OpenRaiser/Intern-Atlas huggingface dataset:
Marketing Communications and the Semantic Web: Theoretical Intersections and Practical Implications
Authors: Teodora Petkova
Venue: WWW
Year: 2021

This research explores the content of marketing communications on the Web in their dialogic and data aspects. Bringing together theories from the fields of digital marketing communications and the Semantic Web, the work investigates how web marketing content is used for building semantic relationships between data nodes (by using schema.org) and across semiotic interpretative routes (by adhering to dialogic principles for communication).
Data-Driven Solutions in Smart Cities: The case of Covid-19
Authors: Nenad Petrovic, Vlado Dimovski, Judita Peterlin, Maja Mesko, Vasja Roblek
Venue: WWW
Year: 2021

This paper aims to give a systemic vision about the data-driven mobile applications in urban data management processes, which is essential to ensure a sustainable smart city ecosystem for what is needed to ensure diversification between stakeholders and data sources. The realization of sustainable data-driven smart solutions based on an urban data platform that will enable citizen wellbeing in the smart city is needed to develop data-driven applications. In this paper, we present five case study mobile applications developed using AppSheet and Google Apps Script technologies to prevent the spread of COVID-19 and provide support to (potentially) infected citizens. Several aspects relevant to coronavirus pandemic are considered: quick COVID-19 patient assessment based on user-provided symptoms integrated with contact tracing; volunteer help during quarantine; UAV-based COVID-19 outdoor safety surveillance; test scheduling and AR-based pharmacy shop assistant.
FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs
Authors: Jan Portisch, Michael Hladik, Heiko Paulheim
Venue: WWW
Year: 2021

This paper presents the FinMatcher system and its results for the FinSim 2021 shared task which is co-located with the Workshop on Financial Technology on the Web (FinWeb) in conjunction with The Web Conference. The FinSim-2 shared task consists of a set of concept labels from the financial services domain. The goal is to find the most relevant top-level concept from a given set of concepts. The FinMatcher system exploits three publicly available knowledge graphs, namely WordNet, Wikidata, and WebIsALOD. The graphs are used to generate explicit features as well as latent features which are fed into a neural classifier to predict the closest hypernym.
Simple Wikidata Analysis for Tracking and Improving Biographies in Catalan Wikipedia
Authors: Toni Hermoso Pulido
Venue: WWW
Year: 2021

The advent of Wikidata represented a breakthrough as a collaborative and constantly advancing knowledgebase. As it was originally envisioned, it simplified the linkage and data reuse among different Wikimedia projects. Catalan Wikipedia is one example project where Wikidata has been heavily adopted by its community base: that is the case of integration with article infoboxes or in automatically generated lists. In the following article we highlight the possibilities of taking advantage of structured data from Wikidata for evaluating new biographical articles, so facilitating users to get engaged into diversity challenges or track potential vandalism and errors.
Reliability of Large Scale GPU Clusters for Deep Learning Workloads
Authors: Junjie Qian, Taeyoon Kim, Myeongjae Jeon
Venue: WWW
Year: 2021

Recent advances on deep learning technologies have made GPU clusters popular as training platforms. In this paper, we study reliability issues while focusing on training job failures from analyzing logs collected from running deep learning workloads on a large-scale GPU cluster in production. These failures are largely grouped into two categories, infrastructure and user, based on their sources, and reveal diverse reasons causing the failures. With insights obtained from the failure analysis, we suggest several different ways to improve the stability of shared GPU clusters designed for DL training and optimize user experience by reducing failure occurrences.
Weakly-Supervised Question Answering with Effective Rank and Weighted Loss over Candidates
Authors: Haozhe Qin, Jiangang Zhu, Beijun Shen
Venue: WWW
Year: 2021

We study the weakly supervised question answering problem. Weak-ly supervised question answering aims to learn how the questions should be answered directly from the pairs without golden solutions/evidences, which makes question answering models much easier to scale to many domains. However, in weak supervision setup, a question typically involves many candidate solutions and the spuriousness of candidate solutions will hurt the performance of the question answering models. In this paper, we present an effective method to learn a question answering model in a weak supervision way. Specifically, in order to reduce the spuriousness of candidate solutions used for training, we design several simple yet effective scoring functions to rank the candidate solutions. Despite its simplicity, this ranking process can improve the quality of the training data significantly with fewer spurious candidates left. Then, different from previous approaches that either treat all candidates equally for training or only select the candidate with the largest likelihood in each iteration, we formulate this problem as a multi-task learning problem by weighing the losses computed from top-k candidates. Experimental results show that, our method1 can outperform previous approaches on both semantic parsing and machine reading comprehension tasks.
Superways: A Datacenter Topology for Incast-heavy workloads
Authors: Hamed Rezaei, Balajee Vamanan
Venue: WWW
Year: 2021

Several important datacenter applications cause incast congestion, which severely degrades flow completion times of short flows and throughput of long flows. Further, because most flows are short and the incast duration is shorter than typical round-trip times, reactive mechanisms that rely on congestion control are not effective. While modern datacenter topologies provide high bisection bandwidth to support all-to-all traffic, incast is fundamentally a many-to-one traffic pattern, and therefore, requires deep buffers or high bandwidth at the network edge. We propose Superways, a heterogeneous datacenter topology that provides higher bandwidth for some servers to absorb incasts, as incasts occur only at a small number of servers that aggregate responses from other senders. Our design is based on the key observation that a small subset of servers which aggregate responses are likely to be network bound, whereas most other servers that communicate only with random servers are not. Superways can be implemented over many of the existing datacenter topologies and can be expanded flexibly without incurring high cost and cabling complexity. We also provide a heuristic for scheduling jobs in our topology to fully utilize the extra capacity. Using a real CloudLab implementation and using ns-3 simulations, we show that Superways significantly improves flow completion times and throughput over existing datacenter topologies. We also analyze cost and cabling complexity, and discuss how to expand our topology.
The Observatory of Anonymity: An Interactive Tool to Understand Re-Identification Risks in 89 countries
Authors: Luc Rocher, Meenatchi Sundaram Muthu Selva Annamalai, Yves-Alexandre de Montjoye
Venue: WWW
Year: 2021

Anonymity offers strong guarantees for people to use technology without fear of mass surveillance, identity theft, and data misuse. To anonymize and share data widely, de-identification is the main tool used in academia and industry. Yet mounting evidence suggest that de-identification may not protect people’s privacy in practice. We present The Observatory of Anonymity, an interactive website demonstrating how few pieces of personal data can easily re-identify us. Taking advantage of modern web technologies, it allows participants to explore their correctness score—the likelihood to be correctly and uniquely identified from their demographics only. Trained on census data from 89 countries, it demonstrates the effectiveness of re-identification attacks on deemed-anonymous data. The website further allows analysts to upload their own data samples to train our machine learning models in real time. The Observatory provides a unique tool for individuals, researchers, and practitioners to assess whether current de-identification practices satisfy the anonymization standards of modern data protection laws such as GDPR and CCPA.
Explainable Demand Forecasting: A Data Mining Goldmine
Authors: Joze M. Rozanec
Venue: WWW
Year: 2021

Demand forecasting is a crucial component of demand management. Value is provided to the organization through accurate forecasts and insights into the reasons driving the forecasts to increase confidence and assist decision-making. In this Ph.D., we aim to develop state-of-the-art demand forecasting models for irregular demand, develop explainability mechanisms to avoid exposing models fine-grained information regarding the model features, create a recommender system to assist users on decision-making and develop mechanisms to enrich knowledge graphs with feedback provided by the users through artificial intelligence-powered feedback modules. We have already developed models for accurate forecasts regarding steady and irregular demand and architecture to provide forecast explanations that preserve sensitive information regarding model features. These explanations highlighting real-world events that provide insights on the general context captured through the dataset features while highlighting actionable items and suggesting datasets for future data enrichment.
EUDETECTOR: Leveraging Language Model to Identify EU-Related News
Authors: Koustav Rudra, Danny Tran, Miroslav Shaltev
Venue: WWW
Year: 2021

News media reflects the present state of a country or region to its audiences. Media outlets of a region post different kinds of news for their local and global audiences. In this paper, we focus on Europe (precisely EU) and propose a method to identify news that has an impact on Europe from any aspect such as financial, business, crime, politics, etc. Predicting the location of the news is itself a challenging task. Most of the approaches restrict themselves towards named entities or handcrafted features. In this paper, we try to overcome that limitation i.e., instead of focusing only on the named entities (Europe location, politicians etc.) and some hand-crafted rules, we also explore the context of news articles with the help of pre-trained language model BERT. The auto-regressive language model based European news detector shows about 9-19% improvement in terms of F-score over baseline models. Interestingly, we observe that such models automatically capture named entities, their origin, etc; hence, no separate information is required. We also evaluate the role of such entities in the prediction and explore the tokens that BERT really looks at for deciding the news category. Entities such as person, location, organization turn out to be good rationale tokens for the prediction.
Learning Spatio-Temporal Behavioural Representations for Urban Activity Forecasting
Authors: Flora D. Salim
Venue: WWW
Year: 2021

Understanding human activity patterns in cities enables a more efficient and sustainable energy, transport, and resource planning. In this invited talk, after laying out the background on spatio-temporal representation, I will present our unsupervised approaches to handle large-scale mutivariate sensor data from heterogeneous sources, prior to modelling them further with the rich contextual signals obtained from the environment. I will also present several spatio-temporal prediction and recommendation problems, leveraging graph-based enrichment and embedding techniques, with applications in continuous trajectory prediction, visitor intent profiling, and urban flow forecasting.
Graph Neural Networks for Friend Ranking in Large-scale Social Platforms
Authors: Aravind Sankar, Yozen Liu, Jun Yu, Neil Shah
Venue: WWW
Year: 2021

Graph Neural Networks (GNNs) have recently enabled substantial advances in graph learning. Despite their rich representational capacity, GNNs remain under-explored for large-scale social modeling applications. One such industrially ubiquitous application is friend suggestion: recommending users other candidate users to befriend, to improve user connectivity, retention and engagement. However, modeling such user-user interactions on large-scale social platforms poses unique challenges: such graphs often have heavy-tailed degree distributions, where a significant fraction of users are inactive and have limited structural and engagement information. Moreover, users interact with different functionalities, communicate with diverse groups, and have multifaceted interaction patterns. We study the application of GNNs for friend suggestion, providing the first investigation of GNN design for this task, to our knowledge. To leverage the rich knowledge of in-platform actions, we formulate friend suggestion as multi-faceted friend ranking with multi-modal user features and link communication features. We design a neural architecture GraFRank to learn expressive user representations from multiple feature modalities and user-user interactions. Specifically, GraFRank employs modality-specific neighbor aggregators and cross-modality attentions to learn multi-faceted user representations. We conduct experiments on two multi-million user datasets from Snapchat, a leading mobile social platform, where GraFRank outperforms several state-of-the-art approaches on candidate retrieval (by 30% MRR) and ranking (by 20% MRR) tasks. Moreover, our qualitative analysis indicates notable gains for critical populations of less-active and low-degree users.
Centralized Borrower and Lender Matching under Uncertainty for P2P Lending
Authors: Soumajyoti Sarkar
Venue: WWW
Year: 2021

Motivated by recent applications of sequential decision making in matching markets, in this paper we attempt at formulating and abstracting market designs for P2P lending. We describe a paradigm to set the stage for how peer to peer investments can be conceived from a matching market perspective, especially when both borrower and lender preferences are respected. We model these specialized markets as an optimization problem and consider different utilities for agents on both sides of the market while also understanding the impact of equitable allocations to borrowers. We devise a technique based on sequential decision making that allow the lenders to adjust their choices based on the dynamics of uncertainty from competition over time and that also impacts the rewards in return for their investments. Using simulated experiments we show the dynamics of the regret based on the optimal borrower-lender matching and find that the lender regret depends on the initial preferences set by the lenders which could affect their learning over decision making steps.
How Fair is Fairness-aware Representative Ranking?
Authors: Akrati Saxena, George Fletcher, Mykola Pechenizkiy
Venue: WWW
Year: 2021

It has been observed in several works that the ranking of candidates based on their score can be biased for candidates belonging to the minority community. In recent works, the fairness-aware representative ranking was proposed for computing fairness-aware re-ranking of results. The proposed algorithm achieves the desired distribution of top-ranked results with respect to one or more protected attributes. In this work, we highlight the bias in fairness-aware representative ranking for an individual and for a group if the group is sub-active on the platform. We define individual unfairness and group unfairness from two different perspectives. We further propose methods to generate ideal individual and group fair representative ranking if the universal representation ratio is known. The paper is concluded with open challenges and further directions.
Fast Linking of Mathematical Wikidata Entities in Wikipedia Articles Using Annotation Recommendation
Authors: Philipp Scharpf, Moritz Schubotz, Bela Gipp
Venue: WWW
Year: 2021

Mathematical information retrieval (MathIR) applications such as semantic formula search and question answering systems rely on knowledge-bases that link mathematical expressions to their natural language names. For database population, mathematical formulae need to be annotated and linked to semantic concepts, which is very time-consuming. In this paper, we present our approach to structure and speed up this process by using an application-driven strategy and AI-aided system. We evaluate the quality and time-savings of AI-generated formula and identifier annotation recommendations on a test selection of Wikipedia articles from the physics domain. Moreover, we evaluate the community acceptance of Wikipedia formula entity links and Wikidata item creation and population to ground the formula semantics. Our evaluation shows that the AI guidance was able to significantly speed up the annotation process by a factor of 1.4 for formulae and 2.4 for identifiers. Our contributions were accepted in 88% of the edited Wikipedia articles and 67% of the Wikidata items. The >>AnnoMathTeX<< annotation recommender system is hosted by Wikimedia at annomathtex.wmflabs.org. In the future, our data refinement pipeline will be integrated seamlessly into the Wikimedia user interfaces.
Assessing the Effects of Friend-to-Friend Texting onTurnout in the 2018 US Midterm Elections
Authors: Aaron Schein, Keyon Vafa, Dhanya Sridhar, Victor Veitch, Jeffrey Quinn, James Moffet, David M. Blei, Donald P. Green
Venue: WWW
Year: 2021

Recent mobile app technology lets people systematize the process of messaging their friends to urge them to vote. Prior to the most recent US midterm elections in 2018, the mobile app Outvote randomized an aspect of their system, hoping to unobtrusively assess the causal effect of their users’ messages on voter turnout. However, properly assessing this causal effect is hindered by multiple statistical challenges, including attenuation bias due to mismeasurement of subjects’ outcomes and low precision due to two-sided non-compliance with subjects’ assignments. We address these challenges, which are likely to impinge upon any study that seeks to randomize authentic friend-to-friend interactions, by tailoring the statistical analysis to make use of additional data about both users and subjects. Using meta-data of users’ in-app behavior, we reconstruct subjects’ positions in users’ queues. We use this information to refine the study population to more compliant subjects who were higher in the queues, and we do so in a systematic way which optimizes a proxy for the study’s power. To mitigate attenuation bias, we then use ancillary data of subjects’ matches to the voter rolls that lets us refine the study population to one with low rates of outcome mismeasurement. Our analysis reveals statistically significant treatment effects from friend-to-friend mobilization efforts ( 8.3, CI = (1.2, 15.3)) that are among the largest reported in the get-out-the-vote (GOTV) literature. While social pressure from friends has long been conjectured to play a role in effective GOTV treatments, the present study is among the first to assess these effects experimentally.
Towards Open-domain Vision and Language Understanding with Wikimedia
Authors: David Semedo
Venue: WWW
Year: 2021

Current state-of-the-art task-agnostic visio-linguistic approaches, such as ViLBERT [2], are limited to domains in which texts have a visual materialization (e.g. a person running). This work describes a project towards achieving the next generation of models, that can deal with open-domain media, and learn visio-linguistic representations that reflect data’s context, by jointly reasoning over media, a domain knowledge-graph and temporal context. This ambition will be leveraged by a Wikimedia data framework, comprised by comprehensive and high-quality data, covering a wide range of social, cultural, political and other type of events. Towards this goal, we propose a research setup comprised by an open-domain data framework and a set of novel independent research tasks.
Efficient Computation of Semantically Cohesive Subgraphs for Keyword-Based Knowledge Graph Exploration
Authors: Yuxuan Shi, Gong Cheng, Trung-Kien Tran, Evgeny Kharlamov, Yulin Shen
Venue: WWW
Year: 2021

A knowledge graph (KG) represents a set of entities and their relations. To explore the content of a large and complex KG, a convenient way is keyword-based querying. Traditional methods assign small weights to salient entities or relations, and answer an exploratory keyword query by computing a group Steiner tree (GST), which is a minimum-weight subgraph that connects all the keywords in the query. Recent studies have suggested improving the semantic cohesiveness of a query answer by minimizing the pairwise semantic distances between the entities in a subgraph, but it remains unclear how to efficiently compute such a semantically cohesive subgraph. In this paper, we formulate it as a quadratic group Steiner tree problem (QGSTP) by extending the classical minimum-weight GST problem which is NP-hard. We design two approximation algorithms for QGSTP and prove their approximation ratios. Furthermore, to improve their practical performance, we present heuristics including pruning and ranking strategies.
#Twiti: Social Listening for Threat Intelligence
Authors: Hyejin Shin, WooChul Shim, Saebom Kim, Sol Lee, Yong Goo Kang, Yong Ho Hwang
Venue: WWW
Year: 2021

Twitter is a popular public source for threat hunting. Many security vendors and security professionals use Twitter in practice for collecting Indicators of Compromise (IOCs). However, little is known about IOCs on Twitter. Their important characteristics such as earliness, uniqueness, and accuracy have never been investigated. Moreover, how to extract IOCs from Twitter with high accuracy is not obvious. In this paper, we present Twiti, a system that automatically extracts various forms of malware IOCs from Twitter. Based on the collected IOCs, we conduct the first empirical assessment and thorough analysis of malware IOCs on Twitter. Twiti extracts IOCs from tweets identified as having malware IOC information by leveraging natural language processing and machine learning techniques. With extensive evaluation, we demonstrate that not only can Twiti extract malware IOCs accurately, but also the extracted IOCs are unique and early. By analyzing IOCs in Twiti from various aspects, we find that Twitter captures ongoing malware threats such as Emotet variants and malware distribution sites better than other public threat intelligence (TI) feeds. We also find that only a tiny fraction of IOCs on Twitter come from commercial vendor accounts and individual Twitter users are the main contributors of the early detected or exclusive IOCs, which indicates that Twitter can provide many valuable IOCs uncovered in commercial domain