OpenRaiser/Intern-Atlas huggingface dataset:
Salient object detection using concavity context
Authors: Yao Lu 0028, Wei Zhang 0016, Hong Lu 0001, Xiangyang Xue 0001
Venue: Conference ICCV 2011
Year: 2011

Convexity (concavity) is a bottom-up cue to assign figure-ground relation in the perceptual organization [18]. It suggests that region on the convex side of a curved boundary tend to be figural. To explore the validity of this cue in the task of salient object detection, we segment the images in a test dataset into superpixels, and then locate the concave arcs and their bounding boxes along boundary of superpixels. Ecological statistics indicate that such bounding box contains salient object with a large probability. To utilize this spatial context information, i.e. concavity context, we follow the multi-scale analysis of human visual perception and design a hierarchical model. The model yields an affinity graph over candidate superpixels, in which weights between vertices are determined by the summation of concavity context on different scales in the hierarchy. Finally a graph-cut algorithm is performed to separate the salient and background objects. Evaluation on MSRA Salient Object Detection (SOD) dataset shows that concavity context is effective, and our approach provides improvement over state-of-the-art feature-based algorithms.
Multiple kernels for object detection
Authors: Andrea Vedaldi, Varun Gulshan, Manik Varma, Andrew Zisserman
Venue: Conference ICCV 2009
Year: 2009

Our objective is to obtain a state-of-the art object category detector by employing a state-of-the-art image classifier to search for the object in all possible image sub-windows. We use multiple kernel learning of Varma and Ray (ICCV 2007) to learn an optimal combination of exponential χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> kernels, each of which captures a different feature channel. Our features include the distribution of edges, dense and sparse visual words, and feature descriptors at different levels of spatial organization. Such a powerful classifier cannot be tested on all image sub-windows in a reasonable amount of time. Thus we propose a novel three-stage classifier, which combines linear, quasi-linear, and non-linear kernel SVMs. We show that increasing the non-linearity of the kernels increases their discriminative power, at the cost of an increased computational complexity. Our contributions include (i) showing that a linear classifier can be evaluated with a complexity proportional to the number of sub-windows (independent of the sub-window area and descriptor dimension); (ii) a comparison of three efficient methods of proposing candidate regions (including the jumping window classifier of Chum and Zisserman (CVPR 2007) based on proposing windows from scale invariant features); and (Hi) introducing overlap-recall curves as a mean to compare and optimize the performance of the intermediate pipeline stages. The method is evaluated on the PASCAL Visual Object Detection Challenge, and exceeds the performances of previously published methods for most of the classes.
Understanding cyclic trends in social choices
Authors: Anish Das Sarma, Sreenivas Gollapudi, Rina Panigrahy, Li Zhang
Venue: Conference WSDM 2012
Year: 2012

Motivated by trends in popularity of products, we present a formal model for studying trends in our choice of products in terms of three parameters: (1) their innate utility; (2) individual boredom associated with repeated usage of an item; and (3) social influences associated with the preferences from other people. Different from previous work, in this paper we introduce boredom to explain the cyclic pattern in individual and social choices. We formally model boredom and show that a rational individual would make cyclic choices when considering the boredom factor. Furthermore, we extend the model to social choices by showing that a society that votes for a particular style or product can be viewed as a single individual cycling through different choices.
#twittersearch: A comparison of microblog search and web search
Authors: Jaime Teevan, Daniel Ramage, Meredith Ringel Morris
Venue: Conference WSDM 2011
Year: 2011

Social networking Web sites are not just places to maintain relationships; they can also be valuable information sources. However, little is known about how and why people search socially-generated content. In this paper we explore search behavior on the popular microblogging/social networking site Twitter. Using analysis of large-scale query logs and supplemental qualitative data, we observe that people search Twitter to find temporally relevant information (e.g., breaking news, real-time content, and popular trends) and information related to people (e.g., content directed at the searcher, information about people of interest, and general sentiment and opinion). Twitter queries are shorter, more popular, and less likely to evolve as part of a session than Web queries. It appears people repeat Twitter queries to monitor the associated search results, while changing and developing Web queries to learn about a topic. The results returned from the different corpora support these different uses, with Twitter results including more social chatter and social events, and Web results containing more basic facts and navigational content. We discuss the implications of these findings for the design of next-generation Web search tools that incorporate social media.
Structural annotation of search queries using pseudo-relevance feedback
Authors: Michael Bendersky, W. Bruce Croft, David A. Smith
Venue: Conference CIKM 2010
Year: 2010

Marking up queries with annotations such as part-of-speech tags, capitalization, and segmentation, is an important part of many approaches to query processing and understanding. Due to their brevity and idiosyncratic structure, search queries pose a challenge to existing annotation tools that are commonly trained on full-length documents. To address this challenge, we view the query as an explicit representation of a latent information need, which allows us to use pseudo-relevance feedback, and to leverage additional information from the document corpus, in order to improve the quality of query annotation.
Monte carlo*-minimax search
Authors: Marc Lanctot, Abdallah Saffidine, Joel Veness, Christopher Archibald, Mark H. M. Winands
Venue: Conference IJCAI 2013
Year: 2013

This paper introduces Monte Carlo *-Minimax Search (MCMS), a Monte Carlo search algorithm for turned-based, stochastic, two-player, zero-sum games of perfect information. The algorithm is designed for the class of of densely stochastic games; that is, games where one would rarely expect to sample the same successor state multiple times at any particular chance node. Our approach combines sparse sampling techniques from MDP planning with classic pruning techniques developed for adversarial expectimax planning. We compare and contrast our algorithm to the traditional *-Minimax approaches, as well as MCTS enhanced with the Double Progressive Widening, on four games: Pig, EinStein Würfelt Nicht!, Can't Stop, and Ra. Our results show that MCMS can be competitive with enhanced MCTS variants in some domains, while consistently outperforming the equivalent classic approaches given the same amount of thinking time.
Metro maps of science
Authors: Dafna Shahaf, Carlos Guestrin, Eric Horvitz
Venue: Conference KDD 2012
Year: 2012

As the number of scientific publications soars, even the most enthusiastic reader can have trouble staying on top of the evolving literature. It is easy to focus on a narrow aspect of one's field and lose track of the big picture. Information overload is indeed a major challenge for scientists today, and is especially daunting for new investigators attempting to master a discipline and scientists who seek to cross disciplinary borders. In this paper, we propose metrics of influence, coverage and connectivity for scientific literature. We use these metrics to create structured summaries of information, which we call metro maps. Most importantly, metro maps explicitly show the relations between papers in a way which captures developments in the field. Pilot user studies demonstrate that our method helps researchers acquire new knowledge efficiently: map users achieved better precision and recall scores and found more seminal papers while performing fewer searches.
Optimised‎ KD‎ -trees for fast image descriptor‎ matching
Authors: Chanop Silpa-Anan, Richard I. Hartley
Venue: Conference CVPR 2008
Year: 2008

In this paper, we look at improving the KD-tree for a specific usage: indexing a large number of SIFT and other types of image descriptors. We have extended priority search, to priority search among multiple trees. By creating multiple KD-trees from the same data set and simultaneously searching among these trees, we have improved the KD-treepsilas search performance significantly.We have also exploited the structure in SIFT descriptors (or structure in any data set) to reduce the time spent in backtracking. By using Principal Component Analysis to align the principal axes of the data with the coordinate axes, we have further increased the KD-treepsilas search performance.
Comment spam detection by sequence mining
Authors: Ravi Kant, Srinivasan H. Sengamedu, Krishnan S. Kumar
Venue: Conference WSDM 2012
Year: 2012

Comments are supported by several web sites to increase user participation. Users can usually comment on a variety of media types - photos, videos, news articles, blogs, etc. Comment spam is one of the biggest challenges facing this feature. The traditional approach to combat spam is to train classifiers using various machine learning techniques. Since the commonly used classifiers work on the entire comment text, it is easy to mislead them by embedding spam content in good content.
Logucb: an explore-exploit algorithm for comments recommendation
Authors: Dhruv Kumar Mahajan, Rajeev Rastogi, Charu Tiwari, Adway Mitra
Venue: Conference CIKM 2012
Year: 2012

The highly dynamic nature of online commenting environments makes accurate ratings prediction for new comments challenging. In such a setting, in addition to exploiting comments with high predicted ratings, it is also critical to explore comments with high uncertainty in the predictions. In this paper, we propose a novel upper confidence bound (UCB) algorithm called LOGUCB that balances exploration with exploitation when the average rating of a comment is modeled using logistic regression on its features. At the core of our LOGUCB algorithm lies a novel variance approximation technique for the Bayesian logistic regression model that is used to compute the UCB value for each comment. In experiments with a real-life comments dataset from Yahoo! News, we show that LOGUCB with bag-of-words and topic features outperforms state-of-the-art explore-exploit algorithms.
Supervised matching of comments with news article segments
Authors: Dyut Kumar Sil, Srinivasan H. Sengamedu, Chiranjib Bhattacharyya
Venue: Conference CIKM 2011
Year: 2011

Comments constitute an important part of Web 2.0. In this paper, we consider comments on news articles. To simplify the task of relating the comment content to the article content the comments are about, we propose the idea of showing comments alongside article segments and explore automatic mapping of comments to article segments. This task is challenging because of the vocabulary mismatch between the articles and the comments. We present supervised and unsupervised techniques for aligning comments to segments the of article the comments are about. More specifically, we provide a novel formulation of supervised alignment problem using the framework of structured classification. Our experimental results show that structured classification model performs better than unsupervised matching and binary classification model.
Evaluating online ad campaigns in a pipeline: causal models at scale
Authors: David Chan, Rong Ge, Ori Gershony, Tim Hesterberg, Diane Lambert
Venue: Conference KDD 2010
Year: 2010

Display ads proliferate on the web, but are they effective? Or are they irrelevant in light of all the other advertising that people see? We describe a way to answer these questions, quickly and accurately, without randomized experiments, surveys, focus groups or expert data analysts. Doubly robust estimation protects against the selection bias that is inherent in observational data, and a nonparametric test that is based on irrelevant outcomes provides further defense. Simulations based on realistic scenarios show that the resulting estimates are more robust to selection bias than traditional alternatives, such as regression modeling or propensity scoring. Moreover, computations are fast enough that all processing, from data retrieval through estimation, testing, validation and report generation, proceeds in an automated pipeline, without anyone needing to see the raw data.
Placing images on the world map: a microblog-based enrichment approach
Authors: Claudia Hauff, Geert-Jan Houben
Venue: Conference SIGIR 2012
Year: 2012

Estimating the geographic location of images is a task which has received increasing attention recently. Large numbers of images uploaded to platforms such as Flickr do not contain GPS-based latitude/longitude coordinates. Obtaining such geographic information is beneficial for a variety of applications including travelogues, visual place descriptions and personalized travel recommendations. While most works in this area only exploit an image's textual meta-data (tags, title, etc.) to estimate at what geographic location the image was taken, we consider an additional textual dimension: the image owner's traces on the social Web. Specifically, we hypothesize that information extracted from a person's microblog stream(s) can be utilized to improve the accuracy with which the geographic location of the images is estimated. In this paper, we investigate this hypothesis on the example of Twitter streams and find it to be confirmed. The median error distance in kilometres decreases by up to 67% in comparison to existing state-of-the-art. The best results are achieved when tweets that were posted up to two days before and after an image was taken are considered. Moreover, we also find another type of additional information useful: population density data.
Probabilistic dyadic data analysis with local and global consistency
Authors: Deng Cai 0001, Xuanhui Wang, Xiaofei He 0001
Venue: Conference ICML 2009
Year: 2009

Dyadic data arises in many real world applications such as social network analysis and information retrieval. In order to discover the underlying or hidden structure in the dyadic data, many topic modeling techniques were proposed. The typical algorithms include Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). The probability density functions obtained by both of these two algorithms are supported on the Euclidean space. However, many previous studies have shown naturally occurring data may reside on or close to an underlying submanifold. We introduce a probabilistic framework for modeling both the topical and geometrical structure of the dyadic data that explicitly takes into account the local manifold structure. Specifically, the local manifold structure is modeled by a graph. The graph Laplacian, analogous to the Laplace-Beltrami operator on manifolds, is applied to smooth the probability density functions. As a result, the obtained probabilistic distributions are concentrated around the data manifold. Experimental results on real data sets demonstrate the effectiveness of the proposed approach. 1.
Activity recognition using the velocity histories of tracked keypoints
Authors: Ross Messing, Christopher J. Pal, Henry A. Kautz
Venue: Conference ICCV 2009
Year: 2009

We present an activity recognition feature inspired by human psychophysical performance. This feature is based on the velocity history of tracked keypoints. We present a generative mixture model for video sequences using this feature, and show that it performs comparably to local spatio-temporal features on the KTH activity recognition dataset. In addition, we contribute a new activity recognition dataset, focusing on activities of daily living, with high resolution video sequences of complex actions. We demonstrate the superiority of our velocity history feature on high resolution video sequences of complicated activities. Further, we show how the velocity history feature can be extended, both with a more sophisticated latent velocity model, and by combining the velocity history feature with other useful information, like appearance, position, and high level semantic information. Our approach performs comparably to established and state of the art methods on the KTH dataset, and significantly outperforms all other methods on our challenging new dataset.
Rise and fall patterns of information diffusion: Model and implications
Authors: Yasuko Matsubara, Yasushi Sakurai, B. Aditya Prakash, Lei Li, Christos Faloutsos
Venue: Conference KDD 2012
Year: 2012

The recent explosion in the adoption of search engines and new media such as blogs and Twitter have facilitated faster propagation of news and rumors. How quickly does a piece of news spread over these media? How does its popularity diminish over time? Does the rising and falling pattern follow a simple universal law?
Multiclass transfer learning from unconstrained priors
Authors: Jie Luo, Tatiana Tommasi, Barbara Caputo
Venue: Conference ICCV 2011
Year: 2011

The vast majority of transfer learning methods proposed in the visual recognition domain over the last years addresses the problem of object category detection, assuming a strong control over the priors from which transfer is done. This is a strict condition, as it concretely limits the use of this type of approach in several settings: for instance, it does not allow in general to use off-the-shelf models as priors. Moreover, the lack of a multiclass formulation for most of the existing transfer learning algorithms prevents using them for object categorization problems, where their use might be beneficial, especially when the number of categories grows and it becomes harder to get enough annotated data for training standard learning methods. This paper presents a multiclass transfer learning algorithm that allows to take advantage of priors built over different features and with different learning methods than the one used for learning the new task. We use the priors as experts, and transfer their outputs to the new incoming samples as additional information. We cast the learning problem within the Multi Kernel Learning framework. The resulting formulation solves efficiently a joint optimization problem that determines from where and how much to transfer, with a principled multiclass formulation. Extensive experiments illustrate the value of this approach.
User grouping behavior in online forums
Authors: Xiaolin Shi, Jun Zhu, Rui Cai, Lei Zhang
Venue: Conference KDD 2009
Year: 2009

Online forums represent one type of social media that is particularly rich for studying human behavior in information seeking and diffusing. The way users join communities is a reflection of the changing and expanding of their interests toward information. In this paper, we study the patterns of user participation behavior, and the feature factors that influence such behavior on different forum datasets. We find that, despite the relative randomness and lesser commitment of structural relationships in online forums, users’ community joining behaviors display some strong regularities. One particularly interesting observation is that the very weak relationships between users defined by online replies have similar diffusion curves as those of real friendships or co-authorships. We build social selection models, Bipartite Markov Random Field (BiMRF), to quantitatively evaluate the prediction performance of those feature factors and their relationships. Using these models, we show that some features carry supplementary information, and the effectiveness of different features vary in different types of forums. Moreover, the results of BiMRF with two-star configurations suggest that the feature of user similarity defined by frequency of communication or number of common friends is inadequate to predict grouping behavior, but adding node-level features can improve the fit of the model.
Intervalrank: isotonic regression with listwise and pairwise constraints
Authors: Taesup Moon, Alexander J. Smola, Yi Chang, Zhaohui Zheng
Venue: Conference WSDM 2010
Year: 2010

Ranking a set of retrieved documents according to their relevance to a given query has become a popular problem at the intersection of web search, machine learning, and information retrieval. Recent work on ranking focused on a number of different paradigms, namely, pointwise, pairwise, and list-wise approaches. Each of those paradigms focuses on a different aspect of the dataset while largely ignoring others. The current paper shows how a combination of them can lead to improved ranking performance and, moreover, how it can be implemented in log-linear time.
Robust subspace clustering via tighter rank approximation
Authors: Zhao Kang, Chong Peng, Qiang Cheng
Venue: Conference CIKM 2015
Year: 2015

Matrix rank minimization problem is in general NP-hard. The nuclear norm is used to substitute the rank function in many recent studies. Nevertheless, the nuclear norm approximation adds all singular values together and the approximation error may depend heavily on the magnitudes of singular values. This might restrict its capability in dealing with many practical problems. In this paper, an arctangent function is used as a tighter approximation to the rank function. We use it on the challenging subspace clustering problem. For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method. Extensive experiments on face clustering and motion segmentation show that the proposed method is effective for rank approximation.
Are web users really markovian?
Authors: Flavio Chierichetti, Ravi Kumar, Prabhakar Raghavan, Tamás Sarlós
Venue: Conference WWW 2012
Year: 2012

User modeling on the Web has rested on the fundamental assumption of Markovian behavior --- a user's next action depends only on her current state, and not the history leading up to the current state. This forms the underpinning of PageRank web ranking, as well as a number of techniques for targeting advertising to users. In this work we examine the validity of this assumption, using data from a number of Web settings. Our main result invokes statistical order estimation tests for Markov chains to establish that Web users are not, in fact, Markovian. We study the extent to which the Markovian assumption is invalid, and derive a number of avenues for further research.
Personal privacy vs population privacy: Learning to attack anonymization
Authors: Graham Cormode
Venue: Conference KDD 2011
Year: 2011

Over the last decade great strides have been made in developing techniques to compute functions privately. In particular, Differential Privacy gives strong promises about conclusions that can be drawn about an individual. In contrast, various syntactic methods for providing privacy (criteria such as k-anonymity and l-diversity) have been criticized for still allowing private information of an individual to be inferred. In this paper, we consider the ability of an attacker to use data meeting privacy definitions to build an accurate classifier. We demonstrate that even under Differential Privacy, such classifiers can be used to infer "private" attributes accurately in realistic data. We compare this to similar approaches for inference-based attacks on other forms of anonymized data. We show how the efficacy of all these attacks can be measured on the same scale, based on the probability of successfully inferring a private attribute. We observe that the accuracy of inference of private attributes for differentially private data and $l$-diverse data can be quite similar.