OpenRaiser/Intern-Atlas huggingface dataset:
GAT-DNS: DNS Multivariate Time Series Prediction Model Based on Graph Attention Network
Authors: Xiaofeng Lu, Xiaoyu Zhang, Pietro Lió
Venue: WWW
Year: 2023

As one of the most basic services of the Internet, DNS has suffered a lot of attacks. Existing attack detection methods rely on the learning of malicious samples, so it is difficult to detect new attacks and long-period attacks. This paper transforms the DNS data flow into time series, and proposes a DNS anomaly detection method based on graph attention network and graph embedding (GAT-DNS). GAT-DNS establishes a multivariate time series model to depict the DNS service status. When the actual flow of a feature exceeds the predicted range, it is considered that abnormal DNS behavior is found. In this paper, vertex dependency is proposed to describe the dependency between features. The features with high vertex dependency values are deleted to achieve model compression. This improves the system efficiency. Experiments on open data sets show that compared with the latest AD-Bop and QLAD methods, GAT-DNS method not only improves the precision, recall and F1 value, but also improves the time efficiency of the model.
Facility Relocation Search For Good: When Facility Exposure Meets User Convenience
Authors: Hui Luo, Zhifeng Bao, J. Shane Culpepper, Mingzhao Li, Yanchang Zhao
Venue: WWW
Year: 2023

In this paper, we propose a novel facility relocation problem where facilities (and their services) are portable, which is a combinatorial search problem with many practical applications. Given a set of users, a set of existing facilities, and a set of potential sites, we decide which of the existing facilities to relocate to potential sites, such that two factors are satisfied: (1) facility exposure: facilities after relocation have balanced exposure, namely serving equivalent numbers of users; (2) user convenience: it is convenient for users to access the nearest facility, which provides services with shorter travel distance. This problem is motivated by applications such as dynamically redistributing vaccine resources to align supply with demand for different vaccination centers, and relocating the bike sharing sites daily to improve the transportation efficiency. We first prove that this problem is NP-hard, and then we propose two algorithms: a non-learning best response algorithm () and a reinforcement learning algorithm (). In particular, the best response algorithm finds a Nash equilibrium to balance the facility-related and the user-related goals. To avoid being confined to only one Nash equilibrium, as found in the method, we also propose the reinforcement learning algorithm for long-term benefits, where each facility is an agent and we determine whether a facility needs to be relocated or not. To verify the effectiveness of our methods, we adopt multiple metrics to evaluate not only our objective, but also several other facility exposure equity and user convenience metrics to understand the benefits after facility relocation. Finally, comprehensive experiments using real-world datasets provide insights into the effectiveness of the two algorithms in practice.
Personalized style recommendation via reinforcement learning
Authors: Jiyun Luo, Kurchi Subhra Hazra, Wenyu Huo, Rui Li, Abhijit Mahabal
Venue: WWW
Year: 2023

Pinterest fashion and home decor searchers often have different style tastes. Some existing work adopts users’ past engagement to infer style preference. These methods cannot help users discover new styles. Other work requires users to provide text or visual signals to describe their style preference, but users often are not familiar with style terms and do not have the right image to start with. In this paper, we propose a reinforcement learning (RL) method to help users explore and exploit style space without requiring extra user input. Experimental results show that our method improves the success rate of Pinterest fashion and home decor searches by 34.8%.
Lower Risks, Better Choices: Stock Correlation Based Portfolio Selection in Stock Markets
Authors: Di Luo, Weiheng Liao, Rui Yan
Venue: WWW
Year: 2023

Over the past few years, we’ve seen a huge interest in applying AI techniques to develop investment strategies both in academia and the finance industry. However, we note that generating returns is not always the sole investment objective. Take large pension funds for example, they are considerably more risk-averse as opposed to profit-seeking. With this observation, we propose a Risk-balanced Deep Portfolio Constructor (RDPC) that takes risk into explicit consideration. RDPC is an end-to-end reinforcement learning-based transformer trained to optimize both returns and risk, with a hard attention mechanism that learns the relationship between asset pairs, imitating the powerful pairs trading strategy widely adopted by many investors. Experiments on real-world data show that RDPC achieves state-of-the-art performance not just on risk metrics such as maximum drawdown, but also on risk-adjusted returns metrics including Sharpe ratio and Calmar ratio.
CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization
Authors: Zheheng Luo, Qianqian Xie, Sophia Ananiadou
Venue: WWW
Year: 2023

Citation graphs can be helpful in generating high-quality summaries of scientific papers, where references of a scientific paper and their correlations can provide additional knowledge for contextualising its background and main contributions. Despite the promising contributions of citation graphs, it is still challenging to incorporate them into summarization tasks. This is due to the difficulty of accurately identifying and leveraging relevant content in references for a source paper, as well as capturing their correlations of different intensities. Existing methods either ignore references or utilize only abstracts indiscriminately from them, failing to tackle the challenge mentioned above. To fill that gap, we propose a novel citation-aware scientific paper summarization framework based on the citation graph, able to accurately locate and incorporate the salient contents from references, as well as capture varying relevance between source papers and their references. Specifically, we first build a domain-specific dataset PubMedCite with about 192K biomedical scientific papers and a large citation graph preserving 917K citation relationships between them. It is characterized by preserving the salient contents extracted from full texts of references, and the weighted correlation between the salient contents of references and the source paper. Based on it, we design a self-supervised citation-aware summarization framework (CitationSum) with graph contrastive learning, which boosts the summarization generation by efficiently fusing the salient information in references with source paper contents under the guidance of their correlations. Experimental results show that our model outperforms the state-of-the-art methods, due to efficiently leveraging the information of references and citation correlations.
IRWArt: Levering Watermarking Performance for Protecting High-quality Artwork Images
Authors: Yuanjing Luo, Tongqing Zhou, Fang Liu, Zhiping Cai
Venue: WWW
Year: 2023

Increasing artwork plagiarism incidents underscores the urgent need for reliable copyright protection for high-quality artwork images. Although watermarking is helpful to this issue, existing methods are limited in imperceptibility and robustness. To provide high-level protection for valuable artwork images, we propose a novel invisible robust watermarking framework, dubbed as IRWArt. In our architecture, the embedding and recovery of the watermark are treated as a pair of image transformations’ inverse problems, and can be implemented through the forward and backward processes of an invertible neural networks (INN), respectively. For high visual quality, we embed the watermark in high-frequency domains with minimal impact on artwork and supervise image reconstruction using a human visual system(HVS)-consistent deep perceptual loss. For strong plagiarism-resistant, we construct a quality enhancement module for the embedded image against possible distortions caused by plagiarism actions. Moreover, the two-stagecontrastive training strategy enables the simultaneous realization of the above two goals. Experimental results on 4 datasets demonstrate the superiority of our IRWArt over other state-of-the-art watermarking methods. Code: https://github.com/1024yy/IRWArt.
Rethinking Structural Encodings: Adaptive Graph Transformer for Node Classification Task
Authors: Xiaojun Ma, Qin Chen, Yi Wu, Guojie Song, Liang Wang, Bo Zheng
Venue: WWW
Year: 2023

Graph Transformers have proved their advantages in graph data mining with elaborate Positional Encodings, especially in graph-level tasks. However, their application in the node classification task has not been fully exploited yet. In the node classification task, existing Graph Transformers with Positional Encodings are limited by the following issues: (i) PEs describing the node’s positional identities are insufficient for the node classification task on complex graphs, where a full portrayal of the local node property is needed. (ii) PEs for graphs are integrated with Transformers in a constant schema, resulting in the ignorance of local patterns that may vary among different nodes. In this paper, we propose Adaptive Graph Transformer (AGT) to tackle above issues. AGT consists of a Learnable Centrality Encoding and a Kernelized Local Structure Encoding. The two modules extract structural patterns from centrality and subgraph views in a learnable and scalable manner. Further, we design the Adaptive Transformer Block to adaptively integrate the attention scores and Structural Encodings in a node-specific manner. AGT achieves state-of-the-art performances on nine real-world web graphs (up to 1.6 million nodes). Furthermore, AGT shows outstanding results on two series of synthetic graphs with ranges of heterophily and noise ratios.
Learning Geolocation by Accurately Matching Customer Addresses via Graph based Active Learning
Authors: Saket Maheshwary, Saurabh Sohoney
Venue: WWW
Year: 2023

We propose a novel adaptation of graph-based active learning for customer address resolution or de-duplication, with the aim to determine if two addresses represent the same physical building or not. For delivery systems, improving address resolution positively impacts multiple downstream systems such as geocoding, route planning and delivery time estimations, leading to an efficient and reliable delivery experience, both for customers as well as delivery agents. Our proposed approach jointly leverages address text, past delivery information and concepts from graph theory to retrieve informative and diverse record pairs to label. We empirically show the effectiveness of our approach on manually curated dataset across addresses from India (IN) and United Arab Emirates (UAE). We achieved absolute improvement in recall on average across IN and UAE while preserving precision over the existing production system. We also introduce delivery point (DP) geocode learning for cold-start addresses as a downstream application of address resolution. In addition to offline evaluation, we also performed online A/B experiments which show that when the production model is augmented with active learnt record pairs, the delivery precision improved by and delivery defects reduced by on an average across shipments from IN and UAE.
Towards Timeline Generation with Abstract Meaning Representation
Authors: Behrooz Mansouri, Ricardo Campos, Adam Jatowt
Venue: WWW
Year: 2023

Timeline summarization (TLS) is a challenging research task that requires researchers to distill extensive and intricate temporal data into a concise and easily comprehensible representation. This paper proposes a novel approach to timeline summarization using Abstract Meaning Representations (AMRs), a graphical representation of the text where the nodes are semantic concepts and the edges denote relationships between concepts. With AMR, sentences with different wordings, but similar semantics, have similar representations. To make use of this feature for timeline summarization, a two-step sentence selection method that leverages features extracted from both AMRs and the text is proposed. First, AMRs are generated for each sentence. Sentences are then filtered out by removing those with no named-entities and keeping the ones with the highest number of named-entities. In the next step, sentences to appear in the timeline are selected based on two scores: Inverse Document Frequency (IDF) of AMR nodes combined with the score obtained by applying a keyword extraction method to the text. Our experimental results on the TLS-Covid19 test collection demonstrate the potential of the proposed approach.
Towards InnoGraph: A Knowledge Graph for AI Innovation
Authors: M. Besher Massri, Blerina Spahiu, Marko Grobelnik, Vladimir Alexiev, Matteo Palmonari, Dumitru Roman
Venue: WWW
Year: 2023

Researchers seeking to comprehend the state-of-the-art innovations in a particular field of study must examine recent patents and scientific articles in that domain. Innovation ecosystems consist of interconnected information about entities such as researchers, institutions, projects, products, and technologies. However, representing such information in a machine-readable format is challenging because concepts like "knowledge" are not easily represented. Nonetheless, even a partial representation of innovation ecosystems provides valuable insights. Therefore, representing innovation ecosystems as knowledge graphs (KGs) would enable advanced data analysis and generate new insights. To this end, we propose InnoGraph, a framework that integrates multiple heterogeneous data sources to build a Knowledge Graph of the worldwide AI innovation ecosystem.
The Web and Linked Data as a Solid Foundation for Dataspaces
Authors: Sascha Meckler, Rene Dorsch, Daniel Henselmann, Andreas Harth
Venue: WWW
Year: 2023

The concepts for dataspaces range from database management systems to cross-company platforms for data and applications. In this short paper, we present the “Solid Data Space” (SDS), a concept for dataspaces that build on top of the (Semantic) Web and Social Linked Data (Solid). Existing Web technologies and Linked Data principles form the foundation for open, decentralized networks for sovereign data exchange between citizens, organizations and companies. Domain-specific dataspace implementations can extend the agreements for communication and collaboration to enable specific functionality. We compare the SDS with principles and components of the emerging International Data Spaces to identify similarities and point out technological differences.
Caught in the Game: On the History and Evolution of Web Browser Gaming
Authors: Naif Mehanna, Walter Rudametkin
Venue: WWW
Year: 2023

Web browsers have come a long way since their inception, evolving from a simple means of displaying text documents over the network to complex software stacks with advanced graphics and network capabilities. As personal computers grew in popularity, developers jumped at the opportunity to deploy cross-platform games with centralized management and a low barrier to entry. Simply going to the right address is now enough to start a game. From text-based to GPU-powered 3D games, browser gaming has evolved to become a strong alternative to traditional console and mobile-based gaming, targeting both casual and advanced gamers. Browser technology has also evolved to accommodate more demanding applications, sometimes even supplanting functions typically left to the operating system. Today, websites display rich, computationally intensive, hardware-accelerated graphics, allowing developers to build ever-more impressive applications and games. In this paper, we present the evolution of browser gaming and the technologies that enabled it, from the release of the first text-based games in the early 1990s to current open-world and game-engine-powered browser games. We discuss the societal impact of browser gaming and how it has allowed a new target audience to access digital gaming. Finally, we review the potential future evolution of the browser gaming industry.
Improving the Relevance of Product Search for Queries with Negations
Authors: Felice Antonio Merra, Omar Zaidan, Fabricio de Sousa Nascimento
Venue: WWW
Year: 2023

Product search engines (PSEs) play an essential role in retail websites as they make it easier for users to retrieve relevant products within large catalogs. Despite the continuous progress that has led to increasingly accurate search engines, a limited focus has been given to their performance on queries with negations. Indeed, while we would expect to retrieve different products for the queries “iPhone 13 cover with ring” and “iPhone 13 cover without ring”, this does not happen in popular PSEs with the latter query containing results with the unwanted ring component. The limitation of modern PSEs in understanding negations motivates the need for further investigation. In this work, we start by defining the negation intent in users queries. Then, we design a transformer-based model, named Negation Detector for Queries (ND4Q), that reaches optimal performance in negation detection (+95% on accuracy metrics). Finally, having built the first negation detector for product search queries, we propose a negation-aware filtering strategy, named Filtering Irrelevant Products (FIP). The promising experimental results in improve the PSE relevance performance using FIP (+9.41% on nDCG@16 for queries where the negation starts with "without") pave the way to additional research effort towards negation-aware PSEs.
HybridEval: A Human-AI Collaborative Approach for Evaluating Design Ideas at Scale
Authors: Sepideh Mesbah, Ines Arous, Jie Yang, Alessandro Bozzon
Venue: WWW
Year: 2023

Evaluating design ideas is necessary to predict their success and assess their impact early on in the process. Existing methods rely either on metrics computed by systems that are effective but subject to errors and bias, or experts’ ratings, which are accurate but expensive and long to collect. Crowdsourcing offers a compelling way to evaluate a large number of design ideas in a short amount of time while being cost-effective. Workers’ evaluation is, however, less reliable and might substantially differ from experts’ evaluation. In this work, we investigate workers’ rating behavior and compare it with experts. First, we instrument a crowdsourcing study where we asked workers to evaluate design ideas from three innovation challenges. We show that workers share similar insights with experts but tend to rate more generously and weigh certain criteria more importantly. Next, we develop a hybrid human-AI approach that combines a machine learning model with crowdsourcing to evaluate ideas. Our approach models workers’ reliability and bias while leveraging ideas’ textual content to train a machine learning model. It is able to incorporate experts’ ratings whenever available, to supervise the model training and infer worker performance. Results show that our framework outperforms baseline methods and requires significantly less training data from experts, thus providing a viable solution for evaluating ideas at scale.
Connectivity
Authors: Robert Melancton Metcalfe
Venue: WWW
Year: 2023

The most important new fact about the human condition is that we are now suddenly connected. When I say “suddenly” I refer to the Internet’s birthday, October 29, 1969 and how two thirds of the human race, five billion people, are already on the Internet, in only 50 years. Suddenly. The Arpanet started the Internet in 1969 by networking time-shared minicomputers serving dumb character terminals. Then in 1973 Xerox Palo Alto Research Center (PARC) decided to put a personal computer on every desk. Ethernet was invented on May 22, 1973 to provide local connectivity among those PCs, one on every desk, if you can imagine that. The PARC Ethernet was formed by combining Jerrold coaxial vampire taps, Manchester on-off keying, and Alohanet randomized retransmissions. Then we wrapped it in internet protocols according to a layered reference model. Then we standardized it all: Ethernet, IP, TCP, TELNET, FTP, Mail, URL, HTML, HTTP. Ethernet evolved rapidly away from its Jerrold-Manchester-Alohanet prototype. Ethernet’s legacy is instead packets to the desktop, abundance of bandwidth, and standardization. Come hear all about it. GPT is writing my lecture now.
A Federated Learning Benchmark for Drug-Target Interaction
Authors: Gianluca Mittone, Filip Svoboda, Marco Aldinucci, Nicholas D. Lane, Pietro Lió
Venue: WWW
Year: 2023

Aggregating pharmaceutical data in the drug-target interaction (DTI) domain can potentially deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests [5, 18]. This work proposes the application of federated learning, which is reconcilable with the industry’s constraints. It does not require sharing any information that would reveal the entities’ data or any other high-level summary. When used on a representative GraphDTA model and the KIBA dataset, it achieves up to 15% improved performance relative to the best available non-privacy preserving alternative. Our extensive battery of experiments shows that, unlike in other domains, the non-IID data distribution in the DTI datasets does not deteriorate FL performance. Additionally, we identify a material trade-off between the benefits of adding new data and the cost of adding more clients.
Querying Container Provenance
Authors: Aniket Modi, Moaz Reyad, Tanu Malik, Ashish Gehani
Venue: WWW
Year: 2023

Containers are lightweight mechanisms for the isolation of operating system resources. They are realized by activating a set of namespaces. Given the use of containers in scientific computing, tracking and managing provenance within and across containers is becoming essential for debugging and reproducibility. In this work, we examine the properties of container provenance graphs that result from auditing containerized applications. We observe that the generated container provenance graphs are hypergraphs because one resource may belong to one or more namespaces. We examine the hierarchical behavior of PID, mount, and user namespaces, that are more commonly activated and show that even when represented as hypergraphs, the resulting container provenance graphs are acyclic. We experiment with recently published container logs and identify hypergraph properties.
Algorithmic Vibe in Information Retrieval
Authors: Ali Montazeralghaem, Nick Craswell, Ryen W. White, Ahmed Hassan Awadallah, Byungki Byun
Venue: WWW
Year: 2023

When information retrieval systems return a ranked list of results in response to a query, they may be choosing from a large set of candidate results that are equally useful and relevant. This means we might be able to identify a difference between rankers A and B, where ranker A systematically prefers a certain type of relevant results. Ranker A may have this systematic difference (different “vibe”) without having systematically better or worse results according to standard information retrieval metrics. We first show that a vibe difference can exist, comparing two publicly available rankers, where the one that is trained on health-related queries will systematically prefer health-related results, even for non-health queries. We define a vibe metric that lets us see the words that a ranker prefers. We investigate the vibe of search engine clicks vs. human labels. We perform an initial study into correcting for vibe differences to make ranker A more like ranker B via changes in negative sampling during training.