OpenRaiser/Intern-Atlas huggingface dataset:
E-fashion Product Discovery via Deep Text Parsing
Authors: Uma Sawant, Vijay Gabale, Anand Prabhu Subramanian
Venue: WWW
Year: 2017

Transforming unstructured text into structured form is important for fashion e-commerce platforms that ingest tens of thousands of fashion products every day. While most of the e-commerce product extraction research focuses on extracting a single product from the product title using known keywords, little attention has been paid to discovering potentially multiple products present in the listing along with their respective relevant attributes, and leveraging the entire title and description text for this purpose. We fill this gap and propose a novel composition of sequence labeling and multi-task learning as an end-to-end trainable deep neural architecture. We systematically evaluate our approach on one of the largest tagged datasets in fashion e-commerce consisting of 25K listings labeled at word-level. Given 23 labels, we discover label-values with F1 score of 92.2%. When applied to 2M listings, we discovered 2.6M fashion items and 9.5M attribute values.
FLOCK: Combating Astroturfing on Livestreaming Platforms
Authors: Neil Shah
Venue: WWW
Year: 2017

Livestreaming platforms have become increasingly popular in recent years as a means of sharing and advertising creative content. Popular content streamers who attract large viewership to their live broadcasts can earn a living by means of ad revenue, donations and channel subscriptions. Unfortunately, this incentivized popularity has simultaneously resulted in incentive for fraudsters to provide services to astroturf, or artificially inflate viewership metrics by providing fake ``live'' views to customers. Our work provides a number of major contributions: (a) formulation: we are the first to introduce and characterize the viewbot fraud problem in livestreaming platforms, (b) methodology: we propose FLOCK, a principled and unsupervised method which efficiently and effectively identifies botted broadcasts and their constituent botted views, and (c) practicality: our approach achieves over 98% precision in identifying botted broadcasts and over 90% precision/recall against sizable synthetically generated viewbot attacks on a real-world livestreaming workload of over 16 million views and 92 thousand broadcasts. FLOCK successfully operates on larger datasets in practice and is regularly used at a large, undisclosed livestreaming corporation.
How Gamification Affects Physical Activity: Large-scale Analysis of Walking Challenges in a Mobile Application
Authors: Ali Shameli, Tim Althoff, Amin Saberi, Jure Leskovec
Venue: WWW
Year: 2017

Gamification represents an effective way to incentivize user behavior across a number of computing applications. However, despite the fact that physical activity is essential for a healthy lifestyle, surprisingly little is known about how gamification and in particular competitions shape human physical activity. Here we study how competitions affect physical activity. We focus on walking challenges in a mobile activity tracking application where multiple users compete over who takes the most steps over a predefined number of days. We synthesize our findings in a series of game and app design implications. In particular, we analyze nearly 2,500 physical activity competitions over a period of one year capturing more than 800,000 person days of activity tracking. We observe that during walking competitions, the average user increases physical activity by 23%. Furthermore, there are large increases in activity for both men and women across all ages, and weight status, and even for users that were previously fairly inactive. We also find that the composition of participants greatly affects the dynamics of the game. In particular, if highly unequal participants get matched to each other, then competition suffers and the overall effect on the physical activity drops significantly. Furthermore, competitions with an equal mix of both men and women are more effective in increasing the level of activities. We leverage these insights to develop a statistical model to predict whether or not a competition will be particularly engaging with significant accuracy. Our models can serve as a guideline to help design more engaging competitions that lead to most beneficial behavioral changes.
Large Scale Distributed Data Science from scratch using Apache Spark 2.0
Authors: James G. Shanahan, Liang Dai
Venue: WWW
Year: 2017

Apache Spark is an open-source cluster computing framework. It has emerged as the next generation big data processing engine, overtaking Hadoop MapReduce which helped ignite the big data revolution. Spark maintains MapReduce's linear scalability and fault tolerance, but extends it in a few important ways: it is much faster (100 times faster for certain applications), much easier to program in due to its rich APIs in Python, Java, Scala, SQL and R (MapReduce has 2 core calls) , and its core data abstraction, the distributed data frame. In addition, it goes far beyond batch applications to support a variety of compute-intensive tasks, including interactive queries, streaming, machine learning, and graph processing. With massive amounts of computational power, deep learning has been shown to produce state-of-the-art results on various tasks in different fields like computer vision, automatic speech recognition, natural language processing and online advertising targeting. Thanks to the open-source frameworks, e.g. Torch, Theano, Caffe, MxNet, Keras and TensorFlow, we can build deep learning model in a much easier way. Among all these framework, TensorFlow is probably the most popular open source deep learning library. TensorFlow 1.0 was released recently, which provide a more stable, flexible and powerful computation tool for numerical computation using data flow graphs. Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. This tutorial will provide an accessible introduction to large-scale distributed machine learning and data mining, and to Spark and its potential to revolutionize academic and commercial data science practices. It is divided into three parts: the first part will cover fundamental Spark concepts, including Spark Core, functional programming ala map-reduce, data frames, the Spark Shell, Spark Streaming, Spark SQL, MLlib, and more; the second part will focus on hands-on algorithmic design and development with Spark (developing algorithms from scratch such as decision tree learning, association rule mining (aPriori), graph processing algorithms such as pagerank/shortest path, gradient descent algorithms such as support vectors machines and matrix factorization. Industrial applications and deployments of Spark will also be presented.; the third part will introduce deep learning concepts, how to implement a deep learning model through TensorFlow, Keras and run the model on Spark. Example code will be made available in python (pySpark) notebooks.
An Efficient Approach to Event Detection and Forecasting in Dynamic Multivariate Social Media Networks
Authors: Minglai Shao, Jianxin Li, Feng Chen, Hongyi Huang, Shuai Zhang, Xunxun Chen
Venue: WWW
Year: 2017

Anomalous subgraph detection has been successfully applied to event detection in social media. However, the subgraph detection problembecomes challenging when the social media network incorporates abundant attributes, which leads to a multivariate network. The multivariate characteristic makes most existing methods incapable to tackle this problem effectively and efficiently, as it involves joint feature selection and subgraph detection that has not been well addressed in the current literature, especially, in the dynamic multivariate networks in which attributes evolve over time.
Behavioral Intention of Using Virtual Reality in Learning
Authors: Chien-wen Shen, Jung-Tsung Ho, Ting-Chang Kuo, Thai Ha Luong
Venue: WWW
Year: 2017

This study integrated the unified theory of acceptance and use of technology (UTAUT) and the four stages of Kolb's learning style-concrete experience, reflective observation, abstract conception, and active experimentation to investigate the factors affecting students' behavioral intention to use a virtual reality headset (VRH) in learning. The research model, constructed using structural equation modeling, included the four constructs of the UTAUT, namely performance expectancy, effort expectancy, social influence, and facilitating condition. Hypotheses on whether the four stages of Kolb's learning style and the four constructs of the UTAUT significantly affect behavioral intention to use VRHs in learning were proposed and tested through inference analysis. The results show that only the concrete experience stage of Kolb's learning style has a positive and significant effect on users' behavioral intention to use VRHs in learning, whereas all four constructs of the UTAUT do. These findings should be applied by educational institutions to increase VRH use in learning.
User Relation Prediction Based on Matrix Factorization and Hybrid Particle Swarm Optimization
Authors: Zhenkun Shi, Wanli Zuo, Weitong Chen, Lin Yue, Jiayu Han, Lizhou Feng
Venue: WWW
Year: 2017

Many real-world domains are relational in nature, consisting of a set of objects related to each other in complex ways. Matrix factorization is an effective method in relationship prediction, However, traditional matrix factorization link prediction methods can only be used for non-negative matrix. In this paper, a generalized framework, itelliPrediction, is presented that is able to deal with positive and negative matrix. The novel itelliPrediction framework is domain independent and with high precision. We validate our approach using two different data sources, an open data sets and a real-word dataset, the result demonstrated that the quality of our approach is comparable to, if not better than, exiting state of the art relation predication framework.
Could Data from Location-Based Social Networks Be Used to Support Urban Planning?
Authors: Rodrigo Smarzaro, Tiago França Melo de Lima, Clodoveu A. Davis Jr.
Venue: WWW
Year: 2017

A great quantity of information is required to support urban planning. Usually there are many (not integrated) data sources, originating from different government bodies, in distinct formats and variable properties (e.g. reliability, completeness). The effort to handle these data, integrate and analyze them is high, taking to much time for the information to be available to help decision making. We argue that data from location-based social networks (LBSN) could be used to provide useful information in reasonable time, despite several limitations they have. To asses this, as a case study, we used data from different LBSN to calculate the Local Availability Index (IOL) for a Brazilian city. This index is part of a methodology to estimate quality of urban life inside cities and is used to support urban planning. The results suggest that data from LBSN are useful and could be used to provide insights for local governments.
Query Processing in Location-Based Social Networks
Authors: Ammar Sohail, David Taniar, Andreas Züfle, Jeong-Ho Park
Venue: WWW
Year: 2017

The widespread proliferation of location-acquisition techniques and GPS-embedded mobile devices resulted in the generation of geo-tagged data at unprecedented scale and have essentially enhanced the user experience in location-based services associated with social networks. Such location-based social networks allow people to record and share their location and are a rich source of information which can be exploited to study people's various attributes and characteristics and can provide various Geo-Social (GS) services. In this demonstration, we propose a new type of query called Top-k Geo-Social (TGS) query, which enriches the semantics of the conventional spatial query by introducing a social relevance attribute. Similarly, users formulate their TGS queries and view the results through browser-based client side interface. On the server side, three approaches namely, 1) Social-First 2) Spatial-First and 3) Hybrid are proposed to efficiently process TGS queries.
Embedding Projection for Query Understanding
Authors: Yan Song, Chia-Jung Lee
Venue: WWW
Year: 2017

Learning reliable embeddings requires large data and is not trivial to be adapted to specific tasks due to certain constraints. For queries, the lack of sufficient context can prohibit learning quality representations and thus effective query understanding. We propose to project embeddings from a source space learned with natural language into a target space on queries. The projection function is learned via an overlap vocabulary set shared by both source and target spaces. Experimental results show that both linear and nonlinear embedding projections can help query intent classification and query slot tagging, even when the amount of data used for learning the projection is limited.
Back To The Source: An Online Approach for Sensor Placement and Source Localization
Authors: Brunella Spinelli, L. Elisa Celis, Patrick Thiran
Venue: WWW
Year: 2017

Source localization, the act of finding the originator of a disease or rumor in a network, has become an important problem in sociology and epidemiology. The localization is done using the infection state and time of infection of a few designated sensor nodes; however, maintaining sensors can be very costly in practice. We propose the first online approach to source localization: We deploy a priori only a small number of sensors (which reveal if they are reached by an infection) and then iteratively choose the best location to place a new sensor in order to localize the source. This approach allows for source localization with a very small number of sensors; moreover, the source can be found while the epidemic is still ongoing. Our method applies to a general network topology and performs well even with random transmission delays.
EVELIN: Exploration of Event and Entity Links in Implicit Networks
Authors: Andreas Spitz, Satya Almasian, Michael Gertz
Venue: WWW
Year: 2017

Implicit networks that describe latent entity relations have been demonstrated to be valuable tools in information retrieval, knowledge extraction, and search in document collections. While such implicit relations offer less insight into the types of connection between entities than traditional knowledge bases, they are much easier to extract from unstructured textual sources. Furthermore, they allow the derivation of relationship strength between entities that can be used to identify and leverage important co-mentions, based on which complex constructs of semantically related entities can be assembled with ease. One example of such implicit networks are LOAD graphs, which encode the textual proximity of location-, organization-, actor-, and date-mentions in document collections for the exploration, identification and summarization of events and entity relations. Here, we present EVELIN as a graphical, web-based interface for the exploration of such implicit networks of entities on the example of a large-scale network constructed from the English Wikipedia. The interface is available for online use at http://evelin.ifi.uni-heidelberg.de/.
LD-DL'17 Workshop Keynote Talk By Many Sporny: Building Better Blockchains Via Linked Data
Authors: Manu Sporny
Venue: WWW
Year: 2017

This presentation explores the current state of Blockchain research as it relates to Linked Data. The topics covered by the presentation include: 1) a basic definition of a blockchain, 2) an analysis of current blockchain capabilities, 3) compelling blockchain use cases where Linked Data would provide compelling benefits, 4) how Linked Data may be used to enhance blockchain capabilities, 5) a list of unresolved research and standardization needs as it relates to the expression of blockchains using Linked Data, and 6) possible venues where future work may be coordinated.
Extended Tracking Powers: Measuring the Privacy Diffusion Enabled by Browser Extensions
Authors: Oleksii Starov, Nick Nikiforakis
Venue: WWW
Year: 2017

Users have come to rely on browser extensions to realize features that are not implemented by browser vendors. Extensions offer users the ability to, among others, block ads, de-clutter websites, enrich pages with third-party content, and take screenshots. At the same time, because of their privileged position inside a user's browser, extensions have access to content and functionality that is not available to webpages, such as, the ability to conduct and read cross-origin requests, as well as get access to a browser's history and cookie jar.
The Spread of Physical Activity Through Social Networks
Authors: David Stück, Haraldur Tómas Hallgrímsson, Greg Ver Steeg, Alessandro Epasto, Luca Foschini
Venue: WWW
Year: 2017

Many behaviors that lead to worsened health outcomes are modifiable, social, and visible. Social influence has thus the potential to foster adoption of habits that promote health and improve disease management. In this study, we consider the evolution of the physical activity of 44.5 thousand Fitbit users as they interact on the Fitbit social network, in relation to their health status. The users collectively recorded 9.3 million days of steps over the period of a year through a Fitbit device. 7,515 of the users also self-reported whether they were diagnosed with a major chronic condition. A time-aggregated analysis shows that ego net size, average alter physical activity, gender, and body mass index (BMI) are significantly predictive of ego physical activity. For users who self-reported chronic conditions, the direction and effect size of associations varied depending on the condition, with diabetic users specifically showing almost a 6-fold increase in additional daily steps for each additional social tie. Subsequently, we consider the co-evolution of activity and friendship longitudinally on a month by month basis. We show that the fluctuations in average alter activity significantly predict fluctuations in ego activity. By leveraging a class of novel non-parametric statistical tests we investigate the causal factors in these fluctuations. We find that under certain stationarity assumptions, non-null causal dependence exists between ego and alter's activity, even in the presence of unobserved stationary individual traits. We believe that our findings provide evidence that the study of online social networks have the potential to improve our understanding of factors affecting adoption of positive habits, especially in the context of chronic condition management.
De-anonymizing Web Browsing Data with Social Networks
Authors: Jessica Su, Ansh Shukla, Sharad Goel, Arvind Narayanan
Venue: WWW
Year: 2017

Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show---theoretically, via simulation, and through experiments on real user data---that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one's feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user's social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time.To gauge the real-world effectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on sufficiently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is---to our knowledge---the largest-scale demonstrated de-anonymization to date.
Pairwise Webpage Coreference Classification Using Distant Supervision
Authors: Shivashankar Subramanian, Timothy Baldwin, Julian Brooke, Trevor Cohn
Venue: WWW
Year: 2017

A person or other entity is often associated with multiple URL endpoints on the web, motivating the task of determining whether a given pair of webpages is coreferent to a given entity. To strike a balance between unsupervised and supervised methods that require annotated data, we build a positive and unlabelled (PU) learning model, where we obtain positive examples using web search-based distant supervision. We evaluate our proposed approach using the SemEval-2007 WePS and ALTA-2016 shared task datasets.
Enabling Participatory Design of 3D Virtual Scenes on Mobile Devices
Authors: Xiaowen Sun, Yafang Wang, Gerard de Melo, Wei Gai, Yuliang Shi, Lu Zhao, Yulong Bian, Juan Liu, Chenglei Yang, Xiangxu Meng
Venue: WWW
Year: 2017

Designing 3D virtual scenes is essential for computer animation, computer game design, and virtual reality (VR) applications such as virtual museums. In this paper, we present a novel paradigm for participatory design of 3D virtual scenes on mobile devices. Designers and users are actively engaged in the design process so as to ensure that the results meet their needs with high standards of usability. Our new system allows the designers to construct an initial virtual scene via two tablet devices, one of which supports the scene assembly in a 2D window, while the other displays the corresponding 3D scene synchronously. Subsequently, the designers adapt the 3D scene based on real-time feedback of users exploring the virtual scene via a VR device, consisting of a smartphone with cardboard 3D glasses. The participants can then discuss how to further fine-tune the virtual scene by replaying the recorded footage of the user's experience process, interacting on their own respective tablets or smartphones. The system has been applied to the design of VR environments for virtual museums, residential decoration, firefighter training, and 3D games. Our user study suggests that this system not only increases the efficiency of the design process, but also gives rise to better designs.