OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective
Authors: Zeyd Boukhers, Christoph Lange, Oya Beyan
Venue: WWW
Year: 2023

Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning. The use of data spaces, where data is exchanged among members in a self-regulated environment, is becoming increasingly popular. However, the current manual practices of managing metadata and vocabularies in these spaces are time-consuming, prone to errors, and may not meet the needs of all stakeholders. By leveraging the power of machine learning, we believe that semantic interoperability in data spaces can be significantly improved. This involves automatically generating and updating metadata, which results in a more flexible vocabulary that can accommodate the diverse terminologies used by different sub-communities. Our vision for the future of data spaces addresses the limitations of conventional data exchange and makes data more accessible and valuable for all members of the community.
MetaCrimes: Criminal accountability for conducts in the Metaverse
Authors: Gian Marco Bovenzi
Venue: WWW
Year: 2023

The research addresses a topic whose precise boundaries are yet to be defined: the criminal accountability for conducts committed in the Metaverse. Following a short introduction motivating the reason why this issue has to be considered as pivotal both for the Web and for the society, the main problem raised by the research will be identified, namely, whether an action taken against a person, that in real-life would be a criminal conduct, is considerable as a crime in the Metaverse as well. A short assessment of the (very little so far) current state of the art, as well as the proposed approach and methodology will be then overviewed; finally, the contribution shows its current results, and concludes stating that countries are highly encouraged to shape respective criminal frameworks when applied to the Metaverse, and that the international community should consider the topic as a priority in its agenda. Nevertheless, further experimental and research work has still to be made.
SISSI: An Architecture for Semantic Interoperable Self-Sovereign Identity-based Access Control on the Web
Authors: Christoph H.-J. Braun, Vasil Papanchev, Tobias Käfer
Venue: WWW
Year: 2023

We present an architecture for authentication and authorization on the Web that is based on the Self-Sovereign Identity paradigm. Using our architecture, we aim to achieve semantic interoperability across different approaches to SSI. We build on the underlying RDF data model of the W3C’s recommendation for Verifiable Credentials and specify semantic access control rules using SHACL. Our communication protocol for an authorization process is based on Decentralised Identifiers and extends the Hyperledger Aries Present Proof protocol. We propose a modular architecture that allows for flexible extension, e. g., for supporting more signature schemes or Decentralised Identifier Methods. For evaluation, we implemented a Proof-of-Concept: We show that a Web-based approach to SSI outperfoms a blockchain-based approach to SSI in terms of End-to-End execution time.
The Web Data Commons Schema.org Data Set Series
Authors: Alexander Brinkmann, Anna Primpeli, Christian Bizer
Venue: WWW
Year: 2023

Millions of websites have started to annotate structured data within their HTML pages using the schema.org vocabulary. Popular entity types annotated with schema.org terms are products, local businesses, events, and job postings. The Web Data Commons project has been extracting schema.org data from the Common Crawl every year since 2013 and offers the extracted data for public download in the form of the schema.org data set series. The latest release in the series consists of 106 billion RDF quads describing 3.1 billion entities. The entity descriptions originate from 12.8 million different websites. From a Web Science perspective, the data set series lays the foundation for analyzing the adoption process of schema.org annotations on the Web over the past decade. From a machine learning perspective, the annotations provide a large pool of training data for tasks such as product matching, product or job categorization, information extraction, or question answering. This poster gives an overview of the content of the Web Data Commons schema.org data set series. It highlights trends in the adoption of schema.org annotations on the Web and discusses how the annotations are being used as training data for machine learning applications.
WAM-studio, a Digital Audio Workstation (DAW) for the Web
Authors: Michel Buffa, Antoine Vidal-Mazuy
Venue: WWW
Year: 2023

This paper presents WAM Studio, an open source, online Digital Audio Workstation (DAW) that takes advantages of several W3C Web APIs, such as Web Audio, Web Assembly, Web Components, Web Midi, Media Devices etc. It also uses the Web Audio Modules proposal that has been designed to facilitate the development of inter-operable audio plugins (effects, virtual instruments, virtual piano keyboards as controllers etc.) and host applications. DAWs are feature-rich software and therefore particularly complex to develop in terms of design, implementation, performances and ergonomics. Very few commercial online DAWs exist today and the only open-source examples lack features (no support for inter-operable plugins, for example) and do not take advantage of the recent possibilities offered by modern W3C APIs (e.g. AudioWorklets/Web Assembly). WAM Studio was developed as an open-source technology demonstrator with the aim of showcasing the potential of the web platform, made possible by these APIs. The paper highlights some of the difficulties we encountered (i.e limitations due to the sandboxed and constrained environments that are Web browsers, latency compensation etc.). An online demo, as well as a GitHub repository for the source code are available.
Injecting data into ODRL privacy policies dynamically with RDF mappings
Authors: Juan Cano-Benito, Andrea Cimmino, Raúl García-Castro
Venue: WWW
Year: 2023

The privacy of the data provided by available sources is one of the major concerns of our era. In order to address this challenge, the W3C has promoted recommendations to allow expressing privacy policies. One of these recommendations is the Open Digital Rights Language (ODRL) vocabulary. Although this standard has wide adoption, it is not suitable in domains such as IoT, Ubiquitous and Mobile Computing, or discovery. The reason behind is the fact that ODRL privacy policies are not able to cope with dynamic information that may come from external sources of data and, therefore, these policies can not define privacy restrictions upon data that is not already written in the policy beforehand. In this demo paper, a solution to this challenge is presented. It is shown how ODRL policies can overcome the aforementioned limitation by being combined with a mapping language for RDF materialisation. The article shows how ODRL policies are able to consider data coming from an external data source when they are solved, in particular, a weather forecast API that provides temperature values. The demonstration defines an ODRL policy that grants access to a resource only when the temperature of the API is above a certain value.
Ready, Aim, Snipe! Analysis of Sniper Bots and their Impact on the DeFi Ecosystem
Authors: Federico Cernera, Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini, Francesco Sassi
Venue: WWW
Year: 2023

In the world of cryptocurrencies, public listing of a new token often generates significant hype, in many cases causing its price to skyrocket in a few seconds. In this scenario, timing is crucial to determine the success or failure of an investment opportunity. In this work, we present an in-depth analysis of sniper bots, automated tools designed to buy tokens as soon as they are listed on the market. We leverage GitHub open-source repositories of sniper bots to analyze their features and how they are implemented. Then, we build a dataset of Ethereum and BNB Smart Chain (BSC) liquidity pools to identify addresses that serially take advantage of sniper bots. Our findings reveal 14,029 sniping operations on Ethereum and 1,395,042 in BSC that bought tokens for a total of $10,144,808 dollars and $18,720,447, respectively. We find that Ethereum operations have a higher success rate but require a larger investment. Finally, we analyze token smart contracts to identify mechanisms that can hinder sniper bots.
An Exploration on Cryptocurrency Corporations' Fiscal Opportunities
Authors: Thomas Charest, Masarah Paquet-Clouston
Venue: WWW
Year: 2023

As the decentralized finance industry gains traction, governments worldwide are creating or modifying legislations to regulate such financial activities. To avoid these new legislations, decentralized finance enterprises may shop for fiscally advantageous jurisdictions. This study explores global tax evasion opportunities for decentralized finance enterprises. Opportunities are identified by considering various jurisdictions’ tax laws on cryptocurrencies along with their corporate income tax rates, corporate capital gains tax rates, level of financial development and level of cryptocurrency adoption. They are visualized with the manifold approximation and projection for dimension reduction (UMAP) technique. The study results show that there exist a substantial number of tax evasion opportunities for decentralized finance enterprises through both traditional offshore jurisdictions and crypto-advantageous jurisdictions. The latter jurisdictions are usually considered high-tax fiscal regimes; but, given that they do not apply tax laws, tax evasion opportunities arise, especially in jurisdictions that have high financial development and high cryptocurrency adoption. Further research should investigate these new opportunities and how they are evolving. Understanding the global landscape surrounding tax evasion opportunities in decentralized finance represents a first step at preventing corporate capital flight of cryptocurrencies.
Atrapos: Real-time Evaluation of Metapath Query Workloads
Authors: Serafeim Chatzopoulos, Thanasis Vergoulis, Dimitrios Skoutas, Theodore Dalamagas, Christos Tryfonopoulos, Panagiotis Karras
Venue: WWW
Year: 2023

Heterogeneous information networks (HINs) represent different types of entities and relationships between them. Exploring and mining HINs relies on metapath queries that identify pairs of entities connected by relationships of diverse semantics. While the real-time evaluation of metapath query workloads on large, web-scale HINs is highly demanding in computational cost, current approaches do not exploit interrelationships among the queries. In this paper, we present Atrapos, a new approach for the real-time evaluation of metapath query workloads that leverages a combination of efficient sparse matrix multiplication and intermediate result caching. Atrapos selects intermediate results to cache and reuse by detecting frequent sub-metapaths among workload queries in real time, using a tailor-made data structure, the Overlap Tree, and an associated caching policy. Our experimental study on real data shows that Atrapos accelerates exploratory data analysis and mining on HINs, outperforming off-the-shelf caching approaches and state-of-the-art research prototypes in all examined scenarios.
Learning Topical Structured Interfaces from Medical Research Literature
Authors: Maitry Chauhan, Anna Pyayt, Michael N. Gubanov
Venue: WWW
Year: 2023

Accessing large-scale structured datasets such as WDC or CORD-191 is very challenging. Even if one topic (e.g. COVID-19 vaccine efficacy) is of interest, all topical tables in different sources/papers have hundreds of different schemas, depending on the authors, which significantly complicates both finding and querying them. Here we demonstrate a scalable Meta-profiler system, capable of constructing a structured standardized interface to a topic of interest in large-scale (semi-)structured datasets. This interface, that we call Meta-profile represents a multi-dimensional meta-data summary for a selected topic of interest, accumulating all differently structured representations of the topical tables in the dataset. Such Meta-profiles can be used as a rich visualization as well as a robust structural query interface simplifying access to large-scale (semi-)structured data for different user segments, such as data scientists and end users.
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search
Authors: Patrick H. Chen, Wei-Cheng Chang, Jyun-Yu Jiang, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh
Venue: WWW
Year: 2023

Approximate K-Nearest Neighbor Search (AKNNS) has now become ubiquitous in modern applications, such as a fast search procedure with two-tower deep learning models. Graph-based methods for AKNNS in particular have received great attention due to their superior performance. These methods rely on greedy graph search to traverse the data points as embedding vectors in a database. Under this greedy search scheme, we make a key observation: many distance computations do not influence search updates so that these computations can be approximated without hurting performance. As a result, we propose FINGER, a fast inference method for efficient graph search in AKNNS. FINGER approximates the distance function by estimating angles between neighboring residual vectors. The approximated distance can be used to bypass unnecessary computations for faster searches. Empirically, when it comes to speeding up the inference of HNSW, which is one of the most popular graph-based AKNNS methods, FINGER significantly outperforms existing acceleration approaches and conventional libraries by 20 to 60 across different benchmark datasets.
Improving Product Search with Season-Aware Query-Product Semantic Similarity
Authors: Haoming Chen, Yetian Chen, Jingjing Meng, Yang Jiao, Yikai Ni, Yan Gao, Michinari Momma, Yi Sun
Venue: WWW
Year: 2023

Product search for online shopping should be season-aware, i.e., presenting seasonally relevant products to customers. In this paper, we propose a simple yet effective solution to improve seasonal relevance in product search by incorporating seasonality into language models for semantic matching. We first identify seasonal queries and products by analyzing implicit seasonal contexts through time-series analysis over the past year. Then we introduce explicit seasonal contexts by enhancing the query representation with a season token according to when the query is issued. A new season-enhanced BERT model (SE-BERT) is also proposed to learn the semantic similarity between the resulting seasonal queries and products. SE-BERT utilizes Multi-modal Adaption Gate (MAG) to augment the season-enhanced semantic embedding with other contextual information such as product price and review counts for robust relevance prediction. To better align with the ranking objective, a listwise loss function (neural NDCG) is used to regularize learning. Experimental results validate the effectiveness of the proposed method, which outperforms existing solutions for query-product relevance prediction in terms of NDCG and Price Weighted Purchases (PWP).
Gateway Entities in Problematic Trajectories
Authors: Xi Leslie Chen, Abhratanu Dutta, Sindhu Kiranmai Ernala, Stratis Ioannidis, Shankar Kalyanaraman, Israel Nir, Udi Weinsberg
Venue: WWW
Year: 2023

Social media platforms like Facebook and YouTube connect people with communities that reflect their own values and experiences. People discover new communities either organically or through algorithmic recommendations based on their interests and preferences. We study online journeys users take through these communities, focusing particularly on ones that may lead to problematic outcomes. In particular, we propose and explore the concept of gateways, namely, entities associated with a higher likelihood of subsequent engagement with problematic content. We show, via a real-world application on Facebook groups, that a simple definition of gateway entities can be leveraged to reduce exposure to problematic content by 1% without any adverse impact on user engagement metrics. Motivated by this finding, we propose several formal definitions of gateways, via both frequentist and survival analysis methods, and evaluate their efficacy in predicting user behavior through offline experiments. Frequentist, duration-insensitive methods predict future harmful engagements with an 0.64–0.83 AUC, while survival analysis methods improve this to 0.72–0.90 AUC.
Fairly Adaptive Negative Sampling for Recommendations
Authors: Xiao Chen, Wenqi Fan, Jingfan Chen, Haochen Liu, Zitao Liu, Zhaoxiang Zhang, Qing Li
Venue: WWW
Year: 2023

Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i.e., clicked by a user) and negative items (i.e., obtained by negative sampling). However, the size of different item groups (specified by item attribute) is usually unevenly distributed. We empirically find that the commonly used uniform negative sampling strategy for pairwise algorithms (e.g., BPR) can inherit such data bias and oversample the majority item group as negative instances, severely countering group fairness on the item side. In this paper, we propose a Fairly adaptive Negative sampling approach (FairNeg), which improves item group fairness via adaptively adjusting the group-level negative sampling distribution in the training process. In particular, it first perceives the model’s unfairness status at each step and then adjusts the group-wise sampling distribution with an adaptive momentum update strategy for better facilitating fairness optimization. Moreover, a negative sampling distribution Mixup mechanism is proposed, which gracefully incorporates existing importance-aware sampling techniques intended for mining informative negative samples, thus allowing for achieving multiple optimization purposes. Extensive experiments on four public datasets show our proposed method’s superiority in group fairness enhancement and fairness-utility tradeoff.
Financial Technology on the Web
Authors: Chung-Chi Chen, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen
Venue: WWW
Year: 2023

This paper shares our observations based on our three-year experience organizing the FinWeb workshop series. In addition to the widely-discussed topic, content analysis, we notice two tendencies for FinTech applications: customers’ behavior analysis and finance-oriented LegalTech. We also briefly share our idea on the research direction about reliable and trustworthy FinWeb from the investment perspective.
A Reference-Dependent Model for Web Search Evaluation: Understanding and Measuring the Experience of Boundedly Rational Users
Authors: Nuo Chen, Jiqun Liu, Tetsuya Sakai
Venue: WWW
Year: 2023

Previous researches demonstrate that users’ actions in search interaction are associated with relative gains and losses to reference points, known as the reference dependence effect. However, this widely confirmed effect is not represented in most user models underpinning existing search evaluation metrics. In this study, we propose a new evaluation metric framework, namely Reference Dependent Metric (ReDeM), for assessing query-level search by incorporating the effect of reference dependence into the modelling of user search behavior. To test the overall effectiveness of the proposed framework, (1) we evaluate the performance, in terms of correlation with user satisfaction, of ReDeMs built upon different reference points against that of the widely-used metrics on three search datasets; (2) we examine the performance of ReDeMs under different task states, like task difficulty and task urgency; and (3) we analyze the statistical reliability of ReDeMs in terms of discriminative power. Experimental results indicate that: (1) ReDeMs integrated with a proper reference point achieve better correlations with user satisfaction than most of the existing metrics, like Discounted Cumulative Gain (DCG) and Rank-Biased Precision (RBP), even though their parameters have already been well-tuned; (2) ReDeMs reach relatively better performance compared to existing metrics when the task triggers a high-level cognitive load; (3) the discriminative power of ReDeMs is far stronger than Expected Reciprocal Rank (ERR), slightly stronger than Precision and similar to DCG, RBP and INST. To our knowledge, this study is the first to explicitly incorporate the reference dependence effect into the user browsing model and offline evaluation metrics. Our work illustrates a promising approach to leveraging the insights about user biases from cognitive psychology in better evaluating user search experience and enhancing user models.
Multiple-Agent Deep Reinforcement Learning for Avatar Migration in Vehicular Metaverses
Authors: Junlong Chen, Jiangtian Nie, Minrui Xu, Lingjuan Lyu, Zehui Xiong, Jiawen Kang, Yongju Tong, Wenchao Jiang
Venue: WWW
Year: 2023

Vehicular Metaverses are widely considered as the next Internet revolution to build a 3D virtual world with immersive virtual-real interaction for passengers and drivers. In vehicular Metaverse applications, avatars are digital representations of on-board users to obtain and manage immersive vehicular services (i.e., avatar tasks) in Metaverses and the data they generate. However, traditional Internet of Vehicles (IoV) data management solutions have serious data security risks and privacy protection. Fortunately, blockchain-based Web 3.0 enables avatars to have an ownership identity to securely manage the data owned by users in a decentralized and transparent manner. To ensure users’ immersive experiences and securely manage their data, avatar tasks often require significant computing resources. Therefore, it is impractical for the vehicles to process avatar tasks locally, massive computation resources are needed to support the avatar tasks. To this end, offloading avatar tasks to nearby RoadSide Units (RSUs) is a promising solution to avoid computation overload. To ensure real-time and continuous Metaverse services, the avatar tasks should be migrated among the RSUs when the vehicle navigation. It is challenging for the vehicles to independently decide whether migrate or not according to current and future avatar states. Therefore, in this paper, we propose a new avatar task migration framework for vehicular Metaverses. We then formulate the avatar task migration problem as a Partially Observable Markov Decision Process (POMDP), and apply a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm to dynamically make migration decisions for avatar tasks. Numerous results show that our proposed algorithm outperforms existing baselines for avatar task migration and enables immersive vehicular Metaverse services.