OpenRaiser/Intern-Atlas huggingface dataset:
SSI, from Specifications to Protocol? Formally Verify Security!
Authors: Christoph H.-J. Braun, Ross Horne, Tobias Käfer, Sjouke Mauw
Venue: WWW
Year: 2024

We evaluate a bundle of specifications from the Self-Sovereign Identity (SSI) paradigm to construct an authentication protocol for the Web. We demonstrate how relevant standards such as W3C Verifiable Credentials (VC), W3C Decentralised Identifiers (DIDs), and components of the Hyperledger Aries Framework are to be assembled methodologically into a protocol. We make those assumptions from standard trust models explicit that underlie the derived protocol, and verify security and privacy properties, notably secrecy, authentication, and unlinkability. This enables us to formally justify the additional precision that we urge these specifications to consider, to ensure that implementors of SSI-based systems do not neglect security-critical controls.
A Novel Dual-Pipeline based Attention Mechanism for Multimodal Social Sentiment Analysis
Authors: Ali Braytee, Andy Shueh-Chih Yang, Ali Anaissi, Kunal Chaturvedi, Mukesh Prasad
Venue: WWW
Year: 2024

Traditionally, sentiment analysis methods rely solely on text or image data. However, most user-generated social media content includes both textual and image content. In this study, we propose a novel Dual-Pipeline based Attentional method that uses different modalities of data, including text and images, to analyse and interpret emotions and sentiments expressed in tweets. Our proposed method simultaneously extracts meaningful local and global contextual features from multiple modalities. Local fusion layers within each pipeline combine modality-specific features using an attention mechanism to enrich the joint multimodal representation. A global fusion layer consolidates the collective sentiment representation by seamlessly intermixing the outputs of both pipelines. We evaluate our proposed method using performance metrics such as accuracy and F1-score. Through extensive experimentation on the MVSA dataset, our method demonstrates superior performance compared to state-of-the-art techniques in identifying the sentiment conveyed in social media data.
SWATTING Spambots: Real-time Detection of Malicious Bots on X
Authors: Cristian Brokate, Manon Richard, Lisa Giordani, Jean Liénard
Venue: WWW
Year: 2024

Spambot activity has become increasingly pervasive on social media platforms, such as X (formerly known as Twitter), leading to concerns over information quality and user experience. This study presents an innovative approach for real-time detection and reporting of spambots on Twitter platform. Using data analytics technique, we adapted a comprehensive framework capable of accurately identifying and categorizing spambot accounts based on their behavioral patterns and characteristics. By providing an efficient solution to this growing issue, our research aims to enhance user trust in social media communication channels and promote a more transparent and authentic online environment for users to engage with each other and share information.
DRAM-like Architecture with Asynchronous Refreshing for Continual Relation Extraction
Authors: Tianci Bu, Kang Yang, Wenchuan Yang, Jiawei Feng, Xiaoyu Zhang, Xin Lu
Venue: WWW
Year: 2024

Continual Relation Extraction (CRE) has found widespread web applications (e.g., search engines) in recent times. One significant challenge in this task is the phenomenon of catastrophic forgetting, where models tend to forget earlier information. Existing approaches in this field predominantly rely on memory-based methods to alleviate catastrophic forgetting, which overlooks the inherent challenge posed by the varying memory requirements of different relations and the need for a suitable memory refreshing strategy. Drawing inspiration from the mechanisms of Dynamic Random Access Memory (DRAM), our study introduces a novel CRE architecture with an asynchronous refreshing strategy to tackle these challenges. We first design a DRAM-like architecture, comprising three key modules: perceptron, controller, and refresher. This architecture dynamically allocates memory, enabling the consolidation of well-remembered relations while allocating additional memory for revisiting poorly learned relations. Furthermore, we propose a compromising asynchronous refreshing strategy to find the pivot between over-memorization and overfitting, which focuses on the current learning task and mixed-memory data asynchronously. Additionally, we explain the existing refreshing strategies in CRE from the DRAM perspective. Our proposed method has experimented on two benchmarks and overall outperforms ConPL (the SOTA method) by an average of 1.50% on accuracy, which demonstrates the efficiency of the proposed architecture and refreshing strategy.
Multi-Granularity Tibetan Textual Adversarial Attack Method Based on Masked Language Model
Authors: Xi Cao, Nuo Qun, Quzong Gesang, Yulei Zhu, Nyima Trashi
Venue: WWW
Year: 2024

In social media, neural network models have been applied to hate speech detection, sentiment analysis, etc., but neural network models are susceptible to adversarial attacks. For instance, in a text classification task, the attacker elaborately introduces perturbations to the original texts that hardly alter the original semantics in order to trick the model into making different predictions. By studying textual adversarial attack methods, the robustness of language models can be evaluated and then improved. Currently, most of the research in this field focuses on English, and there is also a certain amount of research on Chinese. However, there is little research targeting Chinese minority languages. With the rapid development of artificial intelligence technology and the emergence of Chinese minority language models, textual adversarial attacks become a new challenge for the information processing of Chinese minority languages. In response to this situation, we propose a multi-granularity Tibetan textual adversarial attack method based on masked language models called TSTricker. We utilize the masked language models to generate candidate substitution syllables or words, adopt the scoring mechanism to determine the substitution order, and then conduct the attack method on several fine-tuned victim models. The experimental results show that TSTricker reduces the accuracy of the classification models by more than 28.70% and makes the classification models change the predictions of more than 90.60% of the samples, which has an evidently higher attack effect than the baseline method.
AI in Health and Social Care: A Methodology for Privacy Risk Modeling and Simulation
Authors: Laura Carmichael, Steve Taylor, Adriane Chapman, Michael J. Boniface
Venue: WWW
Year: 2024

As health and social care data networks evolve and adapt to greater digitalization and datafication of health, data and analytics systems are developing and bringing forward new ways to share, access and analyze data. Organizations and individuals making data sharing decisions for AI-enabled health and social care services need to be able to balance the benefits of such uses with the possible risks that may ensue - including those related to issues of privacy and security. In this paper, we provide an overview of our approach to privacy risk assessment for cross-domain access and re-use of sensitive data for research purposes using Spyderisk - an automated risk assessment tool. We apply Spyderisk to a real AI research scenario and consider the ways in which such techniques could support multiple stakeholders to assess privacy and security risks.
Fast and Accurate Fair k-Center Clustering in Doubling Metrics
Authors: Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci
Venue: WWW
Year: 2024

We study the classic k-center clustering problem under the additional constraint that each cluster should be fair. In this setting, each point is marked with one or more colors, which can be used to model protected attributes (e.g., gender or ethnicity). A cluster is deemed fair if, for every color, the fraction of its points marked with that color is within some prespecified range. We present a coreset-based approach to fair k-center clustering for general metric spaces which attains almost the best approximation quality of the current state of the art solutions, while featuring running times which can be orders of magnitude faster for large datasets of low doubling dimension. We devise sequential, streaming and MapReduce implementations of our approach and conduct a thorough experimental analysis to provide evidence of their practicality, scalability, and effectiveness.
Knowledge Guided Conditional Diffusion Model for Controllable Mobile Traffic Generation
Authors: Haoye Chai, Tong Li, Fenyu Jiang, Shiyuan Zhang, Yong Li
Venue: WWW
Year: 2024

Generating mobile traffic in urban contexts is important for network optimization. However, existing solutions show weakness in capturing complex temporal features of mobile traffic. In this paper, we propose a Knowledge-Guided Conditional Diffusion model (KGDiff) for controllable mobile traffic generation, where a customized denoising network of diffusion model is designed to explore the temporal features of mobile traffic. Specifically, we design a frequency attention mechanism that incorporates an Urban Knowledge Graph (UKG) to adaptively capture implicit correlations between mobile traffic and urban environments in the frequency domain. This approach enables the model to generate network traffic corresponding to different environments in a controlled manner, enhancing the model's controllability. Experiments on one real-world dataset show that the proposed framework has good controllability and can improve generation fidelity with gains surpassing 19%.
Sociotechnical Considerations for Accessibility and Equity in AI for Healthcare
Authors: Adriane Chapman, Chloe L. Harrison, Caroline Jones, James Thornton, Rose Worley, Jeremy C. Wyatt
Venue: WWW
Year: 2024

As AI systems are built and deployed to support mental health services, it is imperative to fully understand the stakeholder acceptability of such systems so that these concerns can be taken into account in system design. As such, we undertook a consultation with staff (therapists) and service-users at Adferiad Recovery (a large mental health charity). The aim was to capture insights about their understanding of trust, and different trust factors for AI in mental health care. Surveys, interviews and focus groups were conducted with service users and therapists. Key takeaways for computer scientists and the developers of AI systems are presented.
Enhancing Cross-Domain Click-Through Rate Prediction via Explicit Feature Augmentation
Authors: Xu Chen, Zida Cheng, Jiangchao Yao, Chen Ju, Weilin Huang, Jinsong Lan, Xiaoyi Zeng, Shuai Xiao
Venue: WWW
Year: 2024

Cross-domain CTR (CDCTR) prediction is an important research topic that studies how to leverage meaningful data from a related domain to help CTR prediction in target domain. Most existing CDCTR works design implicit ways to transfer knowledge across domains such as parameter-sharing that regularizes the model training in target domain. More effectively, recent researchers propose explicit techniques to extract user interest knowledge and transfer this knowledge to target domain. However, the proposed method mainly faces two issues: 1) it usually requires a super domain, i.e. an extremely large source domain, to cover most users or items of target domain, and 2) the extracted user interest knowledge is static no matter what the context is in target domain. These limitations motivate us to develop a more flexible and efficient technique to explicitly transfer knowledge. In this work, we propose a cross-domain augmentation network (CDAnet) being able to perform explicit knowledge transfer between two domains. Specifically, CDAnet contains a designed translation network and an augmentation network which are trained sequentially. The translation network computes latent features from two domains and learns meaningful cross-domain knowledge of each input in target domain by using a designed cross-supervised feature translator. Later the augmentation network employs the explicit cross-domain knowledge as augmented information to boost the target domain CTR prediction. Through extensive experiments on two public benchmarks and one industrial production dataset, we show CDAnet can learn meaningful translated features and largely improve the performance of CTR prediction. CDAnet has been conducted online A/B test in image2product retrieval at Taobao app, bringing an absolute 0.11 point CTR improvement, a relative 0.64% deal growth and a relative 1.26% GMV increase.
Unveiling Climate Drivers via Feature Importance Shift Analysis in New Zealand
Authors: Bowen Chen, Gillian Dobbie, Neelesh Rampal, Yun Sing Koh
Venue: WWW
Year: 2024

In the face of rising surface temperatures from climate change, impacting biodiversity, extreme weather events, and agricultural productivity, understanding the drivers behind temperature changes is imperative. Traditional global climate models (GCMs) are computationally expensive, limiting their applicability, while machine learning approaches, though promising, face interpretability challenges due to their "black box" nature, especially in a dynamic setting where the data is constantly evolving. We propose DUO, a framework to identify shifts in important features and feature combinations as the data distribution changes over time. Our model independently assesses the importance of features and their interactions while also evaluating their relevance when combined with additional features, contributing to the target class. As a case study, we apply DUO to assess the shifts in climate drivers for station-level temperatures in six locations across New Zealand from 1980 to 2020, we identify specific humidity, geopotential height, and air temperature at high atmospheric pressure levels as the most important features for describing temperature variability. By revealing how climate drivers change over time, DUO contributes to a deeper understanding of temperature change patterns, enabling practitioners to develop targeted and adaptive mitigation strategies.
OODREB: Benchmarking State-of-the-Art Methods for Out-Of-Distribution Generalization on Relation Extraction
Authors: Haotian Chen, Houjing Guo, Bingsheng Chen, Xiangdong Zhou
Venue: WWW
Year: 2024

Relation extraction (RE) methods have achieved striking performance when training and test data are independently and identically distributed (i.i.d). However, in real-world scenarios where RE models are trained to acquire knowledge in the wild, the assumption can hardly be satisfied due to the different and unknown testing distributions. In this paper, we serve as the first effort to study out-of-distribution (OOD) problems in RE by constructing an out-of-distribution relation extraction benchmark (OODREB) and then investigating the abilities of state-of-the-art (SOTA) RE methods on OODREB in both i.i.d. and OOD settings. Our proposed benchmark and analysis reveal new findings and insights: (1) Existing SOTA RE methods struggle to achieve satisfying performance on OODREB in both i.i.d. and OOD settings due to the complex training data and biased model selection method. Rethinking the developing protocols of RE methods is of great urgency. (2) The SOTA RE methods fail to learn causality due to the diverse linguistic expressions of causal information. The failure limits their robustness and generalization ability; (3) Current RE methods based on language models are far away from being deployed in real-world applications. We appeal to future work to take the OOD generalization and causality learning ability into consideration. We make our annotation and code publicly available at https://github.com/Hytn/OODREB.
Open Metaverse: Issues, Evolution, and Future
Authors: Zefeng Chen, Wensheng Gan, Jiayi Sun, Jiayang Wu, Philip S. Yu
Venue: WWW
Year: 2024

With the content evolution on the web and the Internet, there is a need for cyberspace that can be used to work, live, and play in digital worlds regardless of geography. The Metaverse provides the possibility of the future Internet, representing a future trend for the web and the Internet. In the future, the Metaverse is a dataspace where the real and the virtual are combined instead of a virtual space. In this paper, we have a comprehensive survey of the compelling Metaverse, including issues of the Metaverse and Metaverse's evolution and future. We hope this survey can provide some helpful prospects and insightful directions for the Metaverse.
Boost Social Recommendation via Adaptive Denoising Network
Authors: Xinran Chen, Chaobo He, Quanlong Guan
Venue: WWW
Year: 2024

Social recommendation aims to integrate social relationships to improve the performance of recommendation, and has attracted increasing attention in the field of recommendation system. Recently, Graph Neural Networks (GNNs) based methods for social recommendation are very competitive, but most of them overlook the fact that social relationships may have potential noises. Through the message passing mechanism of GNNs, these noises could be propagated and amplified, ultimately reducing the performance of recommendation. In view of this, we propose a novel GNN-based Adaptive Denoising Social Recommendation (ADSRec) method. It devises a denoising network, which can alleviate the impact of social relationships noises via the adaptive weight adjustment strategy. By further introducing the contrastive learning, the representations of users and items can be enhanced, leading to better recommendation results. Extensive experiments on three widely used datasets demonstrate the superiority of ADSRec over baselines.
Exploring Representational Similarity Analysis to Protect Federated Learning from Data Poisoning
Authors: Gengxiang Chen, Kai Li, Ahmed M. Abdelmoniem, Linlin You
Venue: WWW
Year: 2024

As a paradigm that preserves privacy, Federated Learning (FL) enables distributed clients to cooperatively train global models using local datasets. However, this approach also provides opportunities for adversaries to compromise system stability by contaminating local data, such as through Label-Flipping Attacks (LFAs). In addressing these security challenges, most existing defense strategies presume the presence of an independent and identically distributed (IID) environment, resulting in suboptimal performance under Non-IID conditions. This paper introduces RSim-FL, a novel and pragmatic defense mechanism that incorporates Representational Similarity Analysis (RSA) into the detection of malevolent updates. This is achieved by calculating the similarity between uploaded local models and the global model. The evaluation, conducted against five state-of-the-art baselines, demonstrates that RSim-FL can accurately identify malicious local models and effectively mitigate divergent Label-Flipping Attacks (LFAs) in a Non-IID setting.
Optimization-Based Budget Pacing in eBay Sponsored Search
Authors: Qinyi Chen, Phuong Ha Nguyen, Djordje Gligorijevic
Venue: WWW
Year: 2024

In online platforms like eBay, sponsored search advertising has become instrumental for businesses aiming for enhanced visibility. However, in automated ad auctions, the sellers (ad campaigns) run the risk of exhausting their budgets prematurely in the absence of proper pacing strategies. In response to this, online platforms have been prompted to employ budget pacing strategies to maintain consistent spending patterns for their sellers. While numerous budget pacing strategies have been introduced, they predominantly stem from either empirical or theoretical perspectives, often functioning in isolation. This paper aims to bridge this gap by investigating the performance of a theoretically inspired optimization-based bid shading method, AdaptivePacing, within eBay's sponsored search environment and proposing variants of the algorithm tailored to real-world environments. Our findings highlight the benefits of applying theoretical pacing approaches in practical contexts. Specifically, the optimization-based AdaptivePacing method offers the platform flexible control over campaign spending patterns, accounts for business constraints, and suggests tailored strategies for distinct advertisers. Furthermore, when evaluating AdaptivePacing alongside established empirical methods, we demonstrate its practical effectiveness and pinpoint areas for further refinement.
Unveiling Misogyny Memes: A Multimodal Analysis of Modality Effects on Identification
Authors: Shijing Chen, Usman Naseem, Imran Razzak, Flora D. Salim
Venue: WWW
Year: 2024

In today's digital era, memes have become a popular means of communication that often reflect societal attitudes as well as prejudices. Misogyny memes are a form of memes that explicitly discriminate against women in various aspects, such as shaming or stereotyping. This research aims to identify misogynous memes through deep learning multimodal analysis and determine which modality, text or image, plays a more significant role in fairness considerations. To achieve this, we utilized the dataset GOAT-benchmarks, which comprises over 6,000 diverse memes covering topics like implicit hate speech, sexism, and cyberbullying. Furthermore, we evaluated the fairness of these models by assessing their performance across different demographic groups. Our findings revealed that while both text and image modalities contribute to identifying misogynous memes, text plays a significant role in misogyny identification, while image contributes further in terms of fairness. This study emphasizes the importance of multimodal analysis in recognizing and mitigating biases in online content. Disclaimer: This paper contains content that may be disturbing to some readers.
OneSparse: A Unified System for Multi-index Vector Search
Authors: Yaoqi Chen, Ruicheng Zheng, Qi Chen, Shuotao Xu, Qianxi Zhang, Xue Wu, Weihao Han, Hua Yuan, Mingqin Li, Yujing Wang, Jason Li, Fan Yang, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang, Mao Yang
Venue: WWW
Year: 2024

Multi-index vector search has become the cornerstone for many applications, such as recommendation systems. Efficient search in such a multi-modal hybrid vector space is challenging since no single index design performs well for all kinds of vector data. Existing approaches to processing multi-index hybrid queries either suffer from algorithmic limitations or processing inefficiency. In this paper, we propose OneSparse, a unified multi-vector index query system that incorporates multiple posting-based vector indices, which enables highly efficient retrieval of multi-modal data-sets. OneSparse introduces a novel multi-index query engine design of inter-index intersection push-down. It also optimizes the vector posting format to expedite multi-index queries. Our experiments show OneSparse achieves more than 6x search performance improvement while maintaining comparable accuracy. OneSparse has already been integrated into Microsoft online web search and advertising systems with 5x+ latency gain for Bing web search and 2.0% Revenue Per Mille (RPM) gain for Bing sponsored search.