OpenRaiser/Intern-Atlas huggingface dataset:
Muse: Multi-query Event Trend Aggregation
Authors: Allison Rozet, Olga Poppe, Chuan Lei, Elke A. Rundensteiner
Venue: CIKM
Year: 2020

Streaming analytics deploy Kleene pattern queries to detect and aggregate event trends on high-rate data streams. Despite increasing workloads, most state-of-the-art systems process each query independently, thus missing cost-saving sharing opportunities. Sharing event trend aggregation poses several technical challenges. First, Kleene patterns are in general difficult to share due to complex nesting and arbitrarily long matches. Second, not all sharing opportunities are beneficial because sharing Kleene patterns incurs non-trivial overhead to ensure the correctness of final aggregation results. We propose MUSE (Multi-query Shared Event trend aggregation), the first framework that shares aggregation queries with Kleene patterns while avoiding expensive trend construction. To find the beneficial sharing plan, the MUSE optimizer effectively selects robust sharing candidates from the exponentially large search space. Our experiments demonstrate that MUSE increases throughput by 4 orders of magnitude compared to state-of-the-art approaches.
Modelling Regional Crime Risk using Directed Graph of Check-ins
Authors: Shakila Khan Rumi, Flora D. Salim
Venue: CIKM
Year: 2020

The location-based social network, Foursquare, reflects the human activities of a city. The mobility dynamics inferred from Foursquare helps us understanding urban social events like crime In this paper, we propose a directed graph from the aggregated movement between regions using Foursquare data. We derive region risk factor from the movement direction, quantity and crime history in different periods of the day. Later, we propose a new set of features, DIrected graph Flow FEatuRes (DIFFER) which are associated with region risk factor. The reliable correlations between DIFFER and crime count are observed. We verify the effectiveness of the DIFFER in monthly crime count using Linear, XGBoost, and Random Forest regression in two cities, Chicago and New York City.
Diverse Enumeration of Maximal Cliques
Authors: Liron Sade, Sara Cohen
Venue: CIKM
Year: 2020

Maximal clique enumeration is a well-studied problem due to its many applications. We present a new algorithm for this problem that enumerates maximal cliques in a diverse ordering. The main idea behind our approach is to adapt the classic Bron-Kerbosch (BK) algorithm by, conceptually, jumping between different nodes in the execution tree. Special care is taken to ensure that (1) each maximal clique is created precisely once, (2) the theoretical runtime remains the same as in the BK algorithm and (3) memory requirements remain reasonable. Experimental results show that we indeed achieve our goals, and moreover, that the cliques are enumerated in a diverse order.
A GAN-based Framework for Modeling Hashtag Popularity Dynamics Using Assistive Information
Authors: Avirup Saha, Niloy Ganguly
Venue: CIKM
Year: 2020

Temporal point process (TPP) models have hitherto been moderately good at nowcasting hashtag popularity, but have been very poor at forecasting due to insufficient modeling of Twitter microdynamics. Recent studies have shown that the highly fluctuating nature of hashtag popularity dynamics is due to the influence of two external factors: (i) hashtag-tweet reinforcement and (ii) inter-hashtag competition. In this paper, we propose a marked TPP based on Generative Adversarial Networks (GANs) which can seamlessly incorporate the assistive information necessary to capture the above effects and successfully forecast distant popularity trends. To achieve this, we employ a unique linear semi-autoregressive model for mark generation and couple the time and mark generative aspects. On seven diverse datasets crawled from Twitter covering several real-world events, our model yields remarkably stable performance in predicting hashtag popularity in diverse situations and offers a substantial improvement over the existing state of the art generative models.
Relation Extraction with Self-determined Graph Convolutional Network
Authors: Sunil Kumar Sahu, Derek Thomas, Billy Chiu, Neha Sengupta, Mohammady Mahdy
Venue: CIKM
Year: 2020

Relation Extraction is a way of obtaining the semantic relationship between entities in text. The state-of-the-art methods use linguistic tools to build a graph for the text in which the entities appear and then a Graph Convolutional Network (GCN) is employed to encode the pre-built graphs. Although their performance is promising, the reliance on linguistic tools results in a non end-to-end process. In this work, we propose a novel model, the Self-determined Graph Convolutional Network (SGCN), which determines a weighted graph using a self-attention mechanism, rather using any linguistic tool. Then, the self-determined graph is encoded using a GCN. We test our model on the TACRED dataset and achieve the state-of-the-art result. Our experiments show that SGCN outperforms the traditional GCN, which uses dependency parsing tools to build the graph.
Automatic Contextual Storytelling in a Natural Language Corpus
Authors: Ishrat Rahman Sami
Venue: CIKM
Year: 2020

Storytelling is an ancient art and science of conveying wisdom through generations for centuries. Data-driven storytelling in the context of a natural language corpus has a huge potential for conveying fast valuable insights about the corpus for better decision making. But high dimensional unstructured nature of natural language text makes automatic extraction of stories extremely difficult. This PhD research project believes that modern storytelling is a hand in hand approach of contextual topic visualization and contextual summarization. While exploratory data visualization can provide valuable insights into the data, these insights can be used to understand and design models for producing abstract summarization. In this project, the context of a story is defined from three perspectives: a single document, a collection of multiple documents about a topic of interest and the whole corpus. In this project, exploratory data visualization is used to understand the context better and now with the achieved insights, research is focusing on abstract summarization for automatic contextual storytelling.
A Framework for Analyzing the Impact of Missing Data in Predictive Models
Authors: Fabiola Santore, Eduardo C. de Almeida, Wagner Hugo Bonat, Eduardo H. M. Pena, Luiz Eduardo Soares de Oliveira
Venue: CIKM
Year: 2020

We propose a stochastic framework to evaluate the impact of missing data on the performance of predictive models. The framework allows full control of important aspects of the data set structure. These include the number and type of the input variables, the correlation between the input variables and their general predictive power, and sample size. The missing process is generated from a multivariate Bernoulli distribution, which allows us to simulate missing patterns corresponding to the MCAR, MAR and MNAR mechanisms. Although the framework may be applied to virtually all types of predictive models, in this article, we focus on the logistic regression model and choose the accuracy as the predictive measure. The simulation results show that the effects of missing data disappear for large sample sizes, as expected. On the other hand, as the number of input variables increases, the accuracy decreases mainly for binary inputs.
Continuous Improvement of Medical Diagnostic Systems with Large Scale Patient Vignette Simulation
Authors: Suhrid Satyal, Nick Fletcher, Shameek Ghosh
Venue: CIKM
Year: 2020

Differential diagnostic systems provide a ranked list of highly prob-able diseases given a patient's profile and symptoms. Evaluation of diagnostic algorithms in literature has been limited to a small set of hand-crafted patient vignettes. Testing with high coverage and gaining insights for improvements are challenging because of thesize and complexity of the knowledge base. Furthermore, scalable practical methodologies for evaluation and deployment of such systems are missing in the literature. Here, we address this challenge using a novel patient vignette simulation algorithm within an iterative clinician-in-the-loop methodology for semi-automatically evaluating and deploying medical diagnostic systems in production.We evaluate our algorithms and methodology through a case study of a real product and knowledge base curated by medical experts.We conduct multiple iterations of the methodology, report novel accuracy measures, and discuss insights from our experience in applying this method to production
Truth be Told: Fake News Detection Using User Reactions on Reddit
Authors: Vinay Setty, Erlend Rekve
Venue: CIKM
Year: 2020

In this paper, we provide a large dataset for fake news detection using social media comments. The dataset consists of 12,597 claims (of which 63% are labelled as fake) from four different sources (Snopes, Poltifact, Emergent and Twitter). The novel part of the dataset is that it also includes over 662K social media discussion comments related to these claims from Reddit. We make this dataset public for the research community. In addition, for the task of fake news detection using social media comments, we provide a simple but strong baseline solution deep neural network model which beats several solutions in the literature.
GeoFlink: A Distributed and Scalable Framework for the Real-time Processing of Spatial Streams
Authors: Salman Ahmed Shaikh, Komal Mariam, Hiroyuki Kitagawa, Kyoung-Sook Kim
Venue: CIKM
Year: 2020

Apache Flink is an open-source system for scalable processing of batch and streaming data. Flink does not natively support efficient processing of spatial data streams, which is a requirement of many applications dealing with spatial data. Besides Flink, other scalable spatial data processing platforms including GeoSpark, Spatial Hadoop, etc. do not support streaming workloads and can only handle static/batch workloads. To fill this gap, we present GeoFlink, which extends Apache Flink to support spatial data types, indexes and continuous queries over spatial data streams. To enable efficient processing of spatial continuous queries and for the effective data distribution across Flink cluster nodes, a gird-based index is introduced. GeoFlink currently supports spatial range, spatial kNN and spatial join queries on point data type. An experimental study on real spatial data streams shows that GeoFlink achieves significantly higher query throughput than ordinary Flink processing.
A Toolkit for Managing Multiple Crowdsourced Top-K Queries
Authors: Caihua Shan, Leong Hou U, Nikos Mamoulis, Reynold Cheng
Venue: CIKM
Year: 2020

Crowdsourced ranking and top-k queries have attracted significant attention recently. Their goal is to combine human cognitive abilities and machine intelligence to rank computer hostile but human friendly items. Many task assignment algorithms and inference approaches have been proposed to publish suitable micro-tasks to the crowd, obtain informative answers, and aggregate the rank from noisy human answers. However, they are all focused on single query processing. To the best of our knowledge, no prior work helps users manage multiple crowdsourced top-k queries. We propose a toolkit, which seamlessly works with most existing inference and task assignment methods, for crowdsourced top-k query management. Our toolkit attempts to optimize human resource allocation and continuously monitors query quality at any stage of the crowdsourcing process. A user can terminate a query early, if the estimated quality already fulfills her requirements. Besides, the toolkit provides user-friendly interfaces for users to initialize queries, monitor execution status, and do more operations by hand.
Introduction to Computer Vision and Realtime Deep Learning-based Object Detection
Authors: James G. Shanahan
Venue: CIKM
Year: 2020

The main focus of object detection, one of the most challenging problems in computer vision (CV), is to predict a set of bounding boxes and category labels for each object of interest in an image or in a point cloud. As such, object detection has a variety of exciting downstream applications such as self-driving cars, checkout-less shopping, smart cities, cancer detection, and more. This field has been revolutionized by deep learning over the past five years, where during this time, two-stage approaches to object detection have given way to simpler, more efficient, one-stage models. Mean average precision (mAP) on benchmark problems such as the COCO Object Detection dataset has improved almost 4X over the course of five years from 15% (Fast RCNN, a two-stage approach) to 55% (EfficientDet7x, a one-stage approach). This tutorial looks under the hood of state-of-the-art object detection systems, such as two-stage, one-stage, and also more recent approaches based upon transformers. It builds out some of their associated detection pipelines in a Jupyter Notebook using Python, OpenCV, PyTorch, Keras and Tensorflow. While the primary focus is on object detection in digital images from cameras and videos, this tutorial will also introduce object detection in 3D point clouds.
Index Obfuscation for Oblivious Document Retrieval in a Trusted Execution Environment
Authors: Jinjin Shao, Shiyu Ji, Alvin Oliver Glova, Yifan Qiao, Tao Yang, Tim Sherwood
Venue: CIKM
Year: 2020

This paper studies privacy-aware inverted index design and document retrieval for multi-keyword document search in a trusted hardware execution environment such as Intel SGX. The previous work uses time-consuming oblivious computing techniques to avoid the leakage of memory access patterns for privacy preservations in such an environment. This paper proposes an efficiency-enhanced design that obfuscates the inverted index structure with posting bucketing and document ID masking, which aims to hide document-term association and avoid the access pattern leakage. This paper describes privacy-aware oblivious document retrieval during online query processing based on such an index. Both privacy and efficiency analyses are provided, followed by evaluation results comparing proposed designs with multiple baselines.
Deep Adaptive Feature Aggregation in Multi-task Convolutional Neural Networks
Authors: Zhen Shen, Chaoran Cui, Jin Huang, Jian Zong, Meng Chen, Yilong Yin
Venue: CIKM
Year: 2020

Convolutional Neural Network (CNN) based multi-task learning methods have been widely used in a variety of applications of computer vision. Towards effective multi-task CNN architectures, recent studies automatically learn the optimal combinations of task-specific features at single network layers. However, they generally construct an unchanged operation of feature aggregation after training, regardless of the characteristics of input features. In this paper, we propose a novel Adaptive Feature Aggregation (AFA) layer for multi-task CNNs, in which a dynamic aggregation mechanism is designed to allow each task to adaptively determine the degree to which the feature aggregation of different tasks is needed according to the feature dependencies. On both pixel-level and image-level tasks, we demonstrate that our approach significantly outperforms the previous state-of-the-art methods of multi-task CNNs.
User and Context Integrated Experience Mining in Online Health Communities
Authors: Jinhe Shi, Yi Chen
Venue: CIKM
Year: 2020

Online Health Communities (OHCs) provide a platform for patients, caregivers, and researchers to exchange information and support each other. Identifying information that describes patient health experiences in OHCs has many important applications, such as trustworthy knowledge discovery and recommendation. To identify patient experience description, we observe that the same word may have different strengths as an indicator of patient experiences when written by different users. Based on this observation, we propose a User-Word Context Vector model, that holistically captures linguistic features of text, user information and context information to classify patient experiences in OHCs. Experimental evaluation shows that the proposed method significantly outperforms the existing methods on patient experience classification.
Neural Logic Reasoning
Authors: Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, Yongfeng Zhang
Venue: CIKM
Year: 2020

Recent years have witnessed the success of deep neural networks in many research areas. The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of cognitive reasoning. However, the concrete ability of reasoning is critical to many theoretical and practical problems. On the other hand, traditional symbolic reasoning methods do well in making logical inference, but they are mostly hard rule-based reasoning, which limits their generalization ability to different tasks since difference tasks may require different rules. Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs. In this paper, we propose Logic-Integrated Neural Network (LINN) to integrate the power of deep learning and logic reasoning. LINN is a dynamic neural architecture that builds the computational graph according to input logical expressions. It learns basic logical operations such as AND, OR, NOT as neural modules, and conducts propositional logical reasoning through the network for inference. Experiments on theoretical task show that LINN achieves significant performance on solving logical equations and variables. Furthermore, we test our approach on the practical task of recommendation by formulating the task into a logical inference problem. Experiments show that LINN significantly outperforms state-of-the-art recommendation models in Top-K recommendation, which verifies the potential of LINN in practice.
GGDs: Graph Generating Dependencies
Authors: Larissa Capobianco Shimomura, George Fletcher, Nikolay Yakovets
Venue: CIKM
Year: 2020

We propose Graph Generating Dependencies (GGDs), a new class of dependencies for property graphs. Extending the expressivity of state of the art constraint languages, GGDs can express both tuple- and equality-generating dependencies on property graphs, both of which find broad application in graph data management. We provide the formal definition of GGDs, analyze the validation problem for GGDs, and demonstrate the practical utility of GGDs.
Helix: DGA Domain Embeddings for Tracking and Exploring Botnets
Authors: Lior Sidi, Yisroel Mirsky, Asaf Nadler, Yuval Elovici, Asaf Shabtai
Venue: CIKM
Year: 2020

Botnets have been using domain generation algorithms (DGA) for over a decade to covertly and robustly identify the domain name of their command and control servers (C&C). Recent advancements in DGA detection has motivated botnet owners to rapidly alter the C&C domain and use adversarial techniques to evade detection. As a result, it has become increasingly difficult to track botnets in DNS traffic. In this paper, we present Helix, a method for tracking and exploring botnets. Helix uses a spatio-temporal deep neural network autoencoder to convert domains into numerical vectors (embeddings) which capture the DGA and seed used to create the domain. This is made possible by leveraging both convolutional (spatial) and recurrent (temporal) layers, and by using techniques such as attention mechanisms and highways. Furthermore, by using an autoencoder architecture, the network can be trained in an unsupervised manner (no labeling of data) which makes the system practical for real world deployments. In our evaluation, we found that Helix can track botnet campaigns, distinguish between DGA families and seeds, and can identify domains generated using the latest adversarial machine learning techniques. Helix is currently being used to track botnets in one of the world's largest Internet Service Providers (ISP), and we include some of the ISP's analysis work using our method.
METEOR: Learning Memory and Time Efficient Representations from Multi-modal Data Streams
Authors: Amila Silva, Shanika Karunasekera, Christopher Leckie, Ling Luo
Venue: CIKM
Year: 2020

Many learning tasks involve multi-modal data streams, where continuous data from different modes convey a comprehensive description about objects. A major challenge in this context is how to efficiently interpret multi-modal information in complex environments. This has motivated numerous studies on learning unsupervised representations from multi-modal data streams. These studies aim to understand higher-level contextual information (e.g., a Twitter message) by jointly learning embeddings for the lower-level semantic units in different modalities (e.g., text, user, and location of a Twitter message). However, these methods directly associate each low-level semantic unit with a continuous embedding vector, which results in high memory requirements. Hence, deploying and continuously learning such models in low-memory devices (e.g., mobile devices) becomes a problem. To address this problem, we present METEOR, a novel MEmory and Time Efficient Online Representation learning technique, which: (1) learns compact representations for multi-modal data by sharing parameters within semantically meaningful groups and preserves the domain-agnostic semantics; (2) can be accelerated using parallel processes to accommodate different stream rates while capturing the temporal changes of the units; and (3) can be easily extended to capture implicit/explicit external knowledge related to multi-modal data streams. We evaluate METEOR using two types of multi-modal data streams (i.e., social media streams and shopping transaction streams) to demonstrate its ability to adapt to different domains. Our results show that METEOR preserves the quality of the representations while reducing memory usage by around 80% compared to the conventional memory-intensive embeddings.
Attribution IQ: Scalable Game Theoretic Attribution in Web Analytics
Authors: Ritwik Sinha, Ivan Andrus, Trevor Paulsen
Venue: CIKM
Year: 2020

Attribution in digital marketing is the task of assigning the credit due to each marketing interaction toward a marketing outcome. Such information helps the brand decide on marketing strategies for the future. In web analytics, attribution goes beyond marketing channels, and can be performed across a broad range of dimensions (e.g. images displayed on a website). This requires attribution algorithms to operate on dimensions with a large number of dimensional elements (hundreds of thousands in the extreme). Additionally, given the many possible metrics a marketer may be interested in, it is infeasible to perform ahead of time computation for all combinations. In this work, we propose a game theoretic attribution model that can be computed at query-time. Our demo of Attribution IQ runs in the Adobe Analytics production system. Our system is highly scalable (it operates on millions of customer journeys), interactive (the user can change the parameters and get updated results instantaneously) and requires no pre-computation (all computation being performed at query time). We demonstrate the effectiveness of Attribution IQ on real-world e-commerce web analytics datasets.