OpenRaiser/Intern-Atlas huggingface dataset:
Filtered-DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters
Authors: Siddharth Gollapudi, Neel Karia, Varun Sivashankar, Ravishankar Krishnaswamy, Nikit Begwani, Swapnil Raz, Yiyong Lin, Yin Zhang, Neelam Mahapatro, Premkumar Srinivasan, Amit Singh, Harsha Vardhan Simhadri
Venue: WWW
Year: 2023

As Approximate Nearest Neighbor Search (ANNS)-based dense retrieval becomes ubiquitous for search and recommendation scenarios, efficiently answering filtered ANNS queries has become a critical requirement. Filtered ANNS queries ask for the nearest neighbors of a query’s embedding from the points in the index that match the query’s labels such as date, price range, language. There has been little prior work on algorithms that use label metadata associated with vector data to build efficient indices for filtered ANNS queries. Consequently, current indices have high search latency or low recall which is not practical in interactive web-scenarios. We present two algorithms with native support for faster and more accurate filtered ANNS queries: one with streaming support, and another based on batch construction. Central to our algorithms is the construction of a graph-structured index which forms connections not only based on the geometry of the vector data, but also the associated label set. On real-world data with natural labels, both algorithms are an order of magnitude or more efficient for filtered queries than the current state of the art algorithms. The generated indices also be queried from an SSD and support thousands of queries per second at over recall@10.
Towards a Sustainability Index Calculator for Smart Cities
Authors: Ramesh Gorantla, Srividya Bansal
Venue: WWW
Year: 2023

In this era of rapid urbanization and our endeavors to create more smart cities, it's crucial to keep track of how our society and neighborhood are getting impacted. It is important to make conscious decisions to keep harmony in sustainability. There are multiple frameworks to evaluate how sustainability is measured and to understand how sustainable a place is, be it a city or a region, and one such framework is the Circles of Sustainability. Though these frameworks offer good solutions, it is a challenge to collect relevant data to make the framework widely usable. This paper focuses on this specific issue by utilizing the methodology introduced in the framework and applying it practically to better understand how sustainable our cities and society are. We present a unique web-based application which utilizes publicly accessible data to compute sustainability scores and rank for every city and presents the results in an intelligent and easy to comprehend visual interface. The paper also discusses the technical difficulties associated with creating such an application, including data collection, data processing, data integration, and scoring algorithm. The paper concludes by discussing the needs for such practical solutions for promoting sustainable urban development.
Provenance Tracking for End-to-End Machine Learning Pipelines
Authors: Stefan Grafberger, Paul Groth, Sebastian Schelter
Venue: WWW
Year: 2023

Software systems that learn from data are being deployed in increasing numbers in real-world application scenarios. It is a difcult and tedious task to ensure at development time that the end-to-end ML pipelines for such applications adhere to sound experimentation practices, such as the strict isolation of train and test data. Furthermore, there is a need to enforce legal and ethical compliance in automated decision-making with ML. For example, to determine whether a model works equally well for diferent groups. To enforce privacy rights (such as the ‘right to be forgotten’), we must identify which models consumed the user’s data for model training to retrain them without this data. Moreover, model predictions can be corrupted due to undetected data distribution shift, e.g., when the train/test data was incorrectly sampled or changed over time (covariate shift) or when the distribution of the target label changed (label shift). Data scientists also require support for pipeline debugging and for uncovering erroneous data, e.g., to identify samples that are not helpful for the classifer and potentially dirty or mislabeled or to identify subsets of data for which a model does not work well. Towards automated low-efort tooling for ML pipelines. Most of the listed issues are typically addressed manually in an ad-hoc way and require signifcant expertise and extra code. In many cases, there is no system support for detecting particular issues. Typically, data has to be integrated frst, as common libraries assume the input to be in a single table. Furthermore, specialised solutions are often incompatible with popular libraries. Even simple tasks like computing fairness metrics can be challenging. This situation is in stark contrast to the software engineering world, with established best practices and infrastructure for testing and CI. Provenance is all you need. At the core of these issues is missing provenance tracking in current tooling. For example, we fnd that can automate the detection of many common correctness issues in ML pipelines with access to (�) the materialised artifacts of a pipeline (its input relations, and its outputs, e.g., the feature matrices, labels, and predictions of a classifer) as well as (��) their whyprovenance [4] (e.g., which input records were used to compute a particular output). This allows us to design screening techniques with low invasiveness for declaratively written ML pipelines. Modeling ML pipelines as datafow computations. We base our approach on a recently introduced model of treating ML pipelines
MassNE: Exploring Higher-Order Interactions with Marginal Effect for Massive Battle Outcome Prediction
Authors: Yin Gu, Kai Zhang, Qi Liu, Xin Lin, Zhenya Huang, Enhong Chen
Venue: WWW
Year: 2023

In online games, predicting massive battle outcomes is a fundamental task of many applications, such as team optimization and tactical formulation. Existing works do not pay adequate attention to the massive battle. They either seek to evaluate individuals in isolation or mine simple pair-wise interactions between individuals, neither of which effectively captures the intricate interactions between massive units (e.g., individuals). Furthermore, as the team size increases, the phenomenon of diminishing marginal utility of units emerges. Such a diminishing pattern is rarely noticed in previous work, and how to capture it from data remains a challenge. To this end, we propose a novel Massive battle outcome predictor with margiNal Effect modules, namely MassNE, which comprehensively incorporates individual effects, cooperation effects (i.e., intra-team interactions) and suppression effects (i.e., inter-team interactions) for predicting battle outcomes. Specifically, we design marginal effect modules to learn how units’ marginal utility changing respect to their number, where the monotonicity assumption is applied to ensure rationality. In addition, we evaluate the current classical models and provide mathematical proofs that MassNE is able to generalize several earlier works in massive settings. Massive battle datasets generated by StarCraft II APIs are adopted to evaluate the performances of MassNE. Extensive experiments empirically demonstrate the effectiveness of MassNE, and MassNE can reveal reasonable cooperation effects, suppression effects, and marginal utilities of combat units from the data.
pFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning
Authors: Tao Guo, Song Guo, Junxiao Wang
Venue: WWW
Year: 2023

Pre-trained vision-language models like CLIP show great potential in learning representations that capture latent characteristics of users. A recently proposed method called Contextual Optimization (CoOp) introduces the concept of training prompt for adapting pre-trained vision-language models. Given the lightweight nature of this method, researchers have migrated the paradigm from centralized to decentralized system to innovate the collaborative training framework of Federated Learning (FL). However, current prompt training in FL mainly focuses on modeling user consensus and lacks the adaptation to user characteristics, leaving the personalization of prompt largely under-explored. Researches over the past few years have applied personalized FL (pFL) approaches to customizing models for heterogeneous users. Unfortunately, we find that with the variation of modality and training behavior, directly applying the pFL methods to prompt training leads to insufficient personalization and performance. To bridge the gap, we present pFedPrompt, which leverages the unique advantage of multimodality in vision-language models by learning user consensus from linguistic space and adapting to user characteristics in visual space in a non-parametric manner. Through this dual collaboration, the learned prompt will be fully personalized and aligned to the user’s local characteristics. We conduct extensive experiments across various datasets under the FL setting with statistical heterogeneity. The results demonstrate the superiority of our pFedPrompt against the alternative approaches with robust performance.
MSQ-BioBERT: Ambiguity Resolution to Enhance BioBERT Medical Question-Answering
Authors: Muzhe Guo, Muhao Guo, Edward T. Dougherty, Fang Jin
Venue: WWW
Year: 2023

Question answering (QA) is a task in the field of natural language processing (NLP) and information retrieval, which has pivotal applications in areas such as online reading comprehension and web search engines. Currently, Bidirectional Encoder Representations from Transformers (BERT) and its biomedical variation (BioBERT) achieve impressive results on the reading comprehension QA datasets and medical-related QA datasets, and so they are widely used for a variety of passage-based QA tasks. However, their performances rapidly deteriorate when encountering passage and context ambiguities. This issue is prevalent and unavoidable in many fields, notably the web-based medical field. In this paper, we introduced a novel approach called the Multiple Synonymous Questions BioBERT (MSQ-BioBERT), which integrates question augmentation, rather than the typical single question used by traditional BioBERT, to elevate BioBERT’s performance on medical QA tasks. In addition, we constructed an ambiguous medical dataset based on the information from Wikipedia web. Experiments with both this web-based constructed medical dataset and open biomedical datasets demonstrate the significant performance gains of the MSQ-BioBERT approach, showcasing a new method for addressing ambiguity in medical QA tasks.
Text Mining-based Social-Psychological Vulnerability Analysis of Potential Victims To Cybergrooming: Insights and Lessons Learned
Authors: Zhen Guo, Pei Wang, Jin-Hee Cho, Lifu Huang
Venue: WWW
Year: 2023

Cybergrooming is a serious cybercrime that primarily targets youths through online platforms. Although reactive predator detection methods have been studied, proactive victim protection and crime prevention can also be achieved through vulnerability analysis of potential youth victims. Despite its significance, vulnerability analysis has not been thoroughly studied in the data science literature, while several social science studies used survey-based methods. To address this gap, we investigate humans’ social-psychological traits and quantify key vulnerability factors to cybergrooming by analyzing text features in the Linguistic Inquiry and Word Count (LIWC). Through pairwise correlation studies, we demonstrate the degrees of key vulnerability dimensions to cybergrooming from youths’ conversational features. Our findings reveal that victims have negative correlations with family and community traits, contrasting with previous social survey studies that indicated family relationships or social support as key vulnerability factors. We discuss the current limitations of text mining analysis and suggest cross-validation methods to increase the validity of research findings. Overall, this study provides valuable insights into understanding the vulnerability factors to cybergrooming and highlights the importance of adopting multidisciplinary approaches.
A First Look at Public Service Websites from the Affordability Lens
Authors: Rumaisa Habib, Aimen Inam, Ayesha Ali, Ihsan Ayyub Qazi, Zafar Ayyub Qazi
Venue: WWW
Year: 2023

Public service websites act as official gateways to services provided by governments. Many of these websites are essential for citizens to receive reliable information and online government services. However, the lack of affordability of mobile broadband services in many developing countries and the rising complexity of websites create barriers for citizens in accessing these government websites. This paper presents the first large-scale analysis of the affordability of public service websites in developing countries. We do this by collecting a corpus of 1900 public service websites, including public websites from nine developing countries and for comparison websites from nine developed countries. Our investigation is driven by website complexity analysis as well as evaluation through a recently proposed affordability index. Our analysis reveals that, in general, public service websites in developing countries do not meet the affordability target set by the UN’s Broadband Commission. However, we show that several countries can be brought within or closer to the affordability target by implementing webpage optimizations to reduce page sizes. We also discuss policy interventions that can help make access to public service website more affordable.
Detecting Social Media Manipulation in Low-Resource Languages
Authors: Samar Haider, Luca Luceri, Ashok Deb, Adam Badawy, Nanyun Peng, Emilio Ferrara
Venue: WWW
Year: 2023

Social media have been deliberately used for malicious purposes, including political manipulation and disinformation. Most research focuses on high-resource languages. However, malicious actors share content across countries and languages, including low-resource ones. Here, we investigate whether and to what extent malicious actors can be detected in low-resource language settings. We discovered that a high number of accounts posting in Tagalog were suspended as part of Twitter’s crackdown on interference operations after the 2016 US Presidential election. By combining text embedding and transfer learning, our framework can detect, with promising accuracy, malicious users posting in Tagalog without any prior knowledge or training on malicious content in that language. We first learn an embedding model for each language, namely a high-resource language (English) and a low-resource one (Tagalog), independently. Then, we learn a mapping between the two latent spaces to transfer the detection model. We demonstrate that the proposed approach significantly outperforms state-of-the-art models and yields marked advantages in settings with very limited training data—the norm when dealing with detecting malicious activity in online platforms.
Socio-Emotional Computational Analysis of Propaganda Campaigns on Social Media Users in the Middle East
Authors: Zain A. Halloush, Ahmed Aleroud, Craig Albert
Venue: WWW
Year: 2023

Society has been significantly impacted by social media platforms in almost every aspect of their life. This impact has been effectively formulating people’s global mindsets and opinions on political, economic, and social events. Such waves of opinion formation are referred to as propagandas and misinformation. Online propaganda influences the emotional and psychological orientation of people. The remarkable leaps in Machine Learning models and Natural Language Processing have helped in analyzing the emotional and psychological effects of cyber social threats such as propaganda campaigns on different nations, specifically in the Middle East, where rates of disputes have risen after the Arab Spring and the ongoing crises. In this paper, we present an approach to detect propagandas and the associated emotional and psychological aspects from social media news headlines that contain such a contextualized cyber social attack. We created a new dataset of headlines containing propaganda tweets and another dataset of potential emotions that the audience might endure when being exposed to such propaganda headlines. We believe that this is the first research to address the detection of emotional reactions linked to propaganda types on social media in the Middle East.
Web Structure Derived Clustering for Optimised Web Accessibility Evaluation
Authors: Alexander Hambley, Yeliz Yesilada, Markel Vigo, Simon Harper
Venue: WWW
Year: 2023

Web accessibility evaluation is a costly and complex process due to limited time, resources and ambiguity. To optimise the accessibility evaluation process, we aim to reduce the number of pages auditors must review by employing statistically representative pages, reducing a site of thousands of pages to a manageable review of archetypal pages. Our paper focuses on representativeness, one of six proposed metrics that form our methodology, to address the limitations we have identified with the W3C Website Accessibility Conformance Evaluation Methodology (WCAG-EM). These include the evaluative scope, the non-probabilistic sampling approach, and the potential for bias within the selected sample. Representativeness, in particular, is a metric to assess the quality and coverage of sampling. To measure this, we systematically evaluate five web page representations with a website of 388 pages, including tags, structure, the DOM tree, content, and a mixture of structure and content. Our findings highlight the importance of including structural components in representations. We validate our conclusions using the same methodology for three additional random sites of 500 pages. As an exclusive attribute, we find that features derived from web content are suboptimal and can lead to lower quality and more disparate clustering for optimised accessibility evaluation.
GELTOR: A Graph Embedding Method based on Listwise Learning to Rank
Authors: Masoud Reyhani Hamedani, Jin-Su Ryu, Sang-Wook Kim
Venue: WWW
Year: 2023

Similarity-based embedding methods have introduced a new perspective on graph embedding by conforming the similarity distribution of latent vectors in the embedding space to that of nodes in the graph; they show significant effectiveness over conventional embedding methods in various machine learning tasks. In this paper, we first point out the three drawbacks of existing similarity-based embedding methods: inaccurate similarity computation, conflicting optimization goal, and impairing in/out-degree distributions. Then, motivated by these drawbacks, we propose AdaSim*, a novel similarity measure for graphs that is conducive to the similarity-based graph embedding. We finally propose GELTOR, an effective embedding method that employs AdaSim* as a node similarity measure and the concept of learning-to-rank in the embedding process. Contrary to existing methods, GELTOR does not learn the similarity scores distribution; instead, for any target node, GELTOR conforms the ranks of its top-t similar nodes in the embedding space to their original ranks based on AdaSim* scores. We conduct extensive experiments with six real-world datasets to evaluate the effectiveness of GELTOR in graph reconstruction, link prediction, and node classification tasks. Our experimental results show that (1) AdaSim* outperforms AdaSim, RWR, and MCT in computing nodes similarity in graphs, (2) our GETLOR outperforms existing state-of-the-arts and conventional embedding methods in most cases of the above machine learning tasks, thereby implying that learning-to-rank is beneficial to graph embedding.
Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation
Authors: Zhongxuan Han, Xiaolin Zheng, Chaochao Chen, Wenjie Cheng, Yang Yao
Venue: WWW
Year: 2023

Cross-Domain Recommendation (CDR) aims to solve the data sparsity problem by integrating the strengths of different domains. Though researchers have proposed various CDR methods to effectively transfer knowledge across domains, they fail to address the following key issues, i.e., (1) they cannot model high-order correlations among users and items in every single domain to obtain more accurate representations; (2) they cannot model the correlations among items across different domains. To tackle the above issues, we propose a novel Intra and Inter Domain HyperGraph Convolutional Network (II-HGCN) framework, which includes two main layers in the modeling process, i.e., the intra-domain layer and the inter-domain layer. In the intra-domain layer, we design a user hypergraph and an item hypergraph to model high-order correlations inside every single domain. Thus we can address the data sparsity problem better and learn high-quality representations of users and items. In the inter-domain layer, we propose an inter-domain hypergraph structure to explore correlations among items from different domains based on their interactions with common users. Therefore we can not only transfer the knowledge of users but also combine embeddings of items across domains. Comprehensive experiments on three widely used benchmark datasets demonstrate that II-HGCN outperforms other state-of-the-art methods, especially when datasets are extremely sparse.
Persona Consistent Dialogue Generation via Contrastive Learning
Authors: Zhenfeng Han, Sai Zhang, Xiaowang Zhang
Venue: WWW
Year: 2023

The inclusion of explicit personas in generation models has gained significant attention as a means of developing intelligent dialogue agents. However, large pretrained generation models often produce inconsistent responses with persona. We investigate the model generation behavior to identify signs of inconsistency and observe inconsistent behavior patterns. In this work, we introduce contrastive learning into persona consistent dialogue generation, building on the idea that humans learn not just from positive feedback, but also from identifying and correcting undesirable behaviors. According to the inconsistent patterns, we design two strategies to construct high-quality negative samples, which are critical for contrastive learning efficacy. Experimental results demonstrate that our method can effectively improve the consistency of the responses while improving its dialogue quality on both automatic and human evaluation.
SpotLight: Visual Insight Recommendation
Authors: Camille Harris, Ryan A. Rossi, Sana Malik, Jane Hoffswell, Fan Du, Tak Yeon Lee, Eunyee Koh, Handong Zhao
Venue: WWW
Year: 2023

Visualization recommendation systems make understanding data more accessible to users of all skill levels by automatically generating visualizations for users to explore. However, most existing visualization recommendation systems focus on ranking all possible visualizations based on the attributes or encodings, which makes it difficult to find the most relevant insights. We therefore introduce a novel class of insight-based visualization recommendation systems that automatically rank and recommend groups of related insights as well as the most important insights within each group. Our approach combines results from different learning-based methods to discover insights automatically and generalizes to a variety of attribute types (e.g., categorical, numerical, and temporal), including non-trivial combinations of these attribute types. To demonstrate the utility of this approach, we implemented a insight-centric visualization recommendation system, SpotLight, and conducted a user study with twelve participants, which showed that users are able to quickly find and understand relevant insights in unfamiliar data.