OpenRaiser/Intern-Atlas huggingface dataset:
EasySpider: A No-Code Visual System for Crawling the Web
Authors: Naibo Wang, Wenjie Feng, Jianwei Yin, See-Kiong Ng
Venue: WWW
Year: 2023

The web is a treasure trove for data that is increasingly used by computer scientists for building large machine learning models as well as non-computer scientists for social studies or marketing analyses. As such, web-crawling is an essential tool for both computational and non-computational scientists to conduct research. However, most of the existing web crawler frameworks and software products either require professional coding skills without an easy-to-use graphic user interface or are expensive and limited in features. They are thus not friendly to newbies and inconvenient for complicated web-crawling tasks. In this paper, we present an easy-to-use visual web crawler system, EasySpider, for designing and executing web crawling tasks without coding. The workflow of a new web crawling task can be visually programmed by following EasySpider’s visual wizard on the target webpages using an intuitive point-and-click interface. The generated crawler task can then be easily invoked locally or as a web service. Our EasySpider is cross-platform and flexible to adapt to different web-resources. It also supports advanced configuration for complicated tasks and extension. The whole system is open-sourced and transparent for free-access at GitHub 1, which avoids possible privacy leakage.
Preserving Missing Data Distribution in Synthetic Data
Authors: Xinyue Wang, Hafiz Salman Asif, Jaideep Vaidya
Venue: WWW
Year: 2023

Data from Web artifacts and from the Web is often sensitive and cannot be directly shared for data analysis. Therefore, synthetic data generated from the real data is increasingly used as a privacy-preserving substitute. In many cases, real data from the web has missing values where the missingness itself possesses important informational content, which domain experts leverage to improve their analysis. However, this information content is lost if either imputation or deletion is used before synthetic data generation. In this paper, we propose several methods to generate synthetic data that preserve both the observable and the missing data distributions. An extensive empirical evaluation over a range of carefully fabricated and real world datasets demonstrates the effectiveness of our approach.
Continual Few-shot Learning with Transformer Adaptation and Knowledge Regularization
Authors: Xin Wang, Yue Liu, Jiapei Fan, Weigao Wen, Hui Xue, Wenwu Zhu
Venue: WWW
Year: 2023

Continual few-shot learning, as a paradigm that simultaneously solves continual learning and few-shot learning, has become a challenging problem in machine learning. An eligible continual few-shot learning model is expected to distinguish all seen classes upon new categories arriving, where each category only includes very few labeled data. However, existing continual few-shot learning methods only consider the visual modality, where the distributions of new categories often indistinguishably overlap with old categories, thus resulting in the severe catastrophic forgetting problem. To tackle this problem, in this paper we study continual few-shot learning with the assistance of semantic knowledge by simultaneously taking both visual modality and semantic concepts of categories into account. We propose a Continual few-shot learning algorithm with Semantic knowledge Regularization (CoSR) for adapting to the distribution changes of visual prototypes through a Transformer-based prototype adaptation mechanism. Specifically, the original visual prototypes from the backbone are fed into the well-designed Transformer with corresponding semantic concepts, where the semantic concepts are extracted from all categories. The semantic-level regularization forces the categories with similar semantics to be closely distributed, while the opposite ones are constrained to be far away from each other. The semantic regularization improves the model’s ability to distinguish between new and old categories, thus significantly mitigating the catastrophic forgetting problem in continual few-shot learning. Extensive experiments on CIFAR100, miniImageNet, CUB200 and an industrial dataset with long-tail distribution demonstrate the advantages of our CoSR model compared with state-of-the-art methods.
MPKGAC: Multimodal Product Attribute Completion in E-commerce
Authors: Kai Wang, Jianzhi Shao, Tao Zhang, Qijin Chen, Chengfu Huo
Venue: WWW
Year: 2023

Product attributes can display the selling points of products, helping users find their desired products in search results. However, product attributes are typically incomplete. In e-commerce, products have multimodal features, including original attributes, images, and texts. How to make full use of the multimodal data to complete the missing attributes is the key challenge. To this end, we propose MPKGAC, a powerful three-stream framework that handles multimodal product data for attribute completion. We build a multimodal product knowledge graph (KG) from the multimodal features, and then convert the attribute completion problem into a multimodal KG completion task. MPKGAC encodes each modality separately, fuses them adaptively, and integrates multimodal decoders for prediction. Experiments show that MPKGAC outperforms the best baseline by 6.2% in Hit@1. MPKGAC is employed to enrich selling points of the women’s clothing industry at Alibaba.com.cn and improves the click-through rate (CTR) by a relative 2.14%.
HyConvE: A Novel Embedding Model for Knowledge Hypergraph Link Prediction with Convolutional Neural Networks
Authors: Chenxu Wang, Xin Wang, Zhao Li, Zirui Chen, Jianxin Li
Venue: WWW
Year: 2023

Knowledge hypergraph embedding, which projects entities and n-ary relations into a low-dimensional continuous vector space to predict missing links, remains a challenging area to be explored despite the ubiquity of n-ary relational facts in the real world. Currently, knowledge hypergraph link prediction methods are essentially simple extensions of those used in knowledge graphs, where n-ary relational facts are decomposed into different subelements. Convolutional neural networks have been shown to have remarkable information extraction capabilities in previous work on knowledge graph link prediction. In this paper, we propose a novel embedding-based knowledge hypergraph link prediction model named HyConvE, which exploits the powerful learning ability of convolutional neural networks for effective link prediction. Specifically, we employ 3D convolution to capture the deep interactions of entities and relations to efficiently extract explicit and implicit knowledge in each n-ary relational fact without compromising its translation property. In addition, appropriate relation and position-aware filters are utilized sequentially to perform two-dimensional convolution operations to capture the intrinsic patterns and position information in each n-ary relation, respectively. Extensive experimental results on real datasets of knowledge hypergraphs and knowledge graphs demonstrate the superior performance of HyConvE compared with state-of-the-art baselines.
Visual Item Selection With Voice Assistants: A systems perspective
Authors: Prashan Wanigasekara, Rafid Al-Humaimidi, Turan Gojayev, Niloofar Gheissari, Achal Dave, Stephen Rawls, Fan Yang, Kechen Qin, Nalin Gupta, Spurthi Sandiri, Chevanthie Dissanayake, Zeynab Raeesy, Emre Barut, Chengwei Su
Venue: WWW
Year: 2023

Interacting with voice assistants, such as Amazon Alexa to aid in day-to-day tasks has become a ubiquitous phenomenon in modern-day households. These voice assistants often have screens to provide visual content (e.g., images, videos) to their users. There is an increasing trend of users shopping or searching for products using these devices, yet, these voice assistants do not support commands or queries that contain visual references to the content shown on screen (e.g., “blue one”, “red dress”). We introduce a novel multi-modal visual shopping experience where the voice assistant is aware of the visual content shown on the screen and assists the user in item selection using natural language multi-modal interactions. We detail a practical, lightweight end-to-end system architecture spanning from model fine-tuning, deployment, to skill invocation on an Amazon Echo family device with a screen. We also define a niche “Visual Item Selection” task and evaluate whether we can effectively leverage publicly available multi-modal models, and embeddings produced from these models for the task. We show that open source contrastive embeddings like CLIP [30] and ALBEF [24] have zero-shot accuracy above for the “Visual Item Selection” task on an internally collected visual shopping dataset. By further fine-tuning the embeddings, we obtain further gains of 8.6% to 24.0% in relative accuracy improvement over a baseline. The technology that enables our visual shopping assistant is available as an Alexa Skill in the Alexa Skills store.
Automatic Feature Selection By One-Shot Neural Architecture Search In Recommendation Systems
Authors: He Wei, Yuekui Yang, Haiyang Wu, Yangyang Tang, Meixi Liu, Jianfeng Li
Venue: WWW
Year: 2023

Feature selection is crucial in large-scale recommendation system, which can not only reduce the computational cost, but also improve the recommendation efficiency. Most existing works rank the features and then select the top-k ones as the final feature subset. However, they assess feature importance individually and ignore the interrelationship between features. Consequently, multiple features with high relevance may be selected simultaneously, resulting in sub-optimal result. In this work, we solve this problem by proposing an AutoML-based feature selection framework that can automatically search the optimal feature subset. Specifically, we first embed the search space into a weight-sharing Supernet. Then, a two-stage neural architecture search method is employed to evaluate the feature quality. In the first stage, a well-designed sampling method considering feature convergence fairness is applied to train the Supernet. In the second stage, a reinforcement learning method is used to search for the optimal feature subset efficiently. The Experimental results on two real datasets demonstrate the superior performance of new framework over other solutions. Our proposed method obtain significant improvement with a 20% reduction in the amount of features on the Criteo. More validation experiments demonstrate the ability and robustness of the framework.
Combining Worker Factors for Heterogeneous Crowd Task Assignment
Authors: Senuri Wijenayake, Danula Hettiachchi, Jorge Gonçalves
Venue: WWW
Year: 2023

Optimising the assignment of tasks to workers is an effective approach to ensure high quality in crowdsourced data - particularly in heterogeneous micro tasks. However, previous attempts at heterogeneous micro task assignment based on worker characteristics are limited to using cognitive skills, despite literature emphasising that worker performance varies based on other parameters. This study is an initial step towards understanding whether and how multiple parameters such as cognitive skills, mood, personality, alertness, comprehension skill, and social and physical context of workers can be leveraged in tandem to improve worker performance estimations in heterogeneous micro tasks. Our predictive models indicate that these parameters have varying effects on worker performance in the five task types considered – sentiment analysis, classification, transcription, named entity recognition and bounding box. Moreover, we note 0.003 - 0.018 reduction in mean absolute error of predicted worker accuracy across all tasks, when task assignment is based on models that consider all parameters vs. models that only consider workers’ cognitive skills. Our findings pave the way for the use of holistic approaches in micro task assignment that effectively quantify worker context.
Migration Reframed? A multilingual analysis on the stance shift in Europe during the Ukrainian crisis
Authors: Sergej Wildemann, Claudia Niederée, Erick Elejalde
Venue: WWW
Year: 2023

The war in Ukraine seems to have positively changed the attitude toward the critical societal topic of migration in Europe – at least towards refugees from Ukraine. We investigate whether this impression is substantiated by how the topic is reflected in online news and social media, thus linking the representation of the issue on the Web to its perception in society. For this purpose, we combine and adapt leading-edge automatic text processing for a novel multilingual stance detection approach. Starting from 5.5M Twitter posts published by 565 European news outlets in one year, beginning September 2021, plus replies, we perform a multilingual analysis of migration-related media coverage and associated social media interaction for Europe and selected European countries. The results of our analysis show that there is actually a reframing of the discussion illustrated by the terminology change, e.g., from “migrant” to “refugee”, often even accentuated with phrases such as “real refugees”. However, concerning a stance shift in public perception, the picture is more diverse than expected. All analyzed cases show a noticeable temporal stance shift around the start of the war in Ukraine. Still, there are apparent national differences in the size and stability of this shift.
qEndpoint: A Wikidata SPARQL endpoint on commodity hardware
Authors: Antoine Willerval, Angela Bonifati, Dennis Diefenbach
Venue: WWW
Year: 2023

In this work, we demonstrate how to setup a Wikidata SPARQL endpoint on commodity hardware resources. We achieve this by using a novel triple store called qEndpoint, which uses a read-only partition based on HDT and a write partition based on RDF4J. We show that qEndpoint can index and query the entire Wikidata dump (currently 17 billion triples) on a machine with 600GB SSD, 10 cores and 10GB of RAM, while keeping the query performance comparable with other SPARQL endpoints indexing Wikidata. In a nutshell, we present the first SPARQL endpoint over Wikidata that can run on commodity hardware while preserving the query run time of existing implementations. Our work goes in the direction of democratizing the access to Wikidata as well as to other large-scale Knowledge Graphs published on the Web. The source code of qEndpoint along with the query workloads are publicly available.
A Chinese Fine-grained Financial Event Extraction Dataset
Authors: Mengjie Wu, Maofu Liu, Luyao Wang, Huijun Hu
Venue: WWW
Year: 2023

The existing datasets are mostly composed of official documents, statements, news articles, and so forth. So far, only a little attention has been paid to the numerals in financial social comments. Therefore, this paper presents CFinNumAttr, a financial numeral attribute dataset in Chinese via annotating the stock reviews and comments collected from social networking platform. We also conduct several experiments on the CFinNumAttr dataset with state-of-the-art methods to discover the importance of the financial numeral attributes. The experimental results on the CFinNumAttr dataset show that the numeral attributes in social reviews or comments contain rich semantic information, and the numeral clue extraction and attribute classification tasks can make a great improvement in financial text understanding.
Know Your Transactions: Real-time and Generic Transaction Semantic Representation on Blockchain & Web3 Ecosystem
Authors: Zhiying Wu, Jieli Liu, Jiajing Wu, Zibin Zheng, Xiapu Luo, Ting Chen
Venue: WWW
Year: 2023

Web3, based on blockchain technology, is the evolving next generation Internet of value. Massive active applications on Web3, e.g. DeFi and NFT, usually rely on blockchain transactions to achieve value transfer as well as complex and diverse custom logic and intentions. Various risky or illegal behaviors such as financial fraud, hacking, money laundering are currently rampant in the blockchain ecosystem, and it is thus important to understand the intent behind the pseudonymous transactions. To reveal the intent of transactions, much effort has been devoted to extracting some particular transaction semantics through specific expert experiences. However, the limitations of existing methods in terms of effectiveness and generalization make it difficult to extract diverse transaction semantics in the rapidly growing and evolving Web3 ecosystem. In this paper, we propose the Motif-based Transaction Semantics representation method (MoTS), which can capture the transaction semantic information in the real-time transaction data workflow. To the best of our knowledge, MoTS is the first general semantic extraction method in Web3 blockchain ecosystem. Experimental results show that MoTS can effectively distinguish different transaction semantics in real-time, and can be used for various downstream tasks, giving new insights to understand the Web3 blockchain ecosystem. Our codes are available at https://github.com/wuzhy1ng/MoTS.
Is IPFS Ready for Decentralized Video Streaming?
Authors: Zhengyu Wu, ChengHao Ryan Yang, Santiago Vargas, Aruna Balasubramanian
Venue: WWW
Year: 2023

InterPlanetary File System (IPFS) is a peer-to-peer protocol for decentralized content storage and retrieval. The IPFS platform has the potential to help users evade censorship and avoid a central point of failure. IPFS is seeing increasing adoption for distributing various kinds of files, including video. However, the performance of video streaming on IPFS has not been well-studied. We conduct a measurement study with over 28,000 videos hosted on the IPFS network and find that video streaming experiences high stall rates due to relatively high Round Trip Times (RTT). Further, videos are encoded using a single static quality, because of which streaming cannot adapt to different network conditions. A natural approach is to use adaptive bitrate (ABR) algorithms for streaming, which encode videos in multiple qualities and streams according to the throughput available. However, traditional ABR algorithms perform poorly on IPFS because the throughput cannot be estimated correctly. The main problem is that video segments can be retrieved from multiple sources, making it difficult to estimate the throughput. To overcome this issue, we have designed Telescope, an IPFS-aware ABR system. We conduct experiments on the IPFS network, where IPFS video providers are geographically distributed across the globe. Our results show that Telescope significantly improves the Quality of Experience (QoE) of videos, for a diverse set of network and cache conditions, compared to traditional ABR.
Violentometer: measuring violence on the Web in real time
Authors: Henrique S. Xavier
Venue: WWW
Year: 2023

This paper describes a system for monitoring in real time the level of violence on Web platforms through the use of an artificial intelligence model to classify textual data according to their content. The system was successfully implemented and tested during the electoral campaign period of the Brazilian 2022 elections by using it to monitor the attacks directed to thousands of candidates on Twitter. We show that, despite an accurate and absolute quantification of violence is not feasible, the system yields differential measures of violence levels that can be useful for understanding human behavior online.
Deep Intention-Aware Network for Click-Through Rate Prediction
Authors: Yaxian Xia, Yi Cao, Sihao Hu, Tong Liu, Lingling Lu
Venue: WWW
Year: 2023

E-commerce platforms provide entrances for customers to enter mini-apps that can meet their specific shopping requirements. Trigger items displayed on entrance icons can attract more entering. However, conventional Click-Through-Rate (CTR) prediction models, which ignore user instant interest in trigger item, fail to be applied to the new recommendation scenario dubbed Trigger-Induced Recommendation in Mini-Apps (TIRA). Moreover, due to the high stickiness of some customers to mini-apps, existing trigger-based methods that over-emphasize the importance of triggers, are undesired for TIRA, since a large portion of customer entries are because of their routine shopping habits instead of triggers. We identify that the key to TIRA is to extract customers’ personalized entering intention and weigh the impact of triggers based on this intention. To achieve this goal, we convert CTR prediction for TIRA into a separate estimation form, and present Deep Intention-Aware Network (DIAN) with three key elements: 1) Intent Net that estimates user’s entering intention, i.e., whether he/she is affected by the trigger or by the habits; 2) Trigger-Aware Net and 3) Trigger-Free Net that estimate CTRs given user’s intention is to the trigger-item and the mini-app respectively. Following a joint learning way, DIAN can both accurately predict user intention and dynamically balance the results of trigger-free and trigger-based recommendations. Experiments show that DIAN advances state-of-the-art performance in a large real-world dataset, and brings a 9.39% lift of online Item Page View and 4.74% CTR for Juhuasuan, a famous mini-app of Taobao.
Multi-head Variational Graph Autoencoder Constrained by Sum-product Networks
Authors: Riting Xia, Yan Zhang, Chunxu Zhang, Xueyan Liu, Bo Yang
Venue: WWW
Year: 2023

Variational graph autoencoder (VGAE) is a promising deep probabilistic model in graph representation learning. However, most existing VGAEs adopt the mean-field assumption, and cannot characterize the graphs with noise well. In this paper, we propose a novel deep probabilistic model for graph analysis, termed Multi-head Variational Graph Autoencoder Constrained by Sum-product Networks (named SPN-MVGAE), which helps to relax the mean-field assumption and learns better latent representation with fault tolerance. Our proposed model SPN-MVGAE uses conditional sum-product networks as constraints to learn the dependencies between latent factors in an end-to-end manner. Furthermore, we introduce the superposition of the latent representations learned by multiple variational networks to represent the final latent representations of nodes. Our model is the first use sum-product networks for graph representation learning, extending the scope of sum-product networks applications. Experimental results show that compared with other baseline methods, our model has competitive advantages in link prediction, fault tolerance, node classification, and graph visualization on real datasets.
Unsupervised Anomaly Detection on Microservice Traces through Graph VAE
Authors: Zhe Xie, Haowen Xu, Wenxiao Chen, Wanxue Li, Huai Jiang, Liangfei Su, Hanzhang Wang, Dan Pei
Venue: WWW
Year: 2023

The microservice architecture is widely employed in large Internet systems. For each user request, a few of the microservices are called, and a trace is formed to record the tree-like call dependencies among microservices and the time consumption at each call node. Traces are useful in diagnosing system failures, but their complex structures make it difficult to model their patterns and detect their anomalies. In this paper, we propose a novel dual-variable graph variational autoencoder (VAE) for unsupervised anomaly detection on microservice traces. To reconstruct the time consumption of nodes, we propose a novel dispatching layer. We find that the inversion of negative log-likelihood (NLL) appears for some anomalous samples, which makes the anomaly score infeasible for anomaly detection. To address this, we point out that the NLL can be decomposed into KL-divergence and data entropy, whereas lower-dimensional anomalies can introduce an entropy gap with normal inputs. We propose three techniques to mitigate this entropy gap for trace anomaly detection: Bernoulli & Categorical Scaling, Node Count Normalization, and Gaussian Std-Limit. On five trace datasets from a top Internet company, our proposed TraceVAE achieves excellent F-scores.