varunr14/newsummary huggingface dataset:
Recently sentiment classification has received considerable attention within the natural language processing research community . However since most recent works regarding sentiment classification have been done in the English language there are accordingly not enough sentiment resources in other languages . Manual construction of reliable sentiment resources is a very difficult and time consuming task . Cross lingual sentiment classification aims to utilize annotated sentiment resources in one language for sentiment classification of text documents in another language . Most existing research works rely on automatic machine translation services to directly project information from one language to another . However different term distribution between original and translated text documents and translation errors are two main problems faced in the case of using only machine translation . To overcome these problems we propose a novel learning model based on active learning and semi supervised co training to incorporate unlabelled data from the target language into the learning process in a bi view framework . This model attempts to enrich training data by adding the most confident automatically labelled examples as well as a few of the most informative manually labelled examples from unlabelled data in an iterative process . Further in this model we consider the density of unlabelled data so as to select more representative unlabelled examples in order to avoid outlier selection in active learning . The proposed model was applied to book review datasets in three different languages . Experiments showed that our model can effectively improve the cross lingual sentiment classification performance and reduce labelling efforts in comparison with some baseline methods . 


Summary: We combine active and semi supervised learning for cross lingual sentiment classification. Density analysis of unlabeled data is used in active learning. We test our proposed model on three different languages. This model reduce manual labelling efforts in cross lingual sentiment classification. Results show that incorporating density analysis can speed up learning process.
The volume of entity centric structured data grows rapidly on the Web . The description of an entity composed of property value pairs has become very large in many applications . To avoid information overload efforts have been made to automatically select a limited number of features to be shown to the user based on certain criteria which is called automatic entity summarization . However to the best of our knowledge there is a lack of extensive studies on how humans rank and select features in practice which can provide empirical support and inspire future research . In this article we present a large scale statistical analysis of the descriptions of entities provided by DBpedia and the abstracts of their corresponding Wikipedia articles to empirically study along several different dimensions which kinds of features are preferable when humans summarize . Implications for automatic entity summarization are drawn from the findings . 


Summary: We empirically study how Wikipedians summarize entity descriptions in practice. We compare entity descriptions in DBpedia with their Wikipedia abstracts. We analyze the length of a summary and the priorities of property values. We analyze the priorities of diversity of and correlation between properties. Implications for automatic entity summarization are drawn from the findings.
In this paper we propose a novel contactless palmprint authentication system where the system uses a CCD camera to capture the user s hand at a distance without any restrictions and touching the device. Furthermore a novel and high performance region of interest ROI extraction method which makes use of nonlinear regression and palm model to extract the ROIs with high success is proposed. Comparative results indicate that the proposed ROI extraction method gives superior performance as compared to the previously proposed point based approaches. To show the performance of the proposed system a novel contactless database has also been created. This database includes images captured from the users who present their hands with various hand positions and orientations in cluttered backgrounds. Furthermore experiments show that the proposed system has achieved a recognition rate of 99.488 and equal error rate of 0.277 on the contactless database of 145 people containing 1752 hand images. 


Summary: A novel unrestricted contactless palmprint image acquisition device is developed. A novel model based ROI extraction method robust to the small errors is proposed. A contactless palmprint DB is constituted by collecting 1752 images from 145 subjects. The images in DB have pose rotation position variations and cluttered backgrounds. The results achieved by the proposed system are encouraging 0.277 EER and 99.488 RR.
Uncertainty is an important idea in information retrieval research but the concept has yet to be fully elaborated and explored . Common assumptions about uncertainty are that it is a negative state and that it will be reduced through information search and retrieval . Research in the domain of uncertainty in illness however has demonstrated that uncertainty is a complex phenomenon that shares a complicated relationship with information . Past research on people living with HIV and individuals who have tested positive for genetic risk for different illnesses has revealed that information and the reduction of uncertainty can in fact produce anxiety and that maintaining uncertainty can be associated with optimism and hope . We review the theory of communication and uncertainty management and offer nine principles based on that theoretical work that can be used to influence IR system design . The principles reflect a view of uncertainty as a multi faceted and dynamic experience one subject to ongoing appraisal and management efforts that include interaction with and use of information in a variety of forms . 


Summary: We review the prevailing discourse around the concept of uncertainty. We describe a communication based theory of uncertainty management. We explore how this theory can inform the design of information retrieval systems.
In many wide area surveillance applications tracking objects is usually accomplished by using network of cameras . A common approach to any multi objects tracking algorithm in a network of cameras comprises of two main steps . First the movement trajectory of each object within the field of view of a camera is extracted and is called object tracklet . Then the set of tracklets are used to determine the persistent trace of each object . In this paper we assume that the tracklets are extracted by a conventional tracking algorithm . The occurrence of occlusion between objects within the viewing scene leads to various types of errors on the extracted tracklets . If these erroneous tracklets are used in a multi object tracking algorithm and ignoring the correction phase then the errors are propagated and affect the results of tracking algorithm . Therefore the true tracklets have to be estimated from the erroneous tracklets . In this paper we propose a variational model for estimating the true tracklets . The variational principle proposed in this model is established by first introducing a variational energy function . Then the erroneous tracklets are used to estimate the true tracklets through optimizing the energy function . The proposed method is evaluated on two well known datasets and a synthetic dataset which is particularly developed to demonstrate the performance of our algorithm under challenging scenarios . The 10 common metrics which are used in other multi objects tracking applications are used for quantitative evaluations . Our experimental results illustrate that our proposed model estimates the true tracklets which improves the overall association performances . 


Summary: Preprocessing of the extracted tracklets before extracting the persistent track An intermediate step on multi object multi camera tracking problem Variational data association model for estimating true tracklets Extracting the least possible true tracklets from erroneous tracklets
Finger vein identification is a new biometric identification technology . While many existing works approach the problem by using shape matching which is the generative method in this paper we introduce a joint discriminative and generative algorithm for the task . Our method considers both the discriminative appearance of local image patches as well as their generative spatial layout . The method is based on the popular vocabulary tree model where we utilize the hidden leaf node layer to calculate a generative confidence to weight the discriminative vote from the leaf node . The training process remains the same as building a conventional vocabulary tree while the prediction process utilizes a proposed point set matching method to support non parametric patch layout matching . In this way the entire model retains the efficiency of the vocabulary tree model which is much lighter than other similar models such as the constellation model . The overall estimation follows the Bayesian theory . Experimental results show that our proposed joint model outperformed the purely generative or discriminative counterpart and can offer competitive performance than existing methods for both the vein authentication and recognition tasks . 


Summary: A joint discriminative and generative vocabulary tree model A non parametric image patch layout model and matching method Evaluation in vein authentication and recognition tasks with good performance Comprehensive in depth discussion of the technique
In this paper we propose an optimization framework to retrieve an optimal group of experts to perform a multi aspect task . While a diverse set of skills are needed to perform a multi aspect task the group of assigned experts should be able to collectively cover all these required skills . We consider three types of multi aspect expert group formation problems and propose a unified framework to solve these problems accurately and efficiently . The first problem is concerned with finding the top k experts for a given task while the required skills of the task are implicitly described . In the second problem the required skills of the tasks are explicitly described using some keywords but each expert has a limited capacity to perform these tasks and therefore should be assigned to a limited number of them . Finally the third problem is the combination of the first and the second problems . Our proposed optimization framework is based on the Facility Location Analysis which is a well known branch of the Operation Research . In our experiments we compare the accuracy and efficiency of the proposed framework with the state of the art approaches for the group formation problems . The experiment results show the effectiveness of our proposed methods in comparison with state of the art approaches . 


Summary: Using facility location analysis as a well known branch of Operation Research to solve an applied problem in information retrieval. Proposing a unified framework to solve three types of problems for expert group formation. Testing our framework on a real test collection. Proof of optimality of the solution.
The study explores the relationship between value attribution and information source use of 17 Chinese business managers during their knowledge management strategic decision making . During semi structured interviews the Chinese business managers half in the telecommunications sector and half in the manufacturing sector were asked to rate 16 information sources on five point Likert Scales . The 16 information sources were grouped into internal external and personal impersonal types . The participants rated the information sources according to five value criteria relevancy comprehensiveness reliability time effort and accessibility . Open ended questions were also asked to get at how and why value attribution affected the participants use of one information source over another during decision making . Findings show that the participants preferred internal personal type of information sources over external impersonal information sources . The differences in value ratings per information source were striking Telecommunications managers rated customers newspapers magazines and conferences trips much lower than the manufacturing managers but they rated corporate library intranet and databases much higher than manufacturing managers . The type of industrial sector therefore highly influenced information source use for decision making by the study s Chinese business managers . Based on this conclusion we added organizational and environmental categories to revise the De Alwis Majid and Chaudhry s typology of factors affecting Chinese managers information source preferences during decision making . 


Summary: We examine information source use during KM strategic decision making. We revised De Alwis et al. s typology of information source preferences. Organizational context is highly determinative of information source use.
This paper presents yet another algorithm for finding polygonal approximations of digital planar curves however with a significant distinction the vertices of an approximating polygon need not lie on the contour itself . This approach gives us more flexibility to reduce the approximation error of the polygon compared to the conventional way where the vertices of the polygon are restricted to lie on the contour . To compute the approximation efficiently we adaptively define a local neighborhood of each point on the contour . The vertices of the polygonal approximation are allowed to move around in the neighborhoods . In addition we demonstrate a general approach where the error measure of an already computed polygonal approximation can possibly be reduced further by vertex relocation without increasing the number of dominant points . Moreover the proposed method is non parametric requiring no parameter to set for any particular application . Suitability of the proposed algorithm is validated by testing on several databases and comparing with existing methods . 


Summary: Polygonal approximations using relaxed approximations allowing the vertices of an approximation to lie outside the contour. Adaptive estimation of contour neighborhoods where the vertices of an approximation are located. A general approach to reduce error measure of any polygonal approximations through vertex relocation.
We propose a new methodology for facial landmark detection . Similar to other state of the art methods we rely on the use of cascaded regression to perform inference and we use a feature representation that results from concatenating 66 HOG descriptors one per landmark . However we propose a novel regression method that substitutes the commonly used Least Squares regressor . This new method makes use of the L 2 1 norm and it is designed to increase the robustness of the regressor to poor initialisations or partial occlusions . Furthermore we propose to use multiple initialisations consisting of both spatial translation and 4 head poses corresponding to different pan rotations . These estimates are aggregated into a single prediction in a robust manner . Both strategies are designed to improve the convergence behaviour of the algorithm so that it can cope with the challenges of in the wild data . We further detail some important experimental details and show extensive performance comparisons highlighting the performance improvement attained by the method proposed here . 


Summary: Facial landmark detection using a cascade of regressors New regression model based on L21 norm Multiple initialisations are used to improve robustness. The method is evaluated on multiple datasets and on the 300W challenge.
Recent research trends in Content based Video Retrieval have shown topic models as an effective tool to deal with the semantic gap challenge. In this scenario this paper has a dual target 1 it is aimed at studying how the use of different topic models pLSA LDA and FSTM affects video retrieval performance 2 a novel incremental topic model IpLSA is presented in order to cope with incremental scenarios in an effective and efficient way. A comprehensive comparison among these four topic models using two different retrieval systems and two reference benchmarking video databases is provided. Experiments revealed that pLSA is the best model in sparse conditions LDA tend to outperform the rest of the models in a dense space and IpLSA is able to work properly in both cases. 


Summary: A study of the use of topic models for video retrieval is presented. A new topic model to deal with incremental retrieval scenarios is proposed. Comparison of four topic models using two different retrieval functions The results highlight the performance differences among the topic models.
Great variances in visual features often present significant challenges in human action recognitions. To address this common problem this paper proposes a statistical adaptive metric learning SAML method by exploring various selections and combinations of multiple statistics in a unified metric learning framework. Most statistics have certain advantages in specific controlled environments and systematic selections and combinations can adapt them to more realistic in the wild scenarios. In the proposed method multiple statistics include means covariance matrices and Gaussian distributions are explicitly mapped or generated in the Riemannian manifolds. Typically d dimensional mean vectors in Rd are mapped to a Rd d space of symmetric positive definite SPD matrices . Subsequently by embedding the heterogeneous manifolds in their tangent Hilbert space subspace combination with minimal deviation is selected from multiple statistics. Then Mahalanobis metrics are introduced to map them back into the Euclidean space. Unified optimizations are finally performed based on the Euclidean distances. In the proposed method subspaces with smaller deviations are selected before metric learning. Therefore by exploring different metric combinations the final learning is more representative and effective than exhaustively learning from all the hybrid metrics. Experimental evaluations are conducted on human action recognitions in both static and dynamic scenarios. Promising results demonstrate that the proposed method performs effectively for human action recognitions in the wild. 


Summary: A statistical adaptive metric learning SAML is proposed to classify action features. SAML explores multiple statistic combinations for feature sets in different scales. Discriminative statistic subspace is learned by a unified metric learning framework. High competitive performances are achieved by SAML on five benchmark databases.
Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades . Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k Nearest Neighbour for classification . Given enough training images the state of the art texture classification methods could achieve very high classification accuracies on some benchmark databases . However when the number of training images is limited which usually happens in real world applications because of the high cost of obtaining labelled data the classification accuracies of those state of the art methods would deteriorate due to the overfitting effect . In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images . By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi manifold analysis framework for texture classification from few training images . A set of new training samples are generated from each training image by a scale and spatial pyramid and then the training samples belonging to each class are modelled by a manifold based on sparse representation . We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors . The framework provides a more compact model than the template matching based texture classification methods and mitigates the overfitting effect . Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images and significantly outperforms the state of the art texture classification approaches on three benchmark datasets . 


Summary: The prime pyramid expands the training dataset with less redundancy. Sparse representation provides a compact model for textural images in each class. Multi manifold analysis improves discriminative power while mitigates overfitting. Reasonably high classification accuracy is achieved with very few training images.
An algorithm for fitting multiple models that characterize the projective relationships between point matches in pairs of images is proposed herein . Specifically the problem of estimating multiple algebraic varieties that relate the projections of 3 dimensional points in one or more views is predominantly turned into a problem of inference over a Markov random field using labels that include outliers and a set of candidate models estimated from subsets of the point matches . Thus not only the MRF can trivially incorporate the errors of fit in singleton factors but the sheer benefit of this approach is the ability to consider the interactions between data points . The proposed method refines the outlier posterior over the course of consecutive inference sweeps until the process settles at a local minimum . The inference engine employed is a Markov Chain Monte Carlo method which samples new labels from clusters of data points . The advantage of this technique pertains to the fact that cluster formation can be manipulated to favor common label assignments between points related to each other by image based criteria . Moreover although CSAMMFIT uses a Potts like pairwise factor the inference algorithm allows for arbitrary prior formulations thereby accommodating the needs for more elaborate feature based constraints . 


Summary: Assignment of projective models becomes a problem of probabilistic inference through clustering. A Markov network formulation that models data points in terms of projective relationships in two views is introduced. An algorithm that fits multiple varieties to data points is specified using MCMC based inference. Use of a global energy measure to capture the quality of convergence. Comparative results indicate less susceptibility to parameter tuning and increased accuracy of convergence.