OpenRaiser/Intern-Atlas huggingface dataset:
ARK-Virus: An ARK Platform Extension for Mindful Risk Governance of Personal Protective Equipment Use in Healthcare
Authors: Lucy McKenna, Junli Liang, Natalia Duda, Nick McDonald, Rob Brennan
Venue: WWW
Year: 2021

In this demonstration we present the Access Risk Knowledge (ARK) Platform - a socio-technical risk governance system. Through the ARK Virus Project, the ARK Platform has been extended for risk management of personal protective equipment (PPE) in healthcare settings during the COVID-19 pandemic. ARK demonstrates the benefits of a Semantic Web approach for supporting both the integration and classification of qualitative and quantitative PPE risk data, across multiple healthcare organisations, in order to generate a unique unified evidence base of risk. This evidence base could be used to inform decision making processes regarding PPE use.
Proposing a Broader Scope of Predictive Features for Modeling Refugee Counts
Authors: Esther Mead, Maryam Maleki, Recep Erol, Nitin Agarwal
Venue: WWW
Year: 2021

The world-wide refugee problem has a long history, but continues to this day, and will unfortunately continue into the foreseeable future. Efforts to anticipate, mitigate and prepare for refugee counts, however, are still lacking. There are many potential causes, but the published research has primarily focused on identifying ways to integrate already existing refugees into the various communities wherein they ultimately reside, rather than on preventive measures. The work proposed herein uses a set of features that can be divided into three basic categories: 1) sociocultural, 2) socioeconomic, and 3) economic, which refer to the nature of each proposed predictive feature. For example, corruption perception is a sociocultural feature, access to healthcare is a socioeconomic feature, and inflation is an economic feature. Forty-five predictive features were collected for various years and countries of interest. As may seem intuitive, the features that fell under the category of "economic" produced the highest predictive value from the regression technique employed. However, additional potential predictive features that have not been previously addressed stood out in our experiments. These include: the global peace index (gpi), freedom of expression (fe), internet users (iu), access to healthcare (hc), cost of living index (coli), local purchasing power index (lppi), homicide rate (hr), access to justice (aj), and women's property rights (wpr). Many of these features are nascent in terms of both their development and collection, as well as the fact that some of these features are not yet collected at a universal level, meaning that the data is missing for some countries and years. Ongoing work regarding these datasets for predicting refugee counts is also discussed in this work.
Towards a Lightweight, Hybrid Approach for Detecting DOM XSS Vulnerabilities with Machine Learning
Authors: William Melicher, Clement Fung, Lujo Bauer, Limin Jia
Venue: WWW
Year: 2021

Client-side cross-site scripting (DOM XSS) vulnerabilities in web applications are common, hard to identify, and difficult to prevent. Taint tracking is the most promising approach for detecting DOM XSS with high precision and recall, but is too computationally expensive for many practical uses. We investigate whether machine learning (ML) classifiers can replace or augment taint tracking when detecting DOM XSS vulnerabilities. Through a large-scale web crawl, we collect over 18 billion JavaScript functions and use taint tracking to label over 180,000 functions as potentially vulnerable. With this data, we train a deep neural network (DNN) to analyze a JavaScript function and predict if it is vulnerable to DOM XSS. We experiment with a range of hyperparameters and present a low-latency, high-recall classifier that could serve as a pre-filter to taint tracking, reducing the cost of stand-alone taint tracking by 3.43 × while detecting 94.5% of unique vulnerabilities. We argue that this combination of a DNN and taint tracking is efficient enough for a range of use cases for which taint tracking by itself is not, including in-browser run-time DOM XSS detection and analyzing large codebases.
Language, Vision and Action are Better Together
Authors: Rada Mihalcea
Venue: WWW
Year: 2021

Much of what we do today is centered around humans — whether it is creating the next generation smartphones, understanding interactions with social media platforms, or developing new mobility strategies. A better understanding of people can not only answer fundamental questions about “us” as humans, but can also facilitate the development of enhanced, personalized technologies. In this talk, I will overview the main challenges (and opportunities) faced by research on multimodal sensing of human behavior, and illustrate these challenges with projects conducted in the Language and Information Technologies lab at Michigan.
PROV4ITDaTa: Transparent and direct transferof personal data to personal stores
Authors: Gertjan De Mulder, Ben De Meester, Pieter Heyvaert, Ruben Taelman, Anastasia Dimou, Ruben Verborgh
Venue: WWW
Year: 2021

Data is scattered across service providers, heterogeneously structured in various formats. By lack of interoperability, data portability is hindered, and thus user control is inhibited. An interoperable data portability solution for transferring personal data is needed. We demo PROV4ITDaTa: a Web application, that allows users to transfer personal data into an interoperable format to their personal data store. PROV4ITDaTa leverages the open-source solutions RML.io, Comunica, and Solid: (i) the RML.io toolset to describe how to access data from service providers and generate interoperable datasets; (ii) Comunica to query these and more flexibly generate enriched datasets; and (iii) Solid Pods to store the generated data as Linked Data in personal data stores. As opposed to other (hard-coded) solutions, PROV4ITDaTa is fully transparent, where each component of the pipeline is fully configurable and automatically generates detailed provenance trails. Furthermore, transforming the personal data into RDF allows for an interopable solution. By maximizing the use of open-source tools and open standards, PROV4ITDaTa facilitates the shift towards a data ecosystem wherein users have control of their data, and providers can focus on their service instead of trying to adhere to interoperability requirements.
WebSocket Adoption and the Landscape of the Real-Time Web
Authors: Paul Murley, Zane Ma, Joshua Mason, Michael D. Bailey, Amin Kharraz
Venue: WWW
Year: 2021

Developers are increasingly deploying web applications which require real-time bidirectional updates, a use case which does not naturally align with the traditional client-server architecture of the web. Many solutions have arisen to address this need over the preceding decades, including HTTP polling, Server-Sent Events, and WebSockets. This paper investigates this ecosystem and reports on the prevalence, benefits, and drawbacks of these technologies, with a particular focus on the adoption of WebSockets. We crawl the Tranco Top 1 Million websites to build a dataset for studying real-time updates in the wild. We find that HTTP Polling remains significantly more common than WebSockets, and WebSocket adoption appears to have stagnated in the past two to three years. We investigate some of the possible reasons for this decrease in the rate of adoption, and we contrast the adoption process to that of other web technologies. Our findings further suggest that even when WebSockets are employed, the prescribed best practices for securing them are often disregarded. The dataset is made available in the hopes that it may help inform the development of future real-time solutions for the web.
Affect Classification in Tweets using Multitask Deep Neural Networks
Authors: Seema Nagar, Achintya Shankhdhar, Ferdous Ahmed Barbhuiya, Kuntal Dey
Venue: WWW
Year: 2021

We propose a multitask deep neural network for detecting affect-retweet pairs for Twitter tweets. Each task given to our network jointly learns a given affect, e.g. hate, sarcasm etc., along with learning retweeting behaviour as an auxiliary task, from a given tweet corpus. On test data, this model allows us to predict retweet behaviour in the absence of any further meta-data, along with identifying affect. This allows us also to predict whether a tweet with affect would go viral or not. Our model delivers F1-scores of 0.93 and 0.91 for hate and sarcasm detection respectively, and predicts retweets with the accuracy of 71% and 60% respectively, delivering state-of-the-art performance on benchmark data.
Biomedical Vocabulary Alignment at Scale in the UMLS Metathesaurus
Authors: Vinh Nguyen, Hong Yung Yip, Olivier Bodenreider
Venue: WWW
Year: 2021

With 214 source vocabularies, the construction and maintenance process of the UMLS (Unified Medical Language System) Metathesaurus terminology integration system is costly, time-consuming, and error-prone as it primarily relies on (1) lexical and semantic processing for suggesting groupings of synonymous terms, and (2) the expertise of UMLS editors for curating these synonymy predictions. This paper aims to improve the UMLS Metathesaurus construction process by developing a novel supervised learning approach for improving the task of suggesting synonymous pairs that can scale to the size and diversity of the UMLS source vocabularies. We evaluate this deep learning (DL) approach against a rule-based approach (RBA) that approximates the current UMLS Metathesaurus construction process. The key to the generalizability of our approach is the use of various degrees of lexical similarity in negative pairs during the training process. Our initial experiments demonstrate the strong performance across multiple datasets of our DL approach in terms of recall (91-92%), precision (88-99%), and F1 score (89-95%). Our DL approach largely outperforms the RBA method in recall (+23%), precision (+2.4%), and F1 score (+14.1%). This novel approach has great potential for improving the UMLS Metathesaurus construction process by providing better synonymy suggestions to the UMLS editors.
Modeling Sparse Information Diffusion at Scale via Lazy Multivariate Hawkes Processes
Authors: Maximilian Nickel, Matthew Le
Venue: WWW
Year: 2021

Multivariate Hawkes Processes (MHPs) are an important class of temporal point processes that have enabled key advances in understanding and predicting social information systems. However, due to their complex modeling of temporal dependencies, MHPs have proven to be notoriously difficult to scale, what has limited their applications to relatively small domains. In this work, we propose a novel model and computational approach to overcome this important limitation. By exploiting a characteristic sparsity pattern in real-world diffusion processes, we show that our approach allows to compute the exact likelihood and gradients of an MHP – independently of the ambient dimensions of the underlying network. We show on synthetic and real-world datasets that our method does not only achieve state-of-the-art modeling results, but also improves runtime performance by multiple orders of magnitude on sparse event sequences. In combination with easily interpretable latent variables and influence structures, this allows us to analyze diffusion processes in networks at previously unattainable scale.
Robust Generalization and Safe Query-Specializationin Counterfactual Learning to Rank
Authors: Harrie Oosterhuis, Maarten de Rijke
Venue: WWW
Year: 2021

Existing work in counterfactual Learning to Rank (LTR) has focussed on optimizing feature-based models that predict the optimal ranking based on document features. LTR methods based on bandit algorithms often optimize tabular models that memorize the optimal ranking per query. These types of model have their own advantages and disadvantages. Feature-based models provide very robust performance across many queries, including those previously unseen, however, the available features often limit the rankings the model can predict. In contrast, tabular models can converge on any possible ranking through memorization. However, memorization is extremely prone to noise, which makes tabular models reliable only when large numbers of user interactions are available. Can we develop a robust counterfactual LTR method that pursues memorization-based optimization whenever it is safe to do? We introduce the Generalization and Specialization (GENSPEC) algorithm, a robust feature-based counterfactual LTR method that pursues per-query memorization when it is safe to do so. Generalization and Specialization (GENSPEC) optimizes a single feature-based model for generalization: robust performance across all queries, and many tabular models for specialization: each optimized for high performance on a single query. GENSPEC uses novel relative high-confidence bounds to choose which model to deploy per query. By doing so, GENSPEC enjoys the high performance of successfully specialized tabular models with the robustness of a generalized feature-based model. Our results show that GENSPEC leads to optimal performance on queries with sufficient click data, while having robust behavior on queries with little or noisy data.
Political Polarization and Platform Migration: : A Study of Parler and Twitter Usage by United States of America Congress Members
Authors: Jacqueline M. Otala, Gillian Kurtic, Isabella Grasso, Yu Liu, Jeanna N. Matthews, Golshan Madraki
Venue: WWW
Year: 2021

Growing dissatisfaction with platform governance decisions at major social media platforms like Twitter, Facebook, and Instagram has led to a number of substantial efforts, originating both on the political right and the political left, to shift to new platforms. In this paper, we examine one of the most impactful of these platform migration efforts, a recent effort primarily on the political right to shift from Twitter to Parler in response to Twitter's increased efforts to flag misinformation in the lead up to the 2020 election in the US. As a case study, we analyze the usage of Parler by all members of the United States Congress and compare that to their usage of Twitter. Even though usage of Parler, even at its peak, was only a small percentage of Twitter usage, Parler usage has been impactful. Specifically, it was linked to the planning of the January 6, 2021 attack on the United States Capitol building. Going forward, Parler itself may not have a large and lasting impact, but it offers important lessons about the relationship between political polarization, platform migration, and the real-world political impacts of platform governance decisions and the splintering of our media landscape.
AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph✱
Authors: Zheyi Pan, Songyu Ke, Xiaodu Yang, Yuxuan Liang, Yong Yu, Junbo Zhang, Yu Zheng
Venue: WWW
Year: 2021

Spatio-temporal graphs are important structures to describe urban sensory data, e.g., traffic speed and air quality. Predicting over spatio-temporal graphs enables many essential applications in intelligent cities, such as traffic management and environment analysis. Recently, many deep learning models have been proposed for spatio-temporal graph prediction and achieved significant results. However, designing neural networks requires rich domain knowledge and expert efforts. To this end, we study automated neural architecture search for spatio-temporal graphs with the application to urban traffic prediction, which meets two challenges: 1) how to define search space for capturing complex spatio-temporal correlations; and 2) how to learn network weight parameters related to the corresponding attributed graph of a spatio-temporal graph. To tackle these challenges, we propose a novel framework, entitled AutoSTG, for automated spatio-temporal graph prediction. In our AutoSTG, spatial graph convolution and temporal convolution operations are adopted in our search space to capture complex spatio-temporal correlations. Besides, we employ the meta learning technique to learn the adjacency matrices of spatial graph convolution layers and kernels of temporal convolution layers from the meta knowledge of the attributed graph. And specifically, such meta knowledge is learned by a graph meta knowledge learner that iteratively aggregates knowledge on the attributed graph. Finally, extensive experiments were conducted on two real-world benchmark datasets to demonstrate that AutoSTG can find effective network architectures and achieve state-of-the-art results. To the best of our knowledge, we are the first to study neural architecture search for spatio-temporal graphs.
GuideBoot: Guided Bootstrap for Deep Contextual Banditsin Online Advertising
Authors: Feiyang Pan, Haoming Li, Xiang Ao, Wei Wang, Yanrong Kang, Ao Tan, Qing He
Venue: WWW
Year: 2021

The exploration/exploitation (E&E) dilemma lies at the core of interactive systems such as online advertising, for which contextual bandit algorithms have been proposed. Bayesian approaches provide guided exploration via uncertainty estimation, but the applicability is often limited due to over-simplified assumptions. Non-Bayesian bootstrap methods, on the other hand, can apply to complex problems by using deep reward models, but lack a clear guidance to the exploration behavior. It still remains largely unsolved to develop a practical method for complex deep contextual bandits. In this paper, we introduce Guided Bootstrap (GuideBoot), combining the best of both worlds. GuideBoot provides explicit guidance to the exploration behavior by training multiple models over both real samples and noisy samples with fake labels, where the noise is added according to the predictive uncertainty. The proposed method is efficient as it can make decisions on-the-fly by utilizing only one randomly chosen model, but is also effective as we show that it can be viewed as a non-Bayesian approximation of Thompson sampling. Moreover, we extend it to an online version that can learn solely from streaming data, which is favored in real applications. Extensive experiments on both synthetic tasks and large-scale advertising environments show that GuideBoot achieves significant improvements against previous state-of-the-art methods.
Emotion-Aware Event Summarization in Microblogs
Authors: Rrubaa Panchendrarajan, Wynne Hsu, Mong-Li Lee
Venue: WWW
Year: 2021

Microblogs have become the preferred means of communication for people to share information and feelings, especially for fast evolving events. Understanding the emotional reactions of people allows decision makers to formulate policies that are likely to be more well-received by the public and hence better accepted especially during policy implementation. However, uncovering the topics and emotions related to an event over time is a challenge due to the short and noisy nature of microblogs. This work proposes a weakly supervised learning approach to learn coherent topics and the corresponding emotional reactions as an event unfolds. We summarize the event by giving the representative microblogs and the emotion distributions associated with the topics over time. Experiments on multiple real-world event datasets demonstrate the effectiveness of the proposed approach over existing solutions.
Where To Next? A Dynamic Model of User Preferences
Authors: Francesco Sanna Passino, Lucas Maystre, Dmitrii Moor, Ashton Anderson, Mounia Lalmas
Venue: WWW
Year: 2021

We consider the problem of predicting users’ preferences on online platforms. We build on recent findings suggesting that users’ preferences change over time, and that helping users expand their horizons is important in ensuring that they stay engaged. Most existing models of user preferences attempt to capture simultaneous preferences: “Users who like A tend to like B as well”. In this paper, we argue that these models fail to anticipate changing preferences. To overcome this issue, we seek to understand the structure that underlies the evolution of user preferences. To this end, we propose the Preference Transition Model (PTM), a dynamic model for user preferences towards classes of items. The model enables the estimation of transition probabilities between classes of items over time, which can be used to estimate how users’ tastes are expected to evolve based on their past history. We test our model’s predictive performance on a number of different prediction tasks on data from three different domains: music streaming, restaurant recommendations and movie recommendations, and find that it outperforms competing approaches. We then focus on a music application, and inspect the structure learned by our model. We find that the PTM uncovers remarkable regularities in users’ preference trajectories over time. We believe that these findings could inform a new generation of dynamic, diversity-enhancing recommender systems.
GOAT at the FinSim-2 task: Learning Word Representations of Financial Data with Customized Corpus
Authors: Yulong Pei, Qian Zhang
Venue: WWW
Year: 2021

In this paper, we present our approaches for the FinSim 2021 Shared Task on Learning Semantic Similarities for the Financial Domain. The aim of the FinSim shared task is to automatically classify a given list of terms from the financial domain into the most relevant hypernym (or top-level) concept in an external ontology. Two different word representations have been compared in our study, i.e., customized word2vec provided by the shared task and FinBERT. We first create a customized corpus from the given prospectuses and relevant articles from Investopedia. Then we train the domain-specific word2vec embeddings using the customized data with customized word2vec and FinBERT as the initialized embeddings respectively. Our experimental results demonstrate that these customized word embeddings can effectively improve the classification performance and achieve better results than the direct utilization of the provided word embeddings. The class imbalance issue of the given data is also explored. We empirically study the classification performance by employing several different strategies for imbalanced classification problems. Our system ranks 2nd on both Average Accuracy and Mean Rank metrics.
Insurance Assistant: An Intelligent Platform for Video Insurance Assessment
Authors: Shuang Peng, Minghui Yang, Fudong Wang, Xiangyang Li, Zujie Wen, Lei Liu
Venue: WWW
Year: 2021

In the insurance industry, the assessor’s role is essential and requires significant efforts conversing with the claimant. This is a highly professional process that involves many complex operations to make a final insurance report. In order to save the cost, the previous offline insurance assessment procedure is gradually moved online. However, for the junior assessor often lacking in practical experience, it is not easy to quickly handle such an online procedure, yet this is important as the insurance company decides how much compensation the claimant should receive based on the assessor’s feedback. In this paper, we present an insurance assistant that applies NLP technologies to help junior insurance assessors do their job better. The insurance assistant recommends appropriate inquiring policies and auto-completes the case report during the insurance assessment procedure. Here, we demonstrate the system via a short video 1.
Visualisation of Temporal Network Data via Time-Aware Static Representations with HOTVis
Authors: Vincenzo Perri, Ingo Scholtes
Venue: WWW
Year: 2021

The visual analysis of temporal network data is often hindered by the cognitively demanding nature of dynamic graphic visualizations. Addressing this issue, the graph visualization tool HOTVis generates time-aware static network visualizations that highlight the causal topology of temporal networks, i.e. which nodes can directly and indirectly influence each other, and are thus considerably easier to interpret than state-of-the-art dynamic graph visualizations.