OpenRaiser/Intern-Atlas huggingface dataset:
Spark-parSketch: A Massively Distributed Indexing of Time Series Datasets
Authors: Oleksandra Levchenko, Djamel Edine Yagoubi, Reza Akbarinia, Florent Masseglia, Boyan Kolev, Dennis E. Shasha
Venue: CIKM
Year: 2018

A growing number of domains (finance, seismology, internet-of-things, etc.) collect massive time series. When the number of series grow to the hundreds of millions or even billions, similarity queries become intractable on a single machine. Further, naive (quadratic) parallelization won't work well. So, we need both efficient indexing and parallelization. We propose a demonstration of Spark-parSketch, a complete solution based on sketches / random projections to efficiently perform both the parallel indexing of large sets of time series and a similarity search on them. Because our method is approximate, we explore the tradeoff between time and precision. A video showing the dynamics of the demonstration can be found by the link http://parsketch.gforge.inria.fr/video/parSketchdemo_720p.mov.
VTeller: Telling the Values Somewhere, Sometime in a Dynamic Network of Urban Systems
Authors: Yan Li, Tingjian Ge, Cindy X. Chen
Venue: CIKM
Year: 2018

Dynamic networks are very common in urban systems today. As data are acquired, unfortunately, they are rarely complete observations of the whole system. It is important to reliably infer the unobserved attribute values anywhere in the graphs, at certain times---either in the past or in the future. Previous work does not sufficiently capture the correlations inherent with graph topology and with time. We propose a machine learning approach using a novel probabilistic graphical model. We devise a series of algorithms to efficiently group the vertices, to learn the model parameters, and to infer the unobserved values for query processing. Furthermore, we propose a method to incrementally and automatically update the model. Finally, we perform an extensive experimental study using two real-world dynamic graph datasets to evaluate our approach.
Neural Retrieval with Partially Shared Embedding Spaces
Authors: Bo Li, Le Jia
Venue: CIKM
Year: 2018

One category of neural information retrieval models tries to learn text representation in a common embedding space for both queries and documents. However, a single embedding space is not always sufficient, since queries and documents are different in terms of length, number of topics covered, etc. We argue that queries and documents should be mapped into different but overlapping embedding spaces, which is named Partially Shared Embedding Space (PSES) model in this paper. PSES consists of two embedding spaces respectively for queries and documents, and a shared embedding space capturing common features of two sources. Those three embeddings are learned by jointly obeying three constraints: a feature separation constraint, a pairwise matching constraint, and a reconstruction constraint. Experiments on standard TREC collections indicate that PSES leads to significant better performance of retrieval over traditional IR models and several neural IR models with only one embedding space.
Extracting Figures and Captions from Scientific Publications
Authors: Pengyuan Li, Xiangying Jiang, Hagit Shatkay
Venue: CIKM
Year: 2018

Figures and captions convey essential information in scientific publications. As such, there is a growing interest in mining published figures and in utilizing their respective captions as a source of knowledge. There is also much interest in image captioning systems that can automatically generate captions for images, whose training requires large datasets of image-caption pairs. Notably, the first fundamental step of obtaining figures and captions from publications is neither well-studied nor yet well-addressed. In this paper, we introduce a new and effective system for figure and caption extraction, PDFigCapX. Unlike current methods that extract figures by handling raw encoded contents of PDF documents, we separate text from graphical contents and utilize layout information to detect and disambiguate figures and captions. Files containing the figures and their associated captions are then produced as output to the end-user. We test PDFigCapX on both a previously used generic dataset and on two new sets of publications within the biomedical domain. Our experiments and results show a significant improvement in performance compared to the state-of-the-art, and demonstrate the effectiveness of our approach. Our system will be available for use at: https://www.eecis.udel.edu/~compbio/PDFigCapX.
CUSNTF: A Scalable Sparse Non-negative Tensor Factorization Model for Large-scale Industrial Applications on Multi-GPU
Authors: Hao Li, Kenli Li, Ji-yao An, Keqin Li
Venue: CIKM
Year: 2018

Given a high-order, large-scale and sparse data from big data and industrial applications, how can we acquire useful patterns in a real-time and low memory overhead manner? Sparse Non-negative tensor factorization (SNTF) possesses high-order representation, non-negativity and dimension reduction inherence. Thus, SNTF has become a useful tool to represent and analyze the sparse data, which has been incorporated with extra contextual information, i.e., time and location, etc, more than the matrix, which can only model the 2 ways data. However, current SNTF techniques suffer from a) non-linear time and space overhead, b) intermediate data explosion, and c) inability on GPU and multi-GPU. To address these issues, a single-thread-based SNTF is proposed, which involves the feature elements rather than on the whole factor matrices, and can avoid the forming of large-scale intermediate matrices. Then, a CUDA parallelizing single-thread-based SNTF (CUSNTF) model is proposed for industrial applications on GPU and multi-GPU (MCUSNTF). Thus, CUSNTF has linear computing and space complexity, and linear communication cost on multi-GPU. We implement CUSNTF and MCUSNTF on 8 P100 GPUs, and compare it with state-of-the-art parallel and distributed methods. Experimental results from several industrial datasets demonstrate that the linear scalability and efficiency of CUSNTF.
Towards Explainable Networked Prediction
Authors: Liangyue Li, Hanghang Tong, Huan Liu
Venue: CIKM
Year: 2018

Networked prediction has attracted lots of research attention in recent years. Compared with the traditional learning setting, networked prediction is even harder to understand due to its coupled, \em multi-level nature. The learning process propagates top-down through the underlying network from the macro level (the entire learning system), to meso level (learning tasks), and to micro level (individual learning examples). In the meanwhile, the networked prediction setting also offers rich context to explain the learning process through the lens of \em multi-aspect, including training examples ( e.g., what are the most influential examples ), the learning tasks ( e.g., which tasks are most important ) and the task network ( e.g., which task connections are the keys ). Thus, we propose a multi-aspect, multi-level approach to explain networked prediction. The key idea is to efficiently quantify the influence on different levels of the learning system due to the perturbation of various aspects. The proposed method offers two distinctive advantages: (1) multi-aspect, multi-level: it is able to explain networked prediction from multiple aspects (i.e., example-task-network) at multiple levels (i.e., macro-meso-micro); (2) efficiency: it has a linear complexity by efficiently evaluating the influences of changes to the networked prediction without retraining.
Weakly-Supervised Generative Adversarial Nets with Auxiliary Information for Wireless Coverage Estimation
Authors: Zhuo Li, Hongwei Wang, Miao Zhao
Venue: CIKM
Year: 2018

Wireless coverage is the received signal strength of a particular region, which is a key prerequisite to provide high quality mobile communication service. In this paper, we aim to estimate the wireless coverage of an area based on the randomly distributed samples of received signal strength collected within the area. Specifically, we propose a weakly-supervised generative adversarial nets with auxiliary information (WS-GAN), which is a data-driven model to make estimation on the wireless coverage. Different from conventional methods (e.g., k-NN) that predict the missing values by simply replacing them with the average of nearby observations, WS-GAN adopts a GAN-based structure, in which the generator approximates the real distribution of observations under the supervision of the discriminator, while the discriminator tries to distinguish the ground truth from "faked" data produced by the generator. WS-GAN differs from the literature in two aspects: (1) WS-GAN is able to use the auxiliary information of local radio environment, e.g., geographical information as terrain and buildings, to improve the estimation performance, since such auxiliary information has significant impact on the variation of received signal strength. (2) The objective function of WS-GAN combines adversarial loss and a new light-weight reconstruction error term for weakly supervision, which are jointly optimized during training. The experiments on a real dataset show that WS-GAN can generally achieve more accurate results than baselines. Moreover, through a case study, we demonstrate that the wireless coverage maps generated by WS-GAN are more rational and practical than those obtained by baselines.
A Two-Layer Algorithmic Framework for Service Provider Configuration and Planning with Optimal Spatial Matching
Authors: Xijun Li, Jianguo Yao, Mingxuan Yuan, Jia Zeng
Venue: CIKM
Year: 2018

Industrial telecommunication applications prefer to run at the optimal capacity configuration to achieve the required Quality of Service (QoS) at the minimum cost. The optimal capacity configuration is usually achieved through the selection of cell towers capacities and locations. Given a set of service providers (e.g., cell towers) and a set of customers (e.g., major residential areas), where each customer has an amount of demand and each provider has multiple candidate capacities and corresponding costs, the optimal capacity selection is configured through spatial matching to satisfy the demand of each customer at the minimum cost. However, existing solutions developed for spatial matching, in which each provider's capacity is fixed, cannot be directly applied to the capacity configuration problem with multiple capacities and location selections. In this paper, we are the first to study Service Provider Configuration and Planning with Optimal Spatial Matching (SPC-POSM) problem, in which the objectives are 1) to select the proper capacity for each provider at the minimum total cost and 2) to assign providers' service to satisfy the demand of each customer on a condition that the matching distance is no more than service quality requirement. We prove that SPC-POSM problem is NP-hard and design an efficient two-layer meta-heuristic framework to solve the problem. Unsupervised learning technique is designed to accelerate the calculation and a novel local search mechanism is embedded to further improve solution quality. Extensive experimental results on both real and synthetic datasets verify the effectiveness and efficiency of the proposed framework.
Long-Term RNN: Predicting Hazard Function for Proactive Maintenance of Water Mains
Authors: Bin Liang, Zhidong Li, Yang Wang, Fang Chen
Venue: CIKM
Year: 2018

Failure event prediction is becoming increasingly important in wide applications, such as the planning of proactive maintenance, the active investment management, and disease surveillance. To address the issue, the hazard function in survival analysis has been employed to describe the pattern of failures. Different from traditional survival analysis, this paper discovers how to apply recurrent neural network (RNN) to the long-term hazard function prediction. The proposed Long-Term RNN (LT-RNN) is able to leverage the precedent information shared by other entities, leading to more reliable long-term predictions. Specifically, our method allows a black-box treatment for modelling the hazard function which is often a pre-defined parametric form in typical survival analysis. The key idea of our approach is to model the hazard function as a nonparameteric function of the history. The same precedent information from other entities is embedded to a stitched vector for LT-RNN to automatically learn a representation of the long-term hazard function. We apply our model to the proactive maintenance problem using a large dataset from a water utility in Australia.
Optimizing Generalized Linear Models with Billions of Variables
Authors: Yanbo Liang, Yongyang Yu, MingJie Tang, Chaozhuo Li, Weiqing Yang, Weichen Xu, Ruifeng Zheng
Venue: CIKM
Year: 2018

The use of large-scale machine learning~(ML) is becoming ubiquitous in various domains ranging from business intelligence to self-driving cars. Many companies are building ML pipelines in a unified data processing environment, and leveraging well-tuned numerical optimization packages for obtaining model parameters. However, most existing optimization tools are specifically designed for a single machine setup, and cannot handle vast volume of data. In this work, we build a distributed computing framework towards optimizing generalized linear models with billions of variables. We at first design a new distributed vector to represent data points from extremely large feature space. Then, we introduce an efficient and scalable approach to compute the second order derivatives of loss function, and optimizes model parameters with limited memory requirement. Experiments on real-world datasets demonstrate that our proposed techniques can scale up for ML models with billions of variables, and achieves better performance than state-of-the-art systems on a wide range of applications, e.g., ad CTR prediction and rideshare price bidding.
An Option Gate Module for Sentence Inference on Machine Reading Comprehension
Authors: Xuming Lin, Ruifang Liu, Yiwei Li
Venue: CIKM
Year: 2018

In machine reading comprehension (MRC) tasks, sentence inference is an important but extremely difficult problem. Most of MRC models directly interact articles with questions from the word level, which ignores inter and intra information of sentences and cannot well focus on problems about sentence reasoning and inference, especially when the answer clues are far apart in the article. In this paper, we propose an option gate approach for reading comprehension. We consider applying a sentence-level option gate module to make the model incorporate sentence information. In our approach we (1) extract key sentences in the article to filter out noise unrelated to the question and the options, (2) encode each sentence in articles, questions and options with dot-product self-attention to obtain intra sentence representations, (3) model inter relationships between the article and the question with bilinear attention and (4) apply an option gate with sentence inference information to each option representation with the question-aware article representation. This module can help better reasoning instead of directly word matching or paraphrasing. And this module can easily supply sentence information for most of the existing reading comprehension models. Experimental results on the RACE dataset show that this easy and simple module helps outperform the baseline models by 2.5% at most (single model), and achieve state-of-the-art results on the RACE-H dataset.
Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News
Authors: Qikai Liu, Xiang Cheng, Sen Su, Shuguang Zhu
Venue: CIKM
Year: 2018

It has been shown that stock price movements are influenced by news. To predict stock movements with news, many existing works rely only on the news title since the news content may contain irrelevancies which seriously degrade the prediction accuracy. However, we observe that there is still useful information in the content which is not reflected in the title, and simply ignoring the content will result in poor performance. In this paper, taking advantage of neural representation learning, we propose a hierarchical complementary attention network (HCAN) to capture valuable complementary information in news title and content for stock movement prediction. In HCAN, we adopt a two-level attention mechanism to quantify the importances of the words and sentences in a given news. Moreover, we design a novel measurement for calculating the attention weights to avoid capturing redundant information in the news title and content. Experimental results on news datasets show that our proposed model outperforms the state-of-the-art techniques.
Generating Keyword Queries for Natural Language Queries to Alleviate Lexical Chasm Problem
Authors: Xiaoyu Liu, Shunda Pan, Qi Zhang, Yu-Gang Jiang, Xuanjing Huang
Venue: CIKM
Year: 2018

In recent years, the task of reformulating natural language queries has received considerable attention from both industry and academic communities. Because of the lexical chasm problem between natural language queries and web documents, if we directly use natural language queries as inputs for retrieval, the results are usually unsatisfactory. In this work, we formulated the task as a translation problem to convert natural language queries into keyword queries. Since the nature language queries users input are diverse and multi-faceted, general encoder-decoder models cannot effectively handle low-frequency words and out-of-vocabulary words. We propose a novel encoder-decoder method with two decoders: the pointer decoder firstly extracts query terms directly from the source text via copying mechanism, then the generator decoder generates query terms using two attention modules simultaneously considering the source text and extracted query terms. For evaluation and training, we also proposed a semi-automatic method to construct a large-scale dataset about natural language query-keyword query pairs. Experimental results on this dataset demonstrated that our model could achieve better performance than the previous state-of-the-art methods.
Adversarial Learning of Answer-Related Representation for Visual Question Answering
Authors: Yun Liu, Xiaoming Zhang, Feiran Huang, Zhoujun Li
Venue: CIKM
Year: 2018

Visual Question Answering (VQA) aims to learn a joint embedding of the question sentence and the corresponding image to infer the answer. Existing approaches learn the joint embedding don't consider the answer-related information, which results in that the learned representation is not effective to reflect the answer of the question. To address this problem, this paper proposes a novel method, i.e., Adversarial Learning of Answer-Related Representation (ALARR) for visual question answering, which seeks an effective answer-related representation for the question-image pair based on adversarial learning between two processes. The embedding learning process aims to generate modality-invariant joint representations for the question-image and question-answer pairs, respectively. Meanwhile, it tries to confuse the other process, embedding discriminator, which tries to discriminate the two representations from different modalities of pairs. Specifically, the joint embedding of the question-image pair is learned by a three-level attention model, and the joint representation of the question-answer pair is learned by a semantic integration model. Through the adversarial leaning, the answer-related representation are better preserved. Then an answer predictor is proposed to infer the answer from the answer-related representation. Experiments conducted on two widely used VQA benchmark datasets demonstrate that the proposed model outperforms the state-of-the-art approaches.
CurEx: A System for Extracting, Curating, and Exploring Domain-Specific Knowledge Graphs from Text
Authors: Michael Loster, Felix Naumann, Jan Ehmueller, Benjamin Feldmann
Venue: CIKM
Year: 2018

The integration of diverse structured and unstructured information sources into a unified, domain-specific knowledge base is an important task in many areas. A well-maintained knowledge base enables data analysis in complex scenarios, such as risk analysis in the financial sector or investigating large data leaks, such as the Paradise or Panama papers. Both the creation of such knowledge bases, as well as their continuous maintenance and curation involves many complex tasks and considerable manual effort. With CurEx, we present a modular system that allows structured and unstructured data sources to be integrated into a domain-specific knowledge base. In particular, we (i) enable the incremental improvement of each individual integration component; (ii) enable the selective generation of multiple knowledge graphs from the information contained in the knowledge base; and (iii) provide two distinct user interfaces tailored to the needs of data engineers and end-users respectively. The former has curation capabilities and controls the integration process, whereas the latter focuses on the exploration of the generated knowledge graph.
Bug Localization by Learning to Rank and Represent Bug Inducing Changes
Authors: Pablo Loyola, Kugamoorthy Gajananan, Fumiko Satoh
Venue: CIKM
Year: 2018

In software development, bug localization is the process finding portions of source code associated to a submitted bug report. This task has been modeled as an information retrieval task at source code file, where the report is the query. In this work, we propose a model that, instead of working at file level, learns feature representations from source changes extracted from the project history at both syntactic and code change dependency perspectives to support bug localization. To that end, we structured an end-to-end architecture able to integrate feature learning and ranking between sets of bug reports and source code changes. We evaluated our model against the state of the art of bug localization on several real world software projects obtaining competitive results in both intra-project and cross-project settings. Besides the positive results in terms of model accuracy, as we are giving the developer not only the location of the bug associated to the report, but also the change that introduced, we believe this could give a broader context for supporting fixing tasks.
Multi-model Databases and Tightly Integrated Polystores: Current Practices, Comparisons, and Open Challenges
Authors: Jiaheng Lu, Irena Holubová, Bogdan Cautis
Venue: CIKM
Year: 2018

One of the most challenging issues in the era of Big Data is the Variety of the data. In general, there are two solutions to directly manage multi-model data currently: a single integrated multi-model database system or a tightly-integrated middleware over multiple single-model data stores. In this tutorial, we review and compare these two approaches giving insights on their advantages, trade-offs, and research opportunities. In particular, we dive into four key aspects of technology for both types of systems, namely (1) theoretical foundation of multi-model data management, (2) storage strategies for multi-model data, (3) query languages across models, and (4) query evaluation and its optimization. We provide a comparison of performance for the two approaches and discuss related open problems and remaining challenges. The slides of this tutorial can be found at http://udbms.cs.helsinki.fi/?tutorials/CIKM2018.