OpenRaiser/Intern-Atlas huggingface dataset:
Medusa: Unveil Memory Exhaustion DoS Vulnerabilities in Protocol Implementations
Authors: Zhengjie Du, Yuekang Li, Yaowen Zheng, Xiaohan Zhang, Cen Zhang, Yi Liu, Sheikh Mahbub Habib, Xinghua Li, Linzhang Wang, Yang Liu, Bing Mao
Venue: WWW
Year: 2024

Web services have brought great convenience to our daily lives. Meanwhile, they are vulnerable to Denial-of-Service (DoS) attacks. DoS attacks launched via vulnerabilities in the services can cause great harm. The vulnerabilities in protocol implementations are especially important because they are the keystones of web services. One vulnerable protocol implementation can affect all the web services built on top of it. Compared to the vulnerabilities that cause the target service to crash, resource exhaustion vulnerabilities are equally if not more important. This is because such vulnerabilities can deplete the system resources, leading to the unavailability of not only the vulnerable service but also other services running on the same machine. Despite the significance of this type of vulnerability, there has been limited research in this area. In this paper, we propose Medusa, a dynamic analysis framework to detect memory exhaustion vulnerabilities in protocol implementations, which are the most common type of resource exhaustion vulnerabilities. Medusa works in two phases: exploration phase and verification. In the exploration phase, a protocol property graph (PPG) is constructed to embed the states with relevant properties including memory consumption information. In the verification phase, the PPG is used to simulate DoS attacks to verify the vulnerabilities. We implemented Medusa and evaluated its performance on 21 implementations of five protocols. The results demonstrate that Medusa outperforms the state-of-the-art techniques by discovering overall 127× maximum memory consumption. Lastly, Medusa has discovered six 0-day vulnerabilities in six protocol implementations for three protocols. Particularly, one of the vulnerabilities was found in Eclipse Mosquitto, which can affect thousands of services and it has been assigned with a CVE ID.
SOIL: Score Conditioned Diffusion Model for Imbalanced Cloud Failure Prediction
Authors: Chiming Duan, Fangkai Yang, Pu Zhao, Lingling Zheng, Yash Dagli, Yudong Liu, Qingwei Lin, Dongmei Zhang
Venue: WWW
Year: 2024

Cloud failure prediction (e.g., disk failure prediction, memory failure prediction, node failure prediction, etc.) is a crucial task for ensuring the reliability and performance of cloud systems.However, the problem of class imbalance poses a huge challenge for accurate prediction as the number of healthy components (majority class) in a cloud system is much larger than the number of failed components (minority class). The consequences of this class imbalance include biased model performance and insufficient learning, as the model may lack adequate information to learn the characteristics associated with cloud failure effectively. Moreover, current methods for addressing the class imbalance problem, such as SMOTE and its variants, exhibit certain drawbacks, such as generating noisy samples and struggling to maintain sample diversity, which limit their effectiveness in addressing the challenges presented by the class imbalance in cloud failure prediction. In this paper, we propose a novel oversampling method for imbalanced classification, named SOIL (Score cOnditioned dIffusion modeL), which employs a score-conditioned diffusion model to generate high-quality synthetic samples for the minority class, more accurately representing real-world cloud failure patterns. By incorporating classification probabilities as conditional scores, SOIL offers supervision to the generation process, effectively limiting noise production while maintaining sample diversity. Through extensive experiments on various public and industrial datasets , upon adopting our method, the cloud failure prediction model's F1-score is improved by an average of 5.39% and consistently outperforms state-of-the-art competitors in addressing the class imbalance problem, which confirm the effectiveness and robustness of SOIL. In addition, SOIL has been successfully applied to a global large-scale cloud platform serving billions of customers, demonstrating its practicability.
Distributed Data Placement and Content Delivery in Web Caches with Non-Metric Access Costs
Authors: S. Rasoul Etesami
Venue: WWW
Year: 2024

Motivated by applications in web caches and content delivery in peer-to-peer networks, we consider the non-metric data placement problem and develop distributed algorithms for computing or approximating its optimal solutions. In this problem, the goal is to store copies of the data points among a set of cache-capacitated servers to minimize overall data storage and clients' access costs. We first show that the non-metric data placement problem is inapproximable up to a logarithmic factor. We then provide a game-theoretic decomposition of the objective function and show that a natural type of Glauber dynamics in which servers update their cache contents with probability proportional to the utility they receive from caching those data will converge to an optimal global solution for a sufficiently large noise parameter. In particular, we establish the polynomial mixing time of the Glauber dynamics for a certain range of noise parameters. Such a game-theoretic decomposition not only provides a good performance guarantee in terms of content delivery but also allows the system to operate in a fully distributed manner, hence reducing its computational load and improving its robustness to failures. Moreover, we provide another auction-based distributed algorithm, which allows us to approximate the optimal solution with a performance guarantee that depends on the ratio of the revenue vs. social welfare obtained from the underlying auction.
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai
Authors: Zhichao Feng, Junjie Xie, Kaiyuan Li, Yu Qin, Pengfei Wang, Qianzhong Li, Bin Yin, Xiang Li, Wei Lin, Shangguang Wang
Venue: WWW
Year: 2024

In the recommender system of Meituan Waimai, we are dealing with ever-lengthening user behavior sequences, which pose an increasing challenge to modeling user preference effectively. A number of existing sequential recommendation models struggle to capture long-term dependencies, or they exhibit high complexity, both of which make it difficult to satisfy the unique business requirements of Meituan Waimai's recommender system. To better model user interests, we consider selecting relevant sub-sequences from users' extensive historical behaviors based on their preferences. In this specific scenario, we've noticed that the contexts in which users interact have a significant impact on their preferences. For this purpose, we introduce a novel method called Context-based Fast Recommendation Strategy (referred to as CoFARS) to tackle the issue of long sequences. We first identify contexts that share similar user preferences with the target context and then locate the corresponding Points of Interest (PoIs) based on these identified contexts. This approach eliminates the necessity to select a sub-sequence for every candidate PoI, thereby avoiding high time complexity. Specifically, we implement a prototype-based approach to pinpoint contexts that mirror similar user preferences. To amplify accuracy and interpretability, we employ Jensen?Shannon(JS) divergence of PoI attributes such as categories and prices as a measure of similarity between contexts. Subsequently, we construct a temporal graph that encompasses both prototype and context nodes to integrate temporal information. We then identify appropriate prototypes considering both target contexts and short-term user preferences. Following this, we utilize contexts aligned with these prototypes to generate a sub-sequence, aimed at predicting CTR and CTCVR scores with target attention. Since its inception in 2023, this strategy has been adopted in Meituan Waimai's display recommender system, leading to a 4.6% surge in CTR and a 4.2% boost in GMV.
Me, the Web and Digital Accessibility
Authors: Reinaldo Ferraz
Venue: WWW
Year: 2024

This essay will briefly narrate the relationship between my professional growth and the evolution of web technology and the internet, especially in Brazil, where I live. I start by briefly discussing my background and how I came to be involved with technology, from my earliest computer experiences to getting online and continuing my schooling. At this time, several significant Web and Internet events occurred, influencing my career path choice. I also write about how my work has focused on digital accessibility and how my engagement has impacted the Web in my country. I close the article by examining my present work environment and how the Web has impacted my life, motivating me to use free and open technology.
WebGraph: The Next Generation (Is in Rust)
Authors: Tommaso Fontana, Sebastiano Vigna, Stefano Zacchiroli
Venue: WWW
Year: 2024

We report the results of a yearlong effort at the Laboratory for Web Algorithmics and Inria to port the WebGraph framework [4] from Java to Rust. For two decades WebGraph has been instrumental in the analysis and distribution of large graphs for the research community of TheWebConf, but the intrinsic limitations of the Java Virtual Machine had become a bottleneck for very large use cases, such as the Software Heritage Merkle graph [2] with its half a trillion arcs. As part of this clean-slate implementation of WebGraph in Rust, we developed a few ancillary projects bringing to the Rust ecosystem some missing features of independent interest, such as easy, consistent and zero-cost memory mapping of data structures. WebGraph in Rust offers impressive performance improvements over the previous implementation, enabling open-source graph analytics on very large datasets like Common Crawl, on top of a modern systems programming language.
Measuring Arbitrage Losses and Profitability of AMM Liquidity
Authors: Robin Fritsch, Andrea Canidio
Venue: WWW
Year: 2024

This paper presents the results of a comprehensive empirical study of losses to arbitrageurs (following the formalization of loss-versus-rebalancing or LVR by [Milionis et al., 2022]) incurred by liquidity on automated market makers (AMMs). Through a systematic comparison between historical returns from trading fees and losses to arbitrageurs, our findings indicate an insufficient compensation from fees for arbitrage losses across many of the largest AMM liquidity pools (on Uniswap). Remarkably, we identify a higher profitability among less capital-efficient Uniswap v2 pools compared to their Uniswap v3 counterparts. Moreover, we investigate one possible LVR mitigation by quantifying how arbitrage losses reduces with shorter block times. We observe notable variations in the manner of decline of arbitrage losses across different trading pairs. For instance, when comparing 100ms block times to Ethereum's current 12-second block times, the decrease in losses to arbitrageurs ranges between 20% to 70%, depending on the specific trading pair.
Comparative Analysis of Discussion Intensity and Semantic Diversity in Early vs. Late Engagers: A Study of Japanese Tweets about ChatGPT
Authors: Tomoki Fukuma, Koki Noda, Yuta Yamamoto, Takaya Hoshi, Yoshiharu Ichikawa, Kyosuke Kambe, Yu Masubuchi, Fujio Toriumi
Venue: WWW
Year: 2024

This study investigates engagement patterns related to OpenAI's ChatGPT on Japanese Twitter, focusing on two distinct user groups - early and late engagers, inspired by the Innovation Theory. Early engagers are defined as individuals who initiated conversations about ChatGPT during its early stages, whereas late engagers are those who began participating at a later date. To examine the nature of the conversations, we conduct a dual methodology, encompassing both quantitative and qualitative analyses. The quantitative analysis reveals that early engagers often engage with more forward-looking and speculative topics, emphasizing the technological advancements and potential transformative impact of ChatGPT. Conversely, the late engagers intereact more with contemporary topics, focusing on the optimization of existing AI capabilities and considering their inherent limitations. Through our qualitative analysis, we propose a method to measure the proportion of shared or unique viewpoints within topics across both groups. We found that early engagers generally concentrate on a more limited range of perspectives, whereas late engagers exhibit a wider range of viewpoints. Interestingly, a weak correlation was found between the volume of tweets and the diversity of discussed topics in both groups. These findings underscore the importance of identifying semantic diversity, rather than relying solely on the volume of tweets, for understanding differences in communication styles between groups within a given topic. Moreover, our versatile dual methodology holds potential for broader applications, such as studying online discourse patterns within different user groups, or in contexts beyond ChatGPT.
Exit Ripple Effects: Understanding the Disruption of Socialization Networks Following Employee Departures
Authors: David Gamba, Yulin Yu, Yuan Yuan, Grant Schoenebeck, Daniel M. Romero
Venue: WWW
Year: 2024

Amidst growing uncertainty and frequent restructurings, the impacts of employee exits are becoming one of the central concerns for organizations. Using rich communication data from a large holding company, we examine the effects of employee departures on socialization networks among the remaining coworkers. Specifically, we investigate how network metrics change among people who historically interacted with departing employees. We find evidence of "breakdown" in communication among the remaining coworkers, who tend to become less connected with fewer interactions after their coworkers' departure. This effect appears to be moderated by both external factors, such as periods of high organizational stress, and internal factors, such as the characteristics of the departing employee. At the external level, periods of high stress correspond to greater communication breakdown; at the internal level, however, we find patterns suggesting individuals may end up better positioned in their networks after a network neighbor's departure. Overall, our study provides critical insights into managing workforce changes and preserving communication dynamics in the face of employee exits.
PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction
Authors: Yuanbo Gao, Peng Lin, Dongyue Wang, Feng Mei, Xiwei Zhao, Sulong Xu, Jinghe Hu
Venue: WWW
Year: 2024

Click-through rate (CTR) prediction is a core task in recommender systems. Existing methods (IDRec for short) rely on unique identities to represent distinct users and items that have prevailed for decades. On one hand, IDRec often faces significant performance degradation on cold-start problem; on the other hand, IDRec cannot use longer training data due to constraints imposed by iteration efficiency. Most prior studies alleviate the above problems by introducing pre-trained knowledge(e.g. pre-trained user model or multi-modal embeddings). However, the explosive growth of online latency can be attributed to the huge parameters in the pre-trained model. Therefore, most of them cannot employ the unified model of end-to-end training with IDRec in industrial recommender systems, thus limiting the potential of the pre-trained model. To this end, we propose a pre-trained plug-in CTR model, namely PPM. PPM employs multi-modal features as input and utilizes large-scale data for pre-training. Then, PPM is plugged in IDRec model to enhance unified model's performance and iteration efficiency. Upon incorporating IDRec model, certain intermediate results within the network are cached, with only a subset of the parameters participating in training and serving. Hence, our approach can successfully deploy an end-to-end model without causing huge latency increases. Comprehensive offline experiments and online A/B testing at JD E-commerce demonstrate the efficiency and effectiveness of PPM.
Lightweight GCN Encoder and Sequential Decoder for Multi-Candidate Carpooling Route Planning in Road Network
Authors: Yucen Gao, Li Ma, Zhemeng Yu, Songjian Zhang, Jun Fang, Xiaofeng Gao, Guihai Chen
Venue: WWW
Year: 2024

Carpooling Route Planning (CRP) has become an important issue with the growth of low-carbon traffic systems. We investigate a meaningful and challenging scenario for CRP in industry, where each passenger may have several potential positions to get on and off the car. Traditional graph search algorithms or indexing methods usually consume a lot of time and space or perform poorly. In this paper, we propose an end-to-end encoder-decoder model to plan a route for each many-to-one carpooling order with various data-driven mechanisms such as graph partitioning and feature crossover. The encoder is a filter-integrated Graph Convolution Network with external information fusion combining a supervised pre-training classification task, while the latter mimics a pointer network with a rule-based mask mechanism and a domain feature crossover module. We validate the effectiveness and efficiency of our model based on both synthetic and real-world datasets.
Simulating Human Society with Large Language Model Agents: City, Social Media, and Economic System
Authors: Chen Gao, Fengli Xu, Xu Chen, Xiang Wang, Xiangnan He, Yong Li
Venue: WWW
Year: 2024

This tutorial will delve into the fascinating realm of simulating human society using Large Language Model (LLM)-driven agents, exploring their applications in cities, social media, and economic systems. Through this tutorial, participants will gain insights into the integration of LLMs into human society simulation, providing a comprehensive understanding of how these models can accurately represent human interactions, decision-making processes, and societal dynamics from cities to social media and to economic systems. The tutorial will introduce the essential background, discuss the motivation and challenges, and elaborate on the recent advances.
Privacy in Web Advertising: Analytics and Modeling
Authors: Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Venue: WWW
Year: 2024

Privacy in general, and differential privacy (DP) in particular, have become important topics in data mining and machine learning. Digital advertising is a critical component of the internet and is powered by large-scale data analytics and machine learning models; privacy concerns around these are on the rise. Despite the central importance of private ad analytics and training privacy-preserving ad prediction models, there has been relatively little exposure of this subject to the broader Web community. In the past three years, the interest in privacy and the interest in online advertising have been steadily growing. The aim of this tutorial is to provide researchers with an introduction to the problems that arise in private analytics and modeling in advertising, survey recent results, and describe the main research challenges in the space.
Generator-Guided Crowd Reaction Assessment
Authors: Sohom Ghosh, Chung-Chi Chen, Sudip Kumar Naskar
Venue: WWW
Year: 2024

In the realm of social media, understanding and predicting post reach is a significant challenge. This paper presents a Crowd Reaction AssessMent (CReAM) task designed to estimate if a given social media post will receive more reaction than another, a particularly essential task for digital marketers and content writers. We introduce the Crowd Reaction Estimation Dataset (CRED), consisting of pairs of tweets from The White House with comparative measures of retweet count. The proposed Generator-Guided Estimation Approach (GGEA) leverages generative Large Language Models (LLMs), such as ChatGPT, FLAN-UL2, and Claude, to guide classification models for making better predictions. Our results reveal that a fine-tuned FLANG-RoBERTa model, utilizing a cross-encoder architecture with tweet content and responses generated by Claude, performs optimally. We further use a T5-based paraphraser to generate paraphrases of a given post and demonstrate GGEA's ability to predict which post will elicit the most reactions. We believe this novel application of LLMs provides a significant advancement in predicting social media post reach.
Dependency Aware Incident Linking in Large Cloud Systems
Authors: Supriyo Ghosh, Karish Grover, Jimmy Wong, Chetan Bansal, Rakesh Namineni, Mohit Verma, Saravan Rajmohan
Venue: WWW
Year: 2024

Despite significant reliability efforts, large-scale cloud services inevitably experience production incidents that can significantly impact service availability and customer satisfaction. Worse, in many cases one incident can lead to multiple downstream failures due to cascading effects that create several related incidents across different dependent services. Often time On-call Engineers (OCEs) examine these incidents in silos that lead to significant amounts of manual effort and increase the overall time-to-mitigate incidents. Therefore, developing efficient incident linking models is of paramount importance for grouping related incidents into clusters so as to quickly resolve major outages and reduce on-call fatigue. Existing incident linking methods mostly leverage textual and contextual information of incidents (e.g., title, description, severity, impacted components), thus failing to leverage the inter-dependencies between services. In this paper, we propose the dependency-aware incident linking (DiLink) framework which leverages both textual and service dependency graph information to improve the accuracy and coverage of incident links not only emerge from same service, but also from different services and workloads. Furthermore, we propose a novel method to align the embeddings of multi-modal (i.e., textual and graphical) data using Orthogonal Procrustes. Extensive experimental results on real-world incidents from 5 workloads of Microsoft demonstrate that our alignment method has an F1-score of 0.96 (14% gain over current state-of-the-art methods). We are also in the process of deploying this solution across 610 services from these 5 workloads for continuously supporting OCEs improving incident management and reducing manual effort.
CardiO: Predicting Cardinality from Online Sources
Authors: Shrestha Ghosh, Simon Razniewski, Damien Graux, Gerhard Weikum
Venue: WWW
Year: 2024

Count questions are an important type of information need, though often present in noisy, contradictory, or semantically not fully aligned form on the Web. In this work, we propose CardiO, a lightweight and modular framework for searching entity counts on the Web. CardiO extracts all counts from a set of relevant Web snippets, and infers the most central count based on semantic and numeric distances from other candidates. In the absence of supporting evidence, the system relies on peer sets of similar size, to provide an estimate. Experiments show that CardiO can produce accurate and traceable counts better than small LLM-only methods. Although larger models have higher precision, when used to enhance CardiO components, they do not contribute to the final precision or recall.