OpenRaiser/Intern-Atlas huggingface dataset:
The Fisher-Rao Metric in Computer Vision
Authors: Stephen John Maybank
Venue: CIKM
Year: 2019

The Fisher-Rao metric is a Riemannian metric defined on any manifold that forms the parameter space for a family of probability distributions. The metric is specified by quadratic forms defined on the tangent spaces of the manifold. If a parameterisation of the manifold is chosen then each quadratic form is given by a symmetric positive definite matrix. Lengths, areas, volumes and hyper-volumes calculated using the Fisher-Rao metric are invariant under reparameterisation. This invariance is essential in practice because the parameterisation can be changed arbitrarily while keeping the data unchanged. The Fisher-Rao metric is obtained as a limit of the expected value of the log likelihood ratio for two nearby probability distributions. The inverse of the Fisher-Rao matrix is the Cramer-Rao lower bound on the covariance of an unbiased estimate of a parameter. The Fisher-Rao metric is used to divide the parameter space for the Hough transform method for detecting structures in data. Each division or accumulator is invariant under reparametrerisation, and the number of accumulators is proportional to the volume of the parameter space. Accurate approximations to the Fisher Rao metric are obtained for lines, catadioptric images of lines, circles, ellipses and the cross ratio. It is shown that the Fisher-Rao metric can be used to compare the amount of information in point features with the amount of information in edge element features.
PODIUM: Probabilistic Datalog Analysis via Contribution Maximization
Authors: Tova Milo, Yuval Moskovitch, Brit Youngmann
Venue: CIKM
Year: 2019

The use of probabilistic datalog programs has been advocated for applications that involve recursive computation and uncertainty. While using such programs allows for a flexible knowledge derivation, it makes the analysis of query results a challenging task. Particularly, given a set O of output tuples and a number k, one would like to understand which k-size subset of the input tuples has affected the most the derivation of O. This is useful for multiple tasks, such as identifying critical sources of errors and understanding surprising results. To this end, we formalize the Contribution Maximization problem and present an efficient algorithm to solve it. Our algorithm injects a refined variant of the classic Magic Sets technique, integrated with a sampling method, into top-performing algorithms for the well-studied Influence Maximization problem. We propose to demonstrate our solution in a system called PODIUM. We will demonstrate the usefulness of PODIUM using real-life data and programs, and illustrate the effectiveness of our algorithm.
Insta-Search: Towards Effective Exploration of Knowledge Graphs
Authors: Madhulika Mohanty, Maya Ramanath
Venue: CIKM
Year: 2019

Knowledge Graphs (KGs) are used to store heterogenous information in the form of graphs. One flexible and non-expert way to query these KGs is to use relationship queries or keyword search. The user can specify a query using keywords referring to entities in the graph. The system then returns a set of relationships among the queried entities. However, effectively querying these graphs is still challenging for a new user. She is not familiar with the entities and relationships in the graph and hence, her queries could often return empty or too few answers. We demonstrate a system called Insta-Search which facilitates effective exploration of KGs using relationship queries. Insta-Search helps the user by giving autocomplete keyword suggestions for partially typed words. It also displays an estimated number of answers that the current query would fetch along with few approximate top-scoring answers. The users also get entity suggestions so that they can iteratively reformulate the query until they find the query with the expected results. On submitting the query, the system returns ranked query results, grouped on the basis of similar information content to enhance result interpretation. No prerequisite knowledge of the data is required by the user to be able to use the system.
Taming Social Bots: Detection, Exploration and Measurement
Authors: Abdullah Mueen, Nikan Chavoshi, Amanda J. Minnich
Venue: CIKM
Year: 2019

Social bots have been around for over a decade since 2008. Social bots are capable of swaying political opinion, spreading false information, and recruiting for terrorist organizations. Social bots use various sophisticated techniques by adopting emotions, sympathy following, synchronous deletions, and profile molting. There are several approaches proposed in the literature for detection, exploration, and measuring social bots. We provide a comprehensive overview of the existing work from data mining and machine learning perspective, discuss relative strengths and weaknesses of various methods, make recommendations for researchers and practitioners, and propose novel directions for future research in taming the social bots. The tutorial also discusses pitfalls in collecting and sharing data on social bots.
ED2: A Case for Active Learning in Error Detection
Authors: Felix Neutatz, Mohammad Mahdavi, Ziawasch Abedjan
Venue: CIKM
Year: 2019

State-of-the-art approaches formulate error detection as a semi-supervised classification problem. Recent research suggests that active learning is insufficiently effective for error detection and proposes the usage of neural networks and data augmentation to reduce the number of these user-provided labels. However, we can show that using the appropriate active learning strategy, it is possible to outperform the more complex models that rely on data augmentation. To this end, we propose a multi-classifier approach with two-stage sampling for active learning. This intuitive and neat sampling method chooses the most promising cells across rows and columns for labeling. On three datasets, ED2 achieves state-of-the-art detection accuracy while for large datasets, the required number of user labels is lower by one order of magnitude compared to the state of the art.
Feature Selection for Facebook Feed Ranking System via a Group-Sparsity-Regularized Training Algorithm
Authors: Xiuyan Ni, Yang Yu, Peng Wu, Youlin Li, Shaoliang Nie, Qichao Que, Chao Chen
Venue: CIKM
Year: 2019

In modern production platforms, large scale online learning models are applied to data of very high dimension. To save computational resource, it is important to have an efficient algorithm to select the most significant features from an enormous feature pool. In this paper, we propose a novel neural-network-suitable feature selection algorithm, which selects important features from the input layer during training. Instead of directly regularizing the training loss, we inject group-sparsity regularization into the (stochastic) training algorithm. In particular, we introduce a group sparsity norm into the proximally regularized stochastical gradient descent algorithm. To fully evaluate the practical performance, we apply our method to Facebook News Feed dataset, and achieve favorable performance compared with state-of-the-arts using traditional regularizers.
LuPe: A System for Personalized and Transparent Data-driven Decisions
Authors: Sarah Oppold, Melanie Herschel
Venue: CIKM
Year: 2019

Machine learning models are commonly used for decision support even though they are far from perfect, e.g., due to bias introduced by imperfect training data or wrong feature selection. While efforts are made and should continue to be put into developing better models, we will likely continue to rely on imperfect models in many applications. In these settings, how could we at least use the "best" model for an individual or a group of users and transparently communicate the risks and weaknesses that apply? We demonstrate LuPe, a system that addresses these questions. LuPe allows to optimize the choice of the applied model for subgroups of the population or individuals, thereby personalizing the model choice to best fit users' profiles, which improves fairness. LuPe further captures data to explain the choices made and the results of the model. We showcase how such data enable users to understand the system performance they can expect. This transparency helps users in making informed decisions or providing informed consent when such systems are used. Our demonstration will focus on several real-world applications showcasing the behavior of LuPe, including credit scoring and income prediction.
Understanding Data in the Blink of an Eye
Authors: Matteo Paganelli, Paolo Sottovia, Antonio Maccioni, Matteo Interlandi, Francesco Guerra
Venue: CIKM
Year: 2019

Many data analysis and knowledge mining tasks require a basic understanding of the content of a dataset prior to any data access. In this demo, we showcase how data descriptions---a set of compact, readable and insightful formulas of boolean predicates---can be used to guide users in understanding datasets. Finding the best description for a dataset is, unfortunately, both computationally hard and task-specific. This demo shows that not only we can generate descriptions at interactive speed, but also that diverse user needs---from anomaly detection to data exploration---can be accommodated through a user-driven process exploiting dynamic programming in concert with a set of heuristics.
DTMBIO 2019: The Thirteenth International Workshop on Data and Text Mining in Biomedical Informatics
Authors: Hyojung Paik, Ruibin Xi, Doheon Lee
Venue: CIKM
Year: 2019

Started in 2006 as a specialized workshop in the field of text mining applied to biomedical informatics, DTMBIO (ACM international workshop on Data and Text Mining in Biomedical Informatics) has been held annually in conjunction with one of the largest data management conferences, CIKM, bringing together researchers working on computer science and bioinformatics area including text mining and genomic data analysis. The purpose of DTMBIO is to foster discussions regarding the state-of-the-art applications of data and text mining on biomedical research problems. DTMBIO 2019 will help scientists navigate emerging trends and opportunities in the evolving area of informatics related techniques and problems in the context of biomedical research.
Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction
Authors: Zheyi Pan, Zhaoyuan Wang, Weifeng Wang, Yong Yu, Junbo Zhang, Yu Zheng
Venue: CIKM
Year: 2019

Predicting urban flow is essential for city risk assessment and traffic management, which profoundly impacts people's lives and property. Recently, some deep learning models, focusing on capturing spatio-temporal (ST) correlations between urban regions, have been proposed to predict urban flows. However, these models overlook latent region functions that impact ST correlations greatly. Thus, it is necessary to have a framework to assist these deep models in tackling the region function issue. However, it is very challenging because of two problems: 1) how to make deep models predict flows taking into consideration latent region functions; 2) how to make the framework generalize to a variety of deep models. To tackle these challenges, we propose a novel framework that employs matrix factorization for spatio-temporal neural networks (MF-STN), capable of enhancing the state-of-the-art deep ST models. MF-STN consists of two components: 1) a ST feature learner, which obtains features of ST correlations from all regions by the corresponding sub-networks in the existing deep models; and 2) a region-specific predictor, which leverages the learned ST features to make region-specific predictions. In particular, matrix factorization is employed on the neural networks, namely, decomposing the region-specific parameters of the predictor into learnable matrices, i.e., region embedding matrices and parameter embedding matrices, to model latent region functions and correlations among regions. Extensive experiments were conducted on two real-world datasets, illustrating that MF-STN can significantly improve the performance of some representative ST models while preserving model complexity.
NAD: Neural Network Aided Design for Textile Pattern Generation
Authors: Zhifei Pang, Sai Wu, Dongxiang Zhang, Yunjun Gao, Gang Chen
Venue: CIKM
Year: 2019

Textile pattern design is a challenging task that can be hardly resolved by a single deep neural network, due to the requirements on high resolution, periodic tiling, copyright protection and aesthetic preference of designers. In this paper, we present our NAD system which can automatically produce high-quality textile patterns for printing industry. Our NAD system splits the work into three steps: layout design, image filtering and pattern style transfer. In the first and last step, we employ different neural models to learn the process of artwork creation by human designers. Specifically, a reinforcement learning model is first developed for layout adjustment, followed by a CNN-based model for style transfer. We have employed our NAD system in an online production system with real customers and the results are very impressive and promising. The NAD system not only frees human designers from the labor intensive design process, but also results in a 2%-5% daily purchase rate.
Approximate Definitional Constructs as Lightweight Evidence for Detecting Classes Among Wikipedia Articles
Authors: Marius Pasca
Venue: CIKM
Year: 2019

A lightweight method applies a few extraction patterns to the task of distinguishing Wikipedia articles that are classes ("Walled garden", "Garden") from other articles ("High Hazels Park"). The method acquires a set of classes, based on patterns targeting phrases that likely refer to either concepts being introduced or defined ("a *walled garden* is a garden [..]"); or to concepts used to introduce or define other concepts ("a walled garden is a *garden* [..]"). Experimental results over multiple evaluation sets are better, when relying on defined phrases alone vs. defining phrases alone; and further improved, when combining complementary evidence from both.
Practicing the Art of Data Science
Authors: Jian Pei
Venue: CIKM
Year: 2019

Data science embraces interdisciplinary methodologies and tools, such as those in statistics, artificial intelligence/machine learning, data management, algorithms, and computation. Practicing data science to empower innovative applications, however, remains an art due to many factors beyond technology, such as sophistication of application scenarios, business demands, and the central role of human being in the loop. The purpose of this keynote speech is to share with the audience two most important rules of thumb that I learned from my practice of data science research, development and applications, as well as my thoughts on the future enterprise and organization data strategies. First, I will demonstrate the importance and challenges in developing domain-oriented, end-to-end solutions. Specifically, I will discuss our experience in transforming algorithms to domain-oriented tools, and review some of our latest techniques in transforming black-box deep learning networks into interpretable white-box models. Second, I will advocate the core value of data science as the connector and transformer between vertical application challenges and general scientific principles and engineering tools. Using network embedding as an example, I will illustrate the innovative value of building connectors and transformers for new types of data and applications so that they can take great advantage of well established scientific methods and engineering tools. I envision data science for social, commercial and ecological good has to build on enterprise and organization data strategies and infrastructure. About future work, I will provide some thoughts on this perspective, such as data value assessing and pricing, as well as privacy preservation.
Analyzing the Effects of Document's Opinion and Credibility on Search Behaviors and Belief Dynamics
Authors: Suppanut Pothirattanachaikul, Takehiro Yamamoto, Yusuke Yamamoto, Masatoshi Yoshikawa
Venue: CIKM
Year: 2019

To obtain accurate information through web searches, people have to search for information carefully. This study investigates how the search behaviors and decision outcomes of searchers were affected by the documents they encountered during their search process. We focus on two document factors: (1) opinion (consistent and inconsistent) with the searchers' beliefs prior to the search task, and (2) credibility (high and low). We conducted a user study in which 260 participants were asked to perform health-related search tasks while controlling a search result with different opinions and credibility levels. The results revealed that (i) the participants spent more effort searching by issuing more queries, when belief-inconsistent documents were presented; (ii) the documents' opinion and credibility affected their belief dynamics, (i.e., how their beliefs changed after the search task); and (iii) their belief dynamics and search efforts had few relationships. These findings suggest that search engines could prevent users from polarization and thus, help them to obtain accurate information, by presenting documents that are inconsistent with users' beliefs on the higher-rank of the results.
Automatic Construction of Multi-layer Perceptron Network from Streaming Examples
Authors: Mahardhika Pratama, Choiru Za&apos, in, Andri Ashfahani, Yew-Soon Ong, Weiping Ding
Venue: CIKM
Year: 2019

Autonomous construction of deep neural network (DNNs) is desired for data streams because it potentially offers two advantages: proper model's capacity and quick reaction to drift and shift. While self-organizing mechanism of DNNs remains an open issue, this task is even more challenging to be developed for standard multi-layer DNNs than that using the different-depth structures, because addition of a new layer results in information loss of previously trained knowledge. A Neural Network with Dynamically Evolved Capacity (NADINE) is proposed in this paper. NADINE features a fully open structure where its network structure, depth and width, can be automatically evolved from scratch in the online manner and without the use of problem-specific thresholds. NADINE is structured under a standard MLP architecture and the catastrophic forgetting issue during the hidden layer addition phase is resolved using the proposal of soft-forgetting and adaptive memory methods. The advantage of NADINE, namely elastic structure and online learning trait, is numerically validated using nine data stream classification and regression problems where it demonstrates performance's improvement over prominent algorithms in all problems. In addition, it is capable of dealing with data stream regression and classification problems equally well.
BePT: A Behavior-based Process Translator for Interpreting and Understanding Process Models
Authors: Chen Qian, Lijie Wen, Akhil Kumar
Venue: CIKM
Year: 2019

Sharing process models on the web has emerged as a common practice. Users can collect and share their experimental process models with others. However, some users always feel confused about the shared process models for lack of necessary guidelines or instructions. Therefore, several process translators have been proposed to explain the semantics of process models in natural language (NL). We find that previous studies suffer from information loss and generate semantically erroneous descriptions that diverge from original model behaviors. In this paper, we propose a novel process translator named BePT (Behavior-based Process Translator) based on the encoder-decoder paradigm, encoding a process model into a middle representation and decoding the representation into NL descriptions. Our theoretical analysis demonstrates that BePT satisfies behavior correctness, behavior completeness and description minimality. The qualitative and quantitative experiments show that BePT outperforms the state-of-the-art baselines.
Rating Mechanisms for Sustainability of Crowdsourcing Platforms
Authors: Chenxi Qiu, Anna Cinzia Squicciarini, Sarah Michele Rajtmajer
Venue: CIKM
Year: 2019

Crowdsourcing leverages the diverse skill sets of large collections of individual contributors to solve problems and execute projects, where contributors may vary significantly in experience, expertise, and interest in completing tasks. Hence, to ensure the satisfaction of its task requesters, most existing crowdsourcing platforms focus primarily on supervising contributors' behavior. This lopsided approach to supervision negatively impacts contributor engagement and platform sustainability. In this paper, we introduce rating mechanisms to evaluate requesters' behavior, such that the health and sustainability of crowdsourcing platform can be improved. We build a game theoretical model to systematically account for the different goals of requesters, contributors, and platform, and their interactions. On the basis of this model, we focus on a specific application, in which we aim to design a rating policy that incentivizes requesters to engage lessexperienced contributors. Considering the hardness of the problem, we develop a time efficient heuristic algorithm with theoretical bound analysis. Finally, we conduct a user study in Amazon Mechanical Turk (MTurk) to validate the central hypothesis of the model. We provide a simulation based on 3 million task records extracted from MTurk demonstrating that our rating policy can appreciably motivate requesters to hire less-experienced contributors.
Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search
Authors: Minghui Qiu, Bo Wang, Cen Chen, Xiaoyi Zeng, Jun Huang, Deng Cai, Jingren Zhou, Forrest Sheng Bao
Venue: CIKM
Year: 2019

Product search and recommendation is a task that every e-commerce platform wants to outperform their peels on. However, training a good search or recommendation model often requires more data than what many platforms have. Fortunately, the search tasks on different platforms share the common underlying structure. Considering each platform as a domain, we propose a cross-domain learning approach to help the task on data-deficient platforms by leveraging the data from data-abundant platforms. In our solution, the importance of features in different domains is addressed by a domain-specific attention network. Meanwhile, a multi-task regularizer based on Wasserstein distance is introduced to help extract both domain-invariant and domain-specific features. Our model consistently outperforms the competing methods on both public and real-world industry datasets. Quantitative evaluation shows that our model can discover important features for different domains, which helps us better understand different user needs across platforms. Last but not least, we have deployed our model online in three big e-commerce platforms namely Taobao, Tmall, and Qintao, and observed better performance than the production models for all the platforms.