OpenRaiser/Intern-Atlas huggingface dataset:
Wenzher: Comprehensive vertical search for healthcare domain
Authors: Liqiang Nie, Tao Li, Mohammad Akbari, Jialie Shen, Tat-Seng Chua
Venue: Conference SIGIR 2014
Year: 2014

Online health seeking has transformed the way of health knowledge exchange and reusability. The existing general and vertical health search engines, however, just routinely return lists of matched documents or question answer (QA) pairs, which may overwhelm the seekers or not sufficiently meet the seekers' expectations. Instead, our multilingual system is able to return one multi-faceted answer that is well-structured and precisely extracted from multiple heterogeneous healthcare sources. Further, should the seekers not be satisfied with the returned search results, our system can automatically route the unsolved questions to the professionals with relevant expertise.
Polarity Analysis of Texts Using Discourse Structure
Authors: Bas Heerschop, Frank Goossen, Alexander Hogenboom, Flavius Frasincar, Uzay Kaymak, Franciska de Jong
Venue: Conference CIKM 2011
Year: 2011

Sentiment analysis has applications in many areas and the exploration of its potential has only just begun. We propose Pathos, a framework which performs document sentiment analysis (partly) based on a document's discourse structure. We hypothesize that by splitting a text into important and less important text spans, and by subsequently making use of this information by weighting the sentiment conveyed by distinct text spans in accordance with their importance, we can improve the performance of a sentiment classifier. A document's discourse structure is obtained by applying Rhetorical Structure Theory on sentence level. When controlling for each considered method's structural bias towards positive classifications, weights optimized by a genetic algorithm yield an improvement in sentiment classification accuracy and macro-level F1 score on documents of 4.5% and 4.7%, respectively, in comparison to a baseline not taking into account discourse structure.
Globally optimal solution to multi-object tracking with merged measurements
Authors: João F. Henriques, Rui Caseiro, Jorge P. Batista
Venue: Conference ICCV 2011
Year: 2011

Multiple object tracking has been formulated recently as a global optimization problem, and solved efficiently with optimal methods such as the Hungarian Algorithm. A severe limitation is the inability to model multiple objects that are merged into a single measurement, and track them as a group, while retaining optimality. This work presents a new graph structure that encodes these multiple-match events as standard one-to-one matches, allowing computation of the solution in polynomial time. Since identities are lost when objects merge, an efficient method to identify groups is also presented, as a flow circulation problem. The problem of tracking individual objects across groups is then posed as a standard optimal assignment. Experiments show increased performance on the PETS 2006 and 2009 datasets compared to state-of-the-art algorithms.
Tracking a large number of objects from multiple views
Authors: Zheng Wu 0003, Nickolay I. Hristov, Tyson L. Hedrick, Thomas H. Kunz, Margrit Betke
Venue: Conference ICCV 2009
Year: 2009

We propose a multi-object multi-camera framework for tracking large numbers of tightly-spaced objects that rapidly move in three dimensions. We formulate the problem of finding correspondences across multiple views as a multidimensional assignment problem and use a greedy randomized adaptive search procedure to solve this NP-hard problem efficiently. To account for occlusions, we relax the one-to-one constraint that one measurement corresponds to one object and iteratively solve the relaxed assignment problem. After correspondences are established, object trajectories are estimated by stereoscopic reconstruction using an epipolar-neighborhood search. We embedded our method into a tracker-to-tracker multi-view fusion system that not only obtains the three-dimensional trajectories of closely-moving objects but also accurately settles track uncertainties that could not be resolved from single views due to occlusion. We conducted experiments to validate our greedy assignment procedure and our technique to recover from occlusions. We successfully track hundreds of flying bats and provide an analysis of their group behavior based on 150 reconstructed 3D trajectories.
A peek into the future: Predicting the evolution of popularity in user generated content
Authors: Mohamed Ahmed, Stella Spagna, Felipe Huici, Saverio Niccolini
Venue: Conference WSDM 2013
Year: 2013

Content popularity prediction finds application in many areas, including media advertising, content caching, movie revenue estimation, traffic management and macro-economic trends forecasting, to name a few. However, predicting this popularity is difficult due to, among others, the effects of external phenomena, the influence of context such as locality and relevance to users,and the difficulty of forecasting information cascades.
CoupledLP: Link prediction in coupled networks
Authors: Yuxiao Dong, Jing Zhang, Jie Tang, Nitesh V. Chawla, Bai Wang
Venue: Conference KDD 2015
Year: 2015

We study the problem of link prediction in coupled networks, where we have the structure information of one (source) network and the interactions between this network and another (target) network. The goal is to predict the missing links in the target network. The problem is extremely challenging as we do not have any information of the target network. Moreover, the source and target networks are usually heterogeneous and have different types of nodes and links. How to utilize the structure information in the source network for predicting links in the target network? How to leverage the heterogeneous interactions between the two networks for the prediction task?
Rapid selection of reliable templates for visual tracking
Authors: Nicolas Alt, Stefan Hinterstoisser, Nassir Navab
Venue: Conference CVPR 2010
Year: 2010

We propose a method that rates the suitability of given templates for template-based tracking in real-time. This is important for applications with online template selection, such as SLAM, where it is essential to track a low number of preferably reliable templates. Our approach is based on simple image features specifically designed to identify texture properties which are problematic for tracking. During a training step, a support vector régresser is learned. It uses a tracking quality measure which considers both convergence rate and speed obtained by simulation of many tracking attempts. Finally, a minimum set of image features is identified to speedup the online selection process. In experiments on real-world video sequences our method improved the detection rate of an existing tracking-by-detection system by 8% on average.
Asnets: A benchmark dataset of aligned social networks for cross-platform user modeling
Authors: Xuezhi Cao, Yong Yu
Venue: Conference CIKM 2016
Year: 2016

Aligning heterogeneous online social networks is a highly beneficial task proposed in recent years. It targets at automatically aligning accounts from multiple networks by whether they are held by the same natural person. Aligning the networks can improve personalized services by cross-platform user modeling, and is the prerequisite for cross-network analysis. However, there is currently no public benchmark dataset available due to its recency. As performances of this task depend highly on the dataset, experiments using different private datasets are not directly comparable. Therefore, in this paper we propose ASNets, a benchmark dataset with two sets of aligned social networks. With this dataset, we can now properly evaluate different approaches and compare them fairly. The two sets of aligned networks have 328,224 and 141,614 aligned users respectively, covering multilingual usage (Chinese and English) and various types of social networks including general purposed networks, review sites and microblogging sites. We describe the collecting methodology and statistics in details, and evaluate several state-of-the-art network aligning approaches. Beside introducing the dataset, we further propose several potential research directions that benefit from ASNets.
Probabilistic spatial context models for scene content understanding
Authors: Amit Singhal 0001, Jiebo Luo 0001, Weiyu Zhu
Venue: Conference CVPR 2003
Year: 2003

Scene content understanding facilitates a large number of applications, ranging from content-based image retrieval to other multimedia applications. Material detection refers to the problem of identifying key semantic material types (such as sky, grass, foliage, water, and snow in images). In this paper, we present a holistic approach to determining scene content, based on a set of individual material detection algorithms, as well as probabilistic spatial context models. A major limitation of individual material detectors is the significant number of misclassifications that occur because of the similarities in color and texture characteristics of various material types. We have developed a spatial context-aware material detection system that reduces misclassification by constraining the beliefs to conform to the probabilistic spatial context models. Experimental results show that the accuracy of materials detection is improved by 13% using the spatial context models over the individual material detectors themselves.
Video-based face recognition using probabilistic appearance manifolds
Authors: Kuang-Chih Lee, Jeffrey Ho, Ming-Hsuan Yang 0001, David J. Kriegman
Venue: Conference CVPR 2003
Year: 2003

This paper presents a method to model and recognize human faces in video sequences. Each registered person is represented by a low-dimensional appearance manifold in the ambient image space, the complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds), and the connectivity among them. Each pose manifold is approximated by an affine plane. To construct this representation, exemplars are sampled from videos, and these exemplars are clustered with a K-means algorithm; each cluster is represented as a plane computed through principal component analysis (PCA). The connectivity between the pose manifolds encodes the transition probability between images in each of the pose manifold and is learned from a training video sequences. A maximum a posteriori formulation is presented for face recognition in test video sequences by integrating the likelihood that the input image comes from a particular pose manifold and the transition probability to this pose manifold from the previous frame. To recognize faces with partial occlusion, we introduce a weight mask into the process. Extensive experiments demonstrate that the proposed algorithm outperforms existing frame-based face recognition methods with temporal voting schemes.
Monitoring least squares models of distributed streams
Authors: Moshe Gabel, Daniel Keren, Assaf Schuster
Venue: Conference KDD 2015
Year: 2015

Least squares regression is widely used to understand and predict data behavior in many fields. As data evolves, regression models must be recomputed, and indeed much work has focused on quick, efficient and accurate computation of linear regression models. In distributed streaming settings, however, periodically recomputing the global model is wasteful: communicating new observations or model updates is required even when the model is, in practice, unchanged. This is prohibitive in many settings, such as in wireless sensor networks, or when the number of nodes is very large. The alternative, monitoring prediction accuracy, is not always sufficient: in some settings, for example, we are interested in the model's coefficients, rather than its predictions. We propose the first monitoring algorithm for multivariate regression models of distributed data streams that guarantees a bounded model error. It maintains an accurate estimate using a fraction of the communication by recomputing only when the precomputed model is sufficiently far from the (hypothetical) current global model. When the global model is stable, no communication is needed.
A cost-effective recommender system for taxi drivers
Authors: Meng Qu, Hengshu Zhu, Junming Liu, Guannan Liu, Hui Xiong
Venue: Conference KDD 2014
Year: 2014

The GPS technology and new forms of urban geography have changed the paradigm for mobile services. As such, the abundant availability of GPS traces has enabled new ways of doing taxi business. Indeed, recent efforts have been made on developing mobile recommender systems for taxi drivers using Taxi GPS traces. These systems can recommend a sequence of pick-up points for the purpose of maximizing the probability of identifying a customer with the shortest driving distance. However, in the real world, the income of taxi drivers is strongly correlated with the effective driving hours. In other words, it is more critical for taxi drivers to know the actual driving routes to minimize the driving time before finding a customer. To this end, in this paper, we propose to develop a cost-effective recommender system for taxi drivers. The design goal is to maximize their profits when following the recommended routes for finding passengers. Specifically, we first design a net profit objective function for evaluating the potential profits of the driving routes. Then, we develop a graph representation of road networks by mining the historical taxi GPS traces and provide a Brute-Force strategy to generate optimal driving route for recommendation. However, a critical challenge along this line is the high computational cost of the graph based approach. Therefore, we develop a novel recursion strategy based on the special form of the net profit function for searching optimal candidate routes efficiently. Particularly, instead of recommending a sequence of pick-up points and letting the driver decide how to get to those points, our recommender system is capable of providing an entire driving route, and the drivers are able to find a customer for the largest potential profit by following the recommendations. This makes our recommender system more practical and profitable than other existing recommender systems. Finally, we carry out extensive experiments on a real-world data set collected from the San Francisco Bay area and the experimental results clearly validate the effectiveness of the proposed recommender system.
Co-clustering by block value decomposition
Authors: Bo Long, Zhongfei (Mark) Zhang, Philip S. Yu
Venue: Conference KDD 2005
Year: 2005

Dyadic data matrices, such as co-occurrence matrix, rating matrix, and proximity matrix, arise frequently in various important applications. A fundamental problem in dyadic data analysis is to find the hidden block structure of the data matrix. In this paper, we present a new coclustering framework, block value decomposition(BVD), for dyadic data, which factorizes the dyadic data matrix into three components, the row-coefficient matrix R, the block value matrix B, and the column-coefficient matrix C. Under this framework, we focus on a special yet very popular case – non-negative dyadic data, and propose a specific novel co-clustering algorithm that iteratively computes the three decomposition matrices based on the multiplicative updating rules. Extensive experimental evaluations also demonstrate the effectiveness and potential of this framework as well as the specific algorithms for co-clustering, and in particular, for discovering the hidden block structure in the dyadic data.
Improving co-cluster quality with application to product recommendations
Authors: Michail Vlachos, Francesco Fusco, Charalampos Mavroforakis, Anastasios Kyrillidis, Vassilios G. Vassiliadis
Venue: Conference CIKM 2014
Year: 2014

Businesses store an ever increasing amount of historical customer sales data. Given the availability of such information, it is advantageous to analyze past sales, both for revealing dominant buying patterns, and for providing more targeted recommendations to clients. In this context, co-clustering has proved to be an important data-modeling primitive for revealing latent connections between two sets of entities, such as customers and products. In this work, we introduce a new algorithm for co-clustering that is both scalable and highly resilient to noise. Our method is inspired by k-Means and agglomerative hierarchical clustering approaches: (i) first it searches for elementary co-clustering structures and (ii) then combines them into a better, more compact, solution. The algorithm is flexible as it does not require an explicit number of co-clusters as input, and is directly applicable on large data graphs. We apply our methodology on real sales data to analyze and visualize the connections between clients and products. We showcase a real deployment of the system, and how it has been used for driving a recommendation engine. Finally, we demonstrate that the new methodology can discover co-clusters of better quality and relevance than state-of-the-art co-clustering techniques.
Towards semantic knowledge propagation from text corpus to web images
Authors: Guojun Qi, Charu C. Aggarwal, Thomas S. Huang
Venue: Conference WWW 2011
Year: 2011

In this paper, we study the problem of transfer learning from text to images in the context of network data in which link based bridges are available to transfer the knowledge between the different domains. The problem of classification of image data is often much more challenging than text data because of the following two reasons: (a) Labeled text data is very widely available for classification purposes. On the other hand, this is often not the case for image data, in which a lot of images are available from many sources, but many of them are often not labeled. (b) The image features are not directly related to semantic concepts inherent in class labels. On the other hand, since text data tends to have natural semantic interpretability (because of their human origins), they are often more directly related to class labels.
Community evolution in dynamic multi-mode networks
Authors: Lei Tang, Huan Liu, Jianping Zhang, Zohreh Nazeri
Venue: Conference KDD 2008
Year: 2008

A multi-mode network typically consists of multiple heterogeneous social actors among which various types of interactions could occur. Identifying communities in a multi-mode network can help understand the structural properties of the network, address the data shortage and unbalanced problems, and assist tasks like targeted marketing and finding influential actors within or between groups. In general, a network and the membership of groups often evolve gradually. In a dynamic multi-mode network, both actor membership and interactions can evolve, which poses a challenging problem of identifying community evolution. In this work, we try to address this issue by employing the temporal information to analyze a multi-mode network. A spectral framework and its scalability issue are carefully studied. Experiments on both synthetic data and real-world large scale networks demonstrate the efficacy of our algorithm and suggest its generality in solving problems with complex relationships.
Entity linking on graph data
Authors: Minghe Yu
Venue: Conference WWW 2014
Year: 2014

With the emergence of massive information networks, graph data have become ubiquitous for various applications. Although many graph processing problems have been studied recently, entity linking on graph data has not received enough attention by the academia and industry, which finds vertex pairs that refer to the same entity from two graphs. There are two main research challenges arising in this problem. The first one is how to determine whether two vertices refer to the same entity which is rather hard for graph data, especially uncertain data, e.g., social networks. The second challenge is to efficiently link the vertices. As existing graph data are rather large, it is very important to devise efficient algorithms to achieve high performance. To address these challenges, in this paper we propose a similarity-based method which takes the vertex pairs with similarity larger than a given threshold as linked entities. We extend existing textual similarity and structural similarity to evaluate similarity between vertices from different graphs. To achieve high quality, we also combine them and propose a hybrid similarity. We also discuss new algorithms to efficiently link entities. We conduct experimental studies on real datasets and the results proves show that our hybrid method achieves high performance and outperform the baseline approaches.
Author disambiguation by hierarchical agglomerative clustering with adaptive stopping criterion
Authors: Lei Cen, Eduard C. Dragut, Luo Si, Mourad Ouzzani
Venue: Conference SIGIR 2013
Year: 2013

Entity disambiguation is an important step in many information retrieval applications. This paper proposes new research for entity disambiguation with the focus of name disambiguation in digital libraries. In particular, pairwise similarity is first learned for publications that share the same author name string (ANS) and then a novel Hierarchical Agglomerative Clustering approach with Adaptive Stopping Criterion (HACASC) is proposed to adaptively cluster a set of publications that share a same ANS to individual clusters of publications with different author identities. The HACASC approach utilizes a mixture of kernel ridge regressions to intelligently determine the threshold in clustering. This obtains more appropriate clustering granularity than non-adaptive stopping criterion. We conduct a large scale empirical study with a dataset of more than 2 million publication record pairs to demonstrate the advantage of the proposed HACASC approach.
Event digest: A holistic view on past events
Authors: Arunav Mishra, Klaus Berberich
Venue: Conference SIGIR 2016
Year: 2016

For a general user, easy access to vast amounts of online information available on past events has made retrospection much harder. We propose a problem of automatic event digest generation to aid effective and efficient retrospection. For this, in addition to text, a digest should maximize the reportage of time, geolocations, and entities to present a holistic view on the past event of interest.
Sparql basic graph pattern optimization using selectivity estimation
Authors: Markus Stocker, Andy Seaborne, Abraham Bernstein, Christoph Kiefer, Dave Reynolds
Venue: Conference WWW 2008
Year: 2008

In this paper, we formalize the problem of Basic Graph Pattern (BGP) optimization for SPARQL queries and main memory graph implementations of RDF data. We define and analyze the characteristics of heuristics for selectivity-based static BGP optimization. The heuristics range from simple triple pattern variable counting to more sophisticated selectivity estimation techniques. Customized summary statistics for RDF data enable the selectivity estimation of joined triple patterns and the development of efficient heuristics. Using the Lehigh University Benchmark (LUBM), we evaluate the performance of the heuristics for the queries provided by the LUBM and discuss some of them in more details.