OpenRaiser/Intern-Atlas huggingface dataset:
Requirements and Building Blocks for Manufacturing Dataspaces
Authors: Rohit A. Deshmukh, Sisay Adugna Chala, Christoph Lange
Venue: WWW
Year: 2023

With the advent and pervasiveness of the Internet of Things (IoT), big data and cloud computing technologies, digitalization in enterprises and factories has rapidly increased in the last few years. Digital platforms have emerged as an effective mechanism for enabling the management and sharing of data from various companies. To enable sharing of data beyond the platform boundaries, definition of new platform federation approaches is on the rise. This makes enabling the federation of digital platforms a key requirement for large-scale dataspaces. Therefore, the identification of platform federation requirements and building blocks for such dataspaces needs to be systematically addressed. In this paper, we try to systematically explore the high-level requirements for enabling a federation of digital platforms in the manufacturing domain and identify a set of building blocks. We integrate the requirements and building blocks into the notion of dataspaces. The identified requirements and building blocks act as a blueprint for designing and instantiating new dataspaces, thereby speeding up the development process and reducing costs. We present a case study to illustrate how the use of common building blocks can act as common guiding principles and result in more complete and interoperable implementations.
Trusting Decentralised Knowledge Graphs and Web Data at the Web Conference
Authors: John Domingue, Aisling Third, Maria-Esther Vidal, Philipp D. Rohde, Juan Cano, Andrea Cimmino, Ruben Verborgh
Venue: WWW
Year: 2023

Knowledge Graphs have become a foundation for sharing data on the web and building intelligent services across many sectors and also within some of the most successful corporations in the world. The over centralisation of data on the web, however, has been raised as a concern by a number of prominent researchers in the field. For example, at the beginning of 2022 a €2.7B civil lawsuit was launched against Meta on the basis that it has abused its market dominance to impose unfair terms and conditions on UK users in order to exploit their personal data. Data centralisation can lead to a number of problems including: lock-in/siloing effects, lack of user control over their personal data, limited incentives and opportunities for interoperability and openness, and the resulting detrimental effects on privacy and innovation. A number of diverse approaches and technologies exist for decentralising data, such as federated querying and distributed ledgers. The main question is, though, what does decentralisation really mean for web data and Knowledge Graphs? What are the main issues and tradeoffs involved? These questions and others are addressed in this workshop.
Towards a Semantic Approach for Linked Dataspace, Model and Data Cards
Authors: Andy Donald, Apostolos Galanopoulos, Edward Curry, Emir Muñoz, Ihsan Ullah, Margaret A. Waskow, Maciej Dabrowski, Manan Kalra
Venue: WWW
Year: 2023

The vast majority of artificial intelligence practitioners overlook the importance of documentation when building and publishing models and datasets. However, due to the recent trend in the explainability and fairness of AI models, several frameworks have been proposed such as Model Cards, and Data Cards, among others, to help in the appropriate re-usage of those models and datasets. In addition, because of the introduction of the dataspace concept for similar datasets in one place, there is potential that similar Model Cards, Data Cards, Service Cards, and Dataspace Cards can be linked to extract helpful information for better decision-making about which model and data can be used for a specific application. This paper reviews the case for considering a Semantic Web approach for exchanging Model/Data Cards as Linked Data or knowledge graphs in a dataspace, making them machine-readable. We discuss the basic concepts and propose a schema for linking Data Cards and Model Cards within a dataspace. In addition, we introduce the concept of a dataspace card which can be a starting point for extracting knowledge about models and datasets in a dataspace. This helps in building trust and reuse of models and data among companies and individuals participating as publishers or consumers of such assets.
Meteor: Improved Secure 3-Party Neural Network Inference with Reducing Online Communication Costs
Authors: Ye Dong, Xiaojun Chen, Weizhan Jing, Kaiyun Li, Weiping Wang
Venue: WWW
Year: 2023

Secure neural network inference has been a promising solution to private Deep-Learning-as-a-Service, which enables the service provider and user to execute neural network inference without revealing their private inputs. However, the expensive overhead of current schemes is still an obstacle when applied in real applications. In this work, we present Meteor, an online communication-efficient and fast secure 3-party computation neural network inference system aginst semi-honest adversary in honest-majority. The main contributions of Meteor are two-fold: i) We propose a new and improved 3-party secret sharing scheme stemming from the linearity of replicated secret sharing, and design efficient protocols for the basic cryptographic primitives, including linear operations, multiplication, most significant bit extraction, and multiplexer. ii) Furthermore, we build efficient and secure blocks for the widely used neural network operators such as Matrix Multiplication, ReLU, and Maxpool, along with exploiting several specific optimizations for better efficiency. Our total communication with the setup phase is a little larger than SecureNN (PoPETs’19) and Falcon (PoPETs’21), two state-of-the-art solutions, but the gap is not significant when the online phase must be optimized as a priority. Using Meteor, we perform extensive evaluations on various neural networks. Compared to SecureNN and Falcon, we reduce the online communication costs by up to 25.6 × and 1.5 ×, and improve the running-time by at most 9.8 × (resp. 8.1 ×) and 1.5 × (resp. 2.1 ×) in LAN (resp. WAN) for the online inference.
A Deep Behavior Path Matching Network for Click-Through Rate Prediction
Authors: Jian Dong, Yisong Yu, Yapeng Zhang, Yimin Lv, Shuli Wang, Beihong Jin, Yongkang Wang, Xingxing Wang, Dong Wang
Venue: WWW
Year: 2023

User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user’s decision-making. For understanding the psychological procedure behind user decisions, we present the behavior path and propose to match the user’s current behavior path with historical behavior paths to predict user behaviors on the app. Further, we design a deep neural network for behavior path matching and solve three difficulties in modeling behavior paths: sparsity, noise interference, and accurate matching of behavior paths. In particular, we leverage contrastive learning to augment user behavior paths, provide behavior path self-activation to alleviate the effect of noise, and adopt a two-level matching mechanism to identify the most appropriate candidate. Our model shows excellent performance on two real-world datasets, outperforming state-of-the-art CTR models. Moreover, our model has been deployed on the Meituan food delivery platform and has accumulated 1.6% improvement in CTR and 1.8% improvement in advertising revenue.
Sanitizing Sentence Embeddings (and Labels) for Local Differential Privacy
Authors: Minxin Du, Xiang Yue, Sherman S. M. Chow, Huan Sun
Venue: WWW
Year: 2023

Differentially private (DP) learning, notably DP stochastic gradient descent (DP-SGD), has limited applicability in fine-tuning gigantic pre-trained language models (LMs) for natural language processing tasks. The culprit is the perturbation of gradients (as gigantic as entire models), leading to significant efficiency and accuracy drops. We show how to achieve metric-based local DP (LDP) by sanitizing (high-dimensional) sentence embedding, extracted by LMs and much smaller than gradients. For potential utility improvement, we impose a consistency constraint on the sanitization. We explore two approaches: One is brand new and can directly output consistent noisy embeddings; the other is an upgradation with post-processing. To further mitigate “the curse of dimensionality,” we introduce two trainable linear maps for mediating dimensions without hurting privacy or utility. Our protection can effectively defend against privacy threats on embeddings. It also naturally extends to inference. Our experiments1 show that we reach the non-private accuracy under properly configured parameters, e.g., 0.92 for SST-2 with a privacy budget ϵ = 10 and the reduced dimension as 16. We also sanitize the label for LDP (with another small privacy budget) with limited accuracy losses to fully protect every sequence-label pair.
Jointly modeling products and resource pages for task-oriented recommendation
Authors: Brendan Andrew Duncan, Surya Kallumadi, Taylor Berg-Kirkpatrick, Julian J. McAuley
Venue: WWW
Year: 2023

Modeling high-level user intent in recommender systems can improve performance, although it is often difficult to obtain a ground truth measure of this intent. In this paper, we investigate a novel way to obtain such an intent signal by leveraging resource pages associated with a particular task. We jointly model product interactions and resource page interactions to create a system which can recommend both products and resource pages to users. Our experiments consider the domain of home improvement product recommendation, where resource pages are DIY (do-it-yourself) project pages from Lowes.com. Each DIY page provides a list of tools, materials, and step-by-step instructions to complete a DIY project, such as building a deck, installing cabinets, and fixing a leaking pipe. We use this data as an indicator of the intended project, which is a natural high-level intent signal for home improvement shoppers. We then extend a state-of-the-art system to incorporate this new intent data, and show a significant improvement in the ability of the system to recommend products. We further demonstrate that our system can be used to successfully recommend DIY project pages to users. We have taken initial steps towards deploying our method for project recommendation in production on the Lowe’s website and for recommendations through marketing emails.
Aspect-based Summarization of Legal Case Files using Sentence Classification
Authors: Nikhil E, Anshul Padhi, Pulkit Parikh, Swati Kanwal, Kamalakar Karlapalem, Natraj Raman
Venue: WWW
Year: 2023

Aspect-based summarization of a legal case file related to regulating bodies allows different stakeholders to consume information of interest therein efficiently. In this paper, we propose a multi-step process to achieve the same. First, we explore the semantic sentence segmentation of SEBI case files via classification. We also propose a dataset of Indian legal adjudicating orders which contain tags from carefully crafted domain-specific sentence categories with the help of legal experts. We experiment with various machine learning and deep learning methods for this multi-class classification. Then, we examine the performance of numerous summarization methods on the segmented document to generate persona-specific summaries. Finally, we develop a pipeline making use of the best methods in both sub-tasks to achieve high recall.
Intent-Aware Propensity Estimation via Click Pattern Stratification
Authors: Ehsan Ebrahimzadeh, Alex Cozzi, Abraham Bagherjeiran
Venue: WWW
Year: 2023

Counterfactual learning to rank via inverse propensity weighting is the most popular approach to train ranking models using biased implicit user feedback from logged search data. Standard click propensity estimation techniques rely on simple models of user browsing behavior that primarily account for the attributes of the presentation context that affect whether the relevance of an item to the search context is observed. Most notably, the inherent effect of the listwise presentation of the items on users’ propensity for engagement is captured in the position of the presented items on the search result page. In this work, we enrich this position bias based click propensity model by proposing an observation model that further incorporates the underlying search intent, as reflected in the user’s click pattern in the search context. Our approach does not require an intent prediction model based on the content of the search context. Instead, we rely on a simple, yet effective, non-causal estimate of the user’s browsing intent from the number of click events in the search context. We empirically characterize the distinct rank decay patterns of the estimated click propensities in the characterized intent classes. In particular, we demonstrate a sharper decay of click propensities in top ranks for the intent class identified by sparse user clicks and the higher likelihood of observing clicks in lower ranks for the intent class identified by higher number of user clicks. We show that the proposed intent-aware propensity estimation technique helps with training ranking models with more effective personalization and generalization power through empirical results for a ranking task in a major e-commerce platform.
Towards Decentralised Learning Analytics (Positioning Paper)
Authors: Audrey Ekuban, John Domingue
Venue: WWW
Year: 2023

When students interact with an online course, the routes they take when navigating through the course can be captured. Learning Analytics is the process of measuring, collecting, recording, and analysing this Student Activity Data. Predictive Learning Analytics, a sub-field of Learning Analytics, can help to identify students who are at risk of dropping out or failing, as well as students who are close to a grade boundary. Course tutors can use the insights provided by the analyses to offer timely assistance to these students. Despite its usefulness, there are privacy and ethical issues with the typically centralised approach to Predictive Learning Analytics. In this positioning paper, it is proposed that the issues associated with Predictive Learning Analytics can be alleviated, in a framework called EMPRESS, by combining 1) self-sovereign data, where data owners control who legitimately has access to data pertaining to them, 2) Federated Learning, where the data remains on the data owner’s device and/or the data is processed by the data owners themselves, and 3) Graph Convolutional Networks for Heterogeneous graphs, which are examples of knowledge graphs.
Concept Regulation in the Social Sciences
Authors: Zachary Elkins
Venue: WWW
Year: 2023

The sciences, notably biology and medicine, operate with highly regulated taxonomies and ontologies. The Social Sciences, on the other hand, muddle through in a proverbial tower of Babel. There may be some real benefits to an undisciplined set of ideas, but also some real costs. Over the last ten years, political scientists have attempted to get their semantic act by cooperating to formalize their vocabulary. The result has been a dramatic improvement in how scholars diagnose and treat problems of democracy, as well as a set of web applications that have changed the way countries write constitutions. Nevertheless, these methods of semantic cooperation have exposed some persistent challenges of “social engineering,” ones that may have tractable web solutions.
Detecting the Hidden Dynamics of Networked Actors Using Temporal Correlations
Authors: Keeley Erhardt, Dina Albassam
Venue: WWW
Year: 2023

Influence campaigns pose a threat to fact-based reasoning, erode trust in institutions, and tear at the fabric of our society. In the 21st century, influence campaigns have rapidly evolved, taking on new online identities. Many of these propaganda campaigns are persistent and well-resourced, making their identification and removal both hard and expensive. Social media companies have predominantly aimed to counter the threat of online propaganda by prioritizing the moderation of "coordinated inauthentic behavior". This strategy focuses on identifying orchestrated campaigns explicitly intended to deceive, rather than individual social media accounts or posts. In this paper, we study the Twitter footprint of a multi-year influence campaign linked to the Russian government. Drawing from the influence model, a generative model that describes the interactions between networked Markov chains, we demonstrate how temporal correlations in the sequential decision processes of individual social media accounts can reveal coordinated inauthentic activity.
Interpreting wealth distribution via poverty map inference using multimodal data
Authors: Lisette Espín-Noboa, János Kertész, Márton Karsai
Venue: WWW
Year: 2023

Poverty maps are essential tools for governments and NGOs to track socioeconomic changes and adequately allocate infrastructure and services in places in need. Sensor and online crowd-sourced data combined with machine learning methods have provided a recent breakthrough in poverty map inference. However, these methods do not capture local wealth fluctuations, and are not optimized to produce accountable results that guarantee accurate predictions to all sub-populations. Here, we propose a pipeline of machine learning models to infer the mean and standard deviation of wealth across multiple geographically clustered populated places, and illustrate their performance in Sierra Leone and Uganda. These models leverage seven independent and freely available feature sources based on satellite images, and metadata collected via online crowd-sourcing and social media. Our models show that combined metadata features are the best predictors of wealth in rural areas, outperforming image-based models, which are the best for predicting the highest wealth quintiles. Our results recover the local mean and variation of wealth, and correctly capture the positive yet non-monotonous correlation between them. We further demonstrate the capabilities and limitations of model transfer across countries and the effects of data recency and other biases. Our methodology provides open tools to build towards more transparent and interpretable models to help governments and NGOs to make informed decisions based on data availability, urbanization level, and poverty thresholds.
AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation
Authors: Rui Fan, Yuanhao Pu, Jin Chen, Zhihao Zhu, Defu Lian, Enhong Chen
Venue: WWW
Year: 2023

The Embarrassingly Shallow Autoencoders (EASE and SLIM) are strong recommendation methods based on implicit feedback, compared to competing methods like iALS and VAE-CF. However, EASE suffers from several major shortcomings. First, the training and inference of EASE can not scale with the increasing number of items since it requires storing and inverting a large dense matrix; Second, though its optimization objective – the square loss– can yield a closed-form solution, it is not consistent with recommendation goal – predicting a personalized ranking on a set of items, so that its performance is far from optimal w.r.t ranking-oriented recommendation metrics. Finally, the regularization coefficients are sensitive w.r.t recommendation accuracy and vary a lot across different datasets, so the fine-tuning of these parameters is important yet time-consuming. To improve training and inference efficiency, we propose a Similarity-Structure Aware Shallow Autoencoder on top of three similarity structures, including Co-Occurrence, KNN and NSW. We then optimize the model with a weighted square loss, which is proven effective for ranking-based recommendation but still capable of deriving closed-form solutions. However, the weight in the loss can not be learned in the training set and is similarly sensitive w.r.t the accuracy to regularization coefficients. To automatically tune the hyperparameters, we design two validation losses on the validation set for guidance, and update the hyperparameters with the gradient of the validation losses. We finally evaluate the proposed method on multiple real-world datasets and show that it outperforms seven competing baselines remarkably, and verify the effectiveness of each part in the proposed method.
MLN4KB: an efficient Markov logic network engine for large-scale knowledge bases and structured logic rules
Authors: Huang Fang, Yang Liu, Yunfeng Cai, Mingming Sun
Venue: WWW
Year: 2023

Markov logic network (MLN) is a powerful statistical modeling framework for probabilistic logic reasoning. Despite the elegancy and effectiveness of MLN, the inference of MLN is known to suffer from an efficiency issue. Even the state-of-the-art MLN engines can not scale to medium-size real-world knowledge bases in the open-world setting, i.e., all unobserved facts in the knowledge base need predictions. In this work, by focusing on a certain class of first-order logic rules that are sufficiently expressive, we develop a highly efficient MLN inference engine called MLN4KB that can leverage the sparsity of knowledge bases. MLN4KB enjoys quite strong theoretical properties; its space and time complexities can be exponentially smaller than existing MLN engines. Experiments on both synthetic and real-world knowledge bases demonstrate the effectiveness of the proposed method. MLN4KB is orders of magnitudes faster (more than 103 times faster on some datasets) than existing MLN engines in the open-world setting. Without any approximation tricks, MLN4KB can scale to real-world knowledge bases including WN-18 and YAGO3-10 and achieve decent prediction accuracy without bells and whistles. We implement MLN4KB as a Julia package called MLN4KB.jl. The package supports both maximum a posteriori (MAP) inference and learning the weights of rules. MLN4KB.jl is public available at https://github.com/baidu-research/MLN4KB .
Claim Extraction and Dynamic Stance Detection in COVID-19 Tweets
Authors: Noushin Salek Faramarzi, Fateme Hashemi Chaleshtori, Hossein Shirazi, Indrakshi Ray, Ritwik Banerjee
Venue: WWW
Year: 2023

The information ecosystem today is noisy, and rife with messages that contain a mix of objective claims and subjective remarks or reactions. Any automated system that intends to capture the social, cultural, or political zeitgeist, must be able to analyze the claims as well as the remarks. Due to the deluge of such messages on social media, and their tremendous power to shape our perceptions, there has never been a greater need to automate these analyses, which play a pivotal role in fact-checking, opinion mining, understanding opinion trends, and other such downstream tasks of social consequence. In this noisy ecosystem, not all claims are worth checking for veracity. Such a check-worthy claim, moreover, must be accurately distilled from subjective remarks surrounding it. Finally, and especially for understanding opinion trends, it is important to understand the stance of the remarks or reactions towards that specific claim. To this end, we introduce a COVID-19 Twitter dataset, and present a three-stage process to (i) determine whether a given Tweet is indeed check-worthy, and if so, (ii) which portion of the Tweet ought to be checked for veracity, and finally, (iii) determine the author’s stance towards the claim in that Tweet, thus introducing the novel task of topic-agnostic stance detection.