2018 IEEE International Conference on Big Knowledge (ICBK)最新文献_第6页

Prediction of Aluminum Electrolysis Superheat Based on Improved Relative Density Noise Filter SMO 基于改进相对密度噪声滤波SMO的铝电解过热预测

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00057

Yunsheng Liu, Shuyin Xia, Hong Yu, Yueguo Luo, Baiyun Chen, Kang Liu, Guoyin Wang

{"title":"Prediction of Aluminum Electrolysis Superheat Based on Improved Relative Density Noise Filter SMO","authors":"Yunsheng Liu, Shuyin Xia, Hong Yu, Yueguo Luo, Baiyun Chen, Kang Liu, Guoyin Wang","doi":"10.1109/ICBK.2018.00057","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00057","url":null,"abstract":"Adjusting superheat is very important in the production of aluminum electrolysis. However, due to the influence of the detection equipment and environment, there usually exist noise data which might have effects on the superheat adjustment. CNSMO(Class Noise based Sequential Minimal Optimization) [1] has a good performance in processing the data containing noise and prediction of superheat, which contains a large number of noise samples such that the generalizability of conventional SVMs deteriorates. The main reason is that the kernel mapping of the noise samples is likely to lead to overfitting in conventional SVMs [2]-[4]. However, ineffectiveness of CNSMO appears in asymmetric data. To deal with the problem, we optimize the relative density threshold and propose the IRDNF-SMO (Improved Relative Density Noise Filter based SMO algorithm). In the IRDNF-SMO, not only the relative density model is used for class noise detection, but the threshold of the relative density is optimized instead of setting to the fixed value such that the ineffectiveness is alleviated in asymmetric data. The experimental results on industry data sets and benchmark data sets demonstrated that the proposed algorithm has higher prediction accuracy in the data sets.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127561405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Depth Recovery from a Single Image Based on L0 Gradient Minimization 基于L0梯度最小化的单幅图像深度恢复

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00052

Fengyun Cao, Fei Xie

引用次数: 0

Welcome Message from Conference Organizers 会议主办方致欢迎辞

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/icbk.2018.00005

F. Aleskerov, Yong Shi, F. Dória

{"title":"Welcome Message from Conference Organizers","authors":"F. Aleskerov, Yong Shi, F. Dória","doi":"10.1109/icbk.2018.00005","DOIUrl":"https://doi.org/10.1109/icbk.2018.00005","url":null,"abstract":"Supply chain optimization and inventory management optimization stand out as prominent concerns within the realm of modern business analytics. Surprisingly, while supply chain optimization has been in the spotlight for many years, its crucial inventory management component has often been neglected. Companies that have invested in supply chain optimization have typically allowed inventory management policies to be determined by outdated textbook models or even “managerial guesswork,” without consideration of employing advanced analytics technology. Recent discoveries have shown, however, that many organizations can save millions of dollars annually by applying state-of-the-art analytics to optimize inventories. Moreover, substantial gains in profits over and above those obtained from “good” analytics approaches result by using special models from a meta-analytics framework, which combines metaheuristics with analytics. We demonstrate this finding by an integrated meta-analytics platform that combines network optimization, netform modeling and simulation optimization for inventory management. We report computational tests that compare our meta-analytics approach to the status quo methodology customarily used for inventory management and to a recent innovation in inventory management reported to save over $90 million for a major U.S. retail firm. The results show that our meta-analytics approach provides dramatic improvements over both of these alternative approaches, yielding appreciably better levels of service and greater cost savings, and having broad implications for modern inventory management policies. Keynote II (Intl. conf. room, 2F, Venture Bldg.) Wednesday, August 17 08:30-09:10 Factor Space: A Mathematical Framework for New Paradigm Driven by Big Data Peizhuang Wang Professor, Intelligent Engineering and Math Institute, Liaoning Technical University, China","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124069202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Events Detection in Temporally Evolving Social Networks 时变社会网络中的事件检测

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00039

S. Bommakanti, S. Panda

{"title":"Events Detection in Temporally Evolving Social Networks","authors":"S. Bommakanti, S. Panda","doi":"10.1109/ICBK.2018.00039","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00039","url":null,"abstract":"Social networks are the social structures that consist of nodes and edges. Nodes are the actors, persons, etc, and edges are the interactions among the nodes. These interactions change frequently over a time in the social networks, make temporally evolving communities. This change over a time and interactions in the communities cause evolution patterns. These evolution patterns are called as events of the Social Networks. In our paper, we detect the patterns of the interactions between the nodes and then detect the events. To achieve that goal we need to detect community. Community detection provides only structural change but it is not finding the network changes that happen over a time period. So, community mining is required to identify both the structural change and network change. In this paper, we introduce a new community mining approach to identify the similar communities and their events evolution. To do this task, we need to find current time frame t_i community changes with respect to community change in past time frame t_(i-1). To achieve this goal we are using DBLP citation dataset. This DBLP dataset represents the author and co-author relationship. In the DBLP citation dataset, we identified the existing communities and the way these communities evolve over a time period.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130291805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

TL-PC: An Interpretable Causal Relationship Networks on Older Adults Fall Influence Factors 老年人跌倒影响因素的可解释因果关系网络

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00036

Zihan Li, W. Ding, Kui Yu, Suzanne G. Leveille, Ping Chen

引用次数: 0

Reinforcement Learning Based Decision Tree Induction Over Data Streams with Concept Drifts 基于强化学习的概念漂移数据流决策树归纳

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00051

Christopher Blake, Eirini Ntoutsi

引用次数: 4

A Standardized, and Extensible Framework for Comparative Analysis of Quantitative Finance Algorithms - An Open-Source Solution, and Examples of Baseline Experiments with Discussion 量化金融算法比较分析的标准化和可扩展框架——开源解决方案和基线实验示例与讨论

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00061

Alasdair Macindoe, Ognjen Arandjelovic

{"title":"A Standardized, and Extensible Framework for Comparative Analysis of Quantitative Finance Algorithms - An Open-Source Solution, and Examples of Baseline Experiments with Discussion","authors":"Alasdair Macindoe, Ognjen Arandjelovic","doi":"10.1109/ICBK.2018.00061","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00061","url":null,"abstract":"Quantitative finance has been receiving an increasing amount of attention, both from industry and research communities. Yet there is no standardized framework which would allow for a straightforward and repeatable comparison of different investment strategies, leading to a lack of clarity on the state of the art and thereby limiting progress in understanding the field. In the present work we introduce a novel, open-source framework which aims at addressing the crucial limitation. In particular, as our first contribution we describe a highly flexible and readily extensible framework which through its modularity and 'agnosticism', is capable of dealing with diverse types of data and research questions. We summarize its design and functionalities, and as an additional contribution present a number of baseline experiments on examples of publicly available financial data sets. We hope that the two contributions will serve to provide a degree of standardization of experimental analyses in the field, increase our understanding of the state of the art, as well as drive future efforts in increasing the repeatability and transparency of research efforts. Lastly, we also described several examples of experiments which demonstrate the use of the framework, and will include the full corresponding source code in the release.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134465188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Bayesian Inference for Survival Analysis in Private Setting 私人环境下生存分析的贝叶斯推理

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00049

T. T. Nguyen, S. Hui

引用次数: 0

Chinese Entity Relation Extraction Based on Syntactic Features 基于句法特征的中文实体关系抽取

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00021

Y. Jiang, Gongqing Wu, Chenyang Bu, Xuegang Hu

{"title":"Chinese Entity Relation Extraction Based on Syntactic Features","authors":"Y. Jiang, Gongqing Wu, Chenyang Bu, Xuegang Hu","doi":"10.1109/ICBK.2018.00021","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00021","url":null,"abstract":"Entity Relation Extraction (ERE) is an important research topic in the field of information extraction. However, to the best of our knowledge, only a few ERE works have been done for Chinese corpus. Because the syntactic features of Chinese sentences and English sentences are very different, existing algorithms for English corpus cannot be directly applied to Chinese corpus. Thus, in this paper, we propose a novel Chinese entity extraction system based on syntactic features (named SF-CERE). The basic idea of SF-CERE is given as follows. Firstly, we extract candidate relation triples based on verbs and verb-nouns as relation keywords to avoid pre-defining relation types. Secondly, the triples are filtered using the positional constraints between relation keywords and entity pairs. Thirdly, we summarize four major Chinese syntactic features to expand the identified relation triples and improve accuracy. Finally, we use the method of relation transfer to mine and infer implicit relation triples. The experimental results on two real-world dataset (i.e., the encyclopedia dataset and the news dataset) show that SF-CERE effectively improves the quality of the relation triples and obtains good extraction performance.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130615969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Detecting Overlapping Communities in Knowledge Graphs: A Density Optimization Based Approach 知识图谱中重叠社区的检测:基于密度优化的方法

2018 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2018-11-01 DOI: 10.1109/ICBK.2018.00010

Zunying Qin, Liyuan Huang, Bo She, Qiang Wang, Jingru Cui, Guodong Li

{"title":"Detecting Overlapping Communities in Knowledge Graphs: A Density Optimization Based Approach","authors":"Zunying Qin, Liyuan Huang, Bo She, Qiang Wang, Jingru Cui, Guodong Li","doi":"10.1109/ICBK.2018.00010","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00010","url":null,"abstract":"Detecting overlapping communities in knowledge graphs is considered a problem of fundamental importance, since the growing trend of interdisciplinary makes it common for a piece of knowledge belong to different realms. Among all algorithms detecting overlapping communities, Speaker-listener Label Propagation Algorithm (SPLA) represents one of the state-of-the-art approaches due to its high accuracy. However, in the face of overlapping communities with instability and unbalanced partitions, the performance of SLPA drastically degrades. To fill such a gap, we propose a novel community detection algorithm based on density optimization. The proposed algorithm leverages Jaccard similarity coefficient to quantify the similarity between two nodes, and then propagate the label of a node to its neighboring node with the highest similarity. In this way, the initial community is obtained. Due to the existence of an oversized community in the initial community, we argue that a good community structure should have a higher density within the community than outside the community. Therefore, the initial community should be divided again. If the density of the new community is larger than that of the original community, the node's label information is updated; otherwise not. Finally, extensive experiments are carried out on both artificial networks and real networks. The results show that the proposed approach can achieve higher NMI index and overlapping modularity, hence outperforming existing methods.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"167 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120882479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0