OpenRaiser/Intern-Atlas huggingface dataset:
DDPC: Automated Data-Driven Power-Performance Controller Design on-the-fly for Latency-sensitive Web Services
Authors: Mehmet Savasci, Ahmed Ali-Eldin, Johan Eker, Anders Robertsson, Prashant J. Shenoy
Venue: WWW
Year: 2023

Traditional power reduction techniques such as DVFS or RAPL are challenging to use with web services because they significantly affect the services’ latency and throughput. Previous work suggested the use of controllers based on control theory or machine learning to reduce performance degradation under constrained power. However, generating these controllers is challenging as every web service applications running in a data center requires a power-performance model and a fine-tuned controller. In this paper, we present DDPC, a system for autonomic data-driven controller generation for power-latency management. DDPC automates the process of designing and deploying controllers for dynamic power allocation to manage the power-performance trade-offs for latency-sensitive web applications such as a social network. For each application, DDPC uses system identification techniques to learn an adaptive power-performance model that captures the application’s power-latency trade-offs which is then used to generate and deploy a Proportional-Integral (PI) power controller with gain-scheduling to dynamically manage the power allocation to the server running application using RAPL. We evaluate DDPC with two realistic latency-sensitive web applications under varying load scenarios. Our results show that DDPC is capable of autonomically generating and deploying controllers within a few minutes reducing the active power allocation of a web-server by more than 50% compared to state-of-the-art techniques while maintaining the latency well below the target of the application.
Improving the Exploration/Exploitation Trade-Off in Web Content Discovery
Authors: Peter Schulam, Ion Muslea
Venue: WWW
Year: 2023

New web content is published constantly, and although protocols such as RSS can notify subscribers of new pages, they are not always implemented or actively maintained. A more reliable way to discover new content is to periodically re-crawl the target sites. Designing such “content discovery crawlers” has important applications, for example, in web search, digital assistants, business, humanitarian aid, and law enforcement. Existing approaches assume that each site of interest has a relatively small set of unknown “source pages” that, when refreshed, frequently provide hyperlinks to the majority of new content. The state of the art (SOTA) uses ideas from the multi-armed bandit literature to explore candidate sources while simultaneously exploiting known good sources. We observe, however, that the SOTA uses a sub-optimal algorithm for balancing exploration and exploitation. We trace this back to a mismatch between the space of actions that the SOTA algorithm models and the space of actions that the crawler must actually choose from. Our proposed approach, the Thompson crawler (named after the Thompson sampler that drives its refresh decisions), addresses this shortcoming by more faithfully modeling the action space. On a dataset of 4,070 source pages drawn from 53 news domains over a period of 7 weeks, we show that, on average, the Thompson crawler discovers 20% more new pages, finds pages 6 hours earlier, and uses 14 fewer refreshes per 100 pages discovered than the SOTA.
SEKA: Seeking Knowledge Graph Anomalies
Authors: Asara Senaratne
Venue: WWW
Year: 2023

Knowledge Graphs (KGs) form the backbone of many knowledge dependent applications such as search engines and digital personal assistants. KGs are generally constructed either manually or automatically using a variety of extraction techniques applied over multiple data sources. Due to the diverse quality of these data sources, there are likely anomalies introduced into any KG. Hence, it is unrealistic to expect a perfect archive of knowledge. Given how large KGs can be, manual validation is impractical, necessitating an automated approach for anomaly detection in KGs. To improve KG quality, and to identify interesting and abnormal triples (edges) and entities (nodes) that are worth investigating, we introduce SEKA, a novel unsupervised approach to detect anomalous triples and entities in a KG using both the structural characteristics and the content of edges and nodes of the graph. While an anomaly can be an interesting or unusual discovery, such as a fraudulent transaction requiring human intervention, anomaly detection can also identify potential errors. We propose a novel approach named Corroborative Path Algorithm to generate a matrix of semantic features, which we then use to train a one-class Support Vector Machine to identify abnormal triples and entities with no dependency on external sources. We evaluate our approach on four real-world KGs demonstrating the ability of SEKA to detect anomalies, and to outperform comparative baselines.
Dual-grained Text-Image Olfactory Matching Model with Mutual Promotion Stages
Authors: Yi Shao, Jiande Sun, Ye Jiang, Jing Li
Venue: WWW
Year: 2023

Olfactory experience has great advantages in awakening human memories and emotions, which may even surpass vision in some cases. Studies have proved that olfactory scene descriptions in images and text content can also arouse human olfactory imagination, but there are still few studies on solving related problems from the perspective of computer vision and NLP. This paper proposes a multimodal model that can detect similar olfactory experience in paired text-image samples. The model builds two stages, coarse-grained and fine-grained. The model adopts the feature fusion method based on pre-trained CLIP for coarse-grained matching training to obtain a preliminary feature extractor to promote fine-grained matching training, and then uses the similarity calculation method based on stacked cross attention for fine-grained matching training to obtain the final feature extractor which in turn promotes coarse-grained matching training. Finally, we manually build an approximate olfactory nouns list during fine-grained matching training, which not only yields significantly better performance when fed back to the fine-grained matching process, but this noun list can be used for future research. Experiments on the MUSTI task dataset of MediaEval2022 prove that the coarse-grained and fine-grained matching stages in proposed model both perform well, and both F1 measures exceed the existing baseline models.
Measuring Potential Performance Gains of Python Web Applications with pyUpgradeSim
Authors: Anthony Shaw, Amin Beheshti
Venue: WWW
Year: 2023

Python is a popular programming language for web development. However, optimizing the performance of Python web applications is a challenging task for developers. This paper presents a new approach to measuring the potential performance gains of upgraded Python web applications. Our approach is based on the provision of an interactive service that assists developers in optimizing their Python code through changes to the underlying system. The service uses profiling and visualization techniques to identify performance bottlenecks. We demonstrate and evaluate the effectiveness of our approach through a series of experiments on real-world Python web applications, measuring performance differences in between versions and the benefits of migrating at a reduced cost. The results show promising improvement in performance without any required code changes.
Identifying Stable States of Large Signed Graphs
Authors: Muhieddine Shebaro, Jelena Tesic
Venue: WWW
Year: 2023

Signed network graphs provide a way to model complex relationships and interdependencies between entities: negative edges allow for a deeper study of social dynamics. One approach to achieving balance in a network is to model the sources of conflict through structural balance. Current methods focus on computing the frustration index or finding the largest balanced clique, but these do not account for multiple ways to reach a consensus or scale well for large, sparse networks. In this paper, we propose an expansion of the frustration cloud computation and compare various tree-sampling algorithms that can discover a high number of diverse balanced states. Then, we compute and compare the frequencies of balanced states produced by each. Finally, we investigate these techniques’ impact on the consensus feature space.
"Why is this misleading?": Detecting News Headline Hallucinations with Explanations
Authors: Jiaming Shen, Jialu Liu, Daniel Finnie, Negar Rahmati, Mike Bendersky, Marc Najork
Venue: WWW
Year: 2023

Automatic headline generation enables users to comprehend ongoing news events promptly and has recently become an important task in web mining and natural language processing. With the growing need for news headline generation, we argue that the hallucination issue, namely the generated headlines being not supported by the original news stories, is a critical challenge for the deployment of this feature in web-scale systems Meanwhile, due to the infrequency of hallucination cases and the requirement of careful reading for raters to reach the correct consensus, it is difficult to acquire a large dataset for training a model to detect such hallucinations through human curation. In this work, we present a new framework named ExHalder to address this challenge for headline hallucination detection. ExHalder adapts the knowledge from public natural language inference datasets into the news domain and learns to generate natural language sentences to explain the hallucination detection results. To evaluate the model performance, we carefully collect a dataset with more than six thousand labeled ⟨ article, headline⟩ pairs. Extensive experiments on this dataset and another six public ones demonstrate that ExHalder can identify hallucinated headlines accurately and justifies its predictions with human-readable natural language explanations.
The 3rd International Workshop on Deep Learning for the Web of Things
Authors: Cheng Shi, Chin-Chen Chang, Eyhab Al-Masri, Abel C. H. Chen, Haishuai Wang, Qichun Zhang, Hsiao-Ting Tseng, K. Shankar, Taehong Kim
Venue: WWW
Year: 2023

Deep learning and the Web of Things (WoT) have become powerful tools for web engineering, leading to increased investigation and publication of research related to deep learning in web engineering. Therefore, this workshop is titled "The 3rd International Workshop on Deep Learning for the Web of Things" for the Web Conference 2023 (WWW’23). This workshop features five research articles: (1) "Multiple-Agent Deep Reinforcement Learning for Avatar Migration in Vehicular Metaverses", (2) "Web 3.0: Future of the Internet", (3) "Weighted Statistically Significant Pattern Mining", (4) "DSNet: Efficient Lightweight Model for Video Salient Object Detection for IoT and WoT Applications", and (5) "The Human-Centric Metaverse: A Survey".
Quantize Sequential Recommenders Without Private Data
Authors: Lingfeng Shi, Yuang Liu, Jun Wang, Wei Zhang
Venue: WWW
Year: 2023

Deep neural networks have achieved great success in sequential recommendation systems. While maintaining high competence in user modeling and next-item recommendation, these models have long been plagued by the numerous parameters and computation, which inhibit them to be deployed on resource-constrained mobile devices. Model quantization, as one of the main paradigms for compression techniques, converts float parameters to low-bit values to reduce parameter redundancy and accelerate inference. To avoid drastic performance degradation, it usually requests a fine-tuning phase with an original dataset. However, the training set of user-item interactions is not always available due to transmission limits or privacy concerns. In this paper, we propose a novel framework to quantize sequential recommenders without access to any real private data. A generator is employed in the framework to synthesize fake sequence samples to feed the quantized sequential recommendation model and minimize the gap with a full-precision sequential recommendation model. The generator and the quantized model are optimized with a min-max game — alternating discrepancy estimation and knowledge transfer. Moreover, we devise a two-level discrepancy modeling strategy to transfer information between the quantized model and the full-precision model. The extensive experiments of various recommendation networks on three public datasets demonstrate the effectiveness of the proposed framework.
Deep Neural Network with LinUCB: A Contextual Bandit Approach for Personalized Recommendation
Authors: Qicai Shi, Feng Xiao, Douglas Pickard, Inga Chen, Liang Chen
Venue: WWW
Year: 2023

Recommender systems are widely used in many Web applications to recommend items which are relevant to a user’s preferences. However, focusing on exploiting user preferences while ignoring exploration will lead to biased feedback and hurt the user’s experience in the long term. The Mutli-Armed Bandit (MAB) is introduced to balance the tradeoff between exploitation and exploration. By utilizing context information in the reward function, contextual bandit algorithms lead to better performance compared to context-free bandit algorithms. However, existing contextual bandit algorithms either assume a linear relation between the expected reward and context features, whose representation power gets limited, or use a deep neural network in the reward function which is impractical in implementation. In this paper, we propose a new contextual bandit algorithm, DeepLinUCB, which leverages the representation power of deep neural network to transform the raw context features in the reward function. Specifically, this deep neural network is dedicated to the recommender system, which is efficient and practical in real-world applications. Furthermore, we conduct extensive experiments in our online recommender system using requests from real-world scenarios and show that DeepLinUCB is efficient and outperforms other bandit algorithms.
Learning to Simulate Crowd Trajectories with Graph Networks
Authors: Hongzhi Shi, Quanming Yao, Yong Li
Venue: WWW
Year: 2023

Crowd stampede disasters often occur, such as recent ones in Indonesia and South Korea, and crowd simulation is particularly important to prevent and avoid such disasters. Most traditional models for crowd simulation, such as the social force model, are hand-designed formulas, which use Newtonian forces to model the interactions between pedestrians. However, such formula-based methods may not be flexible enough to capture the complex interaction patterns in diverse crowd scenarios. Recently, due to the development of the Internet, a large amount of pedestrian movement data has been collected, allowing us to study crowd simulation in a data-driven way. Inspired by the recent success of graph network-based simulation (GNS), we propose a novel method under the framework of GNS, which simulates the crowd in a data-driven way. Specifically, we propose to model the interactions among people and the environment using a heterogeneous graph. Then, we design a heterogeneous gated message-passing network to learn the interaction pattern that depends on the visual field. Finally, the randomness is introduced by modeling the context’s different influences on pedestrians with a probabilistic emission function. Extensive experiments on synthetic data, controlled-environment data and real-world data are performed. Extensive results show that our model can generally capture the three main factors which contribute to crowd trajectories while adapting to the data characteristics beyond the strong assumption of formulas-based methods. As a result, the proposed method outperforms existing methods by a large margin.
RICC: Robust Collective Classification of Sybil Accounts
Authors: Dongwon Shin, Suyoung Lee, Sooel Son
Venue: WWW
Year: 2023

A Sybil attack is a critical threat that undermines the trust and integrity of web services by creating and exploiting a large number of fake (i.e., Sybil) accounts. To mitigate this threat, previous studies have proposed leveraging collective classification to detect Sybil accounts. Recently, researchers have demonstrated that state-of-the-art adversarial attacks are able to bypass existing collective classification methods, posing a new security threat. To this end, we propose RICC, the first robust collective classification framework, designed to identify adversarial Sybil accounts created by adversarial attacks. RICC leverages the novel observation that these adversarial attacks are highly tailored to a target collective classification model to optimize the attack budget. Owing to this adversarial strategy, the classification results for adversarial Sybil accounts often significantly change when deploying a new training set different from the original training set used for assigning prior reputation scores to user accounts. Leveraging this observation, RICC achieves robustness in collective classification by stabilizing classification results across different training sets randomly sampled in each round. RICC achieves false negative rates of 0.01, 0.11, 0.00, and 0.01 in detecting adversarial Sybil accounts for the Enron, Facebook, Twitter_S, and Twitter_L datasets, respectively. It also attains respective AUCs of 0.99, 1.00, 0.89, and 0.74 for these datasets, achieving high performance on the original task of detecting Sybil accounts. RICC significantly outperforms all existing Sybil detection methods, demonstrating superior robustness and efficacy in the collective classification of Sybil accounts.
Entity and Event Topic Extraction from Podcast Episode Title and Description Using Entity Linking
Authors: Christian Siagian, Amina Shabbeer
Venue: WWW
Year: 2023

To improve Amazon Music podcast services and customer engagements, we introduce Entity-Linked Topic Extraction (ELTE) to identify well-known entity and event topics from podcast episodes. An entity can be a person, organization, work-of-art, etc., while an event, such as the Opioid epidemic, occurs at specific point(s) in time. ELTE first extracts key-phrases from episode title and description metadata. It then uses entity linking to canonicalize them against Wikipedia knowledge base (KB), ensuring that the topics exist in the real world. ELTE also models NIL-predictions for entity or event topics that are not in the KB, as well as topics that are not of entity or event type. To test the model, we construct a podcast topic database of 1166 episodes from various categories. Each episode comes with a Wiki-link annotated main topic or NIL-prediction. ELTE produces the best overall Exact Match EM score of .84, with by-far the best EM of .89 among the entity or event type episodes, as well as NIL-predictions for episodes without entity or event main topic (EM score of .86).
Child Sexual Abuse Awareness and Support Seeking on Reddit: A thematic Analysis
Authors: Siva Sahitya Simhadri, Tatiana R. Ringenberg
Venue: WWW
Year: 2023

Child sexual abuse (CSA) is a pervasive issue in both online and physical contexts. Social media has grown in popularity as a platform for offering awareness, support, and community for those seeking help or advice regarding CSA. One popular social media platform in which such communities has formed is Reddit. In this study, we use both LDA and a reflexive thematic analysis to understand the types of engagements users have with subreddits aimed at CSA awareness. Through the reflexive thematic analysis, we identified six themes including strong negative emotions and phrasing, seeking help, personal experiences and their impact, measurement strategies to prevent abuse, provisioning of support, and the problematic nature of the Omegle platform. This research has implications for those creating awareness materials around CSA safety as well as for child advocacy groups.
Multi-Objective Ranking to Boost Navigational Suggestions in eCommerce AutoComplete
Authors: Sonali Singh, Sachin Farfade, Prakash Mandayam Comar
Venue: WWW
Year: 2023

Query AutoComplete (QAC) helps customers complete their search queries quickly by suggesting completed queries. QAC on eCommerce sites usually employ Learning to Rank (LTR) approaches based on customer behaviour signals such as clicks and conversion rates to optimize business metrics. However, they do not exclusively optimize for the quality of suggested queries which results in lack of navigational suggestions like product categories and attributes, e.g., "sports shoes" and "white shoes" for query "shoes". We propose to improve the quality of query suggestions by introducing navigational suggestions without impacting the business metrics. For this purpose, we augment the customer behaviour (CB) based objective with Query-Quality (QQ) objective and assemble them with trainable mixture weights to define multi-objective optimization function. We propose to optimize this multi-objective function by implementing ALMO algorithm to obtain a model robust against any mixture weight. We show that this formulation improves query relevance on an eCommerce QAC dataset by at least 13% over the baseline Deep Pairwise LTR (DeepPLTR) with minimal impact on MRR and results in a lift of 0.26% in GMV in an online A/B test. We also evaluated our approach on public search logs datasets and got improvement in query relevance by using query coherence as QQ objective.
DSNet: Efficient Lightweight Model for Video Salient Object Detection for IoT and WoT Applications
Authors: Hemraj Singh, Mridula Verma, Ramalingaswamy Cheruku
Venue: WWW
Year: 2023

The most challenging aspects of deploying deep models in IoT and embedded systems are extensive computational complexity and large training and inference time. Although various lightweight versions of state-of-the-art models are also being designed, maintaining the performance of such models is difficult. To overcome these problems, an efficient, lightweight, Deformable Separable Network (DSNet) is proposed for video salient object detection tasks, mainly for mobile and embedded vision applications. DSNet is equipped with a Deformable Convolution Network (DeCNet), Separable Convolution Network (SCNet), and Depth-wise Attention Response Propagation (DARP) module, which makes it maintain the trade-off between accuracy and latency. The proposed model generates saliency maps considering both the background and foreground simultaneously, making it perform better in unconstrained scenarios (such as partial occlusion, deformable background/objects, and illumination effect). The extensive experiments conducted on six benchmark datasets demonstrate that the proposed model outperforms state-of-art approaches in terms of computational complexity, number of parameters, and latency measures.
hp-frac: An index to determine Awarded Researchers
Authors: Aashay Singhal, Kamalakar Karlapalem
Venue: WWW
Year: 2023

In order to advance academic research, it is important to assess and evaluate the academic influence of researchers and the findings they produce. Citation metrics are universally used methods to evaluate researchers. Amongst the several variations of citation metrics, the h-index proposed by Hirsch has become the leading measure. Recent work shows that h-index is not an effective measure to determine scientific impact - due to changing authorship patterns. This can be mitigated by using h-index of a paper to compute h-index of an author. We show that using fractional allocation of h-index gives better results. In this work, we reapply two indices based on the h-index of a single paper. The indices are referred to as: hp-index and hp-frac-index. We run large-scale experiments in three different fields with about a million publications and 3,000 authors. Our experiments show that hp-frac-index provides a unique ranking when compared to h-index. It also performs better than h-index in providing higher ranks to the awarded researcher.