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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yu, Ye Wu, Yao Liang, Xi Ma, Cheng Lu, Qiang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9781665405270

Academic collaboration has become one of the topics of large-scale social network analysis. The information extracted from academic collaboration also shows the contextual value in combination with other data. For academic collaborative social networks with intricate collaborative relationships, previous research methods mainly focused on network structure and community discovery, lacking in mining the characteristics of relationships in collaborative networks. There were some limitations in the research perspective. In this work, we propose a collaborative relationship representation method that is more suitable for academic collaborative social networks. It marks the attribute characteristics of nodes in collaborative relationships and highlights collaborative relationships in visualization. By adopting this new representation of collaborative relationships, we designed a visual analysis framework NcoVis, to visualize academic collaborative social networks. We further visually analyze the link relationship groups guided by nodes, and explore the potential representation of the structure and attribute characteristics of link relationships, so that analysts can explore the theme and structural change characteristics of link relationships, communities and the whole network in collaborative social networks. To prove the proposed framework, we conducted a case study on the academic collaboration literature data set, and analyzed the questionnaire with Likert scale through the user evaluation experiment, which verified the effectiveness of our method and the practicability of NcoVis. © 2022 IEEE.

关键词： Visualization

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Review of graph construction and graph learning in stock price prediction 9th

Review of graph construction and graph learning in stock pri...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Wang, Yunong Qu, Yi Chen, Zhensong Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Management and Engineering Capital University of Economics and Business Beijing100070 China

Precise prediction of stock prices leads to more profits and more effective risk prevention, which is of great significance to both investors and regulators. Recent years, various kinds of information not directly-relevant with stock prices have received more attention, such as texts, images or connections. These external information has the potential of reflecting or influencing fluctuations, and thus, given the utilization of advanced analyzing techniques, the forecasting performance of stock prices could be promoted substantially. For instance, graph neural network models have expanded into many other disciplines including stock price prediction, and exhibited impressive representation learning ability. However, in stock markets, well-defined graphs are rarely seen and how to formulate the graph structures needed remains a challenging problem. Towards this end, this article presents a comprehensive overview of graph construction and graph learning in stock price prediction, by reviewing the existing studies, summarizing its general paradigm, special cases and proposing possible prospects. Our research not only systematically reveals the feasible ways of constructing graphs in financial markets, but also provides insights for further implementations of graph learning models into stock prediction tasks. © 2022 The Authors. Published by Elsevier B.V.

关键词： Financial markets

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Adaptive load shedding for data stream using PI feedback controller

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Journal of Computational Information Systems 2007年第5期3卷 1827-1832页

作者： Huang, Chongzheng Cai, Shaojiang Xie, Zhijun Zhang, Yuanyu Chen, Hong School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering China School of Electronics and Information Engineering Xi'an Jiaotong University Xi'an 710000 China

Load shedding has been widely used in data stream management systems (DSMSs) to keep DSMSs running steadily. One key problem in load shedding is determining how much system load to shed. Existing works tend to adapt coarse algorithm (CA) to solve this problem. In this paper, we present an adaptive PI controller-based load shedding framework for data stream. The main contribution of this paper is our use of feedback control theory to design the load shedding scheme. In contrast to the existing approaches, we firstly apply system identification to establish a dynamic model to describe DSMS, which enables us analyze DSMS quantitatively. Then, based on the model, we use the Root Locus method to design the PI controller with proven performance guarantees. The adaptive framework has been implemented by modifying Borealis system. Theoretic analysis and experimental results demonstrate that our approach is robust even when system load changes frequently. Comparing to existing strategies, our approach also achieves significantly better performance.

关键词： Feedback control

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Top-k entities query processing on uncertainly fused multi-sensory data

Top-k entities query processing on uncertainly fused multi-s...

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作者： Liu, Dexi Wan, Changxuan Xiong, Naixue Park, Jong Hyuk Rho, Seungmin Jiangxi Key Laboratory of Data and Knowledge Engineering School of Information Technology Jiangxi University of Finance and Economics Nanchang China Department of Computer Science Georgia State University Atlanta GA United States Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of School of Electrical Engineering Korea University Seoul Korea Republic of

Sensor fusion is the combining of sensory data from disparate sources such that the resulting information is in some sense better than would be possible when these sources were used individually. The natural uncertainty exists in these data because sensors are not precise enough. Hence, the intuitive method to store this kind of data is using uncertain database. Finding the top-k entities according to one or more attributes is a powerful technique when the uncertain database contains large quantity of data. However, compared to top-k in traditional databases, queries over uncertain database are more complicated because of the existence of exponential possible worlds. We propose a method to process entity-based global top-k aggregate queries in uncertain database, which returns the top-k entities that have the highest aggregate value. Our method has two levels, entity state generation and G-topk-E query processing. In the former level, entity states, which satisfy the properties of x-tuple, are generated one after the other according to their aggregate values, while in the latter level, dynamic programming-based global top-k entity query processing is employed to return the answers. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions. © 2012 Springer-Verlag London Limited.

关键词： Query processing

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Initializing convolutional filters with semantic features for text classification

Initializing convolutional filters with semantic features fo...

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2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： Li, Shen Zhao, Zhe Liu, Tao Hu, Renfen Du, Xiaoyong Institute of Chinese Information Processing Beijing Normal University China UltraPower-BNU Joint Laboratory for Artificial Intelligence Beijing Normal University China College of Chinese Language and Culture Beijing Normal University China School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China

ISBN: (纸本)9781945626838

Convolutional Neural Networks (CNNs) are widely used in NLP tasks. This paper presents a novel weight initialization method to improve the CNNs for text classification. Instead of randomly initializing the convolutional filters, we encode semantic features into them, which helps the model focus on learning useful features at the beginning of the training. Experiments demonstrate the effectiveness of the initialization technique on seven text classification tasks, including sentiment analysis and topic classification. © 2017 Association for Computational Linguistics.

关键词： Sentiment analysis

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QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation

QGEval: Benchmarking Multi-dimensional Evaluation for Questi...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Continuing Education Xi'an Jiaotong University Xi'an China MOE KLINNS Lab School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798891761643

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Face micro-expression recognition algorithm based on ResNet depth model

Face micro-expression recognition algorithm based on ResNet ...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Liquan Wang Shu Zhan School of Computer and Information Engineering Hefei University of Technology Key Laboratory of Big Data Knowledge Engineering Ministry of Education Hefei China

Micro Expression (ME) is the subtle facial expressions that people show when they express their inner feelings. To address the problem that micro-expression recognition is difficult and less accurate due to the small number of samples and uneven distribution of different categories, we propose a model framework to improve the accuracy of micro-expression recognition. The peak frames containing more key expression information in the micro-expression video sequences are extracted; SE-ResNeXt-50, an improved residual network with SE module, is used to extract features from the peak frames of micro-expressions, where the SE module can better learn the key information in the features, and ResNeXt simplifies the structure by replacing the dense structure with the sparse structure through group convolution, which improves the recognition efficiency. The recognition efficiency is improved by replacing the dense structure with the sparse structure by group convolution. At the same time, the Focal Loss loss function can better solve the model performance problem caused by the imbalance of micro-expression data. Simulation experiments are conducted on the micro-expression dataset CASMEⅡ, and it is found that the improved residual network and peak frame improve the accuracy and F1 value of micro-expression recognition. The improved residual network and peak frame can reduce the effect of small data set, make the model have good fitting effect, and improve the performance of different categories, improve the recognition accuracy of micro-expressions, and have better recognition performance for micro-expression recognition.

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Rethinking data-Free Quantization as a Zero-Sum Game

arXiv

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arXiv 2023年

作者： Qian, Biao Wang, Yang Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China

data-free quantization (DFQ) recovers the performance of quantized network (Q) without accessing the real data, but generates the fake sample via a generator (G) by learning from full-precision network (P) instead. However, such sample generation process is totally independent of Q, specialized as failing to consider the adaptability of the generated samples, i.e., beneficial or adversarial, over the learning process of Q, resulting into non-ignorable performance loss. Building on this, several crucial questions — how to measure and exploit the sample adaptability to Q under varied bit-width scenarios? how to generate the samples with desirable adaptability to benefit the quantized network? — impel us to revisit DFQ. In this paper, we answer the above questions from a game-theory perspective to specialize DFQ as a zero-sum game between two players — a generator and a quantized network, and further propose an Adaptability-aware Sample Generation (AdaSG) method. Technically, AdaSG reformulates DFQ as a dynamic maximization-vs-minimization game process anchored on the sample adaptability. The maximization process aims to generate the sample with desirable adaptability, such sample adaptability is further reduced by the minimization process after calibrating Q for performance recovery. The Balance Gap is defined to guide the stationarity of the game process to maximally benefit Q. The theoretical analysis and empirical studies verify the superiority of AdaSG over the state-of-the-arts. Our code is available at https://***/hfutqian/AdaSG. Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

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Ontology-based Categorization of Web Search Results Using YAGO

Ontology-based Categorization of Web Search Results Using YA...

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The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

作者： Anjian Ren Xiaoyong Du Puwei Wang Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing 100782 China

One of the major limitations of current search engines is that users could not quickly locate what they want if the input query is too general. Some existing techniques try to cluster web search results into groups so as to user's quick browsing. In this paper, we present a new approach to categorize the web search results by using YAGO ontology. It utilizes the YAGO ontology to automatically generate categories for the user's specific query and classify the search results into appropriate categories. Our experimental results indicate that our method is feasible and effectiveness.

关键词： Ontologies Web search Feedback Image retrieval Information retrieval Image databases Humans Set theory Information systems Radio frequency

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ParaCube: A Scalable OLAP Model Based on Distributed Aggregate Computing with Sibling Cubes

ParaCube: A Scalable OLAP Model Based on Distributed Aggrega...

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International Asia-Pacific Web Conference (APWEB)

作者： Yansong Zhang Shan Wang Wei Huang Key Laboratory of Ministry of Education of Data Engineering and Knowledge Engineering Beijing China School of Information Renmin University of China Beijing China

The requirements of OLAP applications increase rapidly by dramatically increased data volume, users, query volume and query complexity. The requirement for shortening update period in data warehouse is another crucial factor for a scalable OLAP application. In this paper, we propose a scalable OLAP prototype to support the query processing with increasing data volume by distributing the whole fact tuples to multiple servers to construct a set of sibling cubes which can be merged together to obtain the whole cube. We employ a light weight distribution policy with fully duplicated dimension tables in each sibling server on the observation of very low proportion of space cost for dimension tables. OLAP query with distributed aggregate functions can be transformed into queries to be performed parallel in sibling servers. For non-distributed computing aggregate functions, such as median, the optimized median aggregate computing algorithm is proposed to reduce transmission volume between servers while computing the global median values. We also present a three-level framework in data warehouse to meet the requirement of shorter update period in "operational business intelligence". An asynchronous tunnel model is proposed to reduce update latency by pre-fetching updated tuples to OLAP processing server. Finally, we set up prototype system ParaCube to evaluate performance in SN (shared-nothing) system and multi-core platforms.

关键词： Aggregates Distributed computing Concurrent computing data warehouses Merging Application software Prototypes Query processing Acceleration Material storage

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