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GTK:A Hybrid-Search Algorithm of Top-Rank-k Frequent Patterns Based on Greedy Strategy

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Computers, Materials & Continua 2020年第6期63卷 1445-1469页

作者： Yuhang Long Wensheng Tang Bo Yang Xinyu Wang Hua Ma Hang Shi Xueyu Cheng Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha410081China College of Information Science and Engineering Hunan UniversityChangsha410082China Clayton State University MorrowGA 30260USA

Currently,the top-rank-k has been widely applied to mine frequent patterns with a rank not exceeding *** the existing algorithms,although a level-wise-search could fully mine the target patterns,it usually leads to the delay of high rank patterns generation,resulting in the slow growth of the support threshold and the mining *** at this problem,a greedy-strategy-based top-rank-k frequent patterns hybrid mining algorithm(GTK)is proposed in this *** this algorithm,top-rank-k patterns are stored in a static doubly linked list called RSL,and the patterns are divided into short patterns and long *** short patterns generated by a rank-first-search always joins the two patterns of the highest rank in RSL that have not yet been *** the basis of the short patterns satisfying specific conditions,the long patterns are extracted through *** reduce redundancy,GTK improves the generation method of subsume index and designs the new pruning strategies of *** algorithm also takes the use of reasonable pruning strategies to reduce the amount of computation to improve the computational *** datasets and synthetic datasets are adopted in experiments to evaluate the proposed *** experimental results show the obvious advantages in both time efficiency and space efficiency of GTK.

关键词： Top-rank-k frequent patterns greedy strategy hybrid-search

Closed-Loop Safe Correction for Reinforcement Learning Policy 1

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4th International Conference on Ubiquitous Security, UbiSec 2024

作者： Yi, Zhi Lv, Qi Chen, Shuhong Liang, Ying Dai, Yinglong Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China Blockchain Innovation Lab Swinburne University of Technology MelbourneVIC3122 Australia Molecular Nutrition Branch National Engineering Research Center of Rice and By-product Deep Processing College of Food Science and Engineering Central South University of Forestry and Technology Hunan Changsha410004 China

ISBN: (数字)9789819648368

ISBN: (纸本)9789819648351

Trial and error learning is an approach with uncertain consequences. How to maintain policy security, stability, and efficiency under controlled circumstances, posing a significant academic challenge. Such as Reinforcement Learning (RL) leverages the neural network parameter space to identify the optimal solution for achieving the optimal mapping of the policy space. However, even a slight fluctuation in the parameter space can lead to policy collapse. To solve it, we propose a method called Closed-loop Safe Correction (CLSC). This method employs twin agents to establish a closed-loop framework for policy and sampling data distribution, gradually improving policy efficiency while ensuring policy safety. Base on theoretical and experimental analysis, CLSC elucidates the performance, influencing factors, and advantages of this approach in policy safety scenarios. These findings provide novel research insights and practical directions for advancing safe RL. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

Exploring the Impact of Non-Verbal Virtual Agent Behavior on User Engagement in Argumentative Dialogues

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arXiv 2024年

作者： Aicher, Annalena Bea Minker, Wolfgang Matsuda, Yuki André, Elisabeth Yasumoto, Keichii Ultes, Stefan Human-Centered Artificial Intelligence University of Augsburg Augsburg Germany Institute of Communications Engineering Ulm University Ulm Germany Faculty of Environmental Life Natural Science and Technology Okayama University Okayama Japan Ubiquitous Computing Systems Laboratory NAIST Ikoma Nara Japan Natural Language Generation and Dialogue Systems University of Bamberg Bamberg Germany

Engaging in discussions that involve diverse perspectives and exchanging arguments on a controversial issue is a natural way for humans to form opinions. In this process, the way arguments are presented plays a crucial role in determining how engaged users are, whether the interaction takes place solely among humans or within human-agent teams. This is of great importance as user engagement plays a crucial role in determining the success or failure of cooperative argumentative discussions. One main goal is to maintain the user’s motivation to participate in a reflective opinion-building process, even when addressing contradicting viewpoints. This work investigates how non-verbal agent behavior, specifically co-speech gestures, influences the user’s engagement and interest during an ongoing argumentative interaction. The results of a laboratory study conducted with 56 participants demonstrate that the agent’s co-speech gestures have a substantial impact on user engagement and interest and the overall perception of the system. Therefore, this research offers valuable insights for the design of future cooperative argumentative virtual agents. © 2024, CC BY-NC-ND.

关键词： Chatbots

Large Generative Model-assisted Talking-face Semantic Communication System

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arXiv 2024年

作者： Jiang, Feibo Tu, Siwei Dong, Li Pan, Cunhua Wang, Jiangzhou You, Xiaohu Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratoryof Artificial Intelligence Hunan University of Technology and Business Changsha China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The National Mobile Communications Research Laboratory Southeast University Nanjing China The Purple Mountain Laboratories Nanjing China

The rapid development of generative Artificial Intelligence (AI) continually unveils the potential of Semantic Communication (SemCom). However, current talking-face SemCom systems still encounter challenges such as low bandwidth utilization, semantic ambiguity, and diminished Quality of Experience (QoE). This study introduces a Large Generative Model-assisted Talking-face Semantic Communication (LGM-TSC) System tailored for the talking-face video communication. Firstly, we introduce a Generative Semantic Extractor (GSE) at the transmitter based on the FunASR model to convert semantically sparse talking-face videos into texts with high information density. Secondly, we establish a private Knowledge Base (KB) based on the Large language Model (LLM) for semantic disambiguation and correction, complemented by a joint knowledge base-semantic-channel coding scheme. Finally, at the receiver, we propose a Generative Semantic Reconstructor (GSR) that utilizes BERTVITS2 and SadTalker models to transform text back into a highQoE talking-face video matching the user’s timbre. Simulation results demonstrate the feasibility and effectiveness of the proposed LGM-TSC system. Copyright © 2024, The Authors. All rights reserved.

关键词： Latent semantic analysis

Hierarchical Policies of Subgoals for Safe Deep Reinforcement Learning 2nd

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Hierarchical Policies of Subgoals for Safe Deep Reinforcem...

2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Yu, Fumin Gao, Feng Yuan, Yao Xing, Xiaofei Dai, Yinglong College of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China College of Liberal Arts and Sciences National University of Defense Technology Changsha410073 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9789819902712

Reinforcement learning is a machine learning method that relies on the agent to learn by trial and error to solve decision optimization problems. It is well known that an agent based on deep reinforcement learning in complex environments is difficult to train. Moreover, the agent will generate unsafe and strange actions due to the lack of sufficient reward feedback from the environment. To make the agent converge to a better policy and make its behavior safer and more controllable under sparse rewards, we propose a subgoal embedding method based on prior knowledge and hierarchical strategy that can make the training process converge faster. The subgoal embedding method can be combined with existing reinforcement learning methods. In this paper, we combine the subgoal embedding method with REINFORCE algorithm and PPO(Proximal Policy Optimization) algorithm to test the method in the MiniGrid-DoorKey game environment of the gym platform. The experiments demonstrate the effectiveness of the subgoal embedding method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

Automatic decision support for public opinion governance of urban public events 4th

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Automatic decision support for public opinion governance of ...

4th International Conference on Intelligent computing, Communication and Devices, ICCD 2018

作者： Li, Zhaoxuan Liu, Wuying Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China Engineering Research Center for Cyberspace Content Security Guangdong University of Foreign Studies Guangzhou510420 China

ISBN: (纸本)9789811394058

Though the process of urbanization in China is accelerating and the size of the cities continues to expand, the urban danger sources are increasing. When public emergencies such as natural disasters, major accidents, environmental hazards, and vandalism occur in the city, people receive information from major media platforms;it is easy to cause rapid spread of public opinion by smart terminals. Therefore, the research on the public opinion governance model of urban public events has great practical significance for ensuring the economic development and social stability. This paper mainly focuses on the prediction, analysis, and governance of the current public sentiment caused by urban public emergencies. Facing different sensational outbreak nodes, the system analyzes the current node’s emotional inclination, assists the government to improve their supervisory capability, and proposes rationalized constructive opinions and solutions. © Springer Nature Singapore Pte Ltd 2020.

关键词： Disasters

Sparse Variational Autoencoder-Based Interpretable Bimodal Word Embeddings

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Sparse Variational Autoencoder-Based Interpretable Bimodal W...

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jingyao Tang Weiyu Zhong Qianhua Cai Guojun Lu Zehao Yan Yun Xue Xinguang Li Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China School of Physics and Telecommunication Engineering South China Normal University Guangzhou China

ISBN: (纸本)9781665466097

Word embedding is a basic task in the field of natural language processing, which is widely applied to a variety of tasks. In spite of delivering the semantic information, there is no meaningful explanation for specific dimension of the word embedding. As such, research is ongoing to explore the interpretability of word embeddings and thus improve their performance in downstream tasks. Current interpretable word embedding models, however, merely focus on the textual information of the words instead of the multimodalities. In line with the cognition principle, we establish a sparse variational autoencoder, which exploits both the textual and visual information to generate interpretable word embedding. Experiments are conducted to verify the interpretability of word embeddings together with their performance in downstream tasks. Comparing to the state-of-arts, experimental results indicate that the proposed interpretable word embeddings not only effectively increase the interpretability, but also obtain better result in most downstream tasks.

关键词： Visualization Semantics Machine learning Natural language processing Cognition Task analysis Cybernetics

Lightweight Vision Model-based Multi-user Semantic Communication Systems

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arXiv 2025年

作者： Jiang, Feibo Tu, Siwei Dong, Li Wang, Kezhi Yang, Kun Liu, Ruiqi Pan, Cunhua Wang, Jiangzhou Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science Brunel University London United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China Wireless and Computing Research Institute ZTE Corporation Beijing100029 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

Semantic Communication (SemCom) is a promising new paradigm for next-generation communication systems, emphasizing the transmission of core information, particularly in environments characterized by uncertainty, noise, and bandwidth constraints. However, existing image SemCom systems face several challenges, such as inefficient knowledge base construction, insufficient semantic encoding, and lack of multi-user semantic sharing. To address these issues, we propose a Lightweight Vision Model-based Multi-user Semantic Communication System (LVM-MSC). First, we construct a Lightweight Knowledge Base (LKB) based on the fast Segment Anything Model (SAM). LKB incorporates the extensive image knowledge of the SAM model while significantly reducing the number of parameters through its convolutional architecture. Next, we design an Efficient Semantic Codec (ESC) based on the Masked AutoEncoder (MAE) architecture. ESC enhances semantic compression at both the pixel and semantic levels and implements lightweight semantic decoding tailored for user devices. Furthermore, we propose a Multi-user Semantic Sharing (MSS) transmission for the multi-user SemCom. By calculating the similarity of semantic information among different users in the sharing semantic space, we unify the transmissions of similar semantic information through broadcasting, further improving the transmission efficiency. Finally, simulation results demonstrate the feasibility and effectiveness of the proposed LVM-MSC system. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis

PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment

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arXiv 2022年

作者： Zhang, Chen D'Haro, Luis Fernando Zhang, Qiquan Friedrichs, Thomas Li, Haizhou The Human Language Technology Group at Electrical & Computer Engineering Department National University of Singapore Singapore Spain Pte Ltd Singapore The Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

Chatbots are expected to be knowledgeable across multiple domains, e.g. for daily chit-chat, exchange of information, and grounding in emotional situations. To effectively measure the quality of such conversational agents, a model-based automatic dialogue evaluation metric (ADEM) is expected to perform well across multiple domains. Despite significant progress, an ADEM that works well in one domain does not necessarily generalize to another. This calls for a dedicated network architecture for domain generalization. To tackle the multi-domain dialogue evaluation task, we propose a Panel of Experts (PoE), a multitask network that consists of a shared transformer encoder and a collection of lightweight adapters. The shared encoder captures the general knowledge of dialogues across domains, while each adapter specializes in one specific domain and serves as a domain expert. To validate the idea, we construct a high-quality multi-domain dialogue dataset leveraging data augmentation and pseudo labeling. The PoE network is comprehensively assessed on 16 dialogue evaluation datasets spanning a wide range of dialogue domains. It achieves state-of-the-art performance in terms of mean Spearman correlation over all the evaluation datasets. It exhibits better zero-shot generalization than existing state-of-the-art ADEMs and the ability to easily adapt to new domains with few-shot transfer learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture