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arXiv 2024年

作者： Huang, Muye Lai, Han Zhang, Xinyu Wu, Wenjun Ma, Jie Zhang, Lingling Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China MOE KLINNS Lab Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Chart understanding enables automated data analysis for humans, which requires models to achieve highly accurate visual comprehension. While existing Visual Language Models (VLMs) have shown progress in chart understanding, the lack of high-quality training data and comprehensive evaluation benchmarks hinders VLM chart comprehension. In this paper, we introduce EvoChart, a novel self-training method for generating synthetic chart data to enhance VLMs’ capabilities in real-world chart comprehension. We also propose EvoChart-QA, a noval benchmark for measuring models’ chart comprehension abilities in real-world scenarios. Specifically, EvoChart is a unique self-training data synthesis approach that simultaneously produces high-quality training corpus and a high-performance chart understanding model. EvoChart-QA consists of 650 distinct real-world charts collected from 140 different websites and 1,250 expert-curated questions that focus on chart understanding. Experimental results on various open-source and proprietary VLMs tested on EvoChart-QA demonstrate that even the best proprietary model, GPT-4o, achieves only 49.8% accuracy. Moreover, the EvoChart method significantly boosts the performance of open-source VLMs on real-world chart understanding tasks, achieving 54.2% accuracy on EvoChart-QA. © 2024, CC BY.

关键词： Visual languages

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Consensus with Voting Theory in Blockchain Environments

Consensus with Voting Theory in Blockchain Environments

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IEEE International Conference on Big knowledge (ICBK)

作者： Lei Li Yongkang Jiang Guanfeng Liu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Computing Department Macquarie University Sydney Australia

A blockchain can be taken as a decentralized and distributed public database. In order to achieve data consistency of the system nodes, the execution of a consensus algorithm is necessary and required in the case of decentralized environments. Simply speaking, the consensus is that every node agrees on some record in the blockchain. There are many kinds of consensus algorithms in blockchain environments, and each consensus algorithm has its own proper application scenario. Here we firstly analysis and compare various popular consensus algorithms in blockchain environments, and then as voting theory has systematically studied the decision-making in a group, the traditional methods of voting theory is summarized and listed, including (Position) scoring rules, Copeland, Maximin, Ranked pairs, Voting trees, Bucklin, Plurality with runoff, Single transferable vote, Baldwin rule, and Nanson rule. Finally, we introduce the voting methods from voting theory to consensus algorithms in the blockchain to improve its performance.

关键词： Blockchain Consensus algorithm Bitcoin Voting knowledge engineering Computer science Decision making

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Impact of Consumer Perceived Characteristics on Fruit Sales: Evidence from Community Group-buying 11

Impact of Consumer Perceived Characteristics on Fruit Sales:...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Jiang, Hailiang Tian, Xin Tao, Ye Wang, Lulu Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Beijing HAOLINJU CVS Co. Ltd Beijing100088 China Shanghai HEADING Information Engineering Co. Ltd. Shanghai201112 China

There is a growing trend among retailers to sell fruits through their community group-buying (CGB) channels. Based on real operational data, we employ econometric models to empirically analyze how consumer perception characteristics in CGB affect fruit sales. Our findings suggest that incorporating subjective experiences into product descriptions can positively impact fruit sales for retailers;fresh produce stores outperform regular stores in fruit sales of CGB;Dark-colored fruits sell more than light-colored fruits in the CGB channel. Our study offers valuable insights for retailers selling fruits through CGB channels. © 2024 The Authors.

关键词： community group-buying dark-colored fruits fresh produce stores subjectivity

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MTSC-GE: A Novel Graph based Method for Multivariate Time Series Clustering

MTSC-GE: A Novel Graph based Method for Multivariate Time Se...

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IEEE International Conference on Big knowledge (ICBK)

作者： Ze Yang Changyang Tai Gongqing Wu Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Few clustering methods show good performance on multivariate time series (MTS) data. Traditional methods rely too much on similarity measures and perform poorly on the MTS data with complex structures. This paper proposes an MTS clustering algorithm based on graph embedding called MTSC-GE to improve the performance of MTS clustering. MTSC-GE can map MTS samples to the feature representations in a low-dimensional space and then cluster them. While mining the information of the samples themselves, MTSC-GE builds the whole time series data into a graph, paying attention to the connections between samples from an overall perspective and discovering the local structural feature of MTS data. The proposed MTSC-G E consists of three stages. The first stage builds a graph using the original dataset, where each of the MTS samples is regarded as a node in the graph. The second stage uses the graph embedding technique to obtain a new representation of each node. Finally, MTSC-G E uses the K - Means algorithm to cluster based on the newly obtained representation. We compare MTSC-GE with six state-of-the-art benchmark methods on five public datasets, experimental results show that MTSC-GE has achieved good performance.

关键词： Deep learning Conferences Clustering methods Time series analysis Clustering algorithms Benchmark testing Stability analysis

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Load pattern window aware power supply device clustering

International Journal of Database Theory and Application

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International Journal of database Theory and Application 2016年第8期9卷 269-280页

作者： Sheng, Wanxing Liu, Ke-Yan Yu, Yixi An, Rungong Zhou, Ningnan Zhang, Xiao China Electric Power Research Institute Beijing100192 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing100872 China School of Information Renmin University of China 100872 China

data-driven decision in big data era is becoming ubiquitous in electronic grid. In particular, daily collected power consumption records enable workload aware device clustering, which is crucial for critical domain applications such as device functionality identification. In this paper, we propose a load pattern window aware method for clustering power supply devices. Our approach overcomes the drawbacks in existing works, such as fuzzy based clustering, K-means based clustering and neutral network based clustering. After investigating the large scale records from power supply devices, our approach partitions device records into disjoint time intervals with parameterized window size, which indicate the load pattern feature for a period of time given a specific device. Devices are then decomposed into a mixture of these features, and those devices with similar dominating features are grouped together. The experimental results demonstrate the effectiveness and efficiency of our solution based on the real data collected from power grid in China. © 2016 SERSC.

关键词： Big data

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Unveiling Funded Research: Impact and Dual Innovation

SSRN

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SSRN 2023年

作者： Yang, Alex Jie School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China International Joint Informatics Laboratory Nanjing University Nanjing210023 China

In the relentless pursuit of scientific advancement, comprehending the profound impact and innovation nature inherent in funded research projects assumes paramount significance. To illuminate this matter, I delve into the realm of research supported by the esteemed National Institutes of Health (NIH) and the distinguished National Science Foundation (NSF). Our evaluative framework encompasses a spectrum of metrics, including the 5-year citation, normalized citation, and citation to patents, as markers of research impact. Moreover, I embrace ex-ante innovation (Novelty) and ex-post innovation (Disruption) as dual indispensable yardsticks for evaluating the innovative nature of research projects. Novelty denotes the manifestation of atypical combinations of existing knowledge, while Disruption signifies the extent of paradigm-shifting potential and the ability to exert a disruptive influence on future research endeavors. First, the analysis reveals that funded research projects manifest a conspicuously heightened impact in comparison to their non-funded counterparts. Second, I uncover a noteworthy finding, namely that funded research demonstrates significantly higher levels of ex-ante innovation (Novelty). However, in a surprising twist, the investigation also uncovers that funded research exhibits significantly lower levels of ex-post innovation (Disruption). Additionally, I undertake a meticulous scrutiny of the robustness of the research findings by scrutinizing patterns across disparate years and fields. This comprehensive analysis further unveils inconspicuous heterogeneity, thus substantiating the multifaceted nature of funded research and the vast potential for its innovation across various disciplines and temporal dimensions. © 2023, The Authors. All rights reserved.

关键词： Patents and inventions

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Two novel semantics of top-k queries processing in uncertain database

Two novel semantics of top-k queries processing in uncertain...

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10th IEEE International Conference on Computer and Information Technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, 10th IEEE Int. Conf. Scalable Computing and Communications, ScalCom-2010

作者： Liu, Dexi Wan, Changxuan Xiong, N. Yang, Laurence T. Chen, Lei 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 St. Francis Xavier University Antigonish NS Canada Department of Computer Science HK University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9780769541082

Top-k query is a powerful technique in uncertain databases because of the existence of exponential possible worlds, and it is necessary to combine score and confidence of tuples to derive top k answers. Different semantics, the combination methods of score and confidence, lead to different results. U-kRanks and Global Top-k are two semantics of Topk queries in uncertain database, which consider every alternative in x-tuple as single one and return the tuple which has the highest probability appearing at top k or a given rank. However, no matter which alternative (tuple) of an x-tuple appears in a possible world, it undoubtedly believes that this xtuple appears in the same possible world accordingly. Thus, instead of ranking every individual tuple, we define two novel Top-k queries semantics in uncertain database, Uncertain xkRanks queries (U-x-kRanks) and Global x-Top-k queries (Gx- Top-k), which return k entities according to the score and the confidence of alternatives in x-tuple, respectively. In order to reduce the search space, we present an efficient algorithm to process U-x-kRanks queries and G-x-Top-k queries. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions. © 2010 IEEE.

关键词： Semantics

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The Tourism-Specific Sentiment Vector Construction Based on Kernel Optimization Function

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Procedia Computer Science 2017年 122卷 1162-1167页

作者： Luyao Zhu Wei Li Kun Guo Yong Shi Yuanchun Zheng School of Economics and Management University of Chinese Academy of Sciences Beijing China Fictitious Economy & Data Science Research Center Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

Sentiment analysis in tourism domain has drawn much attention in past few years, which calls for more precise sentiment word embedding method. The article proposes a kernel optimization function for sentiment word embedding. And the method aims at integrating the semantic information, statistics information and sentiment information and maintains the similarity between sentiment words in terms of sentiment orientation. The experiment result shows that the optimal sentiment vectors successfully extract the features in terms of sentiment information and the difference between concretization and abstraction of a sentiment words.

关键词： kernel function sentiment vector word embedding sentiment analysis

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Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

arXiv

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arXiv 2023年

作者： Liu, Yixuan Zhao, Suyun Xiong, Li Liu, Yuhan Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China China Engineering Research Center Ministry of Education on Database and BI China Information School Renmin University of China China Department of Computer Science Emory University United States

Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users’ attitudes need to be satisfied locally, while a strict privacy guarantee for the global model is also required centrally. Personalized Local Differential Privacy (PLDP) is suitable for preserving users’ varying local privacy, yet only provides a central privacy guarantee equivalent to the worst-case local privacy level. Thus, achieving strong central privacy as well as personalized local privacy with a utility-promising model is a challenging problem. In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model. To tighten the privacy bound, we quantify the heterogeneous contributions to the central privacy user by user. The contributions are characterized by the ability of generating "echos" from the perturbation of each user, which is carefully measured by proposed methods Neighbor Divergence and Clip-Laplace Mechanism. Furthermore, we propose a refined framework (S-APES) with the post-sparsification technique to reduce privacy loss in high-dimension scenarios. To the best of our knowledge, the impact of shuffling on personalized local privacy is considered for the first time. We provide a strong privacy amplification effect, and the bound is tighter than the baseline result based on existing methods for uniform local privacy. Experiments demonstrate that our frameworks ensure comparable or higher accuracy for the global model. © 2023, CC BY.

关键词： Learning systems

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Multiagent Q-Learning for Multicrew Dynamic Scheduling and Routing in Road Network Restoration

Multiagent Q-Learning for Multicrew Dynamic Scheduling and R...

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第34届中国控制与决策会议

作者： Yufeng Shen Lianqi Duan Qianshun Zhu Zhaopin Su Guofu Zhang School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) Anhui Province Key Laboratory of Industry Safety and Emergency Technology (Hefei University of Technology)

Road network restoration is an important issue in the post-disaster disposal and rescue,especially when extraordinarily serious natural disasters(e.g.,floods and earthquakes) *** to this endeavour is the problem of determining how to reasonably schedule and route the repair crew to quickly restore the damaged road network and establish reliable supply lines from supply nodes to demand ***,most existing work focuses on the activities of the single repair crew in the static road network,which is unable to adapt to the dynamic changes of the road network caused by secondary disasters,such as rock slides and debris ***,this work is concentrated on multicrew dynamic scheduling and routing in road network ***,a model of multicrew dynamic scheduling and routing is first ***,the Markov decision process is adopted to construct a decision model for repair crews,based on which a multiagent Q-learning algorithm is developed to find the best scheduling and routing ***,experimental results demonstrate that the proposed method can make repair crews adjust their scheduling and routing strategies according to the dynamic changes of the damaged road network and provides a useful attempt to restore the damaged road network in complex,dynamic emergency scenarios of post-disaster.

关键词： Road Network Restoration Multiple Repair Crews Dynamic Scheduling and Routing Multiagent Q-Learning

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