OpenRaiser/Intern-Atlas huggingface dataset:
Reliable QoS monitoring based on client feedback
Authors: Radu Jurca, Boi Faltings, Walter Binder
Venue: Conference WWW 2007
Year: 2007

Service-level agreements (SLAs) establish a contract between service providersand clients concerning Quality of Service (QoS) parameters. Without properpenalties, service providers have strong incentives to deviate from theadvertised QoS, causing losses to the clients. Reliable QoS monitoring (andproper penalties computed on the basis of delivered QoS) are thereforeessential for the trustworthiness of a service-oriented environment. In thispaper, we present a novel QoS monitoring mechanism based on quality ratings from theclients. A reputation mechanism collects the ratings and computes theactual quality delivered to the clients. The mechanism provides incentives forthe clients to report honestly, and pays special attention to minimizing costand overhead1.
Faircorels, an open-source library for learning fair rule lists
Authors: Ulrich Aïvodji, Julien Ferry, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
Venue: Conference CIKM 2021
Year: 2021

FairCORELS is an open-source Python module for building fair rule lists. It is a multi-objective variant of CORELS, a branch-and-bound algorithm to learn certifiably optimal rule lists. FairCORELS supports six statistical fairness metrics, proposes several exploration parameters and leverages on the fairness constraints to prune the search space efficiently. It can easily generate sets of accuracy-fairness trade-offs. The models learnt are interpretable by design and a sparsity parameter can be used to control their length.
Semantics and complexity of graphql
Authors: Olaf Hartig, Jorge Pérez
Venue: Conference WWW 2018
Year: 2018

GraphQL is a recently proposed, and increasingly adopted, conceptual framework for providing a new type of data access interface on the Web. The framework includes a new graph query language whose semantics has been specified informally only. This has prevented the formal study of the main properties of the language. We embark on the formalization and study of GraphQL. To this end, we first formalize the semantics of GraphQL queries based on a labeled-graph data model. Thereafter, we analyze the language and show that it admits really efficient evaluation methods. In particular, we prove that the complexity of the GraphQL evaluation problem is NL-complete. Moreover, we show that the enumeration problem can be solved with constant delay. This implies that a server can answer a GraphQL query and send the response byte-by-byte while spending just a constant amount of time between every byte sent. Despite these positive results, we prove that the size of a GraphQL response might be prohibitively large for an internet scenario. We present experiments showing that current practical implementations suffer from this issue. We provide a solution to cope with this problem by showing that the total size of a GraphQL response can be computed in polynomial time. Our results on polynomial-time size computation plus the constant-delay enumeration can help developers to provide more robust GraphQL interfaces on the Web.
TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional Networks
Authors: Min-Jeong Kim, Yeon-Chang Lee, Sang-Wook Kim
Venue: Conference SIGIR 2023
Year: 2023

The problem of signed network embedding (SNE) aims to represent nodes in a given signed network as low-dimensional vectors. While several SNE methods based on graph convolutional networks (GCN) have been proposed, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which measures the trustworthiness on edge signs for high-order relationships inferred by balance theory and corrects incorrect embedding propagation based on the trustworthiness. The experiments on four real-world signed network datasets demonstrate that TrustSGCN consistently outperforms five state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN.
CGStream: Continuous Correlated Graph Query for Data Streams
Authors: Shirui Pan, Xingquan Zhu
Venue: Conference CIKM 2012
Year: 2012

In this paper, we propose to query correlated graph in a data stream scenario, where given a query graph q an algorithm is required to retrieve all the subgraphs whose Pearson's correlation coefficients with q are greater than a threshold Θ over some graph data flowing in a stream fashion. Due to the dynamic changing nature of the stream data and the inherent complexity of the graph query process, treating graph streams as static datasets is computationally infeasible or ineffective. In the paper, we propose a novel algorithm, CGStream, to identify correlated graphs from data stream, by using a sliding window which covers a number of consecutive batches of stream data records. Our theme is to regard stream query as the traversing along a data stream and the query is achieved at a number of outlooks over the data stream. For each outlook, we derive a lower frequency bound to mine a set of frequent subgraph candidates, where the lower bound guarantees that no pattern is missing from the current outlook to the next outlook. On top of that, we derive an upper correlation bound and a heuristic rule to prune the candidate size, which helps reduce the computation cost at each outlook. Experimental results demonstrate that the proposed algorithm is several times, or even an order of magnitude, more efficient than the straightforward algorithm. Meanwhile, our algorithm achieves good performance in terms of query precision.
Bicmts: Bidirectional coupled multivariate learning of irregular time series with missing values
Authors: Qinfen Wang, Siyuan Ren, Yong Xia, Longbing Cao
Venue: Conference CIKM 2021
Year: 2021

Multivariate time series (MTS) such as multiple medical measures in intensive care units (ICU) are irregularly acquired and hold missing values. Conducting learning tasks on such irregular MTS with missing values, e.g., predicting the mortality of ICU patients, poses significant challenge to existing MTS forecasting models and recurrent neural networks (RNNs), which capture the temporal dependencies within a time series. This work proposes a bidirectional coupled MTS learning (BiCMTS) method to represent both forward and backward value couplings within a time series by RNNs and between MTS by self-attention networks; the learned bidirectional intra- and inter-time series coupling representations are fused to estimate missing values. We test BiCMTS on both data imputation and mortality prediction for ICU patients, showing a great potential of leveraging the deep and hidden relations captured in RNNs by the BiCMTS-learned intra- and inter-time series value couplings in MTS.
Grouping students in educational settings
Authors: Rakesh Agrawal, Behzad Golshan, Evimaria Terzi
Venue: Conference KDD 2014
Year: 2014

Given a class of large number of students, each exhibiting a different ability level, how can we group them into sections so that the overall gain for students is maximized? This question has been a topic of central concern and debate amongst social scientists and policy makers for a long time. We propose a framework for rigorously studying this question, taking a computational perspective. We present a formal definition of the grouping problem and investigate some of its variants. Such variants are determined by the desired number of groups as well as the definition of the gain for each student in the group. We focus on two natural instantiations of the gain function and we show that for both of them the problem of identifying a single group of students that maximizes the gain among its members can be solved in polynomial time. The corresponding partitioning problem, where the goal is to partition the students into non-overlapping groups appear to be much harder. However, the algorithms for the single-group version can be leveraged for solving the more complex partitioning problem. Our experiments with generated data coming from different distributions demonstrate that our algorithm is significantly better than the current strategies in vogue for dividing students in a class into sections.
Uncertainty- Aware Probabilistic Travel Time Prediction for On-Demand Ride-Hailing at DiDi
Authors: Hao Liu 0026, Wenzhao Jiang, Shui Liu, Xi Chen 0080
Venue: Conference KDD 2023
Year: 2023

Travel Time Estimation (TTE) aims to accurately forecast the expected trip duration from an origin to a destination. As one of the world's largest ride-hailing platforms, DiDi answers billions of TTE queries per day. The quality of TTE directly decides the customer's experience and the effectiveness of passenger-to-driver matching. However, existing studies mainly regard TTE as a deterministic regression problem and focus on improving the prediction accuracy of a single label, which overlooks the travel time uncertainty induced by various dynamic contextual factors. To this end, in this paper, we propose a probabilistic framework, ProbTTE, for uncertainty-aware travel time prediction. Specifically, the framework first transforms the single-label regression task to a multi-class classification problem to estimate the implicit travel time distribution. Moreover, we propose an adaptive local label-smoothing scheme to capture the ordinal inter-class relationship among soft travel time labels. Furthermore, we construct a route-wise log-normal distribution regularizer to absorb prior knowledge from large-scale historical trip data. By explicitly considering the travel uncertainty, the proposed approach not only improves the TTE accuracy but also provides additional travel time information to benefit downstream tasks in ride-hailing. Extensive experiments on real-world datasets demonstrate the superiority of the proposed framework compared with state-of-the-art travel time prediction algorithms. In addition, ProbTTE has been deployed in production at DiDi in late 2022 to empower various order dispatching services, and improves passenger and driver experiences significantly.
Using early view patterns to predict the popularity of youtube videos
Authors: Henrique Pinto, Jussara M. Almeida, Marcos André Gonçalves
Venue: Conference WSDM 2013
Year: 2013

Predicting Web content popularity is an important task for supporting the design and evaluation of a wide range of systems, from targeted advertising to effective search and recommendation services. We here present two simple models for predicting the future popularity of Web content based on historical information given by early popularity measures. Our approach is validated on datasets consisting of videos from the widely used YouTube video-sharing portal. Our experimental results show that, compared to a state-of-the-art baseline model, our proposed models lead to significant decreases in relative squared errors, reaching up to 20% reduction on average, and larger reductions (of up to 71%) for videos that experience a high peak in popularity in their early days followed by a sharp decrease in popularity.
A dual markov chain topic model for dynamic environments
Authors: Ayan Acharya, Joydeep Ghosh, Mingyuan Zhou
Venue: Conference KDD 2018
Year: 2018

The abundance of digital text has led to extensive research on topic models that reason about documents using latent representations. Since for many online or streaming textual sources such as news outlets, the number, and nature of topics change over time, there have been several efforts that attempt to address such situations using dynamic versions of topic models. Unfortunately, existing approaches encounter more complex inferencing when their model parameters are varied over time, resulting in high computation complexity and performance degradation. This paper introduces the DM-DTM, a dual Markov chain dynamic topic model, for characterizing a corpus that evolves over time. This model uses a gamma Markov chain and a Dirichlet Markov chain to allow the topic popularities and word-topic assignments, respectively, to vary smoothly over time. Novel applications of the Negative-Binomial augmentation trick result in simple, efficient, closed-form updates of all the required conditional posteriors, resulting in far lower computational requirements as well as less sensitivity to initial conditions, as compared to existing approaches. Moreover, via a gamma process prior, the number of desired topics is inferred directly from the data rather than being pre-specified and can vary as the data changes. Empirical comparisons using multiple real-world corpora demonstrate a clear superiority of DM-DTM over strong baselines for both static and dynamic topic models.
Voting with their feet: Inferring user preferences from app management activities
Authors: Huoran Li, Wei Ai, Xuanzhe Liu, Jian Tang, Gang Huang, Feng Feng, Qiaozhu Mei
Venue: Conference WWW 2016
Year: 2016

Smartphone users have adopted an explosive number of mobile applications (a.k.a., apps) in the recent years. App marketplaces for iOS, Android and Windows Phone platforms host millions of apps which have been downloaded for more than 100 billion times. Investigating how people manage mobile apps in their everyday lives creates a unique opportunity to understand the behavior and preferences of mobile users, to infer the quality of apps, and to improve the user experience. Existing literature provides very limited knowledge about app management activities, due to the lack of user behavioral data at scale. This paper takes the initiative to analyze a very large app management log collected through a leading Android app marketplace. The data set covers five months of detailed downloading, updating, and uninstallation activities, involving 17 million anonymized users and one million apps. We present a surprising finding that the metrics commonly used by app stores to rank apps do not truly reflect the users' real attitudes towards the apps. We then identify useful patterns from the app management activities that much more accurately predict the user preferences of an app even when no user rating is available.
Smartphone app categorization for interest targeting in advertising marketplace
Authors: Vladan Radosavljevic, Mihajlo Grbovic, Nemanja Djuric, Narayan Bhamidipati, Daneo Zhang, Jack Wang, Jiankai Dang, Haiying Huang, Ananth Nagarajan, Peiji Chen
Venue: Conference WWW 2016
Year: 2016

Last decade has witnessed a tremendous expansion of mobile devices, which brought an unprecedented opportunity to reach a large number of mobile users at any point in time. This resulted in a surge of interest of mobile operators and ad publishers to understand usage patterns of mobile apps and allow more relevant content recommendations. Due to a large input space, a critical step in understanding app usage patterns is reducing sparseness by classifying apps into predefined interest taxonomies. However, besides short name and noisy description majority of apps have very limited information available, which makes classification a challenging task. We address this issue and present a novel method to classify apps into interest categories by: 1) embedding apps into low-dimensional space using a neural language model applied on smartphone logs; and 2) applying k-nearest-neighbors classification in the embedding space. To validate the method we run experiments on more than one billion device logs covering hundreds of thousands of apps. To the best of our knowledge this is the first app categorization study at this scale. Empirical results show that the proposed method outperforms the current state-of-the-art.
Scopa: Soft code-switching and pairwise alignment for zero-shot cross-lingual transfer
Authors: Dohyeon Lee, Jaeseong Lee, Gyewon Lee, Byung-Gon Chun, Seung-won Hwang
Venue: Conference CIKM 2021
Year: 2021

The recent advent of cross-lingual embeddings, such as multilingual BERT (mBERT), provides a strong baseline for zero-shot cross-lingual transfer. There also exists increasing research attention to reduce the alignment discrepancy of cross-lingual embeddings between source and target languages, via generating code-switched sentences by substituting randomly selected words in the source languages with their counterparts of the target languages. Although these approaches improve the performance, naively code-switched sentences can have inherent limitations. In this paper, we propose SCOPA, a novel technique to improve the performance of zero-shot cross-lingual transfer. Instead of using the embeddings of code-switched sentences directly, SCOPA mixes them softly with the embeddings of original sentences. In addition, SCOPA utilizes an additional pairwise alignment objective, which aligns the vector differences of word pairs instead of word-level embeddings, in order to transfer contextualized information between different languages while preserving language-specific information. Experiments on the PAWS-X and MLDoc dataset show the effectiveness of SCOPA.
Macular: A multi-task adversarial framework for cross-lingual natural language understanding
Authors: Haoyu Wang 0004, Yaqing Wang 0001, Feijie Wu, Hongfei Xue, Jing Gao 0004
Venue: Conference KDD 2023
Year: 2023

Cross-lingual natural language understanding~(NLU) aims to train NLU models on a source language and apply the models to NLU tasks in target languages, and is a fundamental task for many cross-language applications. Most of the existing cross-lingual NLU models assume the existence of parallel corpora so that words and sentences in source and target languages could be aligned. However, the construction of such parallel corpora is expensive and sometimes infeasible. Motivated by this challenge, recent works propose data augmentation or adversarial training methods to reduce the reliance on external parallel corpora. In this paper, we propose an orthogonal and novel perspective to tackle this challenging cross-lingual NLU task (i.e., when parallel corpora are unavailable). We propose to conduct multi-task learning across different tasks for mutual performance improvement on both source and target languages. The proposed multi-task learning framework is complementary to existing studies and could be integrated with existing methods to further improve their performance on challenging cross-lingual NLU tasks.
Tmmda: A new token mixup multimodal data augmentation for multimodal sentiment analysis
Authors: Xianbing Zhao, Yixin Chen, Sicen Liu, Xuan Zang, Yang Xiang, Buzhou Tang
Venue: Conference WWW 2023
Year: 2023

Existing methods for Multimodal Sentiment Analysis (MSA) mainly focus on integrating multimodal data effectively on limited multimodal data. Learning more informative multimodal representation often relies on large-scale labeled datasets, which are difficult and unrealistic to obtain. To learn informative multimodal representation on limited labeled datasets as more as possible, we proposed TMMDA for MSA, a new Token Mixup Multimodal Data Augmentation, which first generates new virtual modalities from the mixed token-level representation of raw modalities, and then enhances the representation of raw modalities by utilizing the representation of the generated virtual modalities. To preserve semantics during virtual modality generation, we propose a novel cross-modal token mixup strategy based on the generative adversarial network. Extensive experiments on two benchmark datasets, i.e., CMU-MOSI and CMU-MOSEI, verify the superiority of our model compared with several state-of-the-art baselines. The code is available at https://github.com/xiaobaicaihhh/TMMDA.
Du-parking: Spatio-temporal big data tells you realtime parking availability
Authors: Yuecheng Rong, Zhimian Xu, Ruibo Yan, Xu Ma
Venue: Conference KDD 2018
Year: 2018

Realtime parking availability information is of great importance to help drivers to find a parking space faster and thus to reduce parking search traffic. While there are limited realtime parking availability systems in a city due to the expensive cost of sensor device and maintaining realtime parking information. In this paper, we estimate the realtime parking availability throughout a city using historical parking availability data reported by a limited number of existing sensors of parking lots and a variety of datasets we observed in the city, such as meteorology, events, map mobility trace data and navigation data from Baidu map, and POIs. We propose a deep-learning-based approach, called Du-Parking, which consists of three major components modeling temporal closeness, period and current general influence, respectively. More specifically, we employ long short-term memory (LSTM) to model the temporal closeness and period, and meanwhile using two fully-connected layers to model the current general factors. Our approach learns to dynamically aggregate the output of the three components, to estimate the final parking availability of given parking lot. Using the proposed approach, we have provided the realtime parking availability information in Baidu map app, in nine cities in China. We evaluated our approach in Beijing and Shenzhen. The results show the advantages of our method over two categories of baselines, including linear interpolations, and the well-known classification model like GBDT.
A framework for detecting frauds from extremely few labels
Authors: Ya-Lin Zhang, Yi-Xuan Sun, Fangfang Fan, Meng Li, Yeyu Zhao, Wei Wang, Longfei Li, Jun Zhou, Jinghua Feng
Venue: Conference WSDM 2023
Year: 2023

In this paper, we present a framework to deal with the fraud detection task with extremely few labeled frauds. We involve human intelligence in the loop in a labor-saving manner and introduce several ingenious designs to the model construction process. Namely, a rule mining module is introduced, and the learned rules will be refined with expert knowledge. The refined rules will be used to relabel the unlabeled samples and get the potential frauds. We further present a model to learn with the reliable frauds, the potential frauds, and the rest normal samples. Note that the label noise problem, class imbalance problem, and confirmation bias problem are all addressed with specific strategies when building the model. Experimental results are reported to demonstrate the effectiveness of the framework.
Classifying and counting with recurrent contexts
Authors: Denis Moreira dos Reis, André Gustavo Maletzke, Diego Furtado Silva, Gustavo E. A. P. A. Batista
Venue: Conference KDD 2018
Year: 2018

Many real-world applications in the batch and data stream settings with data shift pose restrictions to the access to class labels after the deployment of a classification or quantification model. However, a significant portion of the data stream literature assumes that actual labels are instantaneously available after issuing their corresponding classifications. In this paper, we explore a different set of assumptions without relying on the availability of class labels. We assume that, although the distribution of the data may change over time, it will switch between one of a handful of well-known distributions. Still, we allow the proportions of the classes to vary. In these conditions, we propose the first method that can accurately identify the correct context of data samples and simultaneously estimate the proportion of the positive class. This estimate can be further used to adjust a classification decision threshold and improve classification accuracy. Finally, the method is very efficient regarding time and memory requirements, fitting data stream applications.