bluuebunny/crossref_metadata_2025 huggingface dataset:
Conceptual Imitation Learning in a Human-Robot Interaction Paradigm
Authors: Hajimirsadeghi Hossein , Ahmadabadi Majid Nili , Araabi Babak Nadjar , Moradi Hadi
Published: October 2012

In general, imitation is imprecisely used to address different levels of social learning from high-level knowledge transfer to low-level regeneration of motor commands. However, true imitation is based on abstraction and conceptualization. This article presents a model for conceptual imitation through interaction with the teacher to abstract spatio-temporal demonstrations based on their functional meaning. Abstraction, concept acquisition, and self-organization of proto-symbols are performed through an incremental and gradual learning algorithm. In this algorithm, Hidden Markov Models (HMMs) are used to abstract perceptually similar demonstrations. However, abstract (relational) concepts emerge as a collection of HMMs irregularly scattered in the perceptual space but showing the same functionality. Performance of the proposed algorithm is evaluated in two experimental scenarios. The first one is a human-robot interaction task of imitating signs produced by hand movements. The second one is a simulated interactive task of imitating whole body motion patterns of a humanoid model. Experimental results show efficiency of our model for concept extraction, proto-symbol emergence, motion pattern recognition, prediction, and generation.
Mining Recurring Concept Drifts with Limited Labeled Streaming Data
Authors: Li Peipei , Wu Xindong , Hu Xuegang
Published: October 2012

Tracking recurring concept drifts is a significant issue for machine learning and data mining that frequently appears in real-world stream classification problems. It is a challenge for many streaming classification algorithms to learn recurring concepts in a data stream environment with unlabeled data, and this challenge has received little attention from the research community. Motivated by this challenge, this article focuses on the problem of recurring contexts in streaming environments with limited labeled data. We propose a semi-supervised classification algorithm for data streams with REcurring concept Drifts and Limited LAbeled data, called REDLLA, in which a decision tree is adopted as the classification model. When growing a tree, a clustering algorithm based on
k
-means is installed to produce concept clusters and unlabeled data are labeled in the method of majority-class at leaves. In view of deviations between history and new concept clusters, potential concept drifts are distinguished and recurring concepts are maintained. Extensive studies on both synthetic and real-world data confirm the advantages of our REDLLA algorithm over three state-of-the-art online classification algorithms of CVFDT, DWCDS, and CDRDT and several known online semi-supervised algorithms, even in the case with more than 90% unlabeled data.
Ensembles of Restricted Hoeffding Trees
Authors: Bifet Albert , Frank Eibe , Holmes Geoff , Pfahringer Bernhard
Published: October 2012

The success of simple methods for classification shows that is is often not necessary to model complex attribute interactions to obtain good classification accuracy on practical problems. In this article, we propose to exploit this phenomenon in the data stream context by building an ensemble of Hoeffding trees that are each limited to a small subset of attributes. In this way, each tree is restricted to model interactions between attributes in its corresponding subset. Because it is not known a priori which attribute subsets are relevant for prediction, we build exhaustive ensembles that consider all possible attribute subsets of a given size. As the resulting Hoeffding trees are not all equally important, we weigh them in a suitable manner to obtain accurate classifications. This is done by combining the log-odds of their probability estimates using sigmoid perceptrons, with one perceptron per class. We propose a mechanism for setting the perceptrons’ learning rate using the change detection method for data streams, and also use to reset ensemble members (i.e., Hoeffding trees) when they no longer perform well. Our experiments show that the resulting ensemble classifier outperforms bagging for data streams in terms of accuracy when both are used in conjunction with adaptive naive Bayes Hoeffding trees, at the expense of runtime and memory consumption. We also show that our stacking method can improve the performance of a bagged ensemble.
A Reliable People Counting System via Multiple Cameras
Authors: Ma Huadong , Zeng Chengbin , Ling Charles X.
Published: October 2012

Reliable and real-time people counting is crucial in many applications. Most previous works can only count moving people from a single camera, which cannot count still people or can fail badly when there is a crowd (i.e., heavy occlusion occurs). In this article, we build a system for robust and fast people counting under occlusion through multiple cameras. To improve the reliability of human detection from a single camera, we use a dimensionality reduction method on the multilevel edge and texture features to handle the large variations in human appearance and poses. To accelerate the detection speed, we propose a novel two-stage cascade-of-rejectors method. To handle the heavy occlusion in crowded scenes, we present a fusion method with error tolerance to combine human detection from multiple cameras. To improve the speed and accuracy of moving people counting, we combine our multiview fusion detection method with particle tracking to count the number of people moving in/out the camera view (“border control”). Extensive experiments and analyses show that our method outperforms state-of-the-art techniques in single- and multicamera datasets for both speed and reliability. We also design a deployed system for fast and reliable people (still or moving) counting by using multiple cameras.
A Fuzzy Logic System for Bargaining in Information Markets
Authors: Kolomvatsos Kostas , Anagnostopoulos Christos , Hadjiefthymiades Stathes
Published: October 2012

Future Web business models involve virtual environments where entities interact in order to sell or buy information goods. Such environments are known as Information Markets (IMs). Intelligent agents are used in IMs for representing buyers or information providers (sellers). We focus on the decisions taken by the buyer in the purchase negotiation process with sellers. We propose a reasoning mechanism on the offers (prices of information goods) issued by sellers based on fuzzy logic. The buyer’s knowledge on the negotiation process is modeled through fuzzy sets. We propose a fuzzy inference engine dealing with the decisions that the buyer takes on each stage of the negotiation process. The outcome of the proposed reasoning method indicates whether the buyer should accept or reject the sellers’ offers. Our findings are very promising for the efficiency of automated transactions undertaken by intelligent agents.
Batch Mode Active Learning for Networked Data
Authors: Shi Lixin , Zhao Yuhang , Tang Jie
Published: October 2012

We study a novel problem of batch mode active learning for networked data. In this problem, data instances are connected with links and their labels are correlated with each other, and the goal of batch mode active learning is to exploit the link-based dependencies and node-specific content information to actively select a batch of instances to query the user for learning an accurate model to label unknown instances in the network. We present three criteria (i.e., minimum redundancy, maximum uncertainty, and maximum impact) to quantify the informativeness of a set of instances, and formalize the batch mode active learning problem as selecting a set of instances by maximizing an objective function which combines both link and content information. As solving the objective function is NP-hard, we present an efficient algorithm to optimize the objective function with a bounded approximation rate. To scale to real large networks, we develop a parallel implementation of the algorithm. Experimental results on both synthetic datasets and real-world datasets demonstrate the effectiveness and efficiency of our approach.
Adversarial Geospatial Abduction Problems
Authors: Shakarian Paulo , Dickerson John P. , Subrahmanian V. S.
Published: October 2012

Geospatial Abduction Problems (GAPs) involve the inference of a set of locations that “best explain” a given set of locations of observations. For example, the observations might include locations where a serial killer committed murders or where insurgents carried out Improvised Explosive Device (IED) attacks. In both these cases, we would like to infer a set of locations that explain the observations, for example, the set of locations where the serial killer lives/works, and the set of locations where insurgents locate weapons caches. However, unlike all past work on abduction, there is a strong adversarial component to this; an adversary actively attempts to prevent us from discovering such locations. We formalize such abduction problems as a two-player game where both players (an “agent” and an “adversary”) use a probabilistic model of their opponent (i.e., a mixed strategy). There is asymmetry as the adversary can
choose
both the locations of the observations and the locations of the explanation, while the agent (i.e., us) tries to discover these. In this article, we study the problem from the point of view of both players. We define
reward functions
axiomatically to capture the similarity between two sets of explanations (one corresponding to the locations chosen by the adversary, one guessed by the agent). Many different reward functions can satisfy our axioms. We then formalize the
Optimal Adversary Strategy
(OAS) problem and the
Maximal Counter-Adversary strategy
(MCA) and show that both are NP-hard, that their associated counting complexity problems are #P-hard, and that MCA has no fully polynomial approximation scheme unless P=NP. We show that approximation guarantees are possible for MCA when the reward function satisfies two simple properties (zero-starting and monotonicity) which many natural reward functions satisfy. We develop a mixed integer linear programming algorithm to solve OAS and two algorithms to (approximately) compute MCA; the algorithms yield different approximation guarantees and one algorithm assumes a monotonic reward function. Our experiments use real data about IED attacks over a 21-month period in Baghdad. We are able to show that both the MCA algorithms work well in practice; while MCA-GREEDY-MONO is both highly accurate and slightly faster than MCA-LS, MCA-LS (to our surprise) always completely and correctly maximized the expected benefit to the agent while running in an acceptable time period.
Learning to Infer the Status of Heavy-Duty Sensors for Energy-Efficient Context-Sensing
Authors: Li Xueying , Cao Huanhuan , Chen Enhong , Tian Jilei
Published: October 2012

With the prevalence of smart mobile devices with multiple sensors, the commercial application of intelligent context-aware services becomes more and more attractive. However, limited by the battery capacity, the energy efficiency of context-sensing is the bottleneck for the success of context-aware applications. Though several previous studies for energy-efficient context-sensing have been reported, none of them can be applied to multiple types of high-energy-consuming sensors. Moreover, applying machine learning technologies to energy-efficient context-sensing is underexplored too. In this article, we propose to leverage machine learning technologies for improving the energy efficiency of multiple high-energy-consuming context sensors by trading off the sensing accuracy. To be specific, we try to infer the status of high-energy-consuming sensors according to the outputs of software-based sensors and the physical sensors that are necessary to work all the time for supporting the basic functions of mobile devices. If the inference indicates the high-energy-consuming sensor is in a stable status, we avoid the unnecessary invocation and instead use the latest invoked value as the estimation. The experimental results on real datasets show that the energy efficiency of GPS sensing and audio-level sensing are significantly improved by the proposed approach while the sensing accuracy is over 90%.
Advertising Keywords Recommendation for Short-Text Web Pages Using Wikipedia
Authors: Zhang Weinan , Wang Dingquan , Xue Gui-Rong , Zha Hongyuan
Published: October 2012

Advertising keywords recommendation is an indispensable component for online advertising with the keywords selected from the target Web pages used for contextual advertising or sponsored search. Several ranking-based algorithms have been proposed for recommending advertising keywords. However, for most of them performance is still lacking, especially when dealing with short-text target Web pages, that is, those containing insufficient textual information for ranking. In some cases, short-text Web pages may not even contain enough keywords for selection. A natural alternative is then to recommend relevant keywords not present in the target Web pages. In this article, we propose a novel algorithm for advertising keywords recommendation for short-text Web pages by leveraging the contents of Wikipedia, a user-contributed online encyclopedia. Wikipedia contains numerous entities with related entities on a topic linked to each other. Given a target Web page, we propose to use a content-biased PageRank on the Wikipedia graph to rank the related entities. Furthermore, in order to recommend high-quality advertising keywords, we also add an advertisement-biased factor into our model. With these two biases, advertising keywords that are both relevant to a target Web page and valuable for advertising are recommended. In our experiments, several state-of-the-art approaches for keyword recommendation are compared. The experimental results demonstrate that our proposed approach produces substantial improvement in the precision of the top 20 recommended keywords on short-text Web pages over existing approaches.
Leveraging Auxiliary Data for Learning to Rank
Authors: Zhou Ke , Bai Jing , Zha Hongyuan , Xue Gui-Rong
Published: October 2012

In learning to rank, both the quality and quantity of the training data have significant impacts on the performance of the learned ranking functions. However, in many applications, there are usually not sufficient labeled training data for the construction of an accurate ranking model. It is therefore desirable to leverage existing training data from other tasks when learning the ranking function for a particular task, an important problem which we tackle in this article utilizing a boosting framework withtransfer learning. In particular, we propose to adaptively learn transferable representations called super-features from the training data of both the target task and the auxiliary task. Those super-features and the coefficients for combining them are learned in an iterative stage-wise fashion. Unlike previous transfer learning methods, the super-features can be adaptively learned by weak learners from the data. Therefore, the proposed framework is sufficiently flexible to deal with complicated common structures among different learning tasks. We evaluate the performance of the proposed transfer learning method for two datasets from the Letor collection and one dataset collected from a commercial search engine, and we also compare our methods with several existing transfer learning methods. Our results demonstrate that the proposed method can enhance the ranking functions of the target tasks utilizing the training data from the auxiliary tasks.
Machine Recognition of Music Emotion
Authors: Yang Yi-Hsuan , Chen Homer H.
Published: October 2012

The proliferation of MP3 players and the exploding amount of digital music content call for novel ways of music organization and retrieval to meet the ever-increasing demand for easy and effective information access. As almost every music piece is created to convey emotion, music organization and retrieval by emotion is a reasonable way of accessing music information. A good deal of effort has been made in the music information retrieval community to train a machine to automatically recognize the emotion of a music signal. A central issue of machine recognition of music emotion is the conceptualization of emotion and the associated emotion taxonomy. Different viewpoints on this issue have led to the proposal of different ways of emotion annotation, model training, and result visualization. This article provides a comprehensive review of the methods that have been proposed for music emotion recognition. Moreover, as music emotion recognition is still in its infancy, there are many open issues. We review the solutions that have been proposed to address these issues and conclude with suggestions for further research.
Robust Video Content Analysis via Transductive Learning
Authors: Ewerth Ralph , Mühling Markus , Freisleben Bernd
Published: October 2012

Reliable video content analysis is an essential prerequisite for effective video search. An important current research question is how to develop robust video content analysis methods that produce satisfactory results for a large variety of video sources, distribution platforms, genres, and content. The work presented in this article exploits the observation that the appearance of objects and events is often related to a particular video sequence, episode, program, or broadcast. This motivates our idea of considering the content analysis task for a single video or episode as a transductive setting: the final classification model must be optimal for the given video only, and not in general, as expected for inductive learning. For this purpose, the unlabeled video test data have to be used in the learning process. In this article, a transductive learning framework for robust video content analysis based on feature selection and ensemble classification is presented. In contrast to related transductive approaches for video analysis (e.g., for concept detection), the framework is designed in a general manner and not only for a single task. The proposed framework is applied to the following video analysis tasks: shot boundary detection, face recognition, semantic video retrieval, and semantic indexing of computer game sequences. Experimental results for diverse video analysis tasks and large test sets demonstrate that the proposed transductive framework improves the robustness of the underlying state-of-the-art approaches, whereas transductive support vector machines do not solve particular tasks in a satisfactory manner.
Video Human Motion Recognition Using a Knowledge-Based Hybrid Method Based on a Hidden Markov Model
Authors: Suk Myunghoon , Ramadass Ashok , Jin Yohan , Prabhakaran B.
Published: October 2012

Human motion recognition in video data has several interesting applications in fields such as gaming, senior/assisted-living environments, and surveillance. In these scenarios, we may have to consider adding new motion classes (i.e., new types of human motions to be recognized), as well as new training data (e.g., for handling different type of subjects). Hence, both the accuracy of classification and training time for the machine learning algorithms become important performance parameters in these cases. In this article, we propose a knowledge-based hybrid (KBH) method that can compute the probabilities for hidden Markov models (HMMs) associated with different human motion classes. This computation is facilitated by appropriately mixing features from two different media types (3D motion capture and 2D video). We conducted a variety of experiments comparing the proposed KBH for HMMs and the traditional Baum-Welch algorithms. With the advantage of computing the HMM parameter in a noniterative manner, the KBH method outperforms the Baum-Welch algorithm both in terms of accuracy as well as in reduced training time. Moreover, we show in additional experiments that the KBH method also outperforms the linear support vector machine (SVM).
Robust Visual Tracking Using an Effective Appearance Model Based on Sparse Coding
Authors: Zhang Shengping , Yao Hongxun , Sun Xin , Liu Shaohui
Published: October 2012

Intelligent video surveillance is currently one of the most active research topics in computer vision, especially when facing the explosion of video data captured by a large number of surveillance cameras. As a key step of an intelligent surveillance system, robust visual tracking is very challenging for computer vision. However, it is a basic functionality of the human visual system (HVS). Psychophysical findings have shown that the receptive fields of simple cells in the visual cortex can be characterized as being spatially localized, oriented, and bandpass, and it forms a sparse, distributed representation of natural images. In this article, motivated by these findings, we propose an effective appearance model based on sparse coding and apply it in visual tracking. Specifically, we consider the responses of general basis functions extracted by independent component analysis on a large set of natural image patches as features and model the appearance of the tracked target as the probability distribution of these features. In order to make the tracker more robust to partial occlusion, camouflage environments, pose changes, and illumination changes, we further select features that are related to the target based on an entropy-gain criterion and ignore those that are not. The target is finally represented by the probability distribution of those related features. The target search is performed by minimizing the Matusita distance between the distributions of the target model and a candidate using Newton-style iterations. The experimental results validate that the proposed method is more robust and effective than three state-of-the-art methods.
Context-Aware Semi-Local Feature Detector
Authors: Ji Rongrong , Yao Hongxun , Tian Qi , Xu Pengfei , Sun Xiaoshuai , Liu Xianming
Published: October 2012

How can interest point detectors benefit from contextual cues? In this articles, we introduce a context-aware semi-local detector (CASL) framework to give a systematic answer with three contributions: (1) We integrate the context of interest points to recurrently refine their detections. (2) This integration boosts interest point detectors from the traditionally local scale to a semi-local scale to discover more discriminative salient regions. (3) Such context-aware structure further enables us to bring forward category learning (usually in the subsequent recognition phase) into interest point detection to locate category-aware, meaningful salient regions.

Our CASL detector consists of two phases. The first phase accumulates multiscale spatial correlations of local features into a difference of contextual Gaussians (DoCG) field. DoCG quantizes detector context to highlight contextually salient regions at a semi-local scale, which also reveals visual attentions to a certain extent. The second phase locates contextual peaks by mean shift search over the DoCG field, which subsequently integrates contextual cues into feature description. This phase enables us to integrate category learning into mean shift search kernels. This learning-based CASL mechanism produces more category-aware features, which substantially benefits the subsequent visual categorization process. We conducted experiments in image search, object characterization, and feature detector repeatability evaluations, which reported superior discriminability and comparable repeatability to state-of-the-art works.
Distinguishing Facial Features for Ethnicity-Based 3D Face Recognition
Authors: Berretti Stefano , Del Bimbo Alberto , Pala Pietro
Published: October 2012

Among different approaches for 3D face recognition, solutions based on local facial characteristics are very promising, mainly because they can manage facial expression variations by assigning different weights to different parts of the face. However, so far, a few works have investigated the individual relevance that local features play in 3D face recognition with very simple solutions applied in the practice.

In this article, a local approach to 3D face recognition is combined with a feature selection model to study the relative relevance of different regions of the face for the purpose of discriminating between different subjects. The proposed solution is experimented using facial scans of the
Face Recognition Grand Challenge
dataset. Results of the experimentation are two-fold: they quantitatively demonstrate the assumption that different regions of the face have different relevance for face discrimination and also show that the relevance of facial regions changes for different ethnic groups.
A Generic Approach for Systematic Analysis of Sports Videos
Authors: Zhang Ning , Duan Ling-Yu , Li Lingfang , Huang Qingming , Du Jun , Gao Wen , Guan Ling
Published: October 2012

Various innovative and original works have been applied and proposed in the field of sports video analysis. However, individual works have focused on sophisticated methodologies with particular sport types and there has been a lack of scalable and holistic frameworks in this field. This article proposes a solution and presents a systematic and generic approach which is experimented on a relatively large-scale sports consortia. The system aims at the event detection scenario of an input video with an orderly sequential process. Initially, domain knowledge-independent local descriptors are extracted homogeneously from the input video sequence. Then the video representation is created by adopting a bag-of-visual-words (BoW) model. The video’s genre is first identified by applying the k-nearest neighbor (k-NN) classifiers on the initially obtained video representation, and various dissimilarity measures are assessed and evaluated analytically. Subsequently, an unsupervised probabilistic latent semantic analysis (PLSA)-based approach is employed at the same histogram-based video representation, characterizing each frame of video sequence into one of four view groups, namely closed-up-view, mid-view, long-view, and outer-field-view. Finally, a hidden conditional random field (HCRF) structured prediction model is utilized for interesting event detection. From experimental results, k-NN classifier using KL-divergence measurement demonstrates the best accuracy at 82.16% for genre categorization. Supervised SVM and unsupervised PLSA have average classification accuracies at 82.86% and 68.13%, respectively. The HCRF model achieves 92.31% accuracy using the unsupervised PLSA based label input, which is comparable with the supervised SVM based input at an accuracy of 93.08%. In general, such a systematic approach can be widely applied in processing massive videos generically.
Dynamic Landmarking for Surface Feature Identification and Change Detection
Authors: Wagstaff Kiri L. , Panetta Julian , Ansar Adnan , Greeley Ronald , Pendleton Hoffer Mary , Bunte Melissa , Schörghofer Norbert
Published: October 2012

Given the large volume of images being sent back from remote spacecraft, there is a need for automated analysis techniques that can quickly identify interesting features in those images. Feature identification in individual images and automated change detection in multiple images of the same target are valuable for scientific studies and can inform subsequent target selection. We introduce a new approach to orbital image analysis called dynamic landmarking. It focuses on the identification and comparison of visually salient features in images. We have evaluated this approach on images collected by five Mars orbiters. These evaluations were motivated by three scientific goals: to study fresh impact craters, dust devil tracks, and dark slope streaks on Mars. In the process we also detected a different kind of surface change that may indicate seasonally exposed bedforms. These experiences also point the way to how this approach could be used in an onboard setting to analyze and prioritize data as it is collected.
AEGIS Automated Science Targeting for the MER Opportunity Rover
Authors: Estlin Tara A. , Bornstein Benjamin J. , Gaines Daniel M. , Anderson Robert C. , Thompson David R. , Burl Michael , Castaño Rebecca , Judd Michele
Published: October 2012

The Autonomous Exploration for Gathering Increased Science (AEGIS) system enables automated data collection by planetary rovers. AEGIS software was uploaded to the Mars Exploration Rover (MER) mission’s Opportunity rover in December 2009 and has successfully demonstrated automated onboard targeting based on scientist-specified objectives. Prior to AEGIS, images were transmitted from the rover to the operations team on Earth; scientists manually analyzed the images, selected geological targets for the rover’s remote-sensing instruments, and then generated a command sequence to execute the new measurements. AEGIS represents a significant paradigm shift---by using onboard data analysis techniques, the AEGIS software uses scientist input to select high-quality science targets with no human in the loop. This approach allows the rover to autonomously select and sequence targeted observations in an opportunistic fashion, which is particularly applicable for narrow field-of-view instruments (such as the MER Mini-TES spectrometer, the MER Panoramic camera, and the 2011 Mars Science Laboratory (MSL) ChemCam spectrometer). This article provides an overview of the AEGIS automated targeting capability and describes how it is currently being used onboard the MER mission Opportunity rover.
Using Clustering and Metric Learning to Improve Science Return of Remote Sensed Imagery
Authors: Hayden David S. , Chien Steve , Thompson David R. , Castaño Rebecca
Published: October 2012

Current and proposed remote space missions, such as the proposed aerial exploration of Titan by an aerobot, often can collect more data than can be communicated back to Earth. Autonomous selective downlink algorithms can choose informative subsets of data to improve the science value of these bandwidth-limited transmissions. This requires statistical descriptors of the data that reflect very abstract and subtle distinctions in science content. We propose a metric learning strategy that teaches algorithms how best to cluster new data based on training examples supplied by domain scientists. We demonstrate that clustering informed by metric learning produces results that more closely match multiple scientists’ labelings of aerial data than do clusterings based on random or periodic sampling. A new metric-learning strategy accommodates training sets produced by multiple scientists with different and potentially inconsistent mission objectives. Our methods are fit for current spacecraft processors (e.g., RAD750) and would further benefit from more advanced spacecraft processor architectures, such as OPERA.
Transfer Metric Learning with Semi-Supervised Extension
Authors: Zhang Yu , Yeung Dit-Yan
Published: October 2012

Distance metric learning plays a very crucial role in many data mining algorithms because the performance of an algorithm relies heavily on choosing a good metric. However, the labeled data available in many applications is scarce, and hence the metrics learned are often unsatisfactory. In this article, we consider a transfer-learning setting in which some related source tasks with labeled data are available to help the learning of the target task. We first propose a convex formulation for multitask metric learning by modeling the task relationships in the form of a task covariance matrix. Then we regard transfer learning as a special case of multitask learning and adapt the formulation of multitask metric learning to the transfer-learning setting for our method, called transfer metric learning (TML). In TML, we learn the metric and the task covariances between the source tasks and the target task under a unified convex formulation. To solve the convex optimization problem, we use an alternating method in which each subproblem has an efficient solution. Moreover, in many applications, some unlabeled data is also available in the target task, and so we propose a semi-supervised extension of TML called STML to further improve the generalization performance by exploiting the unlabeled data based on the manifold assumption. Experimental results on some commonly used transfer-learning applications demonstrate the effectiveness of our method.
Mining Travel Patterns from Geotagged Photos
Authors: Zheng Yan-Tao , Zha Zheng-Jun , Chua Tat-Seng
Published: October 2012

Recently, the phenomenal advent of photo-sharing services, such as Flickr and Panoramio, have led to volumous community-contributed photos with text tags, timestamps, and geographic references on the Internet. The photos, together with their time- and geo-references, become the digital footprints of photo takers and implicitly document their spatiotemporal movements. This study aims to leverage the wealth of these enriched online photos to analyze people’s travel patterns at the local level of a tour destination. Specifically, we focus our analysis on two aspects: (1) tourist movement patterns in relation to the regions of attractions (RoA), and (2) topological characteristics of travel routes by different tourists. To do so, we first build a statistically reliable database of travel paths from a noisy pool of community-contributed geotagged photos on the Internet. We then investigate the tourist traffic flow among different RoAs by exploiting the Markov chain model. Finally, the topological characteristics of travel routes are analyzed by performing a sequence clustering on tour routes. Testings on four major cities demonstrate promising results of the proposed system.
Factorization Machines with libFM
Authors: Rendle Steffen
Published: October 2012

Factorization approaches provide high accuracy in several important prediction problems, for example, recommender systems. However, applying factorization approaches to a new prediction problem is a nontrivial task and requires a lot of expert knowledge. Typically, a new model is developed, a learning algorithm is derived, and the approach has to be implemented.

Factorization machines (FM) are a generic approach since they can mimic most factorization models just by feature engineering. This way, factorization machines combine the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain.
libFM
is a software implementation for factorization machines that features stochastic gradient descent (SGD) and alternating least-squares (ALS) optimization, as well as Bayesian inference using Markov Chain Monto Carlo (MCMC). This article summarizes the recent research on factorization machines both in terms of modeling and learning, provides extensions for the ALS and MCMC algorithms, and describes the software tool
libFM
.
Bootstrapping a Game with a Purpose for Commonsense Collection
Authors: Herdağdelen Amaç , Baroni Marco
Published: October 2012

Text mining has been very successful in extracting huge amounts of commonsense knowledge from data, but the extracted knowledge tends to be extremely noisy. Manual construction of knowledge repositories, on the other hand, tends to produce high-quality data in very small amounts. We propose an architecture to combine the best of both worlds: A game with a purpose that induces humans to clean up data automatically extracted by text mining. First, a text miner trained on a set of known commonsense facts harvests many more candidate facts from corpora. Then, a simple slot-machine-with-a-purpose game presents these candidate facts to the players for verification by playing. As a result, a new dataset of high precision commonsense knowledge is created. This combined architecture is able to produce significantly better commonsense facts than the state-of-the-art text miner alone. Furthermore, we report that bootstrapping (i.e., training the text miner on the output of the game) improves the subsequent performance of the text miner.
Identify Online Store Review Spammers via Social Review Graph
Authors: Wang Guan , Xie Sihong , Liu Bing , Yu Philip S.
Published: October 2012

Online shopping reviews provide valuable information for customers to compare the quality of products, store services, and many other aspects of future purchases. However, spammers are joining this community trying to mislead consumers by writing fake or unfair reviews to confuse the consumers. Previous attempts have used reviewers’ behaviors such as text similarity and rating patterns, to detect spammers. These studies are able to identify certain types of spammers, for instance, those who post many similar reviews about one target. However, in reality, there are other kinds of spammers who can manipulate their behaviors to act just like normal reviewers, and thus cannot be detected by the available techniques.

In this article, we propose a novel concept of review graph to capture the relationships among all reviewers, reviews and stores that the reviewers have reviewed as a heterogeneous graph. We explore how interactions between nodes in this graph could reveal the cause of spam and propose an iterative computation model to identify suspicious reviewers. In the review graph, we have three kinds of nodes, namely, reviewer, review, and store. We capture their relationships by introducing three fundamental concepts, the trustiness of reviewers, the honesty of reviews, and the reliability of stores, and identifying their interrelationships: a reviewer is more trustworthy if the person has written more honesty reviews; a store is more reliable if it has more positive reviews from trustworthy reviewers; and a review is more honest if many other honest reviews support it. This is the first time such intricate relationships have been identified for spam detection and captured in a graph model. We further develop an effective computation method based on the proposed graph model. Different from any existing approaches, we do not use an review text information. Our model is thus complementary to existing approaches and able to find more difficult and subtle spamming activities, which are agreed upon by human judges after they evaluate our results.
Using Stochastic Models to Describe and Predict Social Dynamics of Web Users
Authors: Lerman Kristina , Hogg Tad
Published: October 2012

The popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both the hosts of social media content and its consumers. Accurate and timely prediction would enable hosts to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the content. Predicting the popularity of content in social media is challenging due to the complex interactions between content quality and how the social media site highlights its content. Moreover, most social media sites selectively present content that has been highly rated by similar users, whose similarity is indicated implicitly by their behavior or explicitly by links in a social network. While these factors make it difficult to predict popularity
a priori
, stochastic models of user behavior on these sites can allow predicting popularity based on early user reactions to new content. By incorporating the various mechanisms through which web sites display content, such models improve on predictions that are based on simply extrapolating from the early votes. Specifically, for one such site, the news aggregator Digg, we show how a stochastic model distinguishes the effect of the increased visibility due to the network from how interested users are in the content. We find a wide range of interest, distinguishing stories primarily of interest to users in the network (“niche interests”) from those of more general interest to the user community. This distinction is useful for predicting a story’s eventual popularity from users’ early reactions to the story.
Latent Community Topic Analysis
Authors: Yin Zhijun , Cao Liangliang , Gu Quanquan , Han Jiawei
Published: October 2012

This article studies the problem of latent community topic analysis in text-associated graphs. With the development of social media, a lot of user-generated content is available with user networks. Along with rich information in networks, user graphs can be extended with text information associated with nodes. Topic modeling is a classic problem in text mining and it is interesting to discover the latent topics in text-associated graphs. Different from traditional topic modeling methods considering links, we incorporate community discovery into topic analysis in text-associated graphs to guarantee the topical coherence in the communities so that users in the same community are closely linked to each other and share common latent topics. We handle topic modeling and community discovery in the same framework. In our model we separate the concepts of community and topic, so one community can correspond to multiple topics and multiple communities can share the same topic. We compare different methods and perform extensive experiments on two real datasets. The results confirm our hypothesis that topics could help understand community structure, while community structure could help model topics.
Introduction to the Special Section on Search and Mining User-Generated Content
Authors: Cortizo José Carlos , Carrero Francisco , Cantador Iván , Troyano José Antonio , Rosso Paolo
Published: October 2012

The primary goal of this special section of
ACM Transactions on Intelligent Systems and Technology
is to foster research in the interplay between Social Media, Data/Opinion Mining and Search, aiming to reflect the actual developments in technologies that exploit user-generated content.
Twitter, MySpace, Digg
Authors: Paltoglou Georgios , Thelwall Mike
Published: October 2012

Sentiment analysis is a growing area of research with significant applications in both industry and academia. Most of the proposed solutions are centered around supervised, machine learning approaches and review-oriented datasets. In this article, we focus on the more common informal textual communication on the Web, such as online discussions, tweets and social network comments and propose an intuitive, less domain-specific, unsupervised, lexicon-based approach that estimates the level of emotional intensity contained in text in order to make a prediction. Our approach can be applied to, and is tested in, two different but complementary contexts: subjectivity detection and polarity classification. Extensive experiments were carried on three real-world datasets, extracted from online social Web sites and annotated by human evaluators, against state-of-the-art supervised approaches. The results demonstrate that the proposed algorithm, even though unsupervised, outperforms machine learning solutions in the majority of cases, overall presenting a very robust and reliable solution for sentiment analysis of informal communication on the Web.
Leveraging Social Bookmarks from Partially Tagged Corpus for Improved Web Page Clustering
Authors: Trivedi Anusua , Rai Piyush , Daumé Hal , Duvall Scott L.
Published: October 2012

Automatic clustering of Web pages helps a number of information retrieval tasks, such as improving user interfaces, collection clustering, introducing diversity in search results, etc. Typically, Web page clustering algorithms use only features extracted from the page-text. However, the advent of social-bookmarking Web sites, such as StumbleUpon.com and Delicious.com, has led to a huge amount of user-generated content such as the social tag information that is associated with the Web pages. In this article, we present a subspace based feature extraction approach that leverages the social tag information to complement the page-contents of a Web page for extracting beter features, with the goal of improved clustering performance. In our approach, we consider page-text and tags as two separate views of the data, and learn a shared subspace that maximizes the correlation between the two views. Any clustering algorithm can then be applied in this subspace. We then present an extension that allows our approach to be applicable even if the Web page corpus is only partially tagged, that is, when the social tags are present for not all, but only for a small number of Web pages. We compare our subspace based approach with a number of baselines that use tag information in various other ways, and show that the subspace based approach leads to improved performance on the Web page clustering task. We also discuss some possible future work including an active learning extension that can help in choosing which Web pages to get tags for, if we only can get the social tags for only a small number of Web pages.
Information Retrieval in the Commentsphere
Authors: Potthast Martin , Stein Benno , Loose Fabian , Becker Steffen
Published: October 2012

This article studies information retrieval tasks related to Web comments. Prerequisite of such a study and a main contribution of the article is a unifying survey of the research field. We identify the most important retrieval tasks related to comments, namely filtering, ranking, and summarization. Within these tasks, we distinguish two paradigms according to which comments are utilized and which we designate as
comment-targeting
and
comment-exploiting
. Within the first paradigm, the comments themselves form the retrieval targets. Within the second paradigm, the commented
items
form the retrieval targets (i.e., comments are used as an additional information source to improve the retrieval performance for the commented items). We report on four case studies to demonstrate the exploration of the commentsphere under information retrieval aspects: comment filtering, comment ranking, comment summarization and cross-media retrieval. The first three studies deal primarily with comment-targeting retrieval, while the last one deals with comment-exploiting retrieval. Throughout the article, connections to information retrieval research are pointed out.
Entity-Relationship Queries over Wikipedia
Authors: Li Xiaonan , Li Chengkai , Yu Cong
Published: October 2012

Wikipedia is the largest user-generated knowledge base. We propose a structured query mechanism,
entity-relationship query
, for searching entities in the Wikipedia corpus by their properties and interrelationships. An entity-relationship query consists of multiple predicates on desired entities. The semantics of each predicate is specified with keywords. Entity-relationship query searches entities directly over text instead of preextracted structured data stores. This characteristic brings two benefits: (1) Query semantics can be intuitively expressed by keywords; (2) It only requires rudimentary entity annotation, which is simpler than explicitly extracting and reasoning about complex semantic information before query-time. We present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model (BCM) for accurate ranking of query answers. We also explore various weighting schemes for further improving the accuracy of BCM. We test our ideas on a 2008 version of Wikipedia using a collection of 45 queries pooled from INEX entity ranking track and our own crafted queries. Experiments show that the ranking and weighting schemes are both effective, particularly on multipredicate queries.