OpenRaiser/Intern-Atlas huggingface dataset:
Bitcoin Price Prediction Through Opinion Mining
Authors: Germán Cheuque Cerda, Juan L. Reutter
Venue: WWW
Year: 2019

The Bitcoin protocol and its underlying cryptocurrency have started to shape the way we view digital currency, and opened up a large list of new and interesting challenges. Amongst them, we focus on the question of how is the price of digital currencies affected, which is a natural question especially when considering the price rollercoaster we witnessed for bitcoin in 2017-2018. We work under the hypothesis that price is affected by the web footprint of influential people, we refer to them as crypto-influencers. In this paper we provide neural models for predicting bitcoin price. We compare what happens when the model is fed only with recent price history versus what happens when fed, in addition, with a measure of the positivity or negativity of the sayings of these influencers, measured through a sentiment analysis of their twitter posts. We show preliminary evidence that twitter data should indeed help to predict the price of bitcoin, even though the measures we use in this paper have a lot of room for refinement. In particular, we also discuss the challenges of measuring the correct sensation of these posts, and discuss the work that should help improving our discoveries even further.
Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering
Authors: Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, Jaeho Choi
Venue: WWW
Year: 2019

Generative Adversarial Networks (GAN) have not only achieved a big success in various generation tasks such as images, but also boosted the accuracy of classification tasks by generating additional labeled data, which is called data augmentation. In this paper, we propose a Rating Augmentation framework with GAN, named RAGAN, aiming to alleviate the data sparsity problem in collaborative filtering (CF), eventually improving recommendation accuracy significantly. We identify a unique challenge that arises when applying GAN to CF for rating augmentation: naive RAGAN tends to generate values biased towards high ratings. Then, we propose a refined version of RAGAN, named RAGANBT, which addresses this challenge successfully. Via our extensive experiments, we validate that our RAGANBT is really effective to solve the data sparsity problem, thereby providing existing CF models with great improvement in accuracy under various situations such as basic top-N recommendation, long-tail item recommendation, and recommendation to cold-start users.
Are Two Heads Better Than One? An Exploration of Ambiguity in Crowd-Collected Language Decisions from the Phrase Detectives Game
Authors: Jon Chamberlain
Venue: WWW
Year: 2019

The online game-with-a-purpose Phrase Detectives (https://www.phrasedetectives.com) [1] has been collecting decisions about anaphoric coreference in human language for over 10 years (4 million judgements from 40,000 players). The game was originally designed to collect multiple valid solutions for a single task, which complicated aggregation but created a very rich (and noisy) dataset [2]. Analysis of the ambiguous player decisions highlights the need for understanding and resolving disagreement that is inherent in language interpretation. This talk will present some of the interesting cases of ambiguity found by the players of Phrase Detectives (a dataset that will be made available to the research community later this year [3]) and discuss the statistical methods we have been working on to harness crowds that disagree with each other [4, 5].
Revisiting Opinion Dynamics with Varying Susceptibility to Persuasion via Non-Convex Local Search
Authors: T.-H. Hubert Chan, Zhibin Liang, Mauro Sozio
Venue: WWW
Year: 2019

We revisit the opinion susceptibility problem that was proposed by Abebe et al. [1], in which agents influence one another's opinions through an iterative process. Each agent has some fixed innate opinion. In each step, the opinion of an agent is updated to some convex combination between its innate opinion and the weighted average of its neighbors' opinions in the previous step. The resistance of an agent measures the importance it places on its innate opinion in the above convex combination. Under non-trivial conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to select the resistance of each agent (from some given range) such that the sum of the equilibrium opinions is minimized. Contrary to the claim in the aforementioned KDD 2018 paper, the objective function is in general non-convex. Hence, formulating the problem as a convex program might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes.
RecBoard: A Web-based Platform for Recommendation System Research and Development
Authors: Mohit Chawla, Kriti Singh, Longqi Yang, Deborah Estrin
Venue: WWW
Year: 2019

This paper introduces RecBoard, a unified web-based platform that facilitates researchers and practitioners to train, test, deploy, and monitor recommendation systems. RecBoard streamlines the end-to-end process of building recommendation systems by providing a collaborative user interface that automates repetitive tasks related to dataset management, model training, visualization, deployments, and monitoring. Our demo prototype demonstrates how RecBoard can empower common tasks in research and development. RecBoard will be open-sourced and publicly available upon publication.
Creating Public Value by Democratizing the Ecosystem of Human Service Providers
Authors: Charalampos Chelmis, Mengfan Yao
Venue: WWW
Year: 2019

Human service providers play a critical role in improving well–being in the United States. However, little is know about (i) how service seekers find the services they are looking for by navigating among available service providers, and (ii) how such organizations collaborate to meet human needs. In this paper, we report the first outcomes of our ongoing project. Specifically, we first describe a data acquisition engine, designed around the particular challenges of capturing, maintaining, and updating data pertaining to human service organizations from semistructured Web sources. We then proceed to illustrate the potential of the resulting comprehensive repository of human service providers through a case study showcasing a mobile app prototype designed to provide a one–stop shop for human service seekers.
SamWalker: Social Recommendation with Informative Sampling Strategy
Authors: Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen
Venue: WWW
Year: 2019

Recommendation from implicit feedback is a highly challenging task due to the lack of reliable negative feedback data. Only positive feedback are observed and the unobserved feedback can be attributed to two reasons: unknow or dislike. Existing methods address this challenge by treating all the un-observed data as negative (dislike) but downweight the confidence of these data. However, this treatment causes two problems: (1) Confidence weights of the unobserved data are usually assigned manually, which lack flexible and may create empirical bias in evaluating user's preference. (2) To handle massive volume of the unobserved feedback data, most of the existing methods rely on stochastic inference and data sampling strategies. However, since users are only aware of a very small fraction of items in a large dataset, it is difficult for existing samplers to select informative training instances in which the user really dislikes the item rather than does not know it. To address the above two problems, we propose a new recommendation method SamWalker that leverages social information to infer data confidence and guide the sampling process. By modeling data confidence with a social context-aware function, SamWalker can adaptively specify different weights to different data based on users' social contexts. Further, a personalized random-walk-based sampling strategy is developed to adaptively draw informative training instances, which can speed up gradient estimation and reduce sampling variance. Extensive experiments on three real-world datasets demonstrate the superiority of the proposed SamWalker method and its sampling strategy.
BoFGAN: Towards A New Structure of Backward-or-Forward Generative Adversarial Nets
Authors: M. K. Sophie Chen, Xinyi Lin, Wei Chen, Rui Yan
Venue: WWW
Year: 2019

Natural Language Generation (NLG), as an important part of Natural Language Processing (NLP), has begun to take full advantage of recent advances in language models. Based on recurrent neural networks (RNNs), NLG has made ground breaking improvement and is widely applied in many tasks. RNNs typically learn a joint probability of words, and the additional information is usually fed to RNNs hidden layer using implicit vector representations. Still, there exists some problem unsolved. Standard RNN is not applicable when we need to impose hard constraints on the language generation tasks: for example, standard RNNs cannot guarantee designated word(s) to appear in a target sentence to generate. In this paper, we propose a Backward-or-Forward Generative Adversarial Nets model (BoFGAN) to address this problem. Starting from a particular given word, a generative model at every time step generates a new preceding or subsequent word conditioned on the generated sequence so far until both sides reach an end. To train the generator, we first model it as a stochastic policy using Reinforcement Learning; then we employ a discriminator to evaluate the quality of a complete sequence as the end reward; and lastly, we apply Monte Carlo (MC) search to estimate the long-term return and update the generator via policy gradient. Experimental results demonstrate the effectiveness and rationality of our proposed BoFGAN model.
Robust Cyberbullying Detection with Causal Interpretation
Authors: Lu Cheng, Ruocheng Guo, Huan Liu
Venue: WWW
Year: 2019

Cyberbullying poses serious threats to preteens and teenagers, therefore, understanding the incentives behind cyberbullying is critical to prevent its happening and mitigate the impact. Most existing work towards cyberbullying detection has focused on the accuracy, and overlooked causes of the outcome. Discovering the causes of cyberbullying from observational data is challenging due to the existence of confounders, variables that can lead to spurious causal relationships between covariates and the outcome. This work studies the problem of robust cyberbullying detection with causal interpretation and proposes a principled framework to identify and block the influence of the plausible confounders, i.e., p-confounders. The de-confounded model is causally interpretable and is more robust to the changes in data distribution. We test our approach using the state-of-the-art evaluation method, causal transportability. The experimental results corroborate the effectiveness of our proposed algorithm. The purpose of this study is to provide a computational means to understanding cyberbullying behavior from observational data. This improves our ability to predict and to facilitate effective strategies or policies to proactively mitigate the impact of cyberbullying.
Mobility as a Service
Authors: Rob Christiaanse
Venue: WWW
Year: 2019

MAAS as a mobility model leans on the idea that a gap between private and public transport systems needs to be bridged as well as on a city, intercity, national and supranational level. The current situation is felt problematic due to the fragmented tools and services often organized in silos to meet a traveler needs to undertake a trip. One of the major concerns designing any platform system like Mobility as a Service is where to start modeling and how to express the notion of the platform system in some language that is understandable for all stakeholders of the platform system. Understandability buttresses the expectation of stakeholders whether some design will probably implement the intended platform services enabling users to actually buy and or use the platform system for what ever purpose. Building on the economic theories of two-sided markets and mechanism design we introduce the concept of value nets extending the Contract Protocol Net. Value net modeling offers a precise abstract representation which provides in the detailed informational requirements in a canonical form and it connects i.e. implements the abstract notion of Service Oriented Architecture characterizing systems without loss of crucial informational elements.
Cross-Network Embedding for Multi-Network Alignment
Authors: Xiaokai Chu, Xinxin Fan, Di Yao, Zhihua Zhu, Jianhui Huang, Jingping Bi
Venue: WWW
Year: 2019

Recently, data mining through analyzing the complex structure and diverse relationships on multi-network has attracted much attention in both academia and industry. One crucial prerequisite for this kind of multi-network mining is to map the nodes across different networks, i.e., so-called network alignment. In this paper, we propose a cross-network embedding method CrossMNA for multi-network alignment problem through investigating structural information only. Unlike previous methods focusing on pair-wise learning and holding the topology consistent assumption, our proposed CrossMNA considers the multi-network scenarios which involve at least two types of networks with diverse network structures. CrossMNA leverages the cross-network information to refine two types of node embedding vectors, i.e., inter-vector for network alignment and intra-vector for other downstream network analysis tasks. Finally, we verify the effectiveness and efficiency of our proposed method using several real-world datasets. The extensive experiments show that our CrossMNA can significantly outperform the existing baseline methods on multi-network alignment task, and also achieve better performance for link prediction task with less memory usage.
TiFi: Taxonomy Induction for Fictional Domains
Authors: Cuong Xuan Chu, Simon Razniewski, Gerhard Weikum
Venue: WWW
Year: 2019

Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin.
MAMS: Multi-Agent MicroServices✱
Authors: Rem W. Collier, Eoin O&apos, Neill, David Lillis, Gregory M. P. O&apos, Hare
Venue: WWW
Year: 2019

This paper explores the intersection between microservices and Multi-Agent Systems (MAS), introducing the notion of a new approach to building MAS known as Multi-Agent MicroServices (MAMS). Our approach is illustrated through a worked example of a Vickrey Auction implemented as a microservice.
A Semi-Supervised Active-learning Truth Estimator for Social Networks
Authors: Hang Cui, Tarek F. Abdelzaher, Lance M. Kaplan
Venue: WWW
Year: 2019

This paper introduces an active-learning-based truth estimator for social networks, such as Twitter, that enhances estimation accuracy significantly by requesting a well-selected (small) fraction of data to be labeled. Data assessment and truth discovery from arbitrary open online sources are a hard problem due to uncertainty regarding source reliability. Multiple truth finding systems were developed to solve this problem. Their accuracy is limited by the noisy nature of the data, where distortions, fabrications, omissions, and duplication are introduced. This paper presents a semi-supervised truth estimator for social networks, in which a portion of inputs are carefully selected to be reliably verified. The challenge is to find the subset of observations to verify that would maximally enhance the overall fact-finding accuracy. This work extends previous passive approaches to recursive truth estimation, as well as semi-supervised approaches where the estimator has no control over the choice of data to be labeled. Results show that by optimally selecting claims to be verified, we improve estimated accuracy by 12% over unsupervised baseline, and by 5% over previous semi-supervised approaches.
TIM: A Tool for Gaining Insights into Psychotherapy
Authors: Ronan Cummins, Michael P. Ewbank, Alan Martin, Valentin Tablan, Ana Catarino, Andrew D. Blackwell
Venue: WWW
Year: 2019

We introduce and demonstrate the usefulness of a tool that automatically annotates therapist utterances in real-time according to the therapeutic role that they perform in an evidence-based psychological dialogue. This is implemented within the context of an on-line service that supports the delivery of one-to-one therapy. When combined with patient outcome measures, this tool allows us to discover the active ingredients in psychotherapy. In particular, we show that particular measures of therapy content are more strongly correlated with patient improvement than others, suggesting that they are a critical part of psychotherapy. As this tool gives us interpretable measures of therapy content, it can enable services to quality control the therapy delivered. Furthermore, we show how specific insights can be presented to the therapist so they can reflect on and improve their practice.
Location Embeddings for Next Trip Recommendation
Authors: Amine Dadoun, Raphaël Troncy, Olivier Ratier, Riccardo Petitti
Venue: WWW
Year: 2019

The amount of information available in social media and specialized blogs has become useful for a user to plan a trip. However, the user is quickly overwhelmed by the list of possibilities offered to him, making his search complex and time-consuming. Recommender systems aim to provide personalized suggestions to users by leveraging different type of information, thus assisting them in their decision-making process. Recently, the use of neural networks and knowledge graphs have proven to be efficient for items recommendation. In our work, we propose an approach that leverages contextual, collaborative and content information in order to recommend personalized destinations to travelers. We compare our approach with a set of state of the art collaborative filtering methods and deep learning based recommender systems.
Local Matching Networks for Engineering Diagram Search
Authors: Zhuyun Dai, Zhen Fan, Hafeezul Rahman, Jamie Callan
Venue: WWW
Year: 2019

Finding diagrams that contain a specific part or a similar part is important in many engineering tasks. In this search task, the query part is expected to match only a small region in a complex image. This paper investigates several local matching networks that explicitly model local region-to-region similarities. Deep convolutional neural networks extract local features and model local matching patterns. Spatial convolution is employed to cross-match local regions at different scale levels, addressing cases where the target part appears at a different scale, position, and/or angle. A gating network automatically learns region importance, removing noise from sparse areas and visual metadata in engineering diagrams. Experimental results show that local matching approaches are more effective for engineering diagram search than global matching approaches. Suppressing unimportant regions via the gating network enhances accuracy. Matching across different scales via spatial convolution substantially improves robustness to scale and rotation changes. A pipelined architecture efficiently searches a large collection of diagrams by using a simple local matching network to identify a small set of candidate images and a more sophisticated network with convolutional cross-scale matching to re-rank candidates.
When a Merperson is a Merman
Authors: Jennifer Daniel
Venue: WWW
Year: 2019

Currently, to support gender inclusive codepoints (for example, gender inclusive can be defined as male/female to an equal degree, can neither be confidently identified as male/female, etc.) all major platforms default to a male or a female design. So, if someone were to send a text to their friend from a Microsoft device, “Love a good mansplain 1F925” their friend, if reading from an iPhone will see, “Love a good mansplain 1F926” even though both of these emojis map to U+1F926. This creates all kinds of cross platform inconsistencies and in some cases reinforces stereotypes.1
Evaluating Anti-Fingerprinting Privacy Enhancing Technologies
Authors: Amit Datta, Jianan Lu, Michael Carl Tschantz
Venue: WWW
Year: 2019

We study how to evaluate Anti-Fingerprinting Privacy Enhancing Technologies (AFPETs). Experimental methods have the advantage of control and precision, and can be applied to new AFPETs that currently lack a user base. Observational methods have the advantage of scale and drawing from the browsers currently in real-world use. We propose a novel combination of these methods, offering the best of both worlds, by applying experimentally created models of a AFPET's behavior to an observational dataset. We apply our evaluation methods to a collection of AFPETs to find the Tor Browser Bundle to be the most effective among them. We further uncover inconsistencies in some AFPETs' behaviors.
Deep Learning for Solving Important Problems
Authors: Jeff Dean
Venue: WWW
Year: 2019

In this keynote we describe progress in work that our research teams have been doing over the past years, including advances in difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to dozens of Google products. Our group has open-sourced the TensorFlow system [2], a widely popular system designed to easily express machine learning ideas, and to quickly train, evaluate and deploy machine learning systems. We then highlight some of our research accomplishments, and relate them to the National Academy of Engineering's Grand Engineering Challenges for the 21st Century. This is joint work with many people at Google.