OpenRaiser/Intern-Atlas huggingface dataset:
DeeProphet: Improving HTTP Adaptive Streaming for Low Latency Live Video by Meticulous Bandwidth Prediction
Authors: Kefan Chen, Bo Wang, Wufan Wang, Xiaoyu Li, Fengyuan Ren
Venue: WWW
Year: 2023

The performance of HTTP adaptive streaming (HAS) depends heavily on the prediction of end-to-end network bandwidth. The increasingly popular low latency live streaming (LLLS) faces greater challenges since it requires accurate, short-term bandwidth prediction, compared with VOD streaming which needs long-term bandwidth prediction and has good tolerance against prediction error. Part of the challenges comes from the fact that short-term bandwidth experiences both large abrupt changes and uncertain fluctuations. Additionally, it is hard to obtain valid bandwidth measurement samples in LLLS due to its inter-chunk and intra-chunk sending idleness. In this work, we present DeeProphet, a system for accurate bandwidth prediction in LLLS to improve the performance of HAS. DeeProphet overcomes the above challenges by collecting valid measurement samples using fine-grained TCP state information to identify the packet bursting intervals, and by combining the time series model and learning-based model to predict both large change and uncertain fluctuations. Experiment results show that DeeProphet improves the overall QoE by 17.7%-359.2% compared with state-of-the-art LLLS ABR algorithms, and reduces the median bandwidth prediction error to 2.7%.
TML: A Temporal-aware Multitask Learning Framework for Time-sensitive Question Answering
Authors: Ziqiang Chen, Shaojuan Wu, Xiaowang Zhang, Zhiyong Feng
Venue: WWW
Year: 2023

Many facts change over time, Time-sensitive Question Answering(TSQA) answers questions about time evolution facts to test the model’s ability in the dimension of the time. The existing methods obtain the representations of questions and documents and then compute their similarity to find the answer spans. These methods perform well in simple moment questions, but they are difficult to solve hard duration problems that need temporal relations and temporal numeric comparisons. In this paper, we propose Temporal-aware Multitask Learning (TML) with three auxiliary tasks to tackle with them. First, we propose a temporal-aware sequence labeling task to help the model distinguish the temporal expressions by detecting temporal types of tokens in the document. Then a temporal-aware masked language modeling task is used to capture the temporal relation between events based on the context. Furthermore, temporal-aware order learning is proposed to inject the ability of numeric comparison into the model. We carried out comprehensive experiments on the TimeQA benchmark, aiming to evaluate the performance of our proposed methodology in handling temporal question answering. TML significantly outperforms the baselines by a relative 10% on the two splits of the dataset.
Web Information Extraction for Social Good: Food Pantry Answering As an Example
Authors: Huan-Yuan Chen, Hong Yu
Venue: WWW
Year: 2023

Social determinants of health (SDH) are the conditions in which we are born, live, work, and age. Food insecurity (FI) is an important domain of SDH. FI is associated with poor health outcomes. Food bank/pantry (food pantry) directly addresses FI. Improving the availability and quality of food from food pantries could reduce FI, leading to improved health outcomes. However, it is difficult for a client to access food pantry information. In this study, we built a food pantry answering framework by combining location-aware information retrieval, web information extraction and domain-specific answering. Our proposed framework first retrieves pantry candidates based on geolocation of the client, and utilizes structural information from markup language to extract semantic chunks related to six common client requests. We use BERT and RoBERTa as information extraction models and compare three different web page segmentation methods in the experiments.
Intent-based Web Page Summarization with Structure-Aware Chunking and Generative Language Models
Authors: Huan-Yuan Chen, Hong Yu
Venue: WWW
Year: 2023

This paper introduces a structure-aware method to segment web pages into chunks based on their web structures. We utilize large language models to select chunks correspond to a given intent and generate the abstractive summary. Experiments on a food pantry dataset developed for mitigating food insecurity show that the proposed framework is promising.
Graph Self-supervised Learning with Augmentation-aware Contrastive Learning
Authors: Dong Chen, Xiang Zhao, Wei Wang, Zhen Tan, Weidong Xiao
Venue: WWW
Year: 2023

Graph self-supervised learning aims to mine useful information from unlabeled graph data, and has been successfully applied to pre-train graph representations. Many existing approaches use contrastive learning to learn powerful embeddings by learning contrastively from two augmented graph views. However, none of these graph contrastive methods fully exploits the diversity of different augmentations, and hence is prone to overfitting and limited generalization ability of learned representations. In this paper, we propose a novel Graph Self-supervised Learning method with Augmentation-aware Contrastive Learning. Our method is based on the finding that the pre-trained model after adding augmentation diversity can achieve better generalization ability. To make full use of the information from the diverse augmentation method, this paper constructs new augmentation-aware prediction task which complementary with the contrastive learning task. Similar to how pre-training requires fast adaptation to different downstream tasks, we simulate train-test adaptation on the constructed tasks for further enhancing the learning ability; this strategy can be deemed as a form of meta-learning. Experimental results show that our method outperforms previous methods and learns better representations for a variety of downstream tasks.
Multi-Agent Reinforcement Learning with Shared Policy for Cloud Quota Management Problem
Authors: Tong Cheng, Hang Dong, Lu Wang, Bo Qiao, Si Qin, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Thomas Moscibroda
Venue: WWW
Year: 2023

Quota is often used in resource allocation and management scenarios to prevent abuse of resource and increase the efficiency of resource utilization. Quota management is usually fulfilled with a set of rules maintained by the system administrator. However, maintaining these rules usually needs deep domain knowledge. Moreover, arbitrary rules usually cannot guarantee both high resource utilization and fairness at the same time. In this paper, we propose a reinforcement learning framework to automatically respond to quota requests in cloud computing platforms with distinctive usage characteristics for users. Extensive experimental results have demonstrated the superior performance of our framework on achieving a great trade-off between efficiency and fairness.
MediSage: An AI Assistant for Healthcare via Composition of Neural-Symbolic Reasoning Operators
Authors: Sutanay Choudhury, Khushbu Agarwal, Colby Ham, Suzanne Tamang
Venue: WWW
Year: 2023

We introduce MediSage, an AI decision support assistant for medical professionals and caregivers that simplifies the way in which they interact with different modalities of electronic health records (EHRs) through a conversational interface. It provides step-by-step reasoning support to an end-user to summarize patient health, predict patient outcomes and provide comprehensive and personalized healthcare recommendations. MediSage provides these reasoning capabilities by using a knowledge graph that combines general purpose clinical knowledge resources with recent-most information from the EHR data. By combining the structured representation of knowledge with the predictive power of neural models trained over both EHR and knowledge graph data, MediSage brings explainability by construction and represents a stepping stone into the future through further integration with biomedical language models.
Creation and Analysis of a Corpus of Scam Emails Targeting Universities
Authors: Grace Ciambrone, Shomir Wilson
Venue: WWW
Year: 2023

Email-based scams pose a threat to the personally identifiable information and financial safety of all email users. Within a university environment, the risks are potentially greater: traditional students (i.e., within an age range typical of college students) often lack the experience and knowledge of older email users. By understanding the topics, temporal trends, and other patterns of scam emails targeting universities, these institutions can be better equipped to reduce this threat by improving their filtering methods and educating their users. While anecdotal evidence suggests common topics and trends in these scams, the empirical evidence is limited. Observing that large universities are uniquely positioned to gather and share information about email scams, we built a corpus of 5,155 English language scam emails scraped from information security websites of five large universities in the United States. We use Latent Dirichlet Allocation (LDA) topic modelling to assess the landscape and trends of scam emails sent to university addresses. We examine themes chronologically and observe that topics vary over time, indicating changes in scammer strategies. For example, scams targeting students with disabilities have steadily risen in popularity since they first appeared in 2015, while password scams experienced a boom in 2016 but have lessened in recent years. To encourage further research to mitigate the threat of email scams, we release this corpus for others to study.
Practical challenges of ODRL and potential courses of action
Authors: Andrea Cimmino, Juan Cano-Benito, Raúl García-Castro
Venue: WWW
Year: 2023

The Open Digital Rights Language (ODRL) is a standard widely adopted to express privacy policies. This article presents several challenges identified in the context of the European project AURORAL in which ODRL is used to express privacy policies for Smart Communities and Rural Areas. The article presents that some challenges should be addressed directly by the ODRL standardisation group to achieve the best course of action, although others exists. For others, the authors have presented a potential solution, in particular, for considering dynamic values coming from external data sources into privacy policies. Finally, the last challenge is an open research question, since it revolves around the interoperability of privacy policies that belong to different systems and that are expressed with different privacy languages.
Propaganda Política Pagada: Exploring U.S. Political Facebook Ads en Español
Authors: Bruno Coelho, Tobias Lauinger, Laura Edelson, Ian Goldstein, Damon McCoy
Venue: WWW
Year: 2023

In 2021, the U.S. Hispanic population totaled 62.5 million people, 68% of whom spoke Spanish in their homes. To date, it is unclear which political advertisers address this audience in their preferred language, and whether they do so differently than for English-speaking audiences. In this work, we study differences between political Facebook ads in English and Spanish during 2020, the latest U.S. presidential election. Political advertisers spent $ 1.48 B in English, but only $ 28.8 M in Spanish, disproportionately little compared to the share of Spanish speakers in the population. We further find a lower proportion of election-related advertisers (which additionally are more liberal-leaning than in the English set), and a higher proportion of government agencies in the set of Spanish ads. We perform multilingual topic classification, finding that the most common ad topics in English were also present in Spanish, but to a different extent, and with a different composition of advertisers. Thus, Spanish speakers are served different types of ads from different types of advertisers than English speakers, and in lower amounts; these results raise the question of whether political communication through Facebook ads may be inequitable and effectively disadvantaging the sizeable minority of Spanish speakers in the U.S. population.
The DEEP Sensorium: a multidimensional approach to sensory domain labelling
Authors: Simona Corciulo, Livio Bioglio, Valerio Basile, Viviana Patti, Rossana Damiano
Venue: WWW
Year: 2023

In this paper, we describe our intuitions about how language technologies can contribute to create new ways to enhance the accessibility of exhibits in cultural contexts by exploiting the knowledge about the history of our senses and the link between perception and language. We evaluate the performance of five multi-class classification models for the task of sensory recognition and introduce the DEEP Sensorium (Deep Engaging Experiences and Practices - Sensorium), a multidimensional dataset that combines cognitive and affective features to inform systematic methodologies for augmenting exhibits with multi-sensory stimuli. For each model, using different feature sets, we show that the features expressing the affective dimension of words combined with sub-lexical features perform better than uni-dimensional training sets.
Counterfactual Reasoning for Decision Model Fairness Assessment
Authors: Giandomenico Cornacchia, Vito Walter Anelli, Fedelucio Narducci, Azzurra Ragone, Eugenio Di Sciascio
Venue: WWW
Year: 2023

The increasing application of Artificial Intelligence and Machine Learning models poses potential risks of unfair behaviour and, in the light of recent regulations, has attracted the attention of the research community. Several researchers focused on seeking new fairness definitions or developing approaches to identify biased predictions. These approaches focus solely on a discrete and limited space; only a few analyze the minimum variations required in the user characteristics to ensure a positive outcome for the individuals (counterfactuals). In that direction, the methodology proposed in this paper aims to unveil unfair model behaviors using counterfactual reasoning in the case of fairness under unawareness. The method also proposes two new metrics that analyse the (estimated) sensitive information of counterfactual samples with the help of an external oracle. Experimental results on three data sets show the effectiveness of our approach for disclosing unfair behaviour of state-of-the-art Machine Learning and debiasing models. Source code is available at https://github.com/giandos200/WWW-23-Counterfactual-Fair-Opportunity-Poster-.
Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction
Authors: Davide Costa, Lucio La Cava, Andrea Tagarelli
Venue: WWW
Year: 2023

Non-Fungible Tokens (NFTs) represent deeds of ownership, based on blockchain technologies and smart contracts, of unique crypto assets on digital art forms (e.g., artworks or collectibles). In the spotlight after skyrocketing in 2021, NFTs have attracted the attention of crypto enthusiasts and investors intent on placing promising investments in this profitable market. However, the NFT financial performance prediction has not been widely explored to date. In this work, we address the above problem based on the hypothesis that NFT images and their textual descriptions are essential proxies to predict the NFT selling prices. To this purpose, we propose MERLIN, a novel multimodal deep learning framework designed to train Transformer-based language and visual models, along with graph neural network models, on collections of NFTs’ images and texts. A key aspect in MERLIN is its independence on financial features, as it exploits only the primary data a user interested in NFT trading would like to deal with, i.e., NFT images and textual descriptions. By learning dense representations of such data, a price-category classification task is performed by MERLIN models, which can also be tuned according to user preferences in the inference phase to mimic different risk-return investment profiles. Experimental evaluation on a publicly available dataset has shown that MERLIN models achieve significant performances according to several financial assessment criteria, fostering profitable investments, and also beating baseline machine-learning classifiers based on financial features.
Controllable Universal Fair Representation Learning
Authors: Yue Cui, Chen Ma, Kai Zheng, Lei Chen, Xiaofang Zhou
Venue: WWW
Year: 2023

Learning fair and transferable representations of users that can be used for a wide spectrum of downstream tasks (specifically, machine learning models) has great potential in fairness-aware Web services. Existing studies focus on debiasing w.r.t. a small scale of (one or a handful of) fixed pre-defined sensitive attributes. However, in real practice, downstream data users can be interested in various protected groups and these are usually not known as prior. This requires the learned representations to be fair w.r.t. all possible sensitive attributes. We name this task universal fair representation learning, in which an exponential number of sensitive attributes need to be dealt with, bringing the challenges of unreasonable computational cost and un-guaranteed fairness constraints. To address these problems, we propose a controllable universal fair representation learning (CUFRL) method. An effective bound is first derived via the lens of mutual information to guarantee parity of the universal set of sensitive attributes while maintaining the accuracy of downstream tasks. We also theoretically establish that the number of sensitive attributes that need to be processed can be reduced from exponential to linear. Experiments on two public real-world datasets demonstrate CUFRL can achieve significantly better accuracy-fairness trade-off compared with baseline approaches.
Graph Embedding for Mapping Interdisciplinary Research Networks
Authors: Eoghan Cunningham, Derek Greene
Venue: WWW
Year: 2023

Representation learning is the first step in automating tasks such as research paper recommendation, classification, and retrieval. Due to the accelerating rate of research publication, together with the recognised benefits of interdisciplinary research, systems that facilitate researchers in discovering and understanding relevant works from beyond their immediate school of knowledge are vital. This work explores different methods of research paper representation (or document embedding), to identify those methods that are capable of preserving the interdisciplinary implications of research papers in their embeddings. In addition to evaluating state of the art methods of document embedding in a interdisciplinary citation prediction task, we propose a novel Graph Neural Network architecture designed to preserve the key interdisciplinary implications of research articles in citation network node embeddings. Our proposed method outperforms other GNN-based methods in interdisciplinary citation prediction, without compromising overall citation prediction performance.
Deep Passage Retrieval in E-Commerce
Authors: Vinay Rao Dandin, Ozan Ersoy, Kyung Hyuk Kim
Venue: WWW
Year: 2023

We have developed a conversational assistant called the Decision Assistant (DA) to help customers make purchase decisions. To answer customer queries successfully, we use a question and answering (QnA) system that retrieves data on product pages and extracts answers. With various data sources available on the product pages, we deal with unique challenges such as different terminologies and data formats for successful answer retrieval. In this paper, we propose two different bi-encoder architectures for retrieving data from each of the two data sources considered – product descriptions and specifications. The proposed architectures beat the baseline approaches while maintaining a high recall and low latency in production. We envision that the proposed approaches can be widely applicable to other e-commerce QnA systems.
Graph Induced Transformer Network for Detection of Politeness and Formality in Text
Authors: Tirthankar Dasgupta, Manjira Sinha, Chundru Geetha Praveen
Venue: WWW
Year: 2023

Formality and politeness are two of the most commonly studied stylistic dimensions of language that convey, authority, amount of shared context, and social distances among the communicators and are known to affect user behavior significantly. Formality in the text refers to the type of language used in situations when the speaker is very careful about the choice of words and sentence structure. In this paper, we propose a graph-induced transformer network (GiTN) to detect formality and politeness in text automatically. The proposed model exploits the latent linguistic features present in the text to identify the aforementioned stylistic factors. The proposed model is evaluated with multiple datasets across domains. We found that the proposed model’s performance surpasses most baseline systems.
The 2nd Workshop on Interactive and Scalable Information Retrieval Methods for eCommerce (ISIR-eCom)
Authors: Vachik S. Dave, Linsey Pang, Xiquan Cui, Lingfei Wu, Hamed Zamani, George Karypis
Venue: WWW
Year: 2023

ACM Reference Format: Vachik S. Dave, Linsey Pang, Xiquan Cui, Lingfei Wu, Hamed Zamani, and George Karypis. 2023. The 2nd Workshop on Interactive and Scalable Information Retrieval Methods for eCommerce (ISIR-eCom). In Companion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion), April 30–May 04, 2023, Austin, TX, USA. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3543873.3589753
A Bridge over the Troll: Non-Complementary Activism Online
Authors: Emyn Dean
Venue: WWW
Year: 2023

Previous research has identified phenomena such as cyberbystander intervention and various other forms of responses to aggressive or hateful behaviours online. In the online media ecosystem, some people from marginalized communities and their allies have attempted to enhance organic engagement by participating in organized activism, which is sometimes characterized as "non-complementary" or "indirect". This paper attempts to identify, recognize, and label this phenomenon, as well as provide suggestions for further research in this area.
LLMs to the Moon? Reddit Market Sentiment Analysis with Large Language Models
Authors: Xiang Deng, Vasilisa Bashlovkina, Feng Han, Simon Baumgartner, Michael Bendersky
Venue: WWW
Year: 2023

Market sentiment analysis on social media content requires knowledge of both financial markets and social media jargon, which makes it a challenging task for human raters. The resulting lack of high-quality labeled data stands in the way of conventional supervised learning methods. In this work, we conduct a case study approaching this problem with semi-supervised learning using a large language model (LLM). We select Reddit as the target social media platform due to its broad coverage of topics and content types. Our pipeline first generates weak financial sentiment labels for Reddit posts with an LLM and then uses that data to train a small model that can be served in production. We find that prompting the LLM to produce Chain-of-Thought summaries and forcing it through several reasoning paths helps generate more stable and accurate labels, while training the student model using a regression loss further improves distillation quality. With only a handful of prompts, the final model performs on par with existing supervised models. Though production applications of our model are limited by ethical considerations, the model’s competitive performance points to the great potential of using LLMs for tasks that otherwise require skill-intensive annotation.