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arXiv 2022年

作者： Cheng, Lizhi Jia, Weijia Yang, Wenmian Shanghai Jiao Tong University Shanghai China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Guangdong Zhuhai China Nanyang Technological University Singapore Singapore

Spoken Language Understanding (SLU), a core component of the task-oriented dialogue system, expects a shorter inference facing the impatience of human users. Existing work increases inference speed by designing non-autoregressive models for single-turn SLU tasks but fails to apply to multi-turn SLU in confronting the dialogue history. The intuitive idea is to concatenate all historical utterances and utilize the non-autoregressive models directly. However, this approach seriously misses the salient historical information and suffers from the uncoordinated-slot problems. To overcome those shortcomings, we propose a novel model for multi-turn SLU named Salient History Attention with Layer-Refined Transformer (SHA-LRT), which composes of an SHA module, a Layer-Refined Mechanism (LRM), and a Slot label Generation (SLG) task. SHA captures salient historical information for the current dialogue from both historical utterances and results via a well-designed history-attention mechanism. LRM predicts preliminary SLU results from Transformer’s middle states and utilizes them to guide the final prediction, and SLG obtains the sequential dependency information for the non-autoregressive encoder. Experiments on public datasets indicate that our model significantly improves multi-turn SLU performance (17.5% on Overall) with accelerating (nearly 15 times) the inference process over the state-of-the-art baseline as well as effective on the single-turn SLU tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech processing

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Critical Density for K-Coverage Under Border Effects in Camera Sensor Networks With Irregular Obstacles Existence

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IEEE INTERNET OF THINGS JOURNAL 2024年第4期11卷 6426-6437页

作者： Liu, Zhimin Jiang, Guiyan Jia, Weijia Wang, Tian Wu, Youke Hunan First Normal Univ Sch Comp Sci Changsha 410205 Peoples R China Wuhan Univ Technol Sch Econ Wuhan 430070 Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Zhuhai 519000 Peoples R China BNU HKBU United Int Coll Guangdong Key Lab AI & Multi Modal Data Proc Zhuhai 519000 Peoples R China Wuyi Univ Sch Econ & Management Jiangmen 529020 Guangdong Peoples R China

From the perspective of saving energy, it is important to maintain a desired coverage ratio with a minimum sensor density in camera sensor networks (CSNs). In some actual applications, the Field of Interest (FoI) is often an irregular bounded area with obstacles existence. The existence of obstacles would have an adverse effect on quality of coverage in CSNs. Besides, due to the camera sensor may be located in the boundary of the FoI, it also has influence on the coverage contribution of the camera sensor. In this article, we assume that heterogeneous camera sensors are randomly deployed in a convex polygon FoI with irregular obstacles existence and consider the border effects to derive the critical density (CD) of camera sensors for a desired coverage ratio in CSNs. We propose the concept of occlusion K-coverage, and present the expected effective possible sensing region to evaluate the coverage contribution of the camera sensor. Next, a series of simulation experiments are conducted to demonstrate the impact of border effects and parameters of camera sensor on the occlusion K-coverage ratio. The results show that our method can effectively estimate the CD for a desired occlusion K-coverage ratio.

关键词： Camera sensor networks (CSNs) convex area K-coverage minimum sensor density obstacles existence

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An Online Resource Scheduling for Maximizing Quality-of-Experience in Meta Computing

arXiv

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arXiv 2023年

作者： Li, Yandi Guo, Jianxiong Li, Yupeng Wang, Tian Jia, Weijia The Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai519087 China The Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China The Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China The Department of Interactive Media Hong Kong Baptist University Hong Kong

Meta Computing is a new computing paradigm, which aims to solve the problem of computing islands in current edge computing paradigms and integrate all the resources on a network by incorporating cloud, edge, and particularly terminal-end devices. It throws light on solving the problem of lacking computing power. However, at this stage, due to technical limitations, it is impossible to integrate the resources of the whole network. Thus, we create a new meta computing architecture composed of multiple meta computers, each of which integrates the resources in a small-scale network. To make meta computing widely applied in society, the service quality and user experience of meta computing cannot be ignored. Consider a meta computing system providing services for users by scheduling meta computers, how to choose from multiple meta computers to achieve maximum Quality-of-Experience (QoE) with limited budgets especially when the true expected QoE of each meta computer is not known as a priori? The existing studies, however, usually ignore the costs and budgets and barely consider the ubiquitous law of diminishing marginal utility. In this paper, we formulate a resource scheduling problem from the perspective of the multi-armed bandit (MAB). To determine a scheduling strategy that can maximize the total QoE utility under a limited budget, we propose an upper confidence bound (UCB) based algorithm and model the utility of service by using a concave function of total QoE to characterize the marginal utility in the real world. We theoretically upper bound the regret of our proposed algorithm with sublinear growth to the budget. Finally, extensive experiments are conducted, and the results indicate the correctness and effectiveness of our algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Budget control

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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LenAtten: An effective length controlling unit for text summarization

arXiv

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arXiv 2021年

作者： Yu, Zhongyi Wu, Zhenghao Zheng, Hao XuanYuan, Zhe Fong, Jefferson Su, Weifeng Computer Science and Technology Programme Division of Science and Technology Data Science Programme Division of Science and Technology Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai China

Fixed length summarization aims at generating summaries with a preset number of words or characters. Most recent researches incorporate length information with word embeddings as the input to the recurrent decoding unit, causing a compromise between length controllability and summary quality. In this work, we present an effective length controlling unit Length Attention (LenAtten) to break this trade-off. Experimental results show that LenAtten not only brings improvements in length controllability and ROGUE scores but also has great generalization ability. In the task of generating a summary with the target length, our model is 732 times better than the best-performing length controllable summarizer in length controllability on the CNN/Daily Mail dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Controllability

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MTMD: multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting

arXiv

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arXiv 2022年

作者： Wang, Mingjie Zhang, Mingze Guo, Jianxiong Jia, Weijia Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

Recently, machine learning methods have shown the prospects of stock trend forecasting. However, the volatile and dynamic nature of the stock market makes it difficult to directly apply machine learning techniques. Previous methods usually use the temporal information of historical stock price patterns to predict future stock trends, but the multi-scale temporal dependence of financial data and stable trading opportunities are still difficult to capture. The main problem can be ascribed to the challenge of recognizing the patterns of real profit signals from noisy information. In this paper, we propose a framework called multi-scale Temporal Memory Learning and Efficient Debiasing (MTMD). Specifically, through self-similarity, we design a learnable embedding with external attention as memory block, in order to reduce the noise issues and enhance the temporal consistency of the model. This framework not only aggregates comprehensive local information in each timestamp, but also concentrates the global important historical patterns in the whole time stream. Meanwhile, we also design the graph network based on global and local information to adaptively fuse the heterogeneous multi-scale information. Extensive ablation studies and experiments demonstrate that MTMD outperforms the state-of-the-art approaches by a significant margin on the benchmark datasets. The source code of our proposed method is available at https://***/MingjieWang0606/MDMT-Public. Copyright © 2022, The Authors. All rights reserved.

关键词： Financial markets

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Spatiotemporal Feature Hierarchy-Based Blind Prediction of Natural Video Quality via Transfer Learning

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IEEE TRANSACTIONS ON BROADCASTING 2023年第1期69卷 130-143页

作者： Xian, Weizhi Zhou, Mingliang Fang, Bin Liao, Xingran Ji, Cheng Xiang, Tao Jia, Weijia Chongqing Univ Sch Comp Sci Chongqing 400044 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Beijing Normal Univ BNU UIC Inst Artificial Intelligence & Future Net Zhuhai 519088 Peoples R China BNU HKBU United Int Coll Guangdong Key Lab AI Multi Modal Data Proc Zhuhai 519087 Guangdong Peoples R China

In this paper, we propose a pyramidal spatiotemporal feature hierarchy (PSFH)-based no-reference (NR) video quality assessment (VQA) method using transfer learning. First, we generate simulated videos by a generative adversarial network (GAN)-based image restoration model. The residual maps between the distorted frames and simulated frames, which can capture rich information, are utilized as one input of the quality regression network. Second, we use 3D convolution operations to construct a PSFH network with five stages. The spatiotemporal features incorporating the shared features transferred from the pretrained image restoration model are fused stage by stage. Third, with the guidance of the transferred knowledge, each stage generates multiple feature mapping layers that encode different semantics and degradation information using 3D convolution layers and gated recurrent units (GRUs). Finally, five approximate perceptual quality scores and a precise prediction score are obtained by fully connected (FC) networks. The whole model is trained under a finely designed loss function that combines pseudo-Huber loss and Pearson linear correlation coefficient (PLCC) loss to improve the robustness and prediction accuracy. According to the extensive experiments, outstanding results can be obtained compared with other state-of-the-art methods. Both the source code and models are available ***://***/WeizhiXian/PSFHNRVQA

关键词： Three-dimensional displays Spatiotemporal phenomena Quality assessment Transfer learning Distortion Image restoration Feature extraction Video quality assessment pyramidal spatiotemporal feature transfer learning 3D convolution generative adversarial network

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Mtmd: multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting

SSRN

引用

SSRN 2022年

Recently, machine learning methods have shown the prospects of stock trend forecasting. However, the volatile and dynamic nature of the stock market makes it difficult to directly apply machine learning techniques. Previous methods usually use the temporal information of historical stock price patterns to predict future stock trends, but the multi-scale temporal dependence of financial data and stable trading opportunities are still difficult to capture. The main problem can be ascribed to the challenge of recognizing the patterns of real profit signals from noisy information. In this paper, we propose a framework called multiscale Temporal Memory Learning and Efficient Debiasing (MTMD). Specifically, through self-similarity, we design a learnable embedding with external attention as memory block, in order to reduce the noise issues and enhance the temporal consistency of the model. This framework not only aggregates comprehensive local information in each timestamp, but also concentrates the global important historical patterns in the whole time stream. Meanwhile, we also design the graph network based on global and local information to adaptively fuse the heterogeneous multi-scale information. Extensive ablation studies and experiments demonstrate that MTMD outperforms the state-of-the-art approaches by a significant margin on the benchmark datasets. The source code of our proposed method is available at https://***/MingjieWang0606/MDMT-Public. © 2022, The Authors. All rights reserved.

关键词： Financial markets

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Joint Task Scheduling and Container Image Caching in Edge Computing 19

Joint Task Scheduling and Container Image Caching in Edge Co...

引用

19th International Conference on Mobility, Sensing and Networking (MSN)

作者： Mou, Fangyi Tang, Zhiging Lou, Jiong Guo, Jianxiong Wang, Wenhua Wang, Tian BNU HKBU United Int Coll Guangdong Key Lab AI & Multi Modal Data Proc Xiangzhou Zhuhai Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Beijing Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Yunnan Key Lab Software Engn Kunming Yunnan Peoples R China

ISBN: (纸本)9798350358261;9798350358278

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration;2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively.

关键词： Task scheduling image caching container edge computing

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Improved ADT Method for the Switched Nonlinear System: Stability Analysis and Application

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第8期69卷 3580-3584页

作者： Jin, Cui-Li Wang, Qing-Guo Wang, Rui Wu, Di Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equip Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Zhuhai 519087 Peoples R China BNU HKBU United Int Coll Guangdong Key Lab AI & Multi Modal Data Proc Zhuhai 519087 Peoples R China Dalian Univ Technol Liaoning Prov & Sch Aeronaut & Astronaut Key Lab Adv Technol Aerosp Vehicles Dalian 116024 Peoples R China

This brief investigates the stabilization problem of switched nonlinear systems via orchestrating unstable modes. The stabilizing-switching-dependent average dwell time method is advanced to provide enough switching behaviors with stabilizing property to compensate for the increment of the Lyapunov functions caused by modes and dwell-time-dependent/independent destabilizing switching times. The stability criteria are established based on the set of switching pairs with the stabilizing property. By limiting the ratio of stabilizing switching times in one switching period, the destabilizing switching behaviors allowably occur before the stabilizing ones. Besides, the stability results are applicable for the scenario where the preset switching signals with information loss. Finally, the simulation part illustrates the feasibility and superiority of the obtained results.

关键词： Switches Switched systems Switching circuits Stability criteria Circuit stability Nonlinear systems Lyapunov methods Switched systems nonlinear system stability unstable mode

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