OpenRaiser/Intern-Atlas huggingface dataset:
Core-Competitiveness in Partially Observable Networked Market
Authors: Bin Li, Dong Hao
Venue: WWW
Year: 2024

In auction theory, a core is a stable outcome where no subgroup of participants can achieve better results for themselves. Core-competitive auctions aim to generate revenue that is achievable in a core. They are particularly important because they not only generate optimized revenue for the seller, but also provide an efficient and stable environment for participants. We generalize the design of core-competitive auctions to encompass partially observable networked markets (PONM). Unlike traditional auctions, which often deal with scenarios of limited trading activity, our approach to core-competitive auctions for PONM captures the nature of real-world transaction markets, which is a large linking world for the economic entities and commodities circulate among the entities in the market. Our generalizing the auction market to PONM can much improve the liquidity of the auction, and is especially meaningful for the web economics. Specifically, we quantify the upper and lower bounds of the minimum core revenue in PONM, and further prove that there does not exist any truthful auction for PONM which is efficient and core-competitive. Governed by this impossible result, we identify the criteria that the allocation rule for PONM should meet. Based on these criteria, we propose a new class of auction mechanisms for PONM that is individually rational, incentive-compatible, and core-competitive.
Decoding Memes: A Comprehensive Analysis of Late and Early Fusion Models for Explainable Meme Analysis
Authors: Faseela Abdullakutty, Usman Naseem
Venue: WWW
Year: 2024

Memes are important because they serve as conduits for expressing emotions, opinions, and social commentary online, providing valuable insight into public sentiment, trends, and social interactions. By combining textual and visual elements, multi-modal fusion techniques enhance meme analysis, enabling the classification of offensive and sentimental memes effectively. Early and late fusion methods effectively integrate multi-modal data but face limitations. Early fusion integrates features from different modalities before classification. Late fusion combines classification outcomes from each modality after individual classification and reclassifies the combined results. This paper compares early and late fusion models in meme analysis. It showcases their efficacy in extracting meme concepts and classifying meme reasoning. Pre-trained vision encoders, including ViT and VGG-16, and language encoders such as BERT, AlBERT, and DistilBERT, were employed to extract image and text features. These features were subsequently utilized for performing both early and late fusion techniques. This paper further compares the explainability of fusion models through SHAP analysis. In comprehensive experiments, various classifiers such as XGBoost and Random Forest, along with combinations of different vision and text features across multiple sentiment scenarios, showcased the superior effectiveness of late fusion over early fusion.
OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search
Authors: Prabhat Agarwal, Minhazul Islam SK, Nikil Pancha, Kurchi Subhra Hazra, Jiajing Xu, Chuck Rosenberg
Venue: WWW
Year: 2024

In this paper, we present OmniSearchSage, a versatile and scalable system for understanding search queries, pins, and products for Pinterest search. We jointly learn a unified query embedding coupled with pin and product embeddings, leading to an improvement of >8% relevance, >7% engagement, and >5% ads CTR in Pinterest's production search system. The main contributors to these gains are improved content understanding, better multi-task learning, and real-time serving. We enrich our entity representations using diverse text derived from image captions from a generative LLM, historical engagement, and user-curated boards. Our multitask learning setup produces a single search query embedding in the same space as pin and product embeddings and compatible with pre-existing pin and product embeddings. We show the value of each feature through ablation studies, and show the effectiveness of a unified model compared to standalone counterparts. Finally, we share how these embeddings have been deployed across the Pinterest search stack, from retrieval to ranking, scaling to serve 300k requests per second at low latency. Our implementation of this work is available at this link https://github.com/pinterest/atg-research/tree/main/omnisearchsage.
Spatio-Temporal Challenges in Understanding your (Smart) City
Authors: Dirk Ahlers
Venue: WWW
Year: 2024

In this talk, we will explore challenges and opportunities of spatio-temporal information access as connecting temporal and spatial dimensions of mining and analysis. We focus on use cases and examples in the development of systems and services in smart sustainable cities, and in urban energy and climate transitions.
Implementing Sustainable Urban Mobility Transitions in Positive Energy Districts
Authors: Dirk Ahlers, Bjørn Ove Berthelsen, Tor Rune Skoglund, Kelly Riedesel
Venue: WWW
Year: 2024

This paper examines Smart Cities transition with the focus on urban mobility. We demonstrate how a multi-stakeholder, multi-disciplinary approach can support integration of systems, data, people, and organisations with a case study on new integrated mobility solutions and urban decarbonisation.
Extracting Small Subgraphs in Road Networks
Authors: Sara Ahmadian, Sreenivas Gollapudi, Gregory Hutchins, Kostas Kollias, Xizhi Tan
Venue: WWW
Year: 2024

Online navigation platforms are well optimized to solve the standard objective of minimizing travel time and typically require precomputation-based architectures (such as Contraction Hierarchies and Customizable Route Planning) to do so in a fast manner. The reason for this dependence is the size of the graph that represents the road network, which is large. The need to go beyond minimizing the travel time and introduce various types of customizations has led to approaches that rely on alternative route computation or, more generally, small subgraph extraction. On a small subgraph, one can run computationally expensive algorithms at query time and compute optimal solutions for multiple routing problems. In this framework, it is critical for the subgraph to (a) be small and (b) include (near) optimal routes for a collection of customizations. This is precisely the setting that we study in this work. We design algorithms that extract a subgraph connecting designated terminals with the objective of minimizing the subgraph's size and the constraint of including near-optimal routes for a set of predefined cost functions. We provide theoretical guarantees for our algorithms and evaluate them empirically using real-world road networks.
Efficient Location Sampling Algorithms for Road Networks
Authors: Sara Ahmadian, Sreenivas Gollapudi, Kostas Kollias, Vivek Kumar, Ameya Velingker, Santhoshini Velusamy
Venue: WWW
Year: 2024

Many geographic information systems applications rely on data provided by user devices in the road network, including traffic monitoring, driving navigation, and road closure detection. The underlying signal is generally collected by sampling locations from user trajectories. The sampling process, though critical for various applications, has not been studied sufficiently in the literature. While the most natural way to sample a trajectory may be to use a frequency based algorithm, e.g., sampling locations every x seconds, such a sampling strategy can be quite wasteful in resources (e.g., server-side processing, user battery) as well as stored user data. In this work, we conduct a horizontal study of various location sampling algorithms (based on frequency, road geography, reservoir sampling, etc.) and assess their trade-offs in terms of the size of the stored data and the induced quality of training for prediction tasks (specifically predicting speeds on road segments).
Prompt-Eng: Healthcare Prompt Engineering: Revolutionizing Healthcare Applications with Precision Prompts
Authors: Awais Ahmed, Mengshu Hou, Rui Xi, Xiaoyang Zeng, Syed Attique Shah
Venue: WWW
Year: 2024

Prompt Engineering has emerged as a pivotal technique in Natural Language Processing, providing a flexible approach for leveraging pre-trained language models. Particularly, a prompt is used to instruct the model to adopt the nature of given prompts, which became a well-adoptable approach in wide areas of domains. Yet, existing prompt-guided frameworks are experiencing various challenges, such as crafting prompts for specific tasks to achieve clarity and conciseness and avoid ambiguity, which requires time and computational resources. Further existing methods heavily rely on the extensive labelled datasets, yet many domain-specific challenges exist, particularly in healthcare. This study presents Prompt-Eng, a novel framework emphasizing its wide-ranging applications in healthcare, where we design precise prompts with positive and negative aspects; we hypothesize that designing prompts in pairs helps models to generalize effectively. We delve into the significance of quick design and optimization, highlighting its influence in shaping model responses. In addition, we explore the increasing demand for prompts that are aware of the context in multimodal data analysis and the incorporation of prompt engineering in new machine-learning approaches. The essence of our approach is in creating tailored prompts, which serve as instructive guidelines for the models during the prediction procedure. The proposed methodology emphasizes utilizing context-aware prompt pairs to facilitate interpreting and extracting healthcare information from a health corpus by models. The study uses the medical MIMIC-III \footnotehttps://physionet.org/content/mimiciii/1.4/ corpus to predict medicine prescriptions. The paper also explores visual and textual prompts for X-ray image analysis for pneumonia prediction on the MIMIC-CXR \footnote\urlhttps://physionet.org/content/mimic-cxr/2.0.0/ dataset. This approach stands out from existing methods by addressing challenges such as clarity, conciseness, and context awareness, thereby enabling improved interpretation and extraction of healthcare information from diverse data sources.
Is the 'Impression Log' Beneficial to Evaluating News Recommender Systems? No, it is Not!
Authors: Jeewon Ahn, Hong-Kyun Bae, Sang-Wook Kim
Venue: WWW
Year: 2024

This paper aims to answer the question of whether to use the impression log in evaluating news recommendation models. We start with a claim that the testing with the impression log composed of only hard-negative news (i.e., impression (IMP)-based test) is not beneficial to evaluating the models precisely. Based on the claim, we discuss a way of evaluating models by employing all kinds of negative news articles (i.e., Total test). Also, we propose a more-efficient way of evaluating models by sampling only a small number of negative articles (i.e., random-sampling (RS)-based test). We verify our claim by extensively comparing the evaluation results on six models from the IMP-based, Total, and RS-based tests: the RS-based test shows more accurate results than the IMP-based test in determining the superiority among the models while providing higher efficiency than the Total test. Therefore, our answer to the question above would be "do not employ the impression log in testing models even if it is available." This result is quite meaningful since it enables news recommendation researchers and practitioners, who have been using the impression log thus going to the wrong way, to turn to the right one.
Compact Interpretable Tensor Graph Multi-Modal News Embeddings
Authors: Dawon Ahn, William Shiao, Arindam Khaled, Andrew Bauer, Stefanos Poulis, Evangelos E. Papalexakis
Venue: WWW
Year: 2024

Online news articles encompass a variety of modalities such as text and images. How can we learn a representation that incorporates information from all those modalities in a compact and interpretable manner? In this paper, we propose CITEM (Compact Interpretable Tensor graph multi-modal news EMbedding), a tensor-based framework for compact and interpretable multi-modal news representations. CITEM generates a tensor graph consisting of a news similarity graph for each modality and employs a tensor decomposition to produce compact and interpretable embeddings, each dimension of which is a heterogeneous co-cluster of news articles and corresponding modalities. We extensively validate CITEM compared to baselines on two news classification tasks: misinformation news detection and news categorization. The experimental results show that CITEM performs within the same range of AUC as state-of-the-art baselines while producing 7x to 10.5x more compact embeddings. In addition, each embedding dimension of CITEM is interpretable, representing a latent co-cluster of articles.
Fediscount: Shopping Online at a Federated Store Using FedUP as SPARQL Federation Engine
Authors: Julien Aimonier-Davat, Minh Hoang Dang, Pascal Molli, Brice Nédelec, Hala Skaf-Molli
Venue: WWW
Year: 2024

Processing SPARQL queries over large federations of SPARQL endpoints is essential for maintaining the Semantic Web decentralized. However, existing federation engines struggle to query more than a dozen of endpoints. We recently proposed FedUP, a new type of federation engine based on unions-over-joins query plans that outperforms state-of-the-art federation engines by orders of magnitude on large federations. This demonstration paper introduces Fediscount, a federated online shopping application based on the FedShop benchmark, illustrating the capabilities of FedUP. The application is based on standard Semantic Web technologies, enabling end-users to shop online in a virtual federated store comprising 20, 100, or even 200 SPARQL endpoints. This breakthrough opens up promising new avenues for developing and deploying federated applications.
FedUP: Querying Large-Scale Federations of SPARQL Endpoints
Authors: Julien Aimonier-Davat, Brice Nédelec, Minh Hoang Dang, Pascal Molli, Hala Skaf-Molli
Venue: WWW
Year: 2024

Processing SPARQL queries over large federations of SPARQL endpoints is crucial for keeping the Semantic Web decentralized. Despite the existence of hundreds of SPARQL endpoints, current federation engines only scale to dozens. One major issue comes from the current definition of the source selection problem, i.e., finding the minimal set of SPARQL endpoints to contact per triple pattern. Even if such a source selection is minimal, only a few combinations of sources may return results. Consequently, most of the query processing time is wasted evaluating combinations that return no results. In this paper, we introduce the concept of Result-Aware query plans. This concept ensures that every subquery of the query plan effectively contributes to the result of the query. To compute a Result-Aware query plan, we propose FedUP, a new federation engine able to produce Result-Aware query plans by tracking the provenance of query results. However, getting query results requires computing source selection, and computing source selection requires query results. To break this vicious cycle, FedUP computes results and provenances on tiny quotient summaries of federations at the cost of source selection accuracy. Experimental results on federated benchmarks demonstrate that FedUP outperforms state-of-the-art federation engines by orders of magnitude in the context of large-scale federations.
Analysis and Detection of "Pink Slime" Websites in Social Media Posts
Authors: Abdullah Aljebreen, Weiyi Meng, Eduard C. Dragut
Venue: WWW
Year: 2024

Local news outlets play a vital role in providing trusted and relevant information to communities and addressing their specific needs and concerns. The emergence of news outlets posing as local sources and their spread on social media present a significant challenge in the digital information landscape. This paper presents a comprehensive study investigating posts featuring "pink slime'' news, which is a term that has been used to refer to these news outlets due to its deceptive nature. By analyzing a large dataset of posts, we gain valuable insights into the patterns of these posts and the origin of these posts. We show in this work that extracting syntactical features proves valuable in developing a classification approach for detecting such posts and that the approach achieves 92.5% accuracy. We also show that our approach achieves near-perfect detection when grouping the posts by URL.
Unraveling the Dynamics of Stable and Curious Audiences in Web Systems
Authors: Rodrigo Alves, Antoine Ledent, Renato Assunção, Pedro O. S. Vaz de Melo, Marius Kloft
Venue: WWW
Year: 2024

We propose the Burst-Induced Poisson Process (BPoP), a model designed to analyze time series data such as feeds or search queries. BPoP can distinguish between the slowly-varying regular activity of a stable audience and the bursty activity of a curious audience, often seen in viral threads. Our model consists of two hidden, interacting processes: a self-feeding process (SFP) that generates bursty behavior related to viral threads, and a non-homogeneous Poisson process (NHPP) with step function intensity that is influenced by the bursts from the SFP. The NHPP models the normal background behavior, driven solely by the overall popularity of the topic among the stable audience. Through extensive empirical work, we have demonstrated that our model fits and characterizes a large number of real datasets more effectively than state-of-the-art models. Most importantly, BPoP can quantify the stable audience of media channels over time, serving as a valuable indicator of their popularity.
Understanding the Impact of COVID-19 on Online Eating Disorder Communities on Reddit
Authors: Md. Al Amin, Lu Liu
Venue: WWW
Year: 2024

The social restrictions, disruptions in daily activities, and psychological stressors arising from the COVID-19 pandemic constitute a psychological burden for people worldwide, which can be especially detrimental for individuals with mental disorders like Eating Disorders (ED). In this research, we aim to comprehend how COVID-19 has affected individuals with eating disorders through a comparative analysis of data obtained from online communities. We collected data spanning two years before and after the declaration of the pandemic from the subreddits r/AnorexiaNervosa, r/BingeEatingDisorder, and r/EatingDisorders. The research presents multi-faceted tasks where we analyze the content of each of the subreddits by applying a strategy that combines topic modeling, social network analysis, and time series modeling for a better understanding of these communities on both content and network levels. Through a comparative analysis, we address the discussion topic changes based on users' content and determine how COVID-19 leads to changes in communication patterns within the communities. Finally, we implement time series models like ARIMA, Prophet, LSTM, and Transformer on daily posts and comments count to forecast users' activities within the subreddit and establish a performance comparison of these time series models. The findings indicate that both the content of users' discussions and the level of communication and online support-seeking related to eating disorders on Reddit underwent significant changes during the pandemic. The data of this study is available at this GitHub https://github.com/alamincse32/Reddit-Data-for-Eating-Disoder-Community-During-Covid-Pandemic
iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations
Authors: Md Saidul Hoque Anik, Pranav Badhe, Rohit Gampa, Ariful Azad
Venue: WWW
Year: 2024

Core computations in Graph Neural Network (GNN) training and inference are often mapped to sparse matrix operations such as sparse-dense matrix multiplication (SpMM). These sparse operations are harder to optimize by manual tuning because their performance depends significantly on the sparsity of input graphs, GNN models, and computing platforms. To address this challenge, we present iSpLib, a PyTorch-based C++ library equipped with auto-tuned sparse operations. iSpLib expedites GNN training with a cache-enabled backpropagation that stores intermediate matrices in local caches. The library offers a user-friendly Python plug-in that allows users to take advantage of our optimized PyTorch operations out-of-the-box for any existing linear algebra-based PyTorch implementation of popular GNNs (Graph Convolution Network, GraphSAGE, Graph Inference Network, etc.) with only two lines of additional code. We demonstrate that iSpLib obtains up to 27x overall training speedup compared to the equivalent PyTorch 2.1.0 and PyTorch Geometric 2.4.0 implementations on the CPU. Our library is publicly available at \hrefhttps://github.com/HipGraph/iSpLib https://github.com/HipGraph/iSpLib \footnote\hrefhttps://doi.org/10.5281/zenodo.10806511 https://doi.org/10.5281/zenodo.10806511 .
WebAndTheCity'24: 10th International Workshop on The Web and Smart Cities
Authors: Leonidas G. Anthopoulos, Marijn Janssen, Vishanth Weerakkody
Venue: WWW
Year: 2024

This is the 10th edition of the workshop series labeled "AW4City - Web Applications and Smart Cities", which started back in Florence in 2015 and kept on taking place every year in conjunction with the WWW conference series. Last year the workshop was held in Austin, Texas. The workshop series aims to investigate the Web and Web applications' role in establishing smart city (SC) promises. The workshop series aim to investigate the role of the Web and of Web applications in SC growth. This year, the workshop focuses on the new era of the Web and web intelligence in cities and communities. In the era of digital twinning and metaverse (so-called citiverse for cities), and under the UN 2030 Agenda for sustainable growth, cities are being transformed into virtual spaces that generate new types of value and new experiences to their citizens and enterprises that can enhance living and offer new opportunities for economic growth. Moreover, AI and web intelligence generate new types of automated transactions in these virtual spaces, while they can utilize data spaces and standardization for optimal data flow. This workshop aims to demonstrate how the Web transforms cities into new virtual environments.
Homogenizing Data Flows in Smart Cities: Value-driven use Cases in the Era of Citiverse
Authors: Leonidas G. Anthopoulos, Ioannis Nikolaou
Venue: WWW
Year: 2024

Context based data receive an increasing attention for Smart City (SC) flow homogenization and standardization. SC hubness can be a solution that can simplify these flows and transform them to standardized message exchanges, which can be easily retrieved. Several use cases can justify the SC hubness' potential and will be summarized in this paper. However, the aim of this article is to analyze and explain their potential in the era of metaverse in cities, the so-called "citiverse". The role of data in citiverse gains an additional importance since it is a data-oriented ecosystem, but, it has to be seen around the new capabilities and values that citiverse may bring.