OpenRaiser/Intern-Atlas huggingface dataset:
Harnessing the web for population-scale physiological sensing: A case study of sleep and performance
Authors: Tim Althoff, Eric Horvitz, Ryen W. White, Jamie M. Zeitzer
Venue: Conference WWW 2017
Year: 2017

Human cognitive performance is critical to productivity, learning, and accident avoidance. Cognitive performance varies throughout each day and is in part driven by intrinsic, near 24-hour circadian rhythms. Prior research on the impact of sleep and circadian rhythms on cognitive performance has typically been restricted to small-scale laboratory-based studies that do not capture the variability of real-world conditions, such as environmental factors, motivation, and sleep patterns in real-world settings. Given these limitations, leading sleep researchers have called for larger in situ monitoring of sleep and performance. We present the largest study to date on the impact of objectively measured real-world sleep on performance enabled through a reframing of everyday interactions with a web search engine as a series of performance tasks. Our analysis includes 3 million nights of sleep and 75 million interaction tasks. We measure cognitive performance through the speed of keystroke and click interactions on a web search engine and correlate them to wearable device-defined sleep measures over time. We demonstrate that real-world performance varies throughout the day and is influenced by both circadian rhythms, chronotype (morning/evening preference), and prior sleep duration and timing. We develop a statistical model that operationalizes a large body of work on sleep and performance and demonstrates that our estimates of circadian rhythms, homeostatic sleep drive, and sleep inertia align with expectations from laboratory-based sleep studies. Further, we quantify the impact of insufficient sleep on real-world performance and show that two consecutive nights with less than six hours of sleep are associated with decreases in performance which last for a period of six days. This work demonstrates the feasibility of using online interactions for large-scale physiological sensing.
Identifying table boundaries in digital documents via sparse line detection
Authors: Ying Liu, Prasenjit Mitra, C. Lee Giles
Venue: Conference CIKM 2008
Year: 2008

Most prior work on information extraction has focused on extracting information from text in digital documents. However, often, the most important information being reported in an article is presented in tabular form in a digital document. If the data reported in tables can be extracted and stored in a database, the data can be queried and joined with other data using database management systems. In order to prepare the data source for table search, accurately detecting the table boundary plays a crucial role for the later table structure decomposition. Table boundary detection and content extraction is a challenging problem because tabular formats are not standardized across all documents. In this paper, we propose a simple but effective preprocessing method to improve the table boundary detection performance by considering the sparse-line property of table rows. Our method easily simplifies the table boundary detection problem into the sparse line analysis problem with much less noise. We design eight line label types and apply two machine learning techniques, Conditional Random Field (CRF) and Support Vector Machines (SVM), on the table boundary detection field. The experimental results not only compare the performances between the machine learning methods and the heuristics-based method, but also demonstrate the effectiveness of the sparse line analysis in the table boundary detection.
Supervised hypergraph labeling
Authors: Toufiq Parag, Ahmed M. Elgammal
Venue: Conference CVPR 2011
Year: 2011

We address the problem of labeling individual datapoints given some knowledge about (small) subsets or groups of them. The knowledge we have for a group is the likelihood value for each group member to satisfy a certain model. This problem is equivalent to hypergraph labeling problem where each datapoint corresponds to a node and the each subset correspond to a hyperedge with likelihood value as its weight. We propose a novel method to model the label dependence using an Undirected Graphical Model and reduce the problem of hypergraph labeling into an inference problem. This paper describes the structure and necessary components of such model and proposes useful cost functions. We discuss the behavior of proposed algorithm with different forms of the cost functions, identify suitable algorithms for inference and analyze required properties when it is theoretically guaranteed to have exact solution. Examples of several real world problems are shown as applications of the proposed method.
Enforcing similarity constraints with integer programming for better scene text recognition
Authors: David L. Smith, Jacqueline L. Feild, Erik G. Learned-Miller
Venue: Conference CVPR 2011
Year: 2011

The recognition of text in everyday scenes is made difficult by viewing conditions, unusual fonts, and lack of linguistic context. Most methods integrate a priori appearance information and some sort of hard or soft constraint on the allowable strings. Weinman and Learned-Miller [14] showed that the similarity among characters, as a supplement to the appearance of the characters with respect to a model, could be used to improve scene text recognition. In this work, we make further improvements to scene text recognition by taking a novel approach to the incorporation of similarity. In particular, we train a similarity expert that learns to classify each pair of characters as equivalent or not. After removing logical inconsistencies in an equivalence graph, we formulate the search for the maximum likelihood interpretation of a sign as an integer program. We incorporate the equivalence information as constraints in the integer program and build an optimization criterion out of appearance features and character bigrams. Finally, we take the optimal solution from the integer program, and compare all “nearby” solutions using a probability model for strings derived from search engine queries. We demonstrate word error reductions of more than 30% relative to previous methods on the same data set.
New robust metric learning model using maximum correntropy criterion
Authors: Jie Xu, Lei Luo, Cheng Deng, Heng Huang
Venue: Conference KDD 2018
Year: 2018

topic with many real-world applications. Most existing metric learning methods aim to learn an optimal Mahalanobis distance matrix M, under which data samples from the same class are forced to be close to each other and those from different classes are pushed far away. The Mahalanobis distance matrix M can be factorized as M = L'L, and the Mahalanobis distance induced by L is equivalent to the Euclidean distance after linear projection of the feature vectors on the rows of L. However, the Euclidean distance is only suitable for characterizing Gaussian noise, thus the traditional metric learning algorithms are not robust to achieve good performance when they are applied to the occlusion data, which often appear in image and video data mining applications. To overcome this limitation, we propose a new robust metric learning approach by introducing the maximum correntropy criterion to deal with real-world malicious occlusions or corruptions. In our new model, we enforce the intra-class reconstruction residual of each sample to be smaller than the inter-class reconstruction residual by a large margin. Meanwhile, we employ correntropy induced metric to fit the reconstruction residual, which has been proved to be useful in non-Gaussian data processing. Leveraging the half-quadratic optimization technique, we derive an efficient algorithm to solve the proposed new model and provide its convergence guarantee as well. Extensive experiments on various occluded data sets indicate that our proposed model can achieve more promising performance than other related methods.
Time-aware rank aggregation for microblog search
Authors: Shangsong Liang, Zhaochun Ren, Wouter Weerkamp, Edgar Meij, Maarten de Rijke
Venue: Conference CIKM 2014
Year: 2014

We tackle the problem of searching microblog posts and frame it as a rank aggregation problem where we merge result lists generated by separate rankers so as to produce a final ranking to be returned to the user. We propose a rank aggregation method, TimeRA, that is able to infer the rank scores of documents via latent factor modeling. It is time-aware and rewards posts that are published in or near a burst of posts that are ranked highly in many of the lists being aggregated. Our experimental results show that it significantly outperforms state-of-the-art rank aggregation and time-sensitive microblog search algorithms.
Collaborative topic regression with denoising autoencoder for content and community co-representation
Authors: Trong T. Nguyen, Hady W. Lauw
Venue: Conference CIKM 2017
Year: 2017

Personalized recommendation of items frequently faces scenarios where we have sparse observations on users' adoption of items. In the literature, there are two promising directions. One is to connect sparse items through similarity in content. The other is to connect sparse users through similarity in social relations. We seek to integrate both types of information, in addition to the adoption information, within a single integrated model. Our proposed method models item content via a topic model, and user communities via an autoencoder model, while bridging a user's community-based preference to her topic-based preference. Experiments on public real-life data showcase the utility of the model, particularly when there is significant compatibility between communities and topics.
Hoorays: High-order optimization of rating distance for recommender systems
Authors: Jingwei Xu, Yuan Yao, Hanghang Tong, Xianping Tao, Jian Lu
Venue: Conference KDD 2017
Year: 2017

Latent factor models have become a prevalent method in recommender systems, to predict users' preference on items based on the historical user feedback. Most of the existing methods, explicitly or implicitly, are built upon the first-order rating distance principle, which aims to minimize the difference between the estimated and real ratings. In this paper, we generalize such first-order rating distance principle and propose a new latent factor model (HoORaYs) for recommender systems. The core idea of the proposed method is to explore high-order rating distance, which aims to minimize not only (i) the difference between the estimated and real ratings of the same (user, item) pair (i.e., the first-order rating distance), but also (ii) the difference between the estimated and real rating difference of the same user across different items (i.e., the second-order rating distance). We formulate it as a regularized optimization problem, and propose an effective and scalable algorithm to solve it. Our analysis from the geometry and Bayesian perspectives indicate that by exploring the high-order rating distance, it helps to reduce the variance of the estimator, which in turns leads to better generalization performance (e.g., smaller prediction error). We evaluate the proposed method on four real-world data sets, two with explicit user feedback and the other two with implicit user feedback. Experimental results show that the proposed method consistently outperforms the state-of-the-art methods in terms of the prediction accuracy.
Leveraging social context for modeling topic evolution
Authors: Janani Kalyanam, Amin Mantrach, Diego Sáez-Trumper, Hossein Vahabi, Gert R. G. Lanckriet
Venue: Conference KDD 2015
Year: 2015

Topic discovery and evolution (TDE) has been a problem which has gained long standing interest in the research community. The goal in topic discovery is to identify groups of keywords from large corpora so that the information in those corpora are summarized succinctly. The nature of text corpora has changed dramatically in the past few years with the advent of social media. Social media services allow users to constantly share, follow and comment on posts from other users. Hence, such services have given a new dimension to the traditional text corpus. The new dimension being that today's corpora have a social context embedded in them in terms of the community of users interested in a particular post, their profiles etc. We wish to harness this social context that comes along with the textual content for TDE. In particular, our goal is to both qualitatively and quantitatively analyze when social context actually helps with TDE. Methodologically, we approach the problem of TDE by a proposing non-negative matrix factorization (NMF) based model that incorporates both the textual information and social context information. We perform experiments on large scale real world dataset of news articles, and use Twitter as the platform providing information about the social context of these news articles. We compare with and outperform several state-of-the-art baselines. Our conclusion is that using the social context information is most useful when faced with topics that are particularly difficult to detect.
Psmart: Parameter server based multiple additive regression trees system
Authors: Jun Zhou, Qing Cui, Xiaolong Li, Peilin Zhao, Shenquan Qu, Jun Huang
Venue: Conference WWW 2017
Year: 2017

In this paper, we describe a Parameter Server based Multiple Additive Regression Trees system, or PSMART for short. Empirically, PSMART scales MART to hundreds of billions of samples and thousands of features in production.
Scaling factorization machines with parameter server
Authors: Erheng Zhong, Yue Shi, Nathan Liu, Suju Rajan
Venue: Conference CIKM 2016
Year: 2016

Factorization Machines (FM) have been recognized as an effective learning paradigm for incorporating complex relations to improve item recommendation in recommender systems. However, one open issue of FM lies in its factorized representation (latent factors) for each feature in the observed feature space, a characteristic often resulting in a large parameter space. Therefore, training FM (in other words, learning a large number of parameters in FM) is a computationally expensive task. Our work targets to improve the scalability of FM by building it in a distributed environment. We propose a new system framework that integrates Parameter Server (PS) with the Map/Reduce (MR) framework. In addition to the data parallelism achieved via MR, our framework particularly benefits from PS for model parallelism, a critical characteristic for learning with a large number of parameters in FM. We further address two specific challenges in our system, namely, communication cost and parameter update collision. Through both offline and online experiments on recommendation tasks, we demonstrate that the proposed system framework succeeds in scaling up FM for very large datasets, while it also maintains competitive performance on recommendation quality compared to alternative baselines.
Modeling image similarity by Gaussian mixture models and the signature quadratic form distance
Authors: Christian Beecks, Anca Maria Ivanescu, Steffen Kirchhoff, Thomas Seidl 0001
Venue: Conference ICCV 2011
Year: 2011

Modeling image similarity for browsing and searching in voluminous image databases is a challenging task of nearly all content-based image retrieval systems. One promising way of defining image similarity consists in applying distance-based similarity measures on compact image representations. Beyond feature histograms and feature signatures, more general feature representations are mixture models of which the Gaussian mixture model is the most prominent one. This feature representation can be compared by employing approximations of the Kullback-Leibler Divergence. Although several of those approximations have been successfully applied to model image similarity, their applicability to mixture models based on high-dimensional feature descriptors is questionable. In this paper, we thus introduce the Signature Quadratic Form Distance to measure the distance between two Gaussian mixture models of high-dimensional feature descriptors. We show the analytical computation of the proposed Gaussian Quadratic Form Distance and evaluate its retrieval performance by making use of different benchmark image databases.
Action recognition using rank-1 approximation of joint self-similarity volume
Authors: Chuan Sun, Imran N. Junejo, Hassan Foroosh
Venue: Conference ICCV 2011
Year: 2011

In this paper, we make three main contributions in the area of action recognition: (i) We introduce the concept of Joint Self-Similarity Volume (Joint SSV) for modeling dynamical systems, and show that by using a new optimized rank-1 tensor approximation of Joint SSV one can obtain compact low-dimensional descriptors that very accurately preserve the dynamics of the original system, e.g. an action video sequence; (ii) The descriptor vectors derived from the optimized rank-1 approximation make it possible to recognize actions without explicitly aligning the action sequences of varying speed of execution or different frame rates; (iii) The method is generic and can be applied using different low-level features such as silhouettes, histogram of oriented gradients, etc. Hence, it does not necessarily require explicit tracking of features in the space-time volume. Our experimental results on three public datasets demonstrate that our method produces remarkably good results and outperforms all baseline methods.
Towards interpretation of node embeddings
Authors: Ayushi Dalmia, Ganesh J, Manish Gupta
Venue: Conference WWW 2018
Year: 2018

Recently there have been a large number of studies on embedding large-scale information networks using low-dimensional, neighborhood and community aware node representations. Though the performance of these embedding models have been better than traditional methods for graph mining applications, little is known about what these representations encode, or why a particular node representation works better for certain tasks. Our work presented here constitutes the first step in decoding the black-box of vector embeddings of nodes by evaluating their effectiveness in encoding elementary properties of a node such as page rank, degree, closeness centrality, clustering coefficient, etc. We believe that a node representation is effective for an application only if it encodes the application-specific elementary properties of nodes. To unpack the elementary properties encoded in a node representation, we evaluate the representations on the accuracy with which they can model each of these properties. Our extensive study of three state-of-the-art node representation models (DeepWalk, node2vec and LINE) on four different tasks and six diverse graphs reveal that node2vec and LINE best encode the network properties of sparse and dense graphs respectively. We correlate the model performance obtained for elementary property prediction tasks with the high-level downstream applications such as link prediction and node classification, and visualize the task performance vector of each model to understand the semantic similarity between the embeddings learned by various models. Our first study of the node embedding models for outlier detection reveals that node2vec and DeepWalk identify outliers well for sparse and dense graphs respectively. Our analysis highlights that the proposed elementary property prediction tasks help in unearthing the important features responsible for the given node embedding model to perform well for a given downstream task. This understanding would facilitate in picking the right model for a given downstream task.
Mining large-scale, sparse gps traces for map inference: comparison of approaches
Authors: Xuemei Liu, James Biagioni, Jakob Eriksson, Yin Wang, George Forman, Yanmin Zhu
Venue: Conference KDD 2012
Year: 2012

We address the problem of inferring road maps from large-scale GPS traces that have relatively low resolution and sampling frequency. Unlike past published work that requires high-resolution traces with dense sampling, we focus on situations with coarse granularity data, such as that obtained from thousands of taxis in Shanghai, which transmit their location as seldom as once per minute. Such data sources can be made available inexpensively as byproducts of existing processes, rather than having to drive every road with high-quality GPS instrumentation just for map building - and having to re-drive roads for periodic updates. Although the challenges in using opportunistic probe data are significant, successful mining algorithms could potentially enable the creation of continuously updated maps at very low cost.
Additive Explanations for Anomalies Detected from Multivariate Temporal Data
Authors: Ioana Giurgiu, Anika Schumann
Venue: Conference CIKM 2019
Year: 2019

Detecting anomalies from high-dimensional multivariate temporal data is challenging, because of the non-linear, complex relationships between signals. Recently, deep learning methods based on autoencoders have been shown to capture these relationships and accurately discern between normal and abnormal patterns of behavior, even in fully unsupervised scenarios. However, validating the anomalies detected is difficult without additional explanations. In this paper, we extend SHAP -- a unified framework for providing additive explanations, previously applied for supervised models -- with influence weighting, in order to explain anomalies detected from multivariate time series with a GRU-based autoencoder. Namely, we extract the signals that contribute most to an anomaly and those that counteract it. We evaluate our approach on two use cases and show that we can generate insightful explanations for both single and multiple anomalies.
Ensemble Models for Data-Driven Prediction of Malware Infections
Authors: Chanhyun Kang, Noseong Park, B. Aditya Prakash, Edoardo Serra, V. S. Subrahmanian
Venue: Conference WSDM 2016
Year: 2016

Given a history of detected malware attacks, can we predict the number of malware infections in a country? Can we do this for different malware and countries? This is an important question which has numerous implications for cyber security, right from designing better anti-virus software, to designing and implementing targeted patches to more accurately measuring the economic impact of breaches. This problem is compounded by the fact that, as externals, we can only detect a fraction of actual malware infections. In this paper we address this problem using data from Symantec covering more than 1.4 million hosts and 50 malware spread across 2 years and multiple countries. We first carefully design domain-based features from both malware and machine-hosts perspectives. Secondly, inspired by epidemiological and information diffusion models, we design a novel temporal non-linear model for malware spread and detection. Finally we present ESM, an ensemble-based approach which combines both these methods to construct a more accurate algorithm. Using extensive experiments spanning multiple malware and countries, we show that ESM can effectively predict malware infection ratios over time (both the actual number and trend) upto 4 times better compared to several baselines on various metrics. Furthermore, ESM's performance is stable and robust even when the number of detected infections is low.