2018 IEEE International Conference on Big Knowledge (ICBK)最新文献

{"title":"Weight-Agnostic Hierarchical Stick-Breaking Process","authors":"M. Das, C. Bhattacharyya","doi":"10.1109/ICBK.2018.00053","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00053","url":null,"abstract":"Learning from multiple groups of observations are often useful due to the advantage of sharing of statistical information. Hierarchical Bayesian models provide a natural mechanism to achieve this, and hierarchical Dirichlet processes (HDPs) have shown significant impact in this field. HDPs define a collection of probability measures one for each group. All the measures provide support on a common countably infinite set of atoms to share information. The fundamental mechanism in all the variants of HDP make the weights on these atoms positively correlated across groups. This structural limitation is impossible to resolve without changing the sharing principle. But this property hinders the applicability of HDP priors to many problems, when an atom may be highly probable in some groups despite being rare in all other groups. This becomes evident in clustering through association of atoms and observations. Some clusters may be weakly present in most of the groups in spite of being prominent in some groups and vice-versa. In this paper, we pose the problem of weight agnosticism, that of constructing a collection of probability measures on a common countably infinite set of atoms with mutually independent weights across groups. This implies that, a cluster can contain observations from all groups, but popularities of a cluster across groups are mutually independent. So the size of a cluster in a group does not interfere in the participation of observations in other groups to that cluster. Our contribution is also to construct a novel hierarchical Bayesian nonparametric prior, Weight-Agnostic hierarchical Stick-breaking process (was), which models weight agnosticism. was extends the framework of stick-breaking process (SBP) in a novel direction. However, was becomes non-exchangeable and that makes inference process non-standard. But, We derive tractable predictive probability functions for was, which is useful in deriving efficient truncation-free MCMC inference competitive with those in HDP settings. We discuss few real life applications of was in topic moeling and information retrieval. Furthermore, experimenting with five real life datasets we show that, was significantly outperforms HDP in various settings.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"26 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":"129032840","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}

{"title":"A Machine Learning Approach to Detecting Start Reading Location of eBooks","authors":"S. Bodapati, S. Ramaswamy, G. Narayanan","doi":"10.1109/ICBK.2018.00038","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00038","url":null,"abstract":"Machine Learning and NLP (Natural Language Processing) have aided the development of new and improved user experience features in many applications. We address the problem of automatically identifying the \"Start Reading Location\" (SRL) of eBooks, i.e. the location of the logical beginning or start of main content. This improves eBook reading experience by taking users automatically to the logical start location without requiring them to flip through several front-matter sections such as \"Dedication\" and \"About the Author\". Automatic identification of SRL is complex since many eBooks do not adhere to any well-defined convention with respect to section naming, formatting and layout patterns. We formulate SRL as a classification problem based on detailed rule-based and NLP-based classification schemes. Our models are being used in production for Kindle eBooks and have led to a 400% increase in coverage (number of books which had SRL stamped) compared to what could be achieved earlier through an entirely manual process, while also maintaining a high accuracy of 95%.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"22 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":"114069753","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}

{"title":"Dynamic Overlapping Community Discovery Based on Core Nodes","authors":"Yan Liu, Hong Yu","doi":"10.1109/ICBK.2018.00041","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00041","url":null,"abstract":"Social networks in the real world are evolutionary and large scale. Detecting the community structure could express the structure and characteristics of complex networks effectively. Many classic incremental clustering and evolutionary clustering algorithms have been proposed to detect the communities in dynamic networks. However, these algorithms rare to consider the importance of nodes, the overlap between different communities during the process of detection. In this paper, an algorithm based on core nodes was proposed which could not only detect dynamic overlapping communities, but also trace the evolution of network communities. Meanwhile, a three-way representation of a community by a pair of sets is introduced to describe the overlapping communities. Experiment results on real-world data sets demonstrate that our proposed method performs better than the well-known dynamic community detection algorithm.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"476 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120941350","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}

{"title":"[Title page i]","authors":"","doi":"10.1109/icbk.2018.00001","DOIUrl":"https://doi.org/10.1109/icbk.2018.00001","url":null,"abstract":"","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"75 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":"122587985","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}

{"title":"Galaxy: Towards Scalable and Interpretable Explanation on High-Dimensional and Spatio-Temporal Correlated Climate Data","authors":"Yong Zhuang, D. Small, Xin Shu, Kui Yu, S. Islam, W. Ding","doi":"10.1109/ICBK.2018.00027","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00027","url":null,"abstract":"Interpretability has become a major criterion for designing predictive models in climate science. High interpretability can provide qualitative understanding between the meteorological variables and the climate phenomena which is helpful for climate scientists to learn causes of climate events. However, detecting the features which have efficient interpretability to observed events is challenging in spatio-temporal climate data because the key features may be overlooked by the redundancy due to the high degree of spatial and temporal correlations among the features, especially in high dimensionality. Furthermore, climate events occurred in different regions or different times may have different explanatory patterns, detecting explanations for overall climate phenomena is also difficult. Here we propose Galaxy, a new interpretable predictive model. Galaxy allows us to represent the explanatory patterns of subpopulations within an overall population of the target. Each explanatory pattern is defined by the smallest feature subset that the conditional distribution of target actually depends on, which we define as the minimal target explanation. Based on the detection of such explanatory patterns, Galaxy can detect the Galaxy space, the explanations for the overall target population, by sequentially detecting target explanation of every individual subpopulation of the target, and then forecast the target variable by their ensemble predictive power. We validate our approach by comparing Galaxy to several state-of-the-art baselines in a set of comparative experiments and then evaluate how Galaxy can be used to identify the explanatory space and give a referential explanation report in a real-world scenario on a given location in the United States.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"63 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":"124703202","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}

{"title":"Deep Embedding Logistic Regression","authors":"Zhicheng Cui, Muhan Zhang, Yixin Chen","doi":"10.1109/ICBK.2018.00031","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00031","url":null,"abstract":"Logistic regression (LR) is used in many areas due to its simplicity and interpretability. While at the same time, those two properties limit its classification accuracy. Deep neural networks (DNNs), instead, achieve state-of-the-art performance in many domains. However, the nonlinearity and complexity of DNNs make it less interpretable. To balance interpretability and classification performance, we propose a novel nonlinear model, Deep Embedding Logistic Regression (DELR), which augments LR with a nonlinear dimension-wise feature embedding. In DELR, each feature embedding is learned through a deep and narrow neural network and LR is attached to decide feature importance. A compact and yet powerful model, DELR offers great interpretability: it can tell the importance of each input feature, yield meaningful embedding of categorical features, and extract actionable changes, making it attractive for tasks such as market analysis and clinical prediction.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"66 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":"125968529","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}

{"title":"Online Feature Selection for Streaming Features with High Redundancy Using Sliding-Window Sampling","authors":"Dianlong You, Xindong Wu, Limin Shen, Zhen Chen, Chuan Ma, Song Deng","doi":"10.1109/ICBK.2018.00035","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00035","url":null,"abstract":"In recent years, online feature selection has received much attention in data mining with the aim to reduce dimensionality of streaming features by removing irrelevant and redundant features in a real time manner. The existing works, such as Alpha-investing, OSFS, and SAOLA have been proposed to serve this purpose but have drawbacks e.g. low predication accuracy, and more numbers of selected features, streaming features can overflow when the streaming features they have high relevance to each other. In this paper, we propose an online learning algorithm, named OSFSW, with a sliding-window strategy to real-time sample streaming features, by the analysis of conditional independence to discard irrelevant and redundant features with the aim to overcome such drawbacks. Through OSFSW, we can get an approximate Markov blanket in a smaller number of selected features with high prediction accuracy. To validate the efficiency, we implement the proposed algorithm and test its performance on a prevalent dataset, i.e., NIPS 2003, and Causality Workbench. Through extensive experimental results, we demonstrate that OSFSW has a significant performance improvement on prediction accuracy and smaller numbers of selected features when comparing to Alpha-investing, OSFS and SAOLA.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"97 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":"129118007","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}

{"title":"Stance Detection with Target and Target Towards Attention","authors":"Wenqiang Gao, Yujiu Yang, Yi Liu","doi":"10.1109/ICBK.2018.00064","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00064","url":null,"abstract":"We propose a Neural Stance Detection model with target and target towards attention mechanism. Stance detection is the task of classifying the attitude towards a given target. Even though a variety of recurrent neural networks have been used in stance detection problems, existing modes only take advantage of target information and ignore target towards information. What's more, these models tend to perform well when the text discusses the target explicitly. However, when the target is implicitly mentioned, these models are not good. To address this problem, we introduce Target and Target towards Attention mechanism which takes not only target but also target towards information into account. This paper considers the more challenging version of this task, where targets are not always mentioned and a specific test target has no training data available. Our model first builds a hierarchical Long Short Term Memory (LSTM)[1] model to represent sentence and text. And then, target and target towards information are considered via attention mechanism over different semantic levels. We conduct our experiment on SemEval-2016 Task 6 dataset. And the results show that our model outperforms several strong baselines.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"13 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":"131262952","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}

{"title":"Short-Attention Mechanism for Generative Dialogue System","authors":"Pengda Si, Yujiu Yang, Yi Liu","doi":"10.1109/ICBK.2018.00043","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00043","url":null,"abstract":"In recent years, generative dialogue has become the hottest topic in the field of Nature Language Process(NLP). Among the many suggested approaches, the Sequence-tosequence network framework, a variant of traditional Recurrent Neural Network(RNN), has attracted the attention of researchers because of its outstanding performance on many tasks. This model consists of an encoder which encoders the input sequence to a vector and a decoder that decodes the vector to the output sequence. Then attention was applied to the model, that is, the model assigns different weights to different parts to compute vector during decoding process. This end-to-end method enhances the ability to generate natural answers in the human-computer conversation process, while also increases its calculation costs. To solve the problem, we propose a novel short-attention mechanism, in which the original sequence is compressed to a shorter sequence before calculating weight and vector. We apply shortattention to dialogue systems tasks and the experimental results show that short-attention can shorten the computation time by about 20% compared to attention.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"288 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":"115217427","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}

{"title":"Forecast of Solar Energy Production - A Deep Learning Approach","authors":"Rui Zhang, Min Feng, Wei Zhang, Siyuan Lu, Fei Wang","doi":"10.1109/ICBK.2018.00018","DOIUrl":"https://doi.org/10.1109/ICBK.2018.00018","url":null,"abstract":"Solar energy penetration both at utility scale and residential scale has been increasing at an exponential rate. However, its stochastic nature poses great challenge to power grid operation. Knowing how much solar energy generation in advance is vital for power grid balancing, planning and optimization. Therefore, solar energy generation forecast is essential for the stability and operation efficiency of today's smart grid. Although the sun path and energy can be computed with physical laws, the prediction of solar energy generation and production remains very challenging problem both in the field of physical simulation and artificial intelligence. The main reason lies in the fact that the actual solar production are impacted by many factors including the sun position, weather condition and the characteristics of photovoltaic panel, curtailment, etc. Especially in cloudy day, where the cloud movement becomes the main factor in solar energy production. However, predicting the movement of cloud is extremely difficult. In this paper, we present several deep convolutional neural networks utilizing high resolution weather forecast data exploring various temporal and spatial connectivities to capture the cloud movement pattern and its effect on forecasting solar energy generation for solar farms. Comparing with state-of-the-art forecast error rate, we have been able to reduce the error rate from about 21% in the persistent model, to 15.1% from the SVR model, and to 11.8% from the convolutional neural networks. These improvements have significant impact on the healthy growth of the solar energy industry, will save billions of dollars for the US utilities and most importantly reduce dependency on fossil fuel and reduction in CO2 emission.","PeriodicalId":144958,"journal":{"name":"2018 IEEE International Conference on Big Knowledge (ICBK)","volume":"12 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":"115296543","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}