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Using EZDA Approach to Handle Original ZG Control Failure for TVNS Systems Output Tracking due to States Tending to Infinity

Using EZDA Approach to Handle Original ZG Control Failure fo...

引用

International Conference on Systems and Informatics (ICSAI)

作者： Yunong Zhang Nini Shi Min Yang Jinjin Guo Yihong Ling Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

This paper investigates the original Zhang-gradient (ZG) control failure for the output tracking, which is caused by the system states tending to infinity in the time-varying nonlinear scalar (TVNS) systems. Aiming at solving this problem, the extra Zhang dynamics aid (EZDA) approach is considered, and EZDA controllers are designed. Meanwhile, mathematical derivations are presented, and numerical experiments are conducted, substantiating that this control strategy proposed by the EZDA approach is good. This means that the original ZG control failure is effectively solved. Specifically, the effectiveness and feasibility of the EZDA approach are verified.

关键词： Gradient methods Nonlinear control systems Time-varying systems

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Point of interest recommendation with social and geographical influence

Point of interest recommendation with social and geographica...

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IEEE International Conference on Big Data

作者： Da-Chuan Zhang Mei Li Chang-Dong Wang Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9781467390064

Point of interest (POI) recommendation, a service which can help people discover useful and interesting locations has emerged rapidly with the development of location-based social networks (LBSNs), like Foursquare, Gowalla and Wechat. The large number of check-in histories make it possible to mine the preference of each user and then to provide accurate personalized POI recommendation. In real-world applications, apart from check-in data, there are some other useful information available for making better POI recommendation, such as social relationship among users and geographical influence. In this paper, a new POI recommendation method called Social and Geographical Fusing Model (SGFM) is designed. The basic idea is summarized as follows. Firstly, the users' check-in records and social influence are integrated in a combinative model. Then the global user impact factors generated by the PageRank algorithm are used to improve the combinative model. Secondly, a geographical influence measurement is used to capture the users' physical check-in characters. Finally, the enhanced combinative model and geographical influence are combined together to form a new framework. Extensive experiments have been conducted on a famous dataset, namely Gowalla. The comparison results confirm that the proposed framework outperforms state-of-the-art POI recommendation methods significantly.

关键词： Collaboration Social network services History Computational modeling Big data Mobile communication Internet

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New 5-Step Discrete-Time Zeroing Neuronet for Time-Dependent Matrix Square Root Finding

New 5-Step Discrete-Time Zeroing Neuronet for Time-Dependent...

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International Conference on Systems and Informatics (ICSAI)

作者： Xiao Liu Chaowei Hu Xiangui Kang Mingzhi Mao Yunong Zhang Binbin Qiu Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

In this paper, for the purpose of finding square root of a time-dependent matrix, a new discrete-time zeroing neuronet (DTZN) is proposed. Firstly, the problem of square root finding of time-dependent matrix is formulated. Then, an explicit continuous-time zeroing neuronet (CTZN) is derived from the problem formulation equation via vectorization technique. Furthermore, based on Taylor expansion, we present a 5-Step Zhang time-discretization (ZTD) formula. The ZTD is used to approximate the 1st-order derivative of the object, of which the truncational error is proportional to the cube of the sampling period. Finally, the 5-Step DTZN for solving the square root of a time-dependent matrix is acquired by using the presented 5- Step ZTD formula to discretize the CTZN. Theoretical analyses shown stable and convergent performance of the proposed 5- Step DTZN for solving the square root of a time-dependent matrix. Computer experiments results present the stability and convergence of the obtained DTZN for solving square root of time-dependent matrix with the maximum steady-state residual errors proportional to the fourth power of sampling period. By comparison with the DTZNs using Euler formula and the previous 5-Step discretization formula, the proposed 5-Step DTZN has an advantage in residual error. In addition, the influences of step size and sampling period are illustrated by computer experiments results.

关键词： Differential equations Convergence Real-time systems Approximation theory Continuous time systems Stability analysis Neurocontrollers

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Network Embedding with Topology-Aware Textual Representations 18th

Network Embedding with Topology-Aware Textual Representation...

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18th Pacific Rim International Conference on Artificial intelligence, PRICAI 2021

作者： Chen, Jiaxing Xu, Zenan Su, Qinliang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9783030891879

Textual network embedding aims to learn meaningful low-dimensional representations for vertices with the consideration of the associated texts. When learning the representations for texts in network embedding, existing methods mostly only exploit information from neighboring texts (i.e., contexts), while rarely taking advantages of the valuable network topological (structural) information. To bridge the gap, in this paper, a model based on adaptive-filter convolutional neural networks (CNN) is developed, in which the filters are adapted to local network topologies, rather than clamped to fixed values as in traditional CNNs. The dependency enables the learned text representations to be aware of local network topologies. It is shown that the proposed topology-aware representations can be viewed as a complement to existing context-aware ones. When the two are used together, experimental results on three real-world benchmarks demonstrate that significant performance improvements on the tasks of link prediction and vertex classification. © 2021, Springer Nature Switzerland AG.

关键词： Network topology

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Exploiting Multiple Features for Hash Codes Learning with Semantic-Alignment-Promoting Variational Auto-encoder 12th

Exploiting Multiple Features for Hash Codes Learning with Se...

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12th National CCF Conference on Natural Language Processing and Chinese computing, NLPCC 2023

作者： Chen, Jiayang Su, Qinliang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Beijing China

ISBN: (纸本)9783031446924

Semantic hashing is an effective technique to empower information retrieval. Currently, considerable efforts have been dedicated to generating high-quality hash codes by modeling document features using generative models and other approaches. However, most of these methods rely solely on a single type of feature, such as TFIDF features, BERT embeddings, etc. As different types of features have distinct but complementary information of documents, e.g. TFIDF mainly contains the keywords information and BERT focuses on the semantics, hash codes generated solely from either may not capture the full essence of the documents. To overcome this challenge, we propose a semantic-alignment-promoting variational auto-encoder to generate hash codes from multiple document features. Specifically, a VAE-based generative model is first developed to model the multiple features. Then, we propose a semantic-alignment-promoting inference network to estimate the parameters of the variational posterior from multiple features. Additionally, the quality of hash codes is further improved by promoting the semantic alignment between the hash codes of connected documents in a constructed connection graph. The results of extensive experiments on three public datasets demonstrate that our proposed model significantly outperforms current state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Software Defined Networking Based On-Demand Routing Protocol in Vehicle Ad Hoc Networks 12

Software Defined Networking Based On-Demand Routing Protocol...

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12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Dong, Baihong Wu, Weigang Yang, Zhiwei Li, Junjie School of Data and Computer Science Sun Yat-Sen University China Guangdong Key Lab of Big Data Analysis and Processing China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9781509056965

This paper comes up with a SDN based On-Demand Routing Protocol, SVAO, which separates data forwarding layer and network control layer, as in SDN, to enhance the data transmission efficiency within VANETs. The Roadside Service Unit plays the role of Local Controller and is in charge of selecting vehicles to forward packet within a road segment. All the vehicles state in the road. Correspondingly, a two-level design is used. Global Level is distributed and adopts a ranked query scheme to collect vehicle information and determine the road segments along which a message should be forwarded. And the Local Level is in charge of selecting forwarding vehicles in each road segment determined by the global level. We compare SVAO with popular ad-hoc network routing protocols, including OLSR, DSR, DSDV, and DB via simulations. We consider the impact of vehicle density, speed on data transmission rate and average packet delay. The simulation results show that in case of large network scales or high vehicle speed, SVAO performs better than the others. © 2016 IEEE.

关键词： Routing protocols

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Learning Joint Spatial-Temporal Transformations for Video Inpainting 16th

Learning Joint Spatial-Temporal Transformations for Video In...

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16th European Conference on Computer Vision, ECCV 2020

作者： Zeng, Yanhong Fu, Jianlong Chao, Hongyang School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Microsoft Research Asia Beijing China

ISBN: (纸本)9783030585167

High-quality video inpainting that completes missing regions in video frames is a promising yet challenging task. State-of-the-art approaches adopt attention models to complete a frame by searching missing contents from reference frames, and further complete whole videos frame by frame. However, these approaches can suffer from inconsistent attention results along spatial and temporal dimensions, which often leads to blurriness and temporal artifacts in videos. In this paper, we propose to learn a joint Spatial-Temporal Transformer Network (STTN) for video inpainting. Specifically, we simultaneously fill missing regions in all input frames by self-attention, and propose to optimize STTN by a spatial-temporal adversarial loss. To show the superiority of the proposed model, we conduct both quantitative and qualitative evaluations by using standard stationary masks and more realistic moving object masks. Demo videos are available at https://***/researchmm/STTN. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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CORE-TEXT: IMPROVING SCENE TEXT DETECTION WITH CONTRASTIVE RELATIONAL REASONING

CORE-TEXT: IMPROVING SCENE TEXT DETECTION WITH CONTRASTIVE R...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Lin, Jingyang Pan, Yingwei Lai, Rongfeng Yang, Xuehang Chao, Hongyang Yao, Ting Sun Yat-sen University Guangzhou China The Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education Guangzhou China JD AI Research Beijing China

ISBN: (纸本)9781665438643

Localizing text instances in natural scenes is regarded as a fundamental challenge in computer vision. Nevertheless, owing to the extremely varied aspect ratios and scales of text instances in real scenes, most conventional text detectors suffer from the sub-text problem that only localizes the fragments of text instance (i.e., sub-texts). In this work, we quantitatively analyze the sub-text problem and present a simple yet effective design, COntrastive RElation (CORE) module, to mitigate that issue. CORE first leverages a vanilla relation block to model the relations among all text proposals (subtexts of multiple text instances) and further enhances relational reasoning via instance-level sub-text discrimination in a contrastive manner. Such way naturally learns instance-aware representations of text proposals and thus facilitates scene text detection. We integrate the CORE module into a two-stage text detector of Mask R-CNN and devise our text detector CORE-Text. Extensive experiments on four benchmarks demonstrate the superiority of CORE-Text. © 2021 IEEE

关键词： Aspect ratio

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Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers 35

Improving Visual Quality of Image Synthesis by A Token-based...

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35th Conference on Neural Information Processing Systems, NeurIPS 2021

作者： Zeng, Yanhong Yang, Huan Wang, Jianbo Fu, Jianlong School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Microsoft Research Asia China University of Tokyo Japan

ISBN: (纸本)9781713845393

We present a new perspective of achieving image synthesis by viewing this task as a visual token generation problem. Different from existing paradigms that directly synthesize a full image from a single input (e.g., a latent code), the new formulation enables a flexible local manipulation for different image regions, which makes it possible to learn content-aware and fine-grained style control for image synthesis. Specifically, it takes as input a sequence of latent tokens to predict the visual tokens for synthesizing an image. Under this perspective, we propose a token-based generator (i.e.,TokenGAN). Particularly, the TokenGAN inputs two semantically different visual tokens, i.e., the learned constant content tokens and the style tokens from the latent space. Given a sequence of style tokens, the TokenGAN is able to control the image synthesis by assigning the styles to the content tokens by attention mechanism with a Transformer. We conduct extensive experiments and show that the proposed TokenGAN has achieved state-of-the-art results on several widelyused image synthesis benchmarks, including FFHQ and LSUN CHURCH with different resolutions. In particular, the generator is able to synthesize high-fidelity images with 1024 × 1024 size, dispensing with convolutions entirely. © 2021 Neural information processing systems foundation. All rights reserved.

关键词： Image enhancement

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Embedding Dynamic Attributed Networks by Modeling the Evolution Processes 28

Embedding Dynamic Attributed Networks by Modeling the Evolut...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Xu, Zenan Ou, Zijing Su, Qinliang Yu, Jianxing Quan, Xiaojun Lin, Zhenkun School of Data and Computer Science Sun Yat-Sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781952148279

Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the embedding techniques for static networks. But in practice, there are many networks that are evolving over time and hence are dynamic, e.g., the social networks. To address this issue, a high-order spatio-temporal embedding model is developed to track the evolutions of dynamic networks. Specifically, an activeness-aware neighborhood embedding method is first proposed to extract the high-order neighborhood information at each given timestamp. Then, an embedding prediction framework is further developed to capture the temporal correlations, in which the attention mechanism is employed instead of recurrent neural networks (RNNs) for its efficiency in computing and flexibility in modeling. Extensive experiments are conducted on four real-world datasets from three different areas. It is shown that the proposed method outperforms all the baselines by a substantial margin for the tasks of dynamic link prediction and node classification, which demonstrates the effectiveness of the proposed methods on tracking the evolutions of dynamic networks. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Recurrent neural networks

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