OpenRaiser/Intern-Atlas huggingface dataset:
Whale Watching in Inland Indonesia: Analyzing a Small, Remote, Internet-Based Community Cellular Network
Authors: Matthew Johnson, Jenny T. Liang, Michelle Lin, Sudheesh Singanamalla, Kurtis Heimerl
Venue: WWW
Year: 2021

While only generating a minuscule percentage of global traffic, largely lost in the noise of large-scale analyses, remote rural networks are the physical frontier of the Internet today. Through tight integration with a local operator’s infrastructure, we gather a unique dataset to characterize and report a year of interaction between finances, utilization, and performance of a novel, remote, data-only Community LTE Network in Bokondini, Indonesia. With visibility to drill down to individual users, we find use highly unbalanced and the network supported by only a handful of relatively heavy consumers. 45% of users are offline more days than online, and the median user consumes only 77 MB per day online and 36 MB per day on average, limiting consumption by frequently “topping up” in small amounts. Outside video and social media, messaging and IP calling provided by over-the-top services like Facebook Messenger, QQ, and WhatsApp comprise a relatively large percentage of traffic consistently across both heavy and light users. Our analysis shows that Internet-only Community Cellular Networks can be profitable despite most users spending less than $1 USD/day, and offers insights into the unique properties of these networks.
ColChain: Collaborative Linked Data Networks
Authors: Christian Aebeloe, Gabriela Montoya, Katja Hose
Venue: WWW
Year: 2021

One of the major obstacles that currently prevents the Semantic Web from exploiting its full potential is that the data it provides access to is sometimes not available or outdated. The reason is rooted deep within its architecture that relies on data providers to keep the data available, queryable, and up-to-date at all times – an expectation that many data providers in reality cannot live up to for an extended (or infinite) period of time. Hence, decentralized architectures have recently been proposed that use replication to keep the data available in case the data provider fails. Although this increases availability, it does not help keeping the data up-to-date or allow users to query and access previous versions of a dataset. In this paper, we therefore propose ColChain (COLlaborative knowledge CHAINs), a novel decentralized architecture based on blockchains that not only lowers the burden for the data providers but at the same time also allows users to propose updates to faulty or outdated data, trace updates back to their origin, and query older versions of the data. Our extensive experiments show that ColChain reaches these goals while achieving query processing performance comparable to the state of the art.
Sketch-based Algorithms for Approximate Shortest Paths in Road Networks
Authors: Gaurav Aggarwal, Sreenivas Gollapudi, Raghavender, Ali Kemal Sinop
Venue: WWW
Year: 2021

Constructing efficient data structures (distance oracles) for fast computation of shortest paths and other connectivity measures in graphs has been a promising area of study in computer science [23, 24, 28]. In this paper, we propose very efficient algorithms, based on a distance oracle, for computing approximate shortest paths and alternate paths in road networks. Specifically, we adopt a distance oracle construction that exploits the existence of small separators in such networks. In other words, the existence of a small cut in a graph admits a partitioning of the graph into balanced components with a small number of inter-component edges. We demonstrate the efficacy of our algorithm by using it to find near optimal shortest paths and show that it also has the desired properties of well-studied goal-oriented path search algorithms such as ALT [12]. We further demonstrate the use of our distance oracle to produce multiple alternative routes in addition to the shortest path. Finally, we empirically demonstrate that our method, while exploring few edges, produces high quality alternates with respect to metrics such as optimality-loss and diversity of paths.
LocWeb2021 Workshop - Chair's Welcome: Eleventh International Workshop on Location and the Web at The Web Conference 2021
Authors: Dirk Ahlers, Erik Wilde, Rossano Schifanella, Jalal S. Alowibdi
Venue: WWW
Year: 2021

LocWeb2021 (Eleventh International Workshop on Location and the Web) is a workshop at The Web Conference 2021, with evolving topics around location-aware information access, Web architecture, spatial social computing, and social good. It is designed as a meeting place for researchers around the location topic at The Web Conference.
Touch Screen Exploration of Visual Artwork for Blind People
Authors: Dragan Ahmetovic, Nahyun Kwon, Uran Oh, Cristian Bernareggi, Sergio Mascetti
Venue: WWW
Year: 2021

This paper investigates how touchscreen exploration and verbal feedback can be used to support blind people to access visual artwork. We present two artwork exploration modalities. The first one, attribute-based exploration, extends prior work on touchscreen image accessibility, and provides fine-grained segmentation of artwork visual elements; when the user touches an element, the associated attributes are read. The second one, hierarchical exploration, is designed with domain experts and provides multi-level segmentation of the artwork; the user initially accesses a general description of the entire artwork and then explores a coarse segmentation of the visual elements with the corresponding high-level descriptions; once selected, coarse segments are subdivided into fine-grained ones, which the user can access for more detailed descriptions. The two exploration modalities, implemented as a mobile web app, were evaluated through a user study with 10 blind participants. Both modalities were appreciated by the participants. Attribute-based exploration is perceived to be easier to access. Instead, the hierarchical exploration was considered more understandable, useful, interesting and captivating, and the participants remembered more details about the artwork with this modality. Participants commented that the two modalities work well together and therefore both should be made available.
Twin Peaks, a Model for Recurring Cascades
Authors: Matteo Almanza, Silvio Lattanzi, Alessandro Panconesi, Giuseppe Re
Venue: WWW
Year: 2021

Understanding information dynamics and their resulting cascades is a central topic in social network analysis. In a recent seminal work, Cheng et al. analyzed multiples cascades on Facebook over several months, and noticed that many of them exhibit a recurring behaviour. They tend to have multiple peaks of popularity, with periods of quiescence in between. In this paper, we propose the first mathematical model that provably explains this interesting phenomenon, besides exhibiting other fundamental properties of information cascades. Our model is simple and shows that it is enough to have a good clustering structure to observe this interesting recurring behaviour with a standard information diffusion model. Furthermore, we complement our theoretical analysis with an experimental evaluation where we show that our model is able to reproduce the observed phenomenon on several social networks.
BrFAST: a Tool to Select Browser Fingerprinting Attributes for Web Authentication According to a Usability-Security Trade-off
Authors: Nampoina Andriamilanto, Tristan Allard
Venue: WWW
Year: 2021

In this demonstration, we put ourselves in the place of a website manager who seeks to use browser fingerprinting for web authentication. The first step is to choose the attributes to implement among the hundreds that are available. To do so, we developed BrFAST, an attribute selection platform that includes FPSelect, an algorithm that rigorously selects the attributes according to a trade-off between security and usability. BrFAST is configured with a set of parameters for which we provide values for BrFAST to be usable as is. We notably include the resources to use two publicly available browser fingerprint datasets. BrFAST can be extended to use other parameters: other attribute selection methods, other measures of security and usability, or other fingerprint datasets. BrFAST helps visualize the exploration of the possibilities during the search of the best attribute set to use, evaluate the properties of attribute sets, and compare several attribute selection methods. During the demonstration, we compare the attribute sets selected by FPSelect with those selected by the usual methods according to the properties of the resulting browser fingerprints (e.g., their usability, their unicity).
Carpooling Platforms in Smart Cities for COVID-19 Pandemic: A Bibliometric Analysis
Authors: Leonidas G. Anthopoulos, Dimitrios Tzimos
Venue: WWW
Year: 2021

Formulation of carpooling schemes for mutual cost benefits between the driver and the passengers has a long history. However, the convenience of driving alone, especially under the current COVID-19 pandemic, the increase of car ownership and the difficulties in finding travelers with matching schedule and route keeps car occupancy low. The technology is a key enabler of online platforms which facilitate the ride matching process and lead the increase of carpooling services. The aim of this work-in-progress article is to clarify the value proposition of carpooling platforms in smart cities, especially under conditions like the pandemic. Thus, an extensive bibliometric analysis of three separate specialized literature collections using the bibliometrix R-Tool combined with a systematic literature review of selected papers is performed. It is identified that smart carpooling platforms could generate additional value for participants and smart cities with real-time ride matching, interconnection with public transportation and other city services, secure transactions, reputation-based services and closed organization carpooling schemes. To deliver this value to a smart city, a multi-sided platform business model is proposed, suitable for a carpooling service provider with multiple customer segments and partners.
Shaping a Digital Transformation Strategy for Smart Cities under the COVID-19 pandemic: Evidence from Greece
Authors: Leonidas G. Anthopoulos, Christos Ziozias, Apostolos Siokis
Venue: WWW
Year: 2021

Planning and establishing digital transformation (DT) is a complex process for all the organizations. City's DT is another challenging and complex process, which demands both the leading and dedicated role of the local government, and the engagement and commitment of the local stakeholders on a commonly agreed vision and plan. European Commission launched its Digital (DCC) and Intelligent Cities Challenge (ICC) initiatives to provide cities with guidance and support to design and implement corresponding digital transformation strategies. Shaping this strategy became hard during the ICC due to the Covid-19 pandemic, which changed all the local priorities and affected the initial city planning. The aim of this work-in-progress paper is to present the strategic planning process for city's digital transformation that was followed by the municipality of Trikala in Greece, which regardless is a famous smart city it had to join the DCC and ICC initiatives in order to methodologically perform it. Useful evidence are depicted with regard to the different stakeholders’ perspectives and priorities within the city's digital transformation and especially whether and how the COVID-19 outbreak re-arranged or re-shaped them.
The Language of Liberty: A preliminary study
Authors: Oscar Araque, Lorenzo Gatti, Kyriaki Kalimeri
Venue: WWW
Year: 2021

Quantifying the moral narratives expressed in the user-generated text, news, or public discourses is fundamental for understanding individuals’ concerns and viewpoints and preventing violent protests and social polarisation. The Moral Foundation Theory (MFT) was developed precisely to operationalise morality in a five-dimensional scale system. Recent developments of the theory urged for the introduction of a new foundation, liberty. Being only recently added to the theory, there are no available linguistic resources to assess liberty from text corpora. Given its importance to current social issues such as the vaccination debate, we propose a data-driven approach to derive a liberty lexicon based on aligned documents from online encyclopedias with different worldviews. Despite the preliminary nature of our study, we show proof of the concept that large encyclopedia corpora can point out differences in the way people with contrasting viewpoints express themselves. Such differences can be used to derive a novel lexicon, identifying linguistic markers of the liberty foundation.
Negative Knowledge for Open-world Wikidata
Authors: Hiba Arnaout, Simon Razniewski, Gerhard Weikum, Jeff Z. Pan
Venue: WWW
Year: 2021

The Wikidata knowledge base (KB) is one of the most popular structured data repositories on the web, containing more than 1 billion statements for over 90 million entities. Like most major KBs, it is nonetheless incomplete and therefore operates under the open-world assumption (OWA) - statements not contained in Wikidata should be assumed to have an unknown truth. The OWA ignores however, that a significant part of interesting knowledge is negative, which cannot be readily expressed in this data model. In this paper, we review the challenges arising from the OWA, as well as some specific attempts Wikidata has made to overcome them. We review a statistical inference method for negative statements, called peer-based inference, and present Wikinegata, a platform that implements this inference over Wikidata. We discuss lessons learned from the development of this platform, as well as how the platform can be used both for learning about interesting negations, as well as about modelling challenges inside Wikidata. Wikinegata is available at https://d5demos.mpi-inf.mpg.de/negation.
A Comparison of Pre-Trained Language Models for Multi-Class Text Classification in the Financial Domain
Authors: Yusuf Arslan, Kevin Allix, Lisa Veiber, Cedric Lothritz, Tegawendé F. Bissyandé, Jacques Klein, Anne Goujon
Venue: WWW
Year: 2021

Neural networks for language modeling have been proven effective on several sub-tasks of natural language processing. Training deep language models, however, is time-consuming and computationally intensive. Pre-trained language models such as BERT are thus appealing since (1) they yielded state-of-the-art performance, and (2) they offload practitioners from the burden of preparing the adequate resources (time, hardware, and data) to train models. Nevertheless, because pre-trained models are generic, they may underperform on specific domains. In this study, we investigate the case of multi-class text classification, a task that is relatively less studied in the literature evaluating pre-trained language models. Our work is further placed under the industrial settings of the financial domain. We thus leverage generic benchmark datasets from the literature and two proprietary datasets from our partners in the financial technological industry. After highlighting a challenge for generic pre-trained models (BERT, DistilBERT, RoBERTa, XLNet, XLM) to classify a portion of the financial document dataset, we investigate the intuition that a specialized pre-trained model for financial documents, such as FinBERT, should be leveraged. Nevertheless, our experiments show that the FinBERT model, even with an adapted vocabulary, does not lead to improvements compared to the generic BERT models.
WiseKG: Balanced Access to Web Knowledge Graphs
Authors: Amr Azzam, Christian Aebeloe, Gabriela Montoya, Ilkcan Keles, Axel Polleres, Katja Hose
Venue: WWW
Year: 2021

SPARQL query services that balance processing between clients and servers become more and more essential to handle the increasing load for open and decentralized knowledge graphs on the Web. To this end, Linked Data Fragments (LDF) have introduced a foundational framework that has sparked research exploring a spectrum of potential Web querying interfaces in between server-side query processing via SPARQL endpoints and client-side query processing of data dumps. Current proposals in between typically suffer from imbalanced load on either the client or the server. In this paper, to the best of our knowledge, we present the first work that combines both client-side and server-side query optimization techniques in a truly dynamic fashion: we introduce WiseKG, a system that employs a cost model that dynamically delegates the load between servers and clients by combining client-side processing of shipped partitions with efficient server-side processing of star-shaped sub-queries, based on current server workload and client capabilities. Our experiments show that WiseKG significantly outperforms state-of-the-art solutions in terms of average total query execution time per client, while at the same time decreasing network traffic and increasing server-side availability.
ATJ-Net: Auto-Table-Join Network for Automatic Learning on Relational Databases
Authors: Jinze Bai, Jialin Wang, Zhao Li, Donghui Ding, Ji Zhang, Jun Gao
Venue: WWW
Year: 2021

A relational database, consisting of multiple tables, provides heterogeneous information across various entities, widely used in real-world services. This paper studies the supervised learning task on multiple tables, aiming to predict one label column with the help of multiple-tabular data. However, classical ML techniques mainly focus on single-tabular data. Multiple-tabular data refers to many-to-many mapping among joinable attributes and n-ary relations, which cannot be utilized directly by classical ML techniques. Besides, current graph techniques, like heterogeneous information network (HIN) and graph neural networks (GNN), are infeasible to be deployed directly and automatically in a multi-table environment, which limits the learning on databases. For automatic learning on relational databases, we propose an auto-table-join network (ATJ-Net). Multiple tables with relationships are considered as a hypergraph, where vertices are joinable attributes and hyperedges are tuples of tables. Then, ATJ-Net builds a graph neural network on the heterogeneous hypergraph, which samples and aggregates the vertices and hyperedges on n-hop sub-graphs as the receptive field. In order to enable ATJ-Net to be automatically deployed to different datasets and avoid the ”no free lunch” dilemma, we use random architecture search to select optimal aggregators and prune redundant paths in the network. For verifying the effectiveness of our methods across various tasks and schema, we conduct extensive experiments on 4 tasks, 8 various schemas, and 19 sub-datasets w.r.t. citing prediction, review classification, recommendation, and task-blind challenge. ATJ-Net achieves the best performance over state-of-the-art approaches on three tasks and is competitive with KddCup Winner solution on task-blind challenge.
Language, Vision and Action are Better Together
Authors: Jason Baldridge
Venue: WWW
Year: 2021

Human knowledge and use of language is inextricably connected to perception, action and the organization of the brain, yet natural language processing is still dominated by text! More research involving language-including speech-in the context of other modalities and environments is needed, and there has never been a better time to do it. Without ever invoking the worn-out, overblown phrase ”how babies learn” in the talk, I’ll cover three of my team’s efforts involving language, vision and action. First: our work on speech-image representation learning and retrieval, where we demonstrate settings in which directly encoding speech outperforms the hard-to-beat strategy of using automatic speech recognition and strong text encoders. Second: two models for text-to-image generation: a multi-stage model which exploits user-guidance in the form of mouse traces and a single-stage one which uses cross-modal contrastive losses. Third: Room-across-Room, a multilingual dataset for vision-and-language navigation, for which we collected spoken navigation instructions, high-quality text transcriptions, and fine-grained alignments between words and pixels in high-definition 360-degree panoramas. I’ll wrap up with some thoughts on how work on computational language grounding more broadly presents new opportunities to enhance and advance our scientific understanding of language and its fundamental role in human intelligence.
High-dimensional Sparse Embeddings for Collaborative Filtering
Authors: Jan Van Balen, Bart Goethals
Venue: WWW
Year: 2021

A widely adopted paradigm in the design of recommender systems is to represent users and items as vectors, often referred to as latent factors or embeddings. Embeddings can be obtained using a variety of recommendation models and served in production using a variety of data engineering solutions. Embeddings also facilitate transfer learning, where trained embeddings from one model are reused in another. In contrast, some of the best-performing collaborative filtering models today are high-dimensional linear models that do not rely on factorization, and so they do not produce embeddings [27, 28]. They also require pruning, amounting to a trade-off between the model size and the density of the predicted affinities. This paper argues for the use of high-dimensional, sparse latent factor models, instead. We propose a new recommendation model based on a full-rank factorization of the inverse Gram matrix. The resulting high-dimensional embeddings can be made sparse while still factorizing a dense affinity matrix. We show how the embeddings combine the advantages of latent representations with the performance of high-dimensional linear models.
Incrementality Testing in Programmatic Advertising: Enhanced Precision with Double-Blind Designs
Authors: Joel Barajas, Narayan Bhamidipati
Venue: WWW
Year: 2021

Measuring the incremental value of advertising (incrementality) is critical for financial planning and budget allocation by advertisers. Running randomized controlled experiments is the gold standard in marketing incrementality measurement. Current literature and industry practices to run incrementality experiments focus on running placebo, intention-to-treat (ITT), or ghost bidding based experiments. A fundamental challenge with these is that the serving engine as treatment administrator is not blind to the user treatment assignment. Similarly, ITT and ghost bidding solutions provide greatly decreased precision since many experiment users never see ads. We present a novel randomized design solution for incrementality testing based on ghost bidding with improved measurement precision. Our design provides faster and cheaper results including double-blind, to the users and to the serving engine, post-auction experiment execution without ad targeting bias. We also identify ghost impressions in open ad exchanges by matching the bidding values or ads sent to external auctions with held-out bid values. This design leads to larger precision than ITT or current ghost bidding solutions. Our proposed design has been fully deployed in a real production system within a commercial programmatic ad network combined with a Demand Side Platform (DSP) that places ad bids in third-party ad exchanges. We have found reductions of up to 85% of the advertiser budget to reach statistical significance with typical ghost bids conversion and winner rates. Moreover, the highest statistical power at 50% control size design of this current practice is reached at 8% of our proposed design. By deploying this design, for an advertiser in the insurance industry, to measure the incrementality of display and native programmatic advertising, we have found conclusive evidence that the last-touch attribution framework (current industry standard) undervalues these channels by 87% when compared to the incremental conversions derived from the experiment.