OpenRaiser/Intern-Atlas huggingface dataset:
Nonlinear Diffusion for Community Detection and Semi-Supervised Learning
Authors: Rania Ibrahim, David F. Gleich
Venue: WWW
Year: 2019

Diffusions, such as the heat kernel diffusion and the PageRank vector, and their relatives are widely used graph mining primitives that have been successful in a variety of contexts including community detection and semi-supervised learning. The majority of existing methods and methodology involves linear diffusions, which then yield simple algorithms involving repeated matrix-vector operations. Recent work, however, has shown that sophisticated and complicated techniques based on network embeddings and neural networks can give empirical results superior to those based on linear diffusions. In this paper, we illustrate a class of nonlinear graph diffusions that are competitive with state of the art embedding techniques and outperform classic diffusions. Our new methods enjoy much of the simplicity underlying classic diffusion methods as well. Formally, they are based on nonlinear dynamical systems that can be realized with an implementation akin to applying a nonlinear function after each matrix-vector product in a classic diffusion. This framework also enables us to easily integrate results from multiple data representations in a principled fashion. Furthermore, we have some theoretical relationships that suggest choices of the nonlinear term. We demonstrate the benefits of these techniques on a variety of synthetic and real-world data.
ExQuisiTe: Explaining Quantities in Text
Authors: Yusra Ibrahim, Gerhard Weikum
Venue: WWW
Year: 2019

Web pages and other documents often contain tables to provide numerical details in a structured manner. Typically, the text explains and highlights important quantities, often using approximate numbers and aggregates such as totals, averages or ratios. For a human reader, it is crucial to navigate between text and tables to understand the key information in its context, drill down into tables when details are needed, and obtain explanations on specific figures from the accompanying text. In this demo, we present ExQuisiTe: a system to align quantity mentions in the text with related quantity mentions in tables, and enable extractive summarization that considers table contents. ExQuisiTe links quantity mentions in the text with quantities in tables, to support user-friendly exploration. ExQuisiTe handles exact single-cell references as well as rounded or truncated numbers and aggregations such as row or column totals.
Can You Give Me a Reason?: Argument-inducing Online Forum by Argument Mining
Authors: Makiko Ida, Gaku Morio, Kosui Iwasa, Tomoyuki Tatsumi, Takaki Yasui, Katsuhide Fujita
Venue: WWW
Year: 2019

This demonstration paper presents an argument-inducing online forum that stimulates participants with lack of premises for their claim in online discussions. The proposed forum provides its participants the following two subsystems: (1) Argument estimator for online discussions automatically generates a visualization of the argument structures in posts based on argument mining. The forum indicates structures such as claim-premise relations in real time by exploiting a state-of-the-art deep learning model. (2) Argument-inducing agent for online discussion (AIAD) automatically generates a reply post based on the argument estimator requesting further reasons to improve the argumentation of participants. Our experimental discussion demonstrates that the argument estimator can detect the argument structures from online discussions, and AIAD can induce premises from the participants. To the best of our knowledge, our argument-inducing online forum is the first approach to either visualize or request a real-time argument for online discussions. Our forum can be used to collect and induce claim-reasons pairs rather than only opinions to understand various lines of reasoning in online arguments such as civic discussions, online debates, and education objectives. The argument estimator code is available at https://github.com/EdoFrank/EMNLP2018-ArgMining-Morio and the demonstration video is available at https://youtu.be/T9fNJfneQV8.
Multimodal Emotion Classification
Authors: Anurag Illendula, Amit P. Sheth
Venue: WWW
Year: 2019

Most NLP and Computer Vision tasks are limited to scarcity of labelled data. In social media emotion classification and other related tasks, hashtags have been used as indicators to label data. With the rapid increase in emoji usage of social media, emojis are used as an additional feature for major social NLP tasks. However, this is less explored in case of multimedia posts on social media where posts are composed of both image and text. At the same time, w.e have seen a surge in the interest to incorporate domain knowledge to improve machine understanding of text. In this paper, we investigate whether domain knowledge for emoji can improve the accuracy of emotion classification task. We exploit the importance of different modalities from social media post for emotion classification task using state-of-the-art deep learning architectures. Our experiments demonstrate that the three modalities (text, emoji and images) encode different information to express emotion and therefore can complement each other. Our results also demonstrate that emoji sense depends on the textual context, and emoji combined with text encodes better information than considered separately. The highest accuracy of 71.98% is achieved with a training data of 550k posts.
Lessons from neuroscience
Authors: Laurent Itti
Venue: WWW
Year: 2019

Visual attention and eye movements in primates have been widely shown to be guided by a combination of stimulus-dependent or 'bottom-up' cues, as well as task-dependent or 'top-down' cues. Both the bottom-up and top-down aspects of attention and eye movements have been modeled computationally. Yet, it is not until recent work which I will describe that bottom-up models have been strictly put to the test, predicting significantly above chance the eye movement patterns, functional neuroimaging activation patterns, or most recently neural activity in the superior colliculus of human or monkey participants inspecting complex static or dynamic scenes. In recent developments, models that increasingly attempt to capture top-down aspects have been proposed. In one system which I will describe, neuromorphic algorithms of bottom-up visual attention are employed to predict, in a task-independent manner, which elements in a video scene might more strongly attract attention and gaze. These bottom-up predictions have more recently been combined with top-down predictions, which allowed the system to learn from examples (recorded eye movements and actions of humans engaged in 3D video games, including flight combat, driving, first-person, or running a hot-dog stand that serves hungry customers) how to prioritize particular locations of interest given the task. Pushing deeper into real-time, joint online analysis of video and eye movements using neuromorphic models, we have recently been able to predict future gaze locations and intentions of future actions when a player is engaged in a task. In a similar approach where computational models provide a normative gold standard against a particular individual's gaze behavior, machine learning systems have been demonstrated which can predict, from eye movement recordings during as little as only 5 minutes of watching TV, whether a person has ADHD or other neurological disorders. Together, these studies suggest that it is possible to build fully computational models that coarsely capture some aspects of both bottom-up and top-down visual attention.
Understanding Travel from Web Queries using Domain Knowledge from Wikipedia
Authors: Chander Iyer, Srinath Ravindran
Venue: WWW
Year: 2019

Developing a deeper understanding of the travel domain is helpful for presenting users with consistent and reliable information, and few sources of data are able to achieve that. Further, such information can serve as background knowledge for evaluating machine learning algorithms. In this paper, we present part of our work towards developing such an understanding. We demonstrate a simple extraction technique and how the extracted data can be used to evaluate an unsupervised embedding model built on search queries with travel intent.
AI for Good at Scale: Challenges in Real Time Emergency Response
Authors: Alex Jaimes
Venue: WWW
Year: 2019

In most emergencies, people use social media platforms to publicly share information. Such data, from multiple sources is extremely useful for emergency response and public safety: the more knowledge that is gathered, the better the response can be. When an emergency event first occurs, getting the right information as quickly as possible is critical in saving lives. When an emergency event is ongoing, information on what is happening can be critical in making decisions to keep people safe and take control of the particular situation unfolding. In both cases, first responders have to quickly make decisions that include what resources to deploy and where. In this talk, I will describe challenges in emergency response and how a computational platform that leverages public data can address them. A platform to detect emergency situations and deliver the right information to first responders has to deal with ingesting thousands of data points per second: sifting through and identifying relevant information, from different sources, in different formats, with varying levels of detail, in real time, so that first responders and others can be alerted at the right level and at the right time. I will describe technical challenges in processing vast amounts of heterogenous data in real time, highlighting the importance of interdisciplinary research and a human-centered approach to address problems in emergency response. I will give specific examples and discuss relevant research topics in Machine Learning, NLP, Information Retrieval, Computer Vision and other fields.
UNVEIL: Capture and Visualise WiFi Data Leakages
Authors: Shubham Jain, Eden Bensaid, Yves-Alexandre de Montjoye
Venue: WWW
Year: 2019

In the past few years, numerous privacy vulnerabilities have been discovered in the WiFi standards and their implementations for mobile devices. These vulnerabilities allow an attacker to collect large amounts of data on the device user, which could be used to infer sensitive information such as religion, gender, and sexual orientation. Solutions for these vulnerabilities are often hard to design and typically require many years to be widely adopted, leaving many devices at risk. In this paper, we present UNVEIL - an interactive and extendable platform to demonstrate the consequences of these attacks. The platform performs passive and active attacks on smartphones to collect and analyze data leaked through WiFi and communicate the analysis results to users through simple and interactive visualizations. The platform currently performs two attacks. First, it captures probe requests sent by nearby devices and combines them with public WiFi location databases to generate a map of locations previously visited by the device users. Second, it creates rogue access points with SSIDs of popular public WiFis (e.g. _Heathrow WiFi, Railways WiFi) and records the resulting internet traffic. This data is then analyzed and presented in a format that highlights the privacy leakage. The platform has been designed to be easily extendable to include more attacks and to be easily deployable in public spaces. We hope that UNVEIL will help raise public awareness of privacy risks of WiFi networks.
Dynamics of Misinformation Cascades
Authors: Jasser Jasser
Venue: WWW
Year: 2019

The study of information-sharing cascades has been a constant endeavor since the emergence of social networks. Internet memes which mostly consist of catchphrases, viral images, or small videos shared over the social network are notorious for attracting the users’ attention and spreading through the web in a fast fashion. Misinformation propagators latch their message to a meme to maximize the influence and spreading of the false news. As a result, the diffusion of misleading content has become a force to be reckoned with in the field of information warfare, as foreign actors seek to change opinions, manipulate ideologies, and create conflicts. In this study, we analyze the rapid dissemination of misinformation, aka, misinformation cascades, focusing on cascade temporal behavior and multi-cascade influence relationships. Twitter data used in this study contains only information associated with the Russian Internet Agency (IRA) and the Iranian Cyber Army (ICA). Our study focuses on analyzing temporal patterns of information dynamics created by these foreign actors for the sole purpose of spreading misinformation. We explore dividing temporal cascades into phases, where each phase differs from the previous regarding the number and characteristics of the information bursts. For this preliminary study, we are focusing on the #Trump and #USA hashtags used by the ICA. By studying the dynamics behind each phase, the forces behind the transition from one phase to another, and the influence relationships between cascades and their phases, we expect to shed some light on the timely subject of how to identify and protect society from information manipulation campaigns.
What's in the Content of Wikipedia's Article for Deletion Discussions?
Authors: Ali Javanmardi, Lu Xiao
Venue: WWW
Year: 2019

As a successful decentralized online peer production system, Wikipedia has a large number of users participating in selfassigned tasks based on their interest. Participants often coordinate on the tasks about editing an article through the article’s talk page discussions. Researchers analyze Wikipedia talk page discussions for various aspects such as coordination [13], conflict management [1], emotion [8], leadership [12], the relationship between Wikipedia article’s talk page discussion and the article editing activity [5], etc. Besides talk page discussions, Wikipedia’s deletion discussions have also caught researchers’ attentions. In Wikipedia, there are four mechanisms with respect to deleting an article from the web site: speedy deletion, proposed deletion, BLP Deletion, and deletion discussion. Deletion discussion mechanism is designed for controversial articles that the deletion needs to be discussed to reach a conclusion. Also, if a proposed article cannot be tagged with one of the first three mechanisms, it will be tagged for deletion discussion. Researchers have explored various factors of the deletion discussion process and outcome. For example, [19] examined voting behavior in deletion discussions, with a specific emphasis on how early opinions in the debate affect later voices. [6]
ClickGraph: Web Page Embedding using Clickstream Data for Multitask Learning
Authors: Porter Jenkins
Venue: WWW
Year: 2019

The rise of big data frameworks has given website administrators the ability to track user clickstream data with more detail than ever before. These clickstreams can represent the user’s intent and purpose in visiting the site. While existing work has explored methods for predicting future user actions, these methods are limited focus solely on one task at a time, ignore graph structure inherent in clickstreams, or model the conversion of the entire clickstream session, ignoring complexities such as multiple conversions in a single session. In this work, we formulate the novel problem of simultaneously predicting future user actions given a user’s clickstream history. We argue that clickstream data contains important signal for predicting future user action. To tackle this new problem, we propose a novel method called ClickGraph, a recurrent neural network that encodes the graph structure of user click trajectories in the learned representations of web pages. We conduct experiments on a real-world dataset and demonstrate that this multitask learning approach is effective at improving the prediction of form fill conversions over strong baselines. In particular, we demonstrate that the ClickGraph model is effective at reducing false positive rates, increasing F1 scores, and improving recall.
SmartDBO: Smart Docker Benchmarking Orchestrator for Web-application
Authors: Devki Nandan Jha, Michael Nee, Zhenyu Wen, Albert Y. Zomaya, Rajiv Ranjan
Venue: WWW
Year: 2019

Containerized web-applications have gained popularity recently due to the advantages provided by the containers including light-weight, packaged, fast start up and shut down and easy scalability. As there are more than 267 cloud providers, finding a flexible deployment option for containerized web-applications is very difficult as each cloud offers numerous deployment infrastructure. Benchmarking is one of the eminent options to evaluate the provisioned resources before product-level deployment. However, benchmarking the massive infrastructure resources provisioned by various cloud providers is a time consuming, tedious and costly process and is not practical to accomplish manually. In this demonstration, we present Smart Docker Benchmarking Orchestrator (SmartDBO), a general orchestration framework that automatically benchmarks (deploys and executes) users' containerized web-applications across different cloud providers while meeting the constraints of budget and deployment configurations. SmartDBO aims to answer two questions: (i) how to automate the benchmarking of containerized web-application across multi-cloud environments?, (ii) how to maximize the diversity in a benchmarking solution which covers maximum numbers of cloud providers and types of provisioned infrastructures without exceeding users' budgets? We evaluate all the features of SmartDBO using SimplCommerce and TPC-W executing across Amazon AWS and Microsoft Azure.
Alleviating Users' Pain of Waiting: Effective Task Grouping for Online-to-Offline Food Delivery Services
Authors: Shenggong Ji, Yu Zheng, Zhaoyuan Wang, Tianrui Li
Venue: WWW
Year: 2019

Ordering take-out food (a.k.a. takeaway food) on online-to-offline (O2O) food ordering and delivery platforms is becoming a new lifestyle for people living in big cities, thanks to its great convenience. Web users and mobile device users can order take-out food (i.e. obtain online food ordering services) on an O2O platform. Then the O2O platform will dispatch food carriers to deliver food from restaurants to users, i.e. providing users with offline food delivery services. For an O2O food ordering and delivery platform, improving food delivery efficiency, given the massive number of food orders each day and the limited number of food carriers, is of paramount importance to reducing the length of time users wait for their food. Thus, in this paper, we study the food delivery task grouping problem so as to improve food delivery efficiency and alleviate the pain of waiting for users, which to the best of our knowledge has not been studied yet. However, the food delivery task grouping problem is challenging, given two reasons. First, the food delivery efficiency is affected by multiple factors, which are non-trivial to formulate and jointly consider. Second, the problem is a typical NP-hard problem and to find near-optimal grouping results is not easy. To address these two issues, we propose an effective task grouping method. On one hand, we provide formal formulations for the factors affecting the food delivery efficiency, and provide an objective to organically combine these factors such that it can better guide the task grouping. On the other hand, we propose heuristic algorithms to efficiently obtain effective task grouping results, consisting of a greedy algorithm and a replacement algorithm. We evaluate our task grouping method using take-out food order data from web users and mobile device users on a real-world O2O food ordering and delivery platform. Experiment results demonstrate that our task grouping method can save ~ 16% (87 seconds) of average waiting time for each user, comparing with many baseline methods. It indicates that our method is able to significantly improve the food delivery efficiency and can provide better food delivery services for users.
Efficient Interaction-based Neural Ranking with Locality Sensitive Hashing
Authors: Shiyu Ji, Jinjin Shao, Tao Yang
Venue: WWW
Year: 2019

Interaction-based neural ranking has been shown to be effective for document search using distributed word representations. However the time or space required is very expensive for online query processing with neural ranking. This paper investigates fast approximation of three interaction-based neural ranking algorithms using Locality Sensitive Hashing (LSH). It accelerates query-document interaction computation by using a runtime cache with precomputed term vectors, and speeds up kernel calculation by taking advantages of limited integer similarity values. This paper presents the design choices with cost analysis, and an evaluation that assesses efficiency benefits and relevance tradeoffs for the tested datasets.
A Tree-Structured Neural Network Model for Household Energy Breakdown
Authors: Yiling Jia, Nipun Batra, Hongning Wang, Kamin Whitehouse
Venue: WWW
Year: 2019

Residential buildings constitute roughly one-fourth of the total energy use across the globe. Numerous studies have shown that providing an energy breakdown increases residents' awareness of energy use and can help save up to 15% energy. A significant amount of prior work has looked into source-separation techniques collectively called non-intrusive load monitoring (NILM), and most prior NILM research has leveraged high-frequency household aggregate data for energy breakdown. However, in practice most smart meters only sample hourly or once every 15 minutes, and existing NILM techniques show poor performance at such a low sampling rate. In this paper, we propose a TreeCNN model for energy breakdown on low frequency data. There are three key insights behind the design of our model: i) households consume energy with regular temporal patterns, which can be well captured by filters learned in CNNs; ii) tree structure isolates the pattern learning of each appliance that helps avoid magnitude variance problem, while preserves relationship among appliances; iii) tree structure enables the separation of known appliance from unknown ones, which de-noises the input time series for better appliance-level reconstruction. Our TreeCNN model outperformed seven existing baselines on a public benchmark dataset with lower estimation error and higher accuracy on detecting the active states of appliances.
User Donations in a User Generated Video System
Authors: Adele Lu Jia, Xiaoxue Shen, Siqi Shen, Yongquan Fu, Liwen Peng
Venue: WWW
Year: 2019

User generated video systems like YouTube and Twitch.tv have been a major internet phenomenon. They have attracted a vast user base with their many and varied contents provided by their users, and a series of social features tailored for online viewing. In hoping for building a more lively community and encouraging the content creators to share more, recently many such systems have introduced crowdsourcing mechanisms wherein creators get tangible rewards through user donations. User donation is a very special form of user relationships. It influences user engagement in the community, and has a great impact on the success of these systems. However, user donations and donation relationships remain trade secrets for most enterprises and to date are still unexplored. It is not clear at what scale are the donations or how users donate in these systems. In this work, we attempt to fill this gap. We obtain and provide a publicly available dataset on user donations in BiliBili, a popular user generated video system in China with 76.4 million average monthly active users. Based on detailed information on over 5 million videos, over 700 thousand content creators, and over 1.5 million user donations, we quantitatively reveal the characteristics of user donations, we examine their correlations with the upload behavior and content popularity of the creators, and we adopt machine-learned classifiers to accurately predict the creators who will receive donations and who will donate in the future.