OpenRaiser/Intern-Atlas huggingface dataset:
Towards Automated Technologies in the Referencing Quality of Wikidata
Authors: Seyed Amir Hosseini Beghaeiraveri
Venue: WWW
Year: 2022

Wikidata is a general-purpose knowledge graph with the content being crowd-sourced through an open wiki, along with bot accounts. The Wikidata data model enables assigning references to every single statement. Currently, there are more than 1 billion statements in Wikidata, of which about 70% have got references. Due to the rapid growth of Wikidata, the quality of Wikidata references is not well covered in the literature. To cover the gap, we suggest using automated tools to verify and improve the quality of Wikidata references. For verifying reference quality, we develop a comprehensive referencing assessment framework based on Data Quality dimensions and criteria. Then, we implement the framework as automated reusable scripts. To improve reference quality, we use Relation Extraction methods to establish a reference-suggesting framework for Wikidata. During the research, we managed to develop a subsetting approach to create a comparison platform and handle the big size of Wikidata. We also investigated reference statistics in 6 Wikidata topical subsets. The results of the latter investigation indicate the need for a wider assessment framework, which we aim to address in this dissertation.
Fair k-Center Clustering in MapReduce and Streaming Settings
Authors: Suman K. Bera, Syamantak Das, Sainyam Galhotra, Sagar Sudhir Kale
Venue: WWW
Year: 2022

Center-based clustering techniques are fundamental to many real-world applications such as data summarization and social network analysis. In this work, we study the problem of fairness aware k-center clustering over large datasets. We are given an input dataset comprising a set of n points, where each point belongs to a specific demographic group characterized by a protected attribute, such as race or gender. The goal is to identify k clusters such that all clusters have considerable representation from all groups and the maximum radius of these clusters is minimized. The majority of the prior techniques do not scale beyond 100K points for k = 50. To address the scalability challenges, we propose an efficient 2-round algorithm for the MapReduce setting that is guaranteed to be a 9-approximation to the optimal solution. Additionally, we develop a 2-pass streaming algorithm that is efficient and has a low memory footprint. These theoretical results are complemented with an empirical evaluation on million-scale datasets, demonstrating that our techniques are effective to identify high-quality fair clusters and efficient as compared to the state-of-the-art.
Consent of the Governed
Authors: Robin Berjon
Venue: WWW
Year: 2022

Informed consent is rooted in human subjects research where it is used to operationalise autonomy. From there it was transplanted into a computing context but in the process it became separated from key assumptions that makes it work for research such an institutional review board to weed out unethical use or rare and voluntary participation. The result is a procedural ghost of consent that undermines personal autonomy irrespective of what user interface constraints are imposed on it, and a hyper-individualistic approach to privacy that is poorly adapted to life in a complex digital society. All is not dark however and there is a future for consent built for people rather than for homo economicus. We can use the legal basis of consent as an implementation brick and evolve consent towards collective, socialised decision-making: consent of the governed.
On The Origins Of Hypertext In The Disasters Of The Short 20th Century
Authors: Mark Bernstein
Venue: WWW
Year: 2022

The development of hypertext and the World Wide Web is most frequently explained by reference to changes in underlying technologies — Moore's Law giving rise to faster computers, more ample memory, increased bandwidth, inexpensive color displays. That story is true, but it is not complete: hypertext and the Web are also built on a foundation of ideas. Specifically, I believe the Web we know arose from ideas rooted in the disasters of the short twentieth century, 1914–1989. The experience of these disasters differed in the Americas and in Eurasia, and this distinction helps explain many long-standing tensions in research and practice alike.
Utilizing Pattern Mining and Classification Algorithms to Identify Risk for Anxiety and Depression in the LGBTQ+ Community During the COVID-19 Pandemic
Authors: Josephine Bierbaum, Melissa Lynn, Louis Yu
Venue: WWW
Year: 2022

In this paper, we examine the results of pattern mining and decision trees applied to a dataset of survey responses about life for individuals in the LGBTQ+ community during COVID, which have the potential to be used as a tool to identify those at risk for anxiety and depression. The world was immensely affected by the pandemic in 2020 through 2022, and our study attempts to use the data from this period to analyze the impact on anxiety and depression. First, we used the FP-growth algorithm for frequent pattern mining, which finds groups of items that frequently occur together, and utilized the resulting patterns and measures to determine which features were significant when inspecting anxiety and depression. Then, we trained a decision tree with the selected features to classify if a person has anxiety or depression. The resulting decision trees can be used to identify individuals at risk for these conditions. From our results, we also identified specific risk factors that helped predict whether an individual was likely to experience anxiety and/or depression, such as satisfaction with their sex life, cutting meals, and worries of healthcare discrimination due to their gender identity or sexual orientation.
A Human-Centered Design Approach for the Development of a Digital Care Platform in a Smart City Environment: Implications for Business Models
Authors: Jelena Bleja, Sara Neumann, Tim Krüger, Uwe Grossmann
Venue: WWW
Year: 2022

Digital solutions are being sought increasingly in the care sector for making services more efficient and to be prepared for demographic change and the future care service and staff shortage. One possibility here is the implementation of a digital care platform that is target group-oriented and built according to the needs of the individual stakeholders. To build this platform successfully, it is also necessary to take a closer look at the business model. This paper examines the mentioned points by applying a human-centered design approach that focuses on all perspectives and allows a deep understanding of the opportunities and challenges of a digital care platform in a smart city environment. A digital care platform was found to be promising for various involved stakeholders due to the value proposition. Consequently, the stakeholders benefit from e.g. the simplification of processes and data management, less bureaucratic effort and intensified care services for the elderly.
Powering Semantic Analysis with Bio-ontologies
Authors: Olivier Bodenreider
Venue: WWW
Year: 2022

In the past three decades, bio-ontologies have moved from esoteric artifacts to key resources for the semantic analysis of biomedical text. In this presentation, we will follow the evolution of the creation and integration of bio-ontologies, as well as their role in biomedical applications, including literature analysis. Bio-ontologies include a variety of resources that provide a source of names for biomedical entities and specify relations among these entities. These resources are generally developed independently by individuals, collectives, institutions, and standard development organizations. Examples of bio-ontologies include the Medical Subject Headings (MeSH), developed by the National Library of Medicine to support the indexing and retrieval of the biomedical literature, the Gene Ontology (GO), developed by the GO Consortium to support consistent annotation and analysis of gene products across organisms, and SNOMED CT, developed by SNOMED International to support clinical documentation and analytics worldwide. While these three examples illustrate resources with a large scope, many other bio-ontologies focus on a specialized subdomain of medicine. Bio-ontologies use different formalisms and various degrees of formality for their representation. Bio-ontologies play an important role in the semantic analysis of biomedical datasets, including the biomedical literature. Bio-ontologies provide a source of vocabulary for biomedical entities, used for named entity recognition (i.e., finding mentions of biomedical entities in text) and entity resolution (i.e., mapping mentions a specific reference). Bio-ontologies also provide semantic categorization for biomedical entities, which is leveraged for word-sense disambiguation and co-reference resolution (especially when a specific entity is referred to with a broader category). Finally, bio-ontologies provide a source of relations among entities, which can form the basis for relation extraction, hypothesis generation and, more generally, literature-based discovery. Since most bio-ontologies are developed independently, but often need to be used together, ontology alignment techniques have been developed to identify correspondences among entities across ontologies. Repositories of bio-ontologies, such as the Unified Medical Language System (UMLS) Metathesaurus, the National Center for Biomedical Ontology (NCBO) BioPortal and the Open Biological and Biomedical Ontology (OBO) Foundry are useful sources of ontologies and contribute to the development of tools to support semantic analysis of biomedical text.
Query-Driven Graph Processing
Authors: Angela Bonifati
Venue: WWW
Year: 2022

Graphs are data model abstractions that are becoming pervasive in several real-life applications and practical use cases. In these settings, users primarily focus on entities and their relationships, further enhanced with multiple labels and properties to form the so-called property graphs. The processing of property graphs relies on graph queries that are used to extract and manipulate entities and relationships [2]. Whereas property graphs can be defined in a schema-less fashion, schema constraints and schema concepts are important assets when considering complex graph processing [1, 3, 6]. As witnessed by ongoing standardization activities for property graphs and their query language [5], graph processing systems are prone to enable query-driven, schema-driven or constraint-driven tasks. In this talk, I will focus on our work on graph processing in the static and streaming environments [7, 9], as well as schema inference methods [6], graph constraint and query workload designs tailored for property graphs [1, 4]. I will conclude by pinpointing future directions for graph processing and graph analytics inspired by our recent community-wide vision paper [8].
Significance and Coverage in Group Testing on the Social Web
Authors: Nassim Bouarour, Idir Benouaret, Sihem Amer-Yahia
Venue: WWW
Year: 2022

We tackle the longstanding question of checking hypotheses on the social Web. In particular, we address the challenges that arise in the context of testing an input hypothesis on many data samples, in our case, user groups. This is referred to as Multiple Hypothesis Testing, a method of choice for data-driven discoveries. Ensuring sound discoveries in large datasets poses two challenges: the likelihood of accepting a hypothesis by chance, i.e., returning false discoveries, and the pitfall of not being representative of the input data. We develop GroupTest, a framework for group testing that addresses both challenges. We formulate CoverTest, a generic top-n problem that seeks n user groups satisfying one-sample, two-sample, or multiple-sample tests, and maximizing data coverage. We show the hardness of CoverTest and develop a greedy algorithm with a provable approximation guarantee as well as a faster heuristic-based algorithm based on α-investing. Our extensive experiments on four real-world datasets demonstrate the necessity to optimize coverage for sound data-driven discoveries, and the efficiency of our heuristic-based algorithm.
Towards Knowledge-Driven Symptom Monitoring & Trigger Detection of Primary Headache Disorders
Authors: Mathias De Brouwer, Nicolas Vandenbussche, Bram Steenwinckel, Marija Stojchevska, Jonas Van Der Donckt, Vic Degraeve, Filip De Turck, Koen Paemeleire, Sofie Van Hoecke, Femke Ongenae
Venue: WWW
Year: 2022

Headache disorders are experienced by many people around the world. In current clinical practice, the follow-up and diagnosis of headache disorder patients only happens intermittently, based on subjective data self-reported by the patient. The mBrain system tries to make this process more continuous, autonomous and objective by additionally collecting contextual and physiological data via a wearable, mobile app and machine learning algorithms. To support the monitoring of headache symptoms during attacks for headache classification and the detection of headache triggers, much knowledge and contextual data is available from heterogeneous sources, which can be consolidated with semantics. This paper presents a demonstrator of knowledge-driven services that perform these tasks using Semantic Web technologies. These services are deployed in a distributed cascading architecture that includes DIVIDE to derive and manage the RDF stream processing queries that perform the contextually relevant filtering in an intelligent and efficient way.
Price Manipulability in First-Price Auctions
Authors: Johannes Brustle, Paul Dütting, Balasubramanian Sivan
Venue: WWW
Year: 2022

First-price auctions have many desirable properties, including uniquely possessing some, like credibility. However, first-price auctions are also inherently non-truthful, and non-truthfulness may result in instability and inefficiencies. Given these pros and cons, we seek to quantify the extent to which first-price auctions are susceptible to manipulation. In this work we adopt a metric that was introduced in the context of bitcoin fee design markets: the percentage change in payment that can be achieved by being strategic. We study the behavior of this metric for single-unit and k-unit auction environments with n i.i.d. buyers, and seek conditions under which the percentage change tends to zero as n grows large. To the best of our knowledge, ours is the first rigorous study of the extent to which large multi-unit first price auctions are susceptible to manipulation. We provide an almost complete picture of the conditions under which they are “truthful in the large,” and exhibit some surprising boundaries.
Fair Effect Attribution in Parallel Online Experiments
Authors: Alexander Buchholz, Vito Bellini, Giuseppe Di Benedetto, Yannik Stein, Matteo Ruffini, Fabian Moerchen
Venue: WWW
Year: 2022

A/B tests serve the purpose of reliably identifying the effect of changes introduced in online services. It is common for online platforms to run a large number of simultaneous experiments by splitting incoming user traffic randomly in treatment and control groups. Despite a perfect randomization between different groups, simultaneous experiments can interact with each other and create a negative impact on average population outcomes such as engagement metrics. These are measured globally and monitored to protect overall user experience. Therefore, it is crucial to measure these interaction effects and attribute their overall impact in a fair way to the respective experimenters. We suggest an approach to measure and disentangle the effect of simultaneous experiments by providing a cost sharing approach based on Shapley values. We also provide a counterfactual perspective, that predicts shared impact based on conditional average treatment effects making use of causal inference techniques. We illustrate our approach in real world and synthetic data experiments.
Rayleigh Portfolios and Penalised Matrix Decomposition
Authors: Francois Buet-Golfouse, Hans Roggeman, Islam Utyagulov
Venue: WWW
Year: 2022

Since the development and growth of personalised financial services online, effective tailor-made and fast statistical portfolio allocation techniques have been sought after. In this paper, we introduce a framework called Rayleigh portfolios, that encompasses many well-known approaches, such as the Sharpe Ratio, maximum diversification or minimum concentration. By showing the commonalities amongst these approaches, we are able to provide a solution to all such optimisation problems via matrix decomposition, and principal component analysis in particular. In addition, thanks to this reformulation, we show how to include sparsity upper bounds in such portfolios, thereby catering for two additional requirements in portfolio construction: robustness and low transaction costs. Importantly, modifications to the usual penalised matrix decomposition algorithms can be applied to other problems in statistics. Finally, empirical applications show promising results.
Web Audio Modules 2.0: An Open Web Audio Plugin Standard
Authors: Michel Buffa, Shihong Ren, Owen Campbell, Tom Burns, Steven Yi, Jari Kleimola, Oliver Larkin
Venue: WWW
Year: 2022

A group of academic researchers and developers from the computer music industry have joined forces for over a year to propose a new version of Web Audio Modules, an open source framework facilitating the development of high-performance Web Audio plugins (instruments, realtime audio effects and MIDI processors). While JavaScript and Web standards are becoming increasingly flexible and powerful, C, C++, and domain-specific languages such as FAUST or Csound remain the prevailing languages used by professional developers of native plugins. Fortunately, it is now possible to compile them in WebAssembly, which means they can be integrated with the Web platform. Our work aims to create a continuum between native and browser based audio app development and to appeal to programmers from both worlds. This paper presents our proposal including guidelines and implementations for an open Web Audio plugin standard - essentially the infrastructure to support high level audio plugins for the browser.
Multimodal Continual Graph Learning with Neural Architecture Search
Authors: Jie Cai, Xin Wang, Chaoyu Guan, Yateng Tang, Jin Xu, Bin Zhong, Wenwu Zhu
Venue: WWW
Year: 2022

Continual graph learning is rapidly emerging as an important role in a variety of real-world applications such as online product recommendation systems and social media. While achieving great success, existing works on continual graph learning ignore the information from multiple modalities (e.g., visual and textual features) as well as the rich dynamic structural information hidden in the ever-changing graph data and evolving tasks. However, considering multimodal continual graph learning with evolving topological structures poses great challenges: i) it is unclear how to incorporate the multimodal information into continual graph learning and ii) it is nontrivial to design models that can capture the structure-evolving dynamics in continual graph learning. To tackle these challenges, in this paper we propose a novel Multimodal Structure-evolving Continual Graph Learning (MSCGL) model, which continually learns both the model architecture and the corresponding parameters for Adaptive Multimodal Graph Neural Network (AdaMGNN). To be concrete, our proposed MSCGL model simultaneously takes social information and multimodal information into account to build the multimodal graphs. In order for continually adapting to new tasks without forgetting the old ones, our MSCGL model explores a new strategy with joint optimization of Neural Architecture Search (NAS) and Group Sparse Regularization (GSR) across different tasks. These two parts interact with each other reciprocally, where NAS is expected to explore more promising architectures and GSR is in charge of preserving important information from the previous tasks. We conduct extensive experiments over two real-world multimodal continual graph scenarios to demonstrate the superiority of the proposed MSCGL model. Empirical experiments indicate that both the architectures and weight sharing across different tasks play important roles in affecting the model performances.
PREP: Pre-training with Temporal Elapse Inference for Popularity Prediction
Authors: Qi Cao, Huawei Shen, Yuanhao Liu, Jinhua Gao, Xueqi Cheng
Venue: WWW
Year: 2022

Predicting the popularity of online content is a fundamental problem in various applications. One practical challenge takes roots in the varying length of observation time or prediction horizon, i.e., a good model for popularity prediction is desired to handle various prediction settings. However, most existing methods adopt a separate training paradigm for each prediction setting and the obtained model for one setting is difficult to be generalized to others, causing a great waste of computational resources and a large demand for downstream labels. To solve the above issues, we propose a novel pre-training framework for popularity prediction, namely PREP, aiming to pre-train a general representation model from the readily available unlabeled diffusion data, which can be effectively transferred into various prediction settings. We design a novel pretext task for pre-training, i.e., temporal elapse inference for two randomly sampled time slices of popularity dynamics, impelling the representation model to learn intrinsic knowledge about popularity dynamics. Experimental results conducted on two real datasets demonstrate the generalization and efficiency of the pre-training framework for different popularity prediction task settings.
Enhancing Crowd Flow Prediction in Various Spatial and Temporal Granularities
Authors: Marco Cardia, Massimiliano Luca, Luca Pappalardo
Venue: WWW
Year: 2022

The diffusion of the Internet of Things allows nowadays to sense human mobility in great detail, fostering human mobility studies and their applications in various contexts, from traffic management to public security and computational epidemiology. A mobility task that is becoming prominent is crowd flow prediction, i.e., forecasting aggregated incoming and outgoing flows in the locations of a geographic region. Although several deep learning approaches have been proposed to solve this problem, their usage is limited to specific types of spatial tessellations and cannot provide sufficient explanations of their predictions. We propose CrowdNet, a solution to crowd flow prediction based on graph convolutional networks. Compared with state-of-the-art solutions, CrowdNet can be used with regions of irregular shapes and provide meaningful explanations of the predicted crowd flows. We conduct experiments on public data varying the spatio-temporal granularity of crowd flows to show the superiority of our model with respect to existing methods, and we investigate CrowdNet’s reliability to missing or noisy input data. Our model is a step forward in the design of reliable deep learning models to predict and explain human displacements in urban environments.
Personal Research Knowledge Graphs
Authors: Prantika Chakraborty, Sudakshina Dutta, Debarshi Kumar Sanyal
Venue: WWW
Year: 2022

Maintaining research-related information in an organized manner can be challenging for a researcher. In this paper, we envision personal research knowledge graphs (PRKGs) as a means to represent structured information about the research activities of a researcher. PRKGs can be used to power intelligent personal assistants, and personalize various applications. We explore what entities and relations could be potentially included in a PRKG, how to extract them from various sources, and how to share a PRKG within a research group.