OpenRaiser/Intern-Atlas huggingface dataset:
A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online Systems
Authors: Tao Huang, Pengfei Chen, Ruipeng Li
Venue: WWW
Year: 2022

Nowadays, the large online systems are constructed on the basis of microservice architecture. A failure in this architecture may cause a series of failures due to the fault propagation. Thus, the large online systems need to be monitored comprehensively to ensure the service quality. Even though many anomaly detection techniques have been proposed, few of them can be directly applied to a given microservice or cloud server in industrial environment. To settle these challenges, this paper presents SLA-VAE, a semi-supervised learning based active anomaly detection framework using variational auto-encoder. SLA-VAE first defines anomalies based on feature extraction module, introduces semi-supervised VAE to identify anomalies in multivariate time series, and employs active learning to update the online model via a small number of uncertain samples. We conduct experiments on the cloud server data from two different types of game business in Tencent. The results show that SLA-VAE significantly outperforms other state-of-the-art methods and is suitable for wide deployment in large online business system.
UserNLP'22: 2022 International Workshop on User-centered Natural Language Processing
Authors: Xiaolei Huang, Lucie Flek, Franck Dernoncourt, Charles Welch, Silvio Amir, Ramit Sawhney, Diyi Yang
Venue: WWW
Year: 2022

We report goals, paper submissions, keynotes, and organizations of this UserNLP workshop. User-centered NLP can fill these gaps by explicitly considering stylistic variations across individuals or groups of individuals and focusing on user-level modeling tasks. While traditional NLP tasks tend to focus on single documents (e.g., sentiment analysis), user-centered NLP aims to make inferences for individual users, on the basis of one or more documents associated with that user. This workshop aims to create a platform where researchers can present rising challenges in building user-centered NLP models and discuss shared issues across multidisciplinary fields. We have received 11 submissions and accepted 6 of the submissions, which were reviewed by our 19 program committee members. The program invited four keynote talks from both academia and industry. We appreciate the valuable contributions from the organizing committee, program committee, keynote speakers, and the manuscript authors.
AUC-oriented Graph Neural Network for Fraud Detection
Authors: Mengda Huang, Yang Liu, Xiang Ao, Kuan Li, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He
Venue: WWW
Year: 2022

Though Graph Neural Networks (GNNs) have been successful for fraud detection tasks, they suffer from imbalanced labels due to limited fraud compared to the overall userbase. This paper attempts to resolve this label-imbalance problem for GNNs by maximizing the AUC (Area Under ROC Curve) metric since it is unbiased with label distribution. However, maximizing AUC on GNN for fraud detection tasks is intractable due to the potential polluted topological structure caused by intentional noisy edges generated by fraudsters. To alleviate this problem, we propose to decouple the AUC maximization process on GNN into a classifier parameter searching and an edge pruning policy searching, respectively. We propose a model named AO-GNN (Short for AUC-oriented GNN), to achieve AUC maximization on GNN under the aforementioned framework. In the proposed model, an AUC-oriented stochastic gradient is applied for classifier parameter searching, and an AUC-oriented reinforcement learning module supervised by a surrogate reward of AUC is devised for edge pruning policy searching. Experiments on three real-world datasets demonstrate that the proposed AO-GNN patently outperforms state-of-the-art baselines in not only AUC but also other general metrics, e.g. F1-macro, G-means.
Unsupervised Superpixel-Driven Parcel Segmentation of Remote Sensing Images Using Graph Convolutional Network
Authors: Fulin Huang, Zhicheng Yang, Hang Zhou, Chen Du, Andy J. Y. Wong, Yuchuan Gou, Mei Han, Jui-Hsin Lai
Venue: WWW
Year: 2022

Accurate parcel segmentation of remote sensing images plays an important role in ensuring various downstream tasks. Traditionally, parcel segmentation is based on supervised learning using precise parcel-level ground truth information, which is difficult to obtain. In this paper, we propose an end-to-end unsupervised Graph Convolutional Network (GCN)-based framework for superpixel-driven parcel segmentation of remote sensing images. The key component is a novel graph-based superpixel aggregation model, which effectively learns superpixels’ latent affinities and better aggregates similar ones in spatial and spectral spaces. We construct a multi-temporal multi-location testing dataset using Sentinel-2 images and the ground truth annotations in four different regions. Extensive experiments are conducted to demonstrate the efficacy and robustness of our proposed model. The best performance is achieved by our model compared with the competing methods.
A Multi-Task Learning Approach for Delayed Feedback Modeling
Authors: Zhigang Huangfu, Gongduo Zhang, Zhengwei Wu, Qintong Wu, Zhiqiang Zhang, Lihong Gu, Jun Zhou, Jinjie Gu
Venue: WWW
Year: 2022

Conversion rate (CVR) prediction is one of the most essential tasks for digital display advertising. In industrial recommender systems, online learning is particularly favored for its capability to capture the dynamic change of data distribution, which often leads to significantly improvement of conversion rates. However, the gap between a click behavior and the corresponding conversion ranges from a few minutes to days; therefore, fresh data may not have accurate label information when they are ingested by the training algorithm, which is called the delayed feedback problem of CVR prediction. To solve this problem, previous works label the delayed positive samples as negative and correct them at their conversion time, then they optimize the expectation of actual conversion distribution via important sampling under the observed distribution. However, these methods approximate the actual feature distribution as the observed feature distribution, which may introduce additional bias to the delayed feedback modeling. In this paper, we prove the observed conversion rate is the product of the actual conversion rate and the observed non-delayed positive rate. Then we propose Multi-Task Delayed Feedback Model (MTDFM), which consists of two sub-networks: actual CVR network and NDPR (non-delayed positive rate) network. We train the actual CVR network by simultaneously optimizing the observed conversion rate and non-delayed positive rate. The proposed method does not require the observed feature distribution to remain the same as the actual distribution. Finally, experimental results on both public and industrial datasets demonstrate that the proposed method outperforms the previous state-of-the-art methods consistently.
Web Mining to Inform Locations of Charging Stations for Electric Vehicles
Authors: Philipp Hummler, Christof Naumzik, Stefan Feuerriegel
Venue: WWW
Year: 2022

The availability of charging stations is an important factor for promoting electric vehicles (EVs) as a carbon-friendly way of transportation. Hence, for city planners, the crucial question is where to place charging stations so that they reach a large utilization. Here, we hypothesize that the utilization of EV charging stations is driven by the proximity to points-of-interest (POIs), as EV owners have a certain limited willingness to walk between charging stations and POIs. To address our research question, we propose the use of web mining: we characterize the influence of different POIs from OpenStreetMap on the utilization of charging stations. For this, we present a tailored interpretable model that takes into account the full spatial distributions of both the POIs and the charging stations. This allows us then to estimate the distance and magnitude of the influence of different POI types. We evaluate our model with data from approx. 300 charging stations and 4,000 POIs in Amsterdam, Netherlands. Our model achieves a superior performance over state-of-the-art baselines and, on top of that, is able to offer an unmatched level of interpretability. To the best of our knowledge, no previous paper has quantified the POI influence on charging station utilization from real-world usage data by estimating the spatial proximity in which POIs are relevant. As such, our findings help city planners in identifying effective locations for charging stations.
Personal Knowledge Graphs: Use Cases in e-learning Platforms
Authors: Eleni Ilkou
Venue: WWW
Year: 2022

Personal Knowledge Graphs (PKGs) are introduced by the semantic web community as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of personalised representation of user data and interests on the top of big, well-established encyclopedic KGs, such as DBpedia [21]. Inspired by the widely recent usage of PKGs in the medical domain to represent patient data, this PhD proposal aims to adopt a similar technique in the educational domain in e-learning platforms by deploying PKGs to represent users and learners. We propose a novel PKG development that relies on ontology and interlinks to Linked Open Data. Hence, adding the dimension of personalisation and explainability in users’ featured data while respecting privacy. This research design is developed in two use cases: a collaborative search learning platform and an e-learning platform. Our preliminary results show that e-learning platforms can get benefited from our approach by providing personalised recommendations and more user and group-specific data.
Towards Benchmarking Data Literacy
Authors: Calum Inverarity, David Tarrant, Emilie Forrest, Phil Greenwood
Venue: WWW
Year: 2022

Data literacy as a term is growing in presence in society. Until recently, most of the educational focus has been around how to equip people with the skills to use data. However the increased impact that data is having on society has demonstrated the need for a different approach, one where people are able to understand and think critically about how data is being collected, used and shared. Going beyond definitions, in this paper we present research on benchmarking data literacy through self assessment based upon the creation of a set of data literacy levels for adults. Although the work highlights the limitations of self assessment, there is clear potential to build on the definitions to create potential IQ-style tests that help boost critical thinking and demonstrate the importance of data literacy education.
Temporal Question Answering in News Article Collections
Authors: Adam Jatowt
Venue: WWW
Year: 2022

The fields of automatic question answering and reading comprehension have been rapidly advancing recently [6]. Open-domain question answering, in particular, assumes answering arbitrary user questions from a large document collection. The existing QA approaches are however generally working on synchronic document collections such as Wikipedia, Web data or short-term news corpora. This talk is about our latest efforts in automatic question answering over temporal news collections which can contain millions of news articles published during several decades’ long time frames. Temporal aspects of both news articles and user questions form an additional challenge for this kind of a temporal question answering task. To correctly answer questions over such collections one usually needs first to find candidate documents that are likely to contain these answers. We will first discuss a re-ranking approach [3] for news articles which works by utilizing temporal information embedded in questions and in the underlying document collection, thus combining methods from Temporal Information Retrieval [1, 2] and Natural Language Processing. Next, we will discuss a dedicated solution for answering ”When” type questions which require finding occurrence dates of events described in input questions based on the underlying news archive [5]. Finally, we will introduce ArchivalQA [4] - a large-scale question answering dataset which has been automatically created from a two decades’ long news article collection, and which contains over 500k question-answer pairs. The dataset has been processed to remove temporally ambiguous questions for which more than one correct answer exist.
Diachronic Analysis of Time References in News Articles
Authors: Adam Jatowt, Antoine Doucet, Ricardo Campos
Venue: WWW
Year: 2022

Time expressions embedded in text are important for many downstream tasks in NLP and IR. They have been, for example, utilized for timeline summarization, named entity recognition, temporal information retrieval, question answering and others. In this paper, we introduce a novel analytical approach to analyzing characteristics of time expressions in diachronic text collections. Based on a collection of news articles published over a 33-years’ long time span, we investigate several aspects of time expressions with a focus on their interplay with publication dates of containing documents. We utilize a graph-based representation of temporal expressions to represent them through their co-occurring named entities. The proposed approach results in several observations that could be utilized in automatic systems that rely on processing temporal signals embedded in text. It could be also of importance for professionals (e.g., historians) who wish to understand fluctuations in collective memories and collective expectations based on large-scale, diachronic document collections.
Learning Neural Ranking Models Online from Implicit User Feedback
Authors: Yiling Jia, Hongning Wang
Venue: WWW
Year: 2022

Existing online learning to rank (OL2R) solutions are limited to linear models, which are incompetent to capture possible non-linear relations between queries and documents. In this work, to unleash the power of representation learning in OL2R, we propose to directly learn a neural ranking model from users’ implicit feedback (e.g., clicks) collected on the fly. We focus on RankNet and LambdaRank, due to their great empirical success and wide adoption in offline settings, and control the notorious explore-exploit trade-off based on the convergence analysis of neural networks using neural tangent kernel. Specifically, in each round of result serving, exploration is only performed on document pairs where the predicted rank order between the two documents is uncertain; otherwise, the ranker’s predicted order will be followed in result ranking. We prove that under standard assumptions our OL2R solution achieves a gap-dependent upper regret bound of O(log 2(T)), in which the regret is defined on the total number of mis-ordered pairs over T rounds. Comparisons against an extensive set of state-of-the-art OL2R baselines on two public learning to rank benchmark datasets demonstrate the effectiveness of the proposed solution.
Multi-task GNN for Substitute Identification
Authors: Tong Jian, Fan Yang, Zhen Zuo, Wenbo Wang, Michinari Momma, Tong Zhao, Chaosheng Dong, Yan Gao, Yi Sun
Venue: WWW
Year: 2022

Substitute product recommendation is important to improve customer satisfaction on E-commerce domain. E-commerce in nature provides rich sources of substitute relationships, e.g., customers purchase a substitute product when the viewed product is sold out, etc. However, existing recommendation systems usually learn the product substitution correlations without jointly considering variant customer behavior sources. In this paper, we propose a unified multi-task heterogeneous graph neural network (M-HetSage), which captures the complementary information across various customer behavior data sources. This allows us to explore synergy across sources with different attributes and quality. Moreover, we introduce a list-aware average precision (LaAP) loss, which exploits correlations among lists of substitutes and non-substitutes by directly optimizing an approximation of the target ranking metric. On top of that, LaAP leverages a list-aware attention mechanism to differentiate substitute qualities for better recommendations. Comprehensive experiments on Amazon proprietary datasets demonstrate the superiority of our proposed M-HetSage framework equipped with LaAP loss, showing 33%+ improvements on NDCG and mAP metrics comparing to traditional HetSage optimized by a single Triplet loss without differentiating customer behavior data sources.
Massive Text Normalization via an Efficient Randomized Algorithm
Authors: Nan Jiang, Chen Luo, Vihan Lakshman, Yesh Dattatreya, Yexiang Xue
Venue: WWW
Year: 2022

Current popular machine learning techniques in natural language processing and data mining rely heavily on high-quality text sources. Nevertheless, real-world text datasets contain a significant amount of spelling errors and improperly punctuated variants where the performance of these models would quickly deteriorate. Moreover, existing text normalization methods are prohibitively expensive to execute over web-scale datasets, can hardly process noisy texts from social networks, or require annotations to learn the corrections in a supervised manner. In this paper, we present Flan (Fast LSH Algorithm for Text Normalization), a scalable randomized algorithm to clean and canonicalize massive text data. Our approach suggests corrections based on the morphology of the words, where lexically similar words are considered the same with high probability. We efficiently handle the pairwise word-to-word comparisons via locality sensitive hashing (LSH). We also propose a novel stabilization process to address the issue of hash collisions between dissimilar words, which is a consequence of the randomized nature of LSH and is exacerbated by the massive scale of real-world datasets. Compared with existing approaches, our method is more efficient, both asymptotically and in empirical evaluations, does not rely on feature engineering, and does not require any annotation. Our experimental results on real-world datasets demonstrate the efficiency and efficacy of Flan. Based on recent advances in densified Minhash, our approach requires much less computational time compared to baseline text normalization techniques on large-scale Twitter and Reddit datasets. In a human evaluation of the quality of the normalization, Flan achieves 5% and 14% improvement against the baselines over the Reddit and Twitter datasets, respectively. Our method also improves performance on Twitter sentiment classification applications and the perturbed GLUE benchmark datasets, where we introduce random errors into the text.
Knowledge Distillation for Discourse Relation Analysis
Authors: Congcong Jiang, Tieyun Qian, Bing Liu
Venue: WWW
Year: 2022

Automatically identifying the discourse relations can help many downstream NLP tasks such as reading comprehension. It can be categorized into explicit and implicit discourse relation recognition (EDRR and IDRR). Due to the lack of connectives, IDRR remains to be a big challenge. In this paper, we take the first step to exploit the knowledge distillation (KD) technique for discourse relation analysis. Our target is to train a focused single-data single-task student with the help of a general multi-data multi-task teacher. Specifically, we first train one teacher for both the top and second level relation classification tasks with explicit and implicit data. We then transfer the feature embeddings and soft labels from the teacher network to the student network. Extensive experimental results on the popular PDTB dataset proves that our model achieves a new state-of-the-art performance. We also show the effectiveness of our proposed KD architecture through detailed analysis.
Graph Neural Network for Higher-Order Dependency Networks
Authors: Di Jin, Yingli Gong, Zhiqiang Wang, Zhizhi Yu, Dongxiao He, Yuxiao Huang, Wenjun Wang
Venue: WWW
Year: 2022

Graph neural network (GNN) has become a popular tool to analyze the graph data. Existing GNNs only focus on networks with first-order dependency, that is, conventional networks following the Markov property. However, many networks in real life own the higher-order dependency, such as click-stream data where the choice of the next page depends not only on the current page but also on previous pages. This kind of sequential data from complex systems (including natural dependencies) are often ignored by existing GNNs which makes them ineffective. To address this problem, we propose for the first time new GNN approaches for higher-order networks in this paper. First, we form sequence fragments by the current node and its predecessor nodes of different orders as candidate higher-order dependencies. When the fragment significantly affects the probability distribution of different successor nodes of the current node, we include it in the higher-order dependency set. We formulize the network with higher-order dependency as an augmented conventional first-order network, and then feed it into GNNs to derive network embeddings. Moreover, we further propose a new end-to-end GNN framework for dealing with higher-order networks directly in the model. Specifically, the higher-order dependency is used as the neighbor aggregation controller when the node is embedded and updated. In the graph convolutional layer, in addition to the first-order neighbor information, we also aggregate the middle node information from the higher-order dependency segment. We finally test the new approaches on three real networks with higher-order dependency, and compare with some state-of-the-art methods. The results show significant improvements of the new approaches which consider higher-order dependency.
How much is the fork? Fast Probability and Profitability Calculation during Temporary Forks
Authors: Aljosha Judmayer, Nicholas Stifter, Philipp Schindler, Edgar R. Weippl
Venue: WWW
Year: 2022

Estimating the probability, as well as the profitability, of different attacks is of utmost importance when assessing the security and stability of prevalent cryptocurrencies. Previous modeling attempts of classic chain-racing attacks have different drawbacks: they either focus on theoretical scenarios such as infinite attack durations, do not account for already contributed blocks, assume honest victims which immediately stop extending their chain as soon as it falls behind, or rely on computationally heavy approaches which render them ill-suited when fast decisions are required. In this paper, we present a simple yet practical model to calculate the success probability of finite attacks, while considering already contributed blocks and victims that do not give up easily. Hereby, we introduce a more fine grained distinction between different actor types and the sides they take during an attack. The presented model simplifies assessing the profitability of forks in practical settings, while also enabling fast and more accurate estimations of the economic security grantees in certain scenarios. By applying and testing our model in the context of bribing attacks, we further emphasize that approaches where the attacker compensates already contributed attack-chain blocks are particularly cheap. Better and more realistic attack models also help to spot and explain certain events observed in the empirical analysis of cryptocurrencies, or provide valuable directions for future studies. For better reproducibility and to foster further research in this area, all source code, artifacts and calculations are made available on GitHub.