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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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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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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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Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection

arXiv

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arXiv 2023年

作者： Zhang, Xiaohui Yi, Jiangyan Tao, Jianhua Wang, Chenglong Zhang, Chuyuan State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Computer and Information Technology University of Beijing Jiaotong Beijing China Department of Automation Tsinghua University Beijing China University of Science and Technology of China Beijing China

Current fake audio detection algorithms have achieved promising performances on most datasets. However, their performance may be significantly degraded when dealing with audio of a different dataset. The orthogonal weight modification to overcome catastrophic forgetting does not consider the similarity of genuine audio across different datasets. To overcome this limitation, we propose a continual learning algorithm for fake audio detection to overcome catastrophic forgetting, called Regularized Adaptive Weight Modification (RAWM). When fine-tuning a detection network, our approach adaptively computes the direction of weight modification according to the ratio of genuine utterances and fake utterances. The adaptive modification direction ensures the network can effectively detect fake audio on the new dataset while preserving its knowledge of old model, thus mitigating catastrophic forgetting. In addition, genuine audio collected from quite different acoustic conditions may skew their feature distribution, so we introduce a regularization constraint to force the network to remember the old distribution in this regard. Our method can easily be generalized to related fields, like speech emotion recognition. We also evaluate our approach across multiple datasets and obtain a significant performance improvement on cross-dataset experiments. © 2023, CC BY.

关键词： Emotion Recognition

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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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Soccer match broadcast video analysis method based on detection and tracking

Soccer match broadcast video analysis method based on detect...

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作者： Li, Hongyu Yang, Meng Yang, Chao Kang, Jianglang Suo, Xiang Meng, Weiliang Li, Zhen Mao, Lijuan Sheng, Bin Qi, Jun School of Information science and Technology Beijing Forestry University Beijing China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing China School of Athletic Performance Shanghai University of Sport Shanghai China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong Univerity Shanghai China Department of Computing Xi'an JiaoTong-Liverpool University Suzhou China

We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two-dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high-speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non-trivial. To mitigate this, we curate a large-scale, high-precision soccer ball detection dataset and devise a robust detection model, which achieved the (Formula presented.) of 80.9%. Additionally, we develop a high-speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real-time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations. © 2024 John Wiley & Sons Ltd.

关键词： Visualization

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Gaolei Li Changchuan Yin Tony Q.S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

关键词： Location awareness Simulation Collaboration Reinforcement learning Gaussian distribution Autonomous aerial vehicles Distance measurement Trajectory Jamming Optimization

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