OpenRaiser/Intern-Atlas huggingface dataset:
Measuring Alexa Skill Privacy Practices across Three Years
Authors: Jide S. Edu, Xavier Ferrer Aran, Jose M. Such, Guillermo Suarez-Tangil
Venue: WWW
Year: 2022

Smart Voice Assistants are transforming the way users interact with technology. This transformation is mostly fostered by the proliferation of voice-driven applications (called skills) offered by third-party developers through an online market. We see how the number of skills has rocked in recent years, with the Amazon Alexa skill ecosystem growing from just 135 skills in early 2016 to about 125k skills in early 2021. Along with the growth in skills, there is increasing concern over the risks that third-party skills pose to users’ privacy. In this paper, we perform a systematic and longitudinal measurement study of the Alexa marketplace. We shed light on how this ecosystem evolves using data collected across three years between 2019 and 2021. We demystify developers’ data disclosure practices and present an overview of the third-party ecosystem. We see how the research community continuously contribute to the market’s sanitation, but the Amazon vetting process still requires significant improvement. We perform a responsible disclosure process reporting 675 skills with privacy issues to both Amazon and all affected developers, out of which 246 skills suffer from important issues (i.e., broken traceability). We see that 107 out of the 246 (43.5%) skills continue to display broken traceability almost one year after being reported. As a result, the overall state of affairs has improved in the ecosystem over the years. Yet, newly submitted skills and unresolved known issues pose an endemic risk.
The International Workshop on Semantics-enabled Biomedical Literature Analytics (SeBiLAn)
Authors: Faezeh Ensan, Halil Kilicoglu, Bridget T. McInnes, Lucy Lu Wang
Venue: WWW
Year: 2022

1 WORKSHOP OBJECTIVES Due to the exponential growth in the number of scientific articles published in the biomedical domain, obtaining the most relevant articles to a topic of interest, extracting biomedical information from texts, integrating different parts of knowledge from various studies, and finding reliable and scientifically sound studies present significant challenges. While traditional term-based information analysis and machine learning techniques can be employed for literature search, information extraction, and information integration, such approaches lack an effective mechanism for analysing scientific articles based on their semantics, which is often ambiguous and contextual. Knowledge representation and Semantics-enabled techniques have already shown the potential to systematically curate, organize, retrieve and interpret content in ways that relates well to human understanding. The recent major collaborative effort for rapid and effective retrieval and analysis of literature around COVID-19 is a strong indication for the need to develop effective techniques specifically tailored for the biomedical domain. The Allen Institute for AI in collaboration with major institutes such as Chan Zuckerberg Initiative, Microsoft Research, and the National Institutes of Health’s National Library of Medicine, among others, shared the COVID-19 Open shared the COVID-19 Open Research Dataset, known as CORD-19, which consists of more than 50,000 scientific articles about the virus known as SARS-CoV-2. One of the objectives behind the release of
Beyond Reproduction, Experiments want to be Understood
Authors: Jérôme Euzenat
Venue: WWW
Year: 2022

The content of experiments must be semantically described. This topic has already been largely covered. However, some neglected benefits of such an approach provide more arguments in favour of scientific knowledge graphs. Beyond being searchable through flat metadata, a knowledge graph of experiment descriptions may be able to provide answers to scientific and methodological questions. This includes identifying non experimented conditions or retrieving specific techniques used in experiments. In turn, this is useful for researchers as this information can be used for repurposing experiments, checking claimed results or performing meta-analyses.
GRAND+: Scalable Graph Random Neural Networks
Authors: Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang
Venue: WWW
Year: 2022

Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate state-of-the-art performance for this problem. However, it is difficult for GRAND to handle large-scale graphs since its effectiveness relies on computationally expensive data augmentation procedures. In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning. To address the above issue, we develop a generalized forward push (GFPush) algorithm in GRAND+ to pre-compute a general propagation matrix and employ it to perform graph data augmentation in a mini-batch manner. We show that both the low time and space complexities of GFPush enable GRAND+ to efficiently scale to large graphs. Furthermore, we introduce a confidence-aware consistency loss into the model optimization of GRAND+, facilitating GRAND+’s generalization superiority. We conduct extensive experiments on seven public datasets of different sizes. The results demonstrate that GRAND+ 1) is able to scale to large graphs and costs less running time than existing scalable GNNs, and 2) can offer consistent accuracy improvements over both full-batch and scalable GNNs across all datasets.
DC-GNN: Decoupled Graph Neural Networks for Improving and Accelerating Large-Scale E-commerce Retrieval
Authors: Chenchen Feng, Yu He, Shiyang Wen, Guojun Liu, Liang Wang, Jian Xu, Bo Zheng
Venue: WWW
Year: 2022

In large-scale E-commerce retrieval, the Graph Neural Networks (GNNs) has become one of the stage-of-the-arts due to its powerful capability on topological feature extraction and relational reasoning. However, the conventional GNNs-based large-scale E-commerce retrieval suffers from low training efficiency, as such scenario normally has billions of entities and tens of billions of relations. Under the limitation on efficiency, only shallow graph algorithms can be employed, which severely hinders the GNNs representation capability and consequently weakens the retrieval quality. In order to deal with the trade-off between training efficiency and representation capability, we propose the Decoupled Graph Neural Networks (DC-GNN) to improve and accelerate the GNNs-based large-scale E-commerce retrieval. Specifically, DC-GNN decouples the conventional framework into three stages: pre-train, deep aggregation, and CTR prediction. By decoupling the graph operations and the CTR prediction, DC-GNN can effectively improve the training efficiency. More importantly, it can enable deeper graph operations to adequately mine higher-order proximity to boost model performance. Extensive experiments on large-scale industrial datasets demonstrate that DC-GNN gains significant improvements in both model performance and training efficiency.
The Gender Perspective in Wikipedia: A Content and Participation Challenge
Authors: Núria Ferran-Ferrer, Marc Miquel-Ribé, Julio Meneses, Julià Minguillón
Venue: WWW
Year: 2022

Wikipedia is one of the most widely used information sources in the world. Although one of the guiding pillars of this digital platform is ensuring access to the diversity of human knowledge from a neutral point of view, there is a clear and persistent gender bias in terms of content about or contributed by women. The challenge is to include women as equal partners in the public sphere, in which Wikipedia is developing a central role as the most used educational resource among students, professionals, and many other profiles. In this paper, we introduce the gender perspective in the analysis of the gender gap in the content and participation of women in Wikipedia. While most studies focus on one of the two dimensions in which the gender gap has been observed, we review both approaches to provide an overview of the available evidence. Firstly we introduce how the gender gap is framed by the Wikimedia Movement strategy, then we evaluate the gender gap on content and participation, especially regarding editor practices. Finally, we provide some insights to broaden the discussion about the consequences of not addressing the gender gap in Wikipedia, and we provide some research topics that can support the generation of recommendations and guidelines for a community that needs both equity and diversity.
JSRehab: Weaning Common Web Interface Components from JavaScript Addiction
Authors: Romain Fouquet, Pierre Laperdrix, Romain Rouvoy
Venue: WWW
Year: 2022

Leveraging JavaScript (JS) for User Interface (UI) interactivity has been the norm on the web for many years. Yet, using JS increases bandwidth and battery consumption as scripts need to be downloaded and processed by the browser. Plus, client-side JS may expose visitors to security vulnerabilities such as Cross-Site Scripting (XSS). This paper introduces a new server-side plugin, called JSRehab, that automatically rewrites common web interface components by alternatives that do not require any JavaScript (JS). The main objective of JSRehab is to drastically reduce—and ultimately remove—the inclusion of JS in a web page to improve its responsiveness and consume less resources. We report on our implementation of JSRehab for Bootstrap, the most popular UI framework by far, and evaluate it on a corpus of 100 webpages. We show through manual validation that it is indeed possible to lower the dependencies of pages on JS while keeping intact its interactivity and accessibility. We observe that JSRehab brings energy savings of at least 5 % for the majority of web pages on the tested devices, while introducing a median on-the-wire overhead of only 5 % to the HTML payload.
Revisiting Neighborhood-based Link Prediction for Collaborative Filtering
Authors: Hao-Ming Fu, Patrick Poirson, Kwot Sin Lee, Chen Wang
Venue: WWW
Year: 2022

Collaborative filtering (CF) is one of the most successful and fundamental techniques in recommendation systems. In recent years, Graph Neural Network (GNN)-based CF models, such as NGCF [31], LightGCN [10] and GTN [9] have achieved tremendous success and significantly advanced the state-of-the-art. While there is a rich literature of such works using advanced models for learning user and item representations separately, item recommendation is essentially a link prediction problem between users and items. Furthermore, while there have been early works employing link prediction for collaborative filtering [5, 6], this trend has largely given way to works focused on aggregating information from user and item nodes, rather than modeling links directly. In this paper, we propose a new linkage (connectivity) score for bipartite graphs, generalizing multiple standard link prediction methods. We combine this new score with an iterative degree update process in the user-item interaction bipartite graph to exploit local graph structures without any node modeling. The result is a simple, non-deep learning model with only six learnable parameters. Despite its simplicity, we demonstrate our approach significantly outperforms existing state-of-the-art GNN-based CF approaches on four widely used benchmarks. In particular, on Amazon-Book, we demonstrate an over 60% improvement for both Recall and NDCG. We hope our work would invite the community to revisit the link prediction aspect of collaborative filtering, where significant performance gains could be achieved through aligning link prediction with item recommendations.
Multi-dimensional Probabilistic Regression over Imprecise Data Streams
Authors: Ran Gao, Xike Xie, Kai Zou, Torben Bach Pedersen
Venue: WWW
Year: 2022

In applications of Web of Things or Web of Events, a massive volume of multi-dimensional streaming data are automatically and continuously generated from different sources, such as GPS, sensors, and other measurement devices, which are essentially imprecise (inaccurate and/or uncertain). It is challenging to monitor and get insights over imprecise and low-level streaming data, in order to capture potentially important data changing trends and to initiate prompt responses. In this work, we investigate solutions for conducting multi-dimensional and multi-granularity probabilistic regression for the imprecise streaming data. The probabilistic nature of streaming data poses big computational challenges to the regression and its aggregation. In this paper, we study a series of techniques on multi-dimensional probabilistic regression, including aggregation, sketching, popular path materialization, and exception-driven querying. Extensive experiments on real and synthetic datasets show the efficiency and scalability of our proposals.
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection
Authors: Diego Garcia-Olano, Yasumasa Onoe, Joydeep Ghosh
Venue: WWW
Year: 2022

Knowledge-Based Visual Question Answering (KBVQA) is a bi-modal task requiring external world knowledge in order to correctly answer a text question and associated image. Recent single modality text work has shown knowledge injection into pre-trained language models, specifically entity enhanced knowledge graph embeddings, can improve performance on downstream entity-centric tasks. In this work, we empirically study how and whether such methods, applied in a bi-modal setting, can improve an existing VQA system’s performance on the KBVQA task. We experiment with two large publicly available VQA datasets, (1) KVQA which contains mostly rare Wikipedia entities and (2) OKVQA which is less entity-centric and more aligned with common sense reasoning. Both lack explicit entity spans, and we study the effect of different weakly supervised and manual methods for obtaining them. Additionally, we analyze how recently proposed bi-modal and single modal attention explanations are affected by the incorporation of such entity enhanced representations. Our results show substantially improved performance on the KBVQA task without the need for additional costly pre-training, and we provide insights for when entity knowledge injection helps improve a model’s understanding. We provide code and enhanced datasets for reproducibility1.
Scaling R-GCN Training with Graph Summarization
Authors: Alessandro Generale, Till Blume, Michael Cochez
Venue: WWW
Year: 2022

Training of Relational Graph Convolutional Networks (R-GCN) is a memory intense task. The amount of gradient information that needs to be stored during training for real-world graphs is often too large for the amount of memory available on most GPUs. In this work, we experiment with the use of graph summarization techniques to compress the graph and hence reduce the amount of memory needed. After training the R-GCN on the graph summary, we transfer the weights back to the original graph and attempt to perform inference on it. We obtain reasonable results on the AIFB, MUTAG and AM datasets. Our experiments show that training on the graph summary can yield a comparable or higher accuracy to training on the original graphs. Furthermore, if we take the time to compute the summary out of the equation, we observe that the smaller graph representations obtained with graph summarization methods reduces the computational overhead. However, further experiments are needed to evaluate additional graph summary models and whether our findings also holds true for very large graphs.
Multi-view Omics Translation with Multiplex Graph Neural Networks
Authors: Costa Georgantas, Jonas Richiardi
Venue: WWW
Year: 2022

The rapid development of high-throughput experimental technologies for biological sampling has made the collection of omics data (e.g., genomics, epigenomics, transcriptomics and metabolomics) possible at a small cost. While multi-view approaches to omics data have a long history, omics-to-omics translation is a relatively new strand of research with useful applications such as recovering missing or censored data and finding new correlations between samples. As the relations between omics can be non-linear and exhibit long-range dependencies between parts of the genome, deep neural networks can be an effective tool. Graph neural networks have been applied successfully in many different areas of research, especially in problems where annotated data is sparse, and have recently been extended to the heterogeneous graph case, allowing for the modelling of multiple kinds of similarities and entities. Here, we propose a meso-scale approach to construct multiplex graphs from multi-omics data, which can construct several graphs per omics and cross-omics graphs. We also propose a neural network architecture for omics-to-omics translation from these multiplex graphs, featuring a graph neural network encoder, coupled with an attention layer. We evaluate the approach on the open The Cancer Genome Atlas dataset (N=3023), showing that for MicroRNA expression prediction our approach has lower prediction error than regularized linear regression or modern generative adversarial networks.
FiNCAT: Financial Numeral Claim Analysis Tool
Authors: Sohom Ghosh, Sudip Kumar Naskar
Venue: WWW
Year: 2022

While making investment decisions by reading financial documents, investors need to differentiate between in-claim and out-of-claim numerals. In this paper, we present a tool which can do this task automatically. It extracts context embeddings of the numerals using a transformer based pre-trained language model – BERT. Subsequently, it uses a Logistic Regression based model to detect whether a numeral is in-claim or out-of-claim. We use the FinNum-3 (English) dataset to train our model. We conducted rigorous experiments and our best model achieved a Macro F1 score of 0.8223 on the validation set. We have open-sourced this tool which can be accessed from https://github.com/sohomghosh/FiNCAT_Financial_Numeral_Claim_Analysis_Tool
SciNoBo: A Hierarchical Multi-Label Classifier of Scientific Publications
Authors: Nikolaos Gialitsis, Sotiris Kotitsas, Haris Papageorgiou
Venue: WWW
Year: 2022

Classifying scientific publications according to Field-of-Science (FoS) taxonomies is of crucial importance, allowing funders, publishers, scholars, companies and other stakeholders to organize scientific literature more effectively. Most existing works address classification either at venue level or solely based on the textual content of a research publication. We present SciNoBo, a novel classification system of publications to predefined FoS taxonomies, leveraging the structural properties of a publication and its citations and references organized in a multilayer network. In contrast to other works, our system supports assignments of publications to multiple fields by considering their multidisciplinarity potential. By unifying publications and venues under a common multilayer network structure made up of citing and publishing relationships, classifications at the venue-level can be augmented with publication-level classifications. We evaluate SciNoBo on a dataset of publications extracted from Microsoft Academic Graph, and we perform a comparative analysis against a state-of-the-art neural-network baseline. The results reveal that our proposed system is capable of producing high-quality classifications of publications.
Search Filter Ranking with Language-Aware Label Embeddings
Authors: Jacek Golebiowski, Felice Antonio Merra, Ziawasch Abedjan, Felix Biessmann
Venue: WWW
Year: 2022

A search on the major eCommerce platforms returns up to thousands of relevant products making it impossible for an average customer to audit all the results. Browsing the list of relevant items can be simplified using search filters for specific requirements (e.g., shoes of the wrong size). The complete list of available filters is often overwhelming and hard to visualize. Thus, successful user interfaces desire to display only the ones relevant to customer queries. In this work, we frame the filter selection task as an extreme multi-label classification (XMLC) problem based on historical interaction with eCommerce sites. We learn from customers’ clicks and purchases which subset of filters is most relevant to their queries treating the relevant/not-relevant signal as binary labels. A common problem in classification settings with a large number of classes is that some classes are underrepresented. These rare categories are difficult to predict. Building on previous work we show that classification performance for rare classes can be improved by accounting for the language structure of the class labels. Furthermore, our results demonstrate that including language structure in category names enables relatively simple deep learning models to achieve better predictive performance than transformer networks with much higher capacity.
Accurate and Explainable Misinformation Detection: Too Good to be True?
Authors: José Manuél Gómez-Pérez
Venue: WWW
Year: 2022

Many of the challenges entailed in detecting online misinformation are related to our own cognitive limitations as human beings: We can only see a small part of the world at once, so we need to rely on others to pre-process part of that information for us. This makes us vulnerable to misinformation and points at AI as a necessary means to amplify our ability to deal with it at scale. Recent advances [1] demonstrate it is possible to build semi-automatic tools to detect online misinformation. However, the limitations are still many: our algorithms are hard to explain to human stakeholders, the reduced availability of ground truth data is a bottleneck to train better models, and our processing pipelines are long and complex, with multiple points of potential failure. To address such limitations, strategies that wisely combine algorithms that learn from data with explicit knowledge representations are fundamental to reason with misinformation while engaging [2] with human stakeholders. In this talk, I advocate for a partnership between humans and AI to deal with online misinformation detection, go through the challenges such partnership faces, and share some of the ongoing work that pursues this vision.
Cross-Site Prediction on Social Influence for Cold-Start Users in Online Social Networks
Authors: Qingyuan Gong, Yang Chen, Xinlei He, Yu Xiao, Pan Hui, Xin Wang, Xiaoming Fu
Venue: WWW
Year: 2022

Online social networks (OSNs) have become a commodity in our daily life. As an important concept in sociology and viral marketing, the study of social influence has received a lot of attentions in academia. Most of the existing proposals work well on dominant OSNs, such as Twitter, since these sites are mature and many users have generated a large amount of data for the calculation of social influence. Unfortunately, cold-start users on emerging OSNs generate much less activity data, which makes it challenging to identify potential influential users among them. In this work, we propose a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user’s information on dominant OSNs. A supervised machine learning-based approach is adopted, transferring the knowledge of both the descriptive information and dynamic activities on dominant OSNs to emerging OSNs. Descriptive features are extracted from the public data on a user’s homepage. In particular, to extract useful information from the fine-grained dynamic activities which cannot be represented by the statistical indices, we use deep learning technologies to deal with the sequential activity data. Using the real data of millions of users collected from Twitter (a dominant OSN) and Medium (an emerging OSN), we evaluate the performance of our proposed framework to predict prospective influential users. Our system achieves a high prediction performance based on different social influence definitions.