi-Task Feature Learning (MSMTFL) algorithm. Moreover, we present a detailed theoretical analysis showing that MSMTFL achieves a better parameter estimation error bound than the convex formulation. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of MSMTFL in comparison with the state of the art multi-task sparse feature learning algorithms.

关键词： Learning learning algorithms Non-Convex Biomedical informatics Theoretical analysis Parameter estimation images computer vision state of the art Dataset Convexity Religious Missions SYNTHETICS

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

arXiv

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arXiv 2024年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore

The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 2024, CC BY.

关键词： Generative adversarial networks

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