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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Zhang, Yansong Zhang, Yu Xiao, Yanqin Wang, Shan Chen, Hong Key Laboratory of the Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Department of Computer Science Harbin Finance College Harbin 150030 China Computer Center Hebei University Baoding Hebei 071002 China

ISBN: (纸本)9783642006715

What-if analysis can provide more meaningful information than classical OLAP. Multi-scenario hypothesis based on historical data needs efficient what-if data view support. In general, delta table for what-if analysis is more general than other solutions such as query re-writing prototype of Sesame. Delta table is independent of base table and is more suitable to represent complex hypothetical updates and multi-version hypothetical updates. Due to low efficiency of traditional delta table merging algorithm which is based on set operation, there are few researches focus on delta table merging algorithm but on query re-writing algorithm. By analyzing the difference between what-if query and what-if analysis and improving the delta table merging algorithm, we propose novel algorithms without set operation of difference. Considering the feature of aggregate operations in OLAP analysis, pre-merge algorithm is presented without the generation of what if data view before group-bys in the scenario of SUM, AVERAGE and COUNT function OLAP queries. In our experiments, the pre-merge algorithm greatly improved the efficiency of delta table merging procedure which is close to base group-by statement and superior to the re-writing algorithm. A complete comparison between all candidate delta table merging algorithms and re-writing algorithm with different what-if update conditions of update, deletion, insertion and mixed what-if updates is exhibited in our experiments, the policy of what-if analysis among different types is also discussed. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Merging

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Web Technologies Research and Development - APWeb 2005 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Yanchun Zhang Katsumi Tanaka Jeffrey Xu Yu Shan Wang Minglu Li

ISBN: (数字)9783540318491

ISBN: (纸本)9783540252078

This volume contains papers selected for presentation at the 7th Asia Pacific Conference on Web Technology (APWeb 2005), which was held in Shanghai, China during March 29–April 1, 2005. APWeb is an international conference series on WWW technologies and is the primary forum for researchers and practitioners from both academia and industry to exchange knowledge on WWW-related technologies and new advanced applications. APWeb 2005 received 420 submissions from 21 countries and regions worldwide, including China, Korea, Australia, Japan, Taiwan, France, UK, Canada, USA, India, Hong Kong, Brazil, Germany, Thailand, Singapore, Turkey, Spain, Greece, Belgium, New Zealand, and UAE. After a thorough review process for each submission by the Program Committee members and expert reviewers recommended by PC members, APWeb 2005 accepted 71 regular research papers (acceptance ratio 16.9%) and 22 short papers (acceptance ratio 5.2%). This volume also includes 6 keynote papers and 11 invited demo papers. The keynote lectures were given by six leading experts: Prof. Ah Chung Tsoi (Australia Research Council), Prof. Zhiyong Liu (National Nature Science Foundation of China), Prof. John Mylopoulos (University of Toronto), Prof. Ramamohanarao (Rao) Kotagiri (University of Melbourne), Prof. Calton Pu (Georgia Tech), and Prof. Zhiwei Xu (Chinese Academy of Sciences).

关键词： Information Storage and Retrieval Information Systems Applications (incl. Internet) Multimedia Information Systems User Interfaces and Human Computer Interaction Computer Communication Networks Computers and Society

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Semantic SLAM Based on Compensated Segmentation and Geometric Constraints in Dynamic Environments

Semantic SLAM Based on Compensated Segmentation and Geometri...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Baofu Fang Shuai Zhou Hao Wang School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Hefei China

Most of the existing slam algorithms are designed based on the assumption of a static environment, this strong assumption limits the practical application of most slam systems. The main reason is that moving objects will cause feature mismatch in the pose estimation process, which in turn affects the accuracy of localization and mapping. In this paper, we propose a SLAM algorithm in a dynamic environment. First, we use the BlendMask network to detect potential moving objects to generate masks for dynamic objects. The geometrically constrained joint optical flow method is used to detect dynamic feature points. Secondly, aiming at the failure of semantic segmentation network segmentation, a missed detection compensation algorithm based on the invariance of adjacent frame speed is proposed. Finally, a keyframe selection strategy is proposed to construct a semantic octree graph containing only static objects. We evaluate our algorithm on TUM RGB-D and real scene datasets. The experimental results show that the algorithm has high accuracy and real-time performance.

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Interval valued fuzzy rough classifier and its application on privacy protection

Interval valued fuzzy rough classifier and its application o...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Su-Yun Zhao Si Lin Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China College of Science Beijing Forestry University Beijing China

Currently, most works on interval valued problems mainly focus on attribute reduction (i.e., feature selection) by using rough set technologies. However, less research work on classifier building on interval-valued problems has been conducted. It is promising to propose an approach to build classifier for interval-valued problems. In this paper, we propose a classification approach based on interval valued fuzzy rough sets. First, the concept of interval valued fuzzy granules are proposed, which is the crucial notion to build the reduction framework for the interval-valued databases. Second, the idea to keep the critical value invariant before and after reduction is selected. Third, the structure of reduction rule is completely studied by using the discernibility vector approach. After the description of rule inference system, a set of rules covering all the objects can be obtained, which is used as a rule based classifier for future classification. Finally, numerical examples are presented to illustrate feasibility and affectivity of the proposed method in the application of privacy protection.

关键词： Abstracts Diabetes Sonar Iris Accuracy

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VRFormer: 360-Degree Video Streaming with FoV Combined Prediction and Super resolution

VRFormer: 360-Degree Video Streaming with FoV Combined Predi...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Zhihao Zhang Haipeng Du Shouqin Huang Weizhan Zhang Qinghua Zheng Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University School of Continuing Education Xi'an Jiaotong University

360-degree video has shown great potential to the mainstream since its immersive experience. However, 360-degree video streaming requires ultrahigh bandwidth and low latency, which limit the improvement of user quality of experience (QoE). Currently, methods combining field of view (FoV) prediction and adaptive video streaming provide an effective method for addressing the above issues. However, existing FoV prediction methods based on recurrent neural networks (RNN) cannot capture long-range dependency from input to output. Current deep reinforcement learning (DRL)-based adaptive strategies fail to estimate the future bandwidth with high accuracy and fully explore the capability of VR devices. To ameliorate these limitations, we design a DRL-based 360-degree video streaming method named VRFormer with FoV combined prediction and super resolution (SR). First, we adopt a content-aware transformer-based encoder-decoder network to make the long-term FoV prediction. It combines the user's head movement history, eye-tracking history, and user attention extracted from a convolutional neural network (CNN)-based network. Second, we introduce a DNN-based SR network running on VR devices to reconstruct high-definition video content. Finally, we apply a DRL-based network to adaptively allocate rates for future tiles and dynamically control video content reconstruction. Experiments have verified that the proposed method can effectively improve the quality of experience (QoE) of the user's viewing experience compared to the state-of-the-art methods.

关键词： Recurrent neural networks Superresolution Bandwidth Reinforcement learning Prediction methods Streaming media Transformers

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Laplacian SVM for Learning from Label Proportions

Laplacian SVM for Learning from Label Proportions

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Limeng Cui Zhensong Chen Fan Meng Yong Shi School of Computer and Control Engineering UCAS School of Economics and Management UCAS Key Laboratory on Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9781509059119

Proportion-SVM has been deeply studied due to its broad application prospects, such as modeling voting behaviors and spam filtering. However, the geometric information has been widely ignored. Thus, current methods usually show sensitivity to noises. To address these problems, in this paper, we combine the proportion learning framework with Laplacian term. We exploit the advantages of Laplacian term. Extensive experiments show the effectiveness of our method.

关键词： Support vector machines Laplace equations Kernel Training Optimization data models data mining

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Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension

Extremely Short Chinese Text Classification Method Based on ...

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作者： Yongzeng Yue Yuhong Zhang Xuegang Hu Peipei Li Key Laboratory of knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology

Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and label data to expand the extremely short texts and ease the problem of feature sparseness;secondly,the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method.

关键词： Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension

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Semi-autoregressive transformer for image captioning

arXiv

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arXiv 2021年

作者： Zhou, Yuanen Zhang, Yong Hu, Zhenzhen Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Tencent AI Lab Key Laboratory of Knowledge Engineering with Big Data Ministry of Education

Current state-of-the-art image captioning models adopt autoregressive decoders, i.e. they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. To tackle this issue, non-autoregressive image captioning models have recently been proposed to significantly accelerate the speed of inference by generating all words in parallel. However, these non-autoregressive models inevitably suffer from large generation quality degradation since they remove words dependence excessively. To make a better trade-off between speed and quality, we introduce a semi-autoregressive model for image captioning (dubbed as SATIC), which keeps the autoregressive property in global but generates words parallelly in local. Based on Transformer, there are only a few modifications needed to implement SATIC. Experimental results on the MSCOCO image captioning benchmark show that SATIC can achieve a good trade-off without bells and whistles. Code is available at https://***/YuanEZhou/satic. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

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Multivariate Time Series Classification via Hierarchical Graph Embedding

Multivariate Time Series Classification via Hierarchical Gra...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wenhao Niu Xingrui Zhuo Gongqing Wu JunWei Lv Zan Zhang Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Anhui China

Multivariate time series classification aims to determine the labels for multivariate time series samples. Although variable interaction relationships and sample similarity relationships exist in multivariate time series, the available related methods usually ignore the rich relationships and are ineffective in exploiting these. To solve this problem, we propose a Hierarchical Graph Embedding for Multivariate Time Series Classification (MTSC-HGE), which consists of a variable-wise attentive graph pooling module and a sample-wise graph convolutional module to obtain the relationships of variables and samples. Specifically, we design an attentive graph pooling module based on self-attention, which can obtain sample features fusing temporal patterns and variable interaction relationships in samples. Furthermore, we propose a graph mapping criterion that converts the MTS dataset into a graph based on dynamic time warping to explicitly reflect the similarity relationships between samples. To capture latent sample relationships, a GCN module is utilized on the sample graph to integrate sample features obtained from the attentive graph pool module. In addition, a classifier takes the rich representation output by the model to get the final predicted class. Extensive experiments on 14 public datasets show that MTSC-HGE significantly outperforms state-of-the-art baselines.

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Production quality early warning method for firearms components based on cloud edge collaboration 4

Production quality early warning method for firearms compone...

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4th International Conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Sun, Jiawei Hao, Jia Zhang, Xiaoning School of Mechanical Engineering Beijing Institute of Technology Beijing10081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314019 China Key Laboratory of Industry Knowledge and Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing10008 China Tianjin Key Laboratory of Space Environment Simulation Technology Tianjing300450 China

ISBN: (纸本)9798400709449

Digital transformation in manufacturing has become a trend with the continuous development of cloud computing and edge computing. The production process of firearms components is highly challenging and quality problems in the production process may directly affect the safety of people's lives and property. Thus, this paper proposes a production-quality early warning method for firearms components based on cloud-edge collaboration. Real-time early warning and rapid processing for abnormal conditions in the production process are realized based on cloud data analysis and edge intelligent prediction, thereby improving production efficiency and quality. The cloud edge collaboration framework is developed for cloud training of the long short-term memory models and real-time sample edge acquisition, enhancing the adaptability of quality early warning algorithms under specific conditions and the real-time quality of early warning for firearms components. © 2023 ACM.

关键词： Big data

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