varunr14/newsummary huggingface dataset:
With ever increasing information being available to the end users search engines have become the most powerful tools for obtaining useful information scattered on the Web . However it is very common that even most renowned search engines return result sets with not so useful pages to the user . Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages . This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information . In this paper various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking . An overview of selected approaches and their comparison by means of the classification criteria has been presented . With the help of this comparison some common concepts and outstanding features have been identified . 


Summary: An exhaustive review of ranking approaches for semantic search on Web. We identified three stages of ranking in semantic search process. We identified criteria for the comparison of semantic search approaches. We examined some open issues relevant to efficient semantic search.
This article describes in depth research on machine learning methods for sentiment analysis of Czech social media . Whereas in English Chinese or Spanish this field has a long history and evaluation datasets for various domains are widely available in the case of the Czech language no systematic research has yet been conducted . We tackle this issue and establish a common ground for further research by providing a large human annotated Czech social media corpus . Furthermore we evaluate state of the art supervised machine learning methods for sentiment analysis . We explore different pre processing techniques and employ various features and classifiers . We also experiment with five different feature selection algorithms and investigate the influence of named entity recognition and preprocessing on sentiment classification performance . Moreover in addition to our newly created social media dataset we also report results for other popular domains such as movie and product reviews . We believe that this article will not only extend the current sentiment analysis research to another family of languages but will also encourage competition potentially leading to the production of high end commercial solutions . 


Summary: We explore state of the art supervised machine learning methods for sentiment analysis of Czech social media. We provide a large human annotated Czech social media corpus. We explore different pre processing techniques and employ various features and classifiers. We experiment with five different feature selection algorithms. Results are also reported on other widely popular domains such as movie and product reviews.
In this paper we present an accelerated system for segmenting flower images based on graph cut technique which formulates the segmentation problem as an energy function minimization. The contribution of this paper consists to propose an improvement of the classical used energy function which is composed of a data consistent term and a boundary term. For this we integrate an additional data consistent term based on the spatial prior and we add gradient information in the boundary term. Then we propose an automated coarse to fine segmentation method composed mainly of two levels coarse segmentation and fine segmentation. First the coarse segmentation level is based on minimizing the proposed energy function. Then the fine segmentation is done by optimizing the energy function through the standard graph cut technique. Experiments were performed on a subset of Oxford flower database and the obtained results are compared to the reimplemented method of Nilsback et al. 1 . The evaluation shows that our method consumes less CPU time and it has a satisfactory accuracy compared with the mentioned method above 1 . 


Summary: An improvement of the classical energy function graph cut is proposed. Integration of spatial priori and gradient information improves segmentation result. A new coarse to fine flower segmentation method is presented and implemented.
Semi supervised sparse feature selection which can exploit the large number unlabeled data and small number labeled data simultaneously has placed an important role in web image annotation. However most of the semi supervised sparse feature selection methods are developed for single view data and these methods cannot naturally deal with the multi view data though it has shown that leveraging information contained in multiple views can dramatically improve the feature selection performance. Recently multi view learning has obtained much research attention because it can reveal and leverage the correlated and complementary information between different views. So in this paper we apply multi view learning into semi supervised sparse feature selection and propose a semi supervised sparse feature selection method based on multi view Laplacian regularization namely multi view Laplacian sparse feature selection MLSFS .1 MLSFS utilizes multi view Laplacian regularization to boost semi supervised sparse feature selection performance. A simple iterative method is proposed to solve the objective function of MLSFS. We apply MLSFS algorithm into image annotation task and conduct experiments on two web image datasets. The experimental results show that the proposed MLSFS outperforms the state of art single view sparse feature selection methods. 


Summary: Multi view Laplacian sparse feature selection MLSFS algorithm is proposed. Multi view learning is utilized to exploit the complementation of different views features. A effective iterative algorithm is introduced to optimize the objective function. The convergence of the algorithm is proven. Experiments demonstrate MLSFS has good performance of feature selection.
We present PubSearch a hybrid heuristic scheme for re ranking academic papers retrieved from standard digital libraries such as the ACM Portal . The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic a time depreciated citation score and a graph theoretic computed score that relates the paper s index terms with each other . We designed and developed a meta search engine that submits user queries to standard digital repositories of academic publications and re ranks the repository results using the hierarchical heuristic scheme . We evaluate our proposed re ranking scheme via user feedback against the results of ACM Portal on a total of 58 different user queries specified from 15 different users . The results show that our proposed scheme significantly outperforms ACM Portal in terms of retrieval precision as measured by most common metrics in Information Retrieval including Normalized Discounted Cumulative Gain Expected Reciprocal Rank as well as a newly introduced lexicographic rule of ranking search results . In particular PubSearch outperforms ACM Portal by more than 77 in terms of ERR by more than 11 in terms of NDCG and by more than 907.5 in terms of LEX . We also re rank the top 10 results of a subset of the original 58 user queries produced by Google Scholar Microsoft Academic Search and ArnetMiner the results show that PubSearch compares very well against these search engines as well . The proposed scheme can be easily plugged in any existing search engine for retrieval of academic publications . 


Summary: We present PubSearch a new system for academic search and retrieval. PubSearch is based on a cascade hierarchy of three heuristics. The heuristics include Term Frequency citation distribution and topics inter relations. We compare PubSearch performance against ACM Portal on 58 user queries. The system outperforms ACM Portal in terms of ERR NDCG LEX metric by large margin.
Sudden illumination changes are a fundamental problem in background modeling applications. Most strategies to solve it are based on determining the particular form of the color transformation which the pixels undergo when an illumination change occurs. Here we present an approach which does not assume any specific form of the color transformation. It is based on a quantitative assessment of the smoothness of the local color transformation from one frame to the background model. In addition to this an assessment of the obtained illumination states of the pixels is carried out with the help of fuzzy logic. Experimental results are presented which demonstrate the performance of our approach in a range of situations. 


Summary: Illumination change detection is carried out without assuming a specific color transformation. The method is designed to improve existing standard foreground detection algorithms. Backup procedures are designed to recover from detection failures. The method is applicable to a wide range of varying illumination conditions.
Practical classification problems often involve some kind of trade off between the decisions a classifier may take . Indeed it may be the case that decisions are not equally good or costly therefore it is important for the classifier to be able to predict the risk associated with each classification decision . Bayesian decision theory is a fundamental statistical approach to the problem of pattern classification . The objective is to quantify the trade off between various classification decisions using probability and the costs that accompany such decisions . Within this framework a loss function measures the rates of the costs and the risk in taking one decision over another . In this paper we give a formal justification for a decision function under the Bayesian decision framework that comprises the minimisation of Bayesian risk and an empirical decision function found by Domingos and Pazzani . This new decision function has a very intuitive geometrical interpretation that can be explored on a Cartesian plane . We use this graphical interpretation to analyse different approaches to find the best decision on four different Na ve Bayes classifiers Gaussian Bernoulli Multinomial and Poisson on different standard collections . We show that the graphical interpretation significantly improves the understanding of the models and opens new perspectives for new research studies . 


Summary: A study of a conditional risk based on both variable and constant losses. A geometrical interpretation of the decision function of a classifier. An analysis of performance of different Na ve Bayes classifiers compared to SVM.
In this paper a novel method is proposed for real world pose invariant face recognition from only a single image in a gallery . A 3D Facial Expression Generic Elastic Model is proposed to reconstruct a 3D model of each human face using only a single 2D frontal image . Then for each person in the database a Sparse Dictionary Matrix is created from all face poses by rotating the 3D reconstructed models and extracting features in the rotated face . Each SDM is subsequently rendered based on triplet angles of face poses . Before matching to SDM an initial estimate of triplet angles of face poses is obtained in the probe face image using an automatic head pose estimation approach . Then an array of the SDM is selected based on the estimated triplet angles for each subject . Finally the selected arrays from SDMs are compared with the probe image by sparse representation classification . Convincing results were acquired to handle pose changes on the FERET CMU PIE LFW and video face databases based on the proposed method compared to several state of the art in pose invariant face recognition . 


Summary: We have presented a method for facial expression invariant face recognition. We propose a novel method for real world pose invariant face recognition. Proposed method use a single image in gallery with any facial expressions. We generate a sparse dictionary matrix for each people based on triplet pose angles. Convincing results were acquired to handle pose on the FERET CMU PIE and LFW databases.
In this work we elaborate on the meaning of metadata quality by surveying efforts and experiences matured in the digital library domain . In particular an overview of the frameworks developed to characterize such a multi faceted concept is presented . Moreover the most common quality related problems affecting metadata both during the creation and the aggregation phase are discussed together with the approaches technologies and tools developed to mitigate them . This survey on digital library developments is expected to contribute to the ongoing discussion on data and metadata quality occurring in the emerging yet more general framework of data infrastructures . 


Summary: A characterization of metadata quality. A discussion on the role of metadata quality in data infrastructures. A survey of metadata quality frameworks to assess metadata quality. An array of solutions to mitigate the effects of metadata quality issues. Directions for future works related to metadata quality in data infrastructures.
The basic goal of scene understanding is to organize the video into sets of events and to find the associated temporal dependencies . Such systems aim to automatically interpret activities in the scene as well as detect unusual events that could be of particular interest such as traffic violations and unauthorized entry . The objective of this work therefore is to learn behaviors of multi agent actions and interactions in a semi supervised manner . Using tracked object trajectories we organize similar motion trajectories into clusters using the spectral clustering technique . This set of clusters depicts the different paths routes i.e . the distinct events taking place at various locations in the scene . A temporal mining algorithm is used to mine interval based frequent temporal patterns occurring in the scene . A temporal pattern indicates a set of events that are linked based on their relationship with other events in the set and we use Allen s interval based temporal logic to describe these relations . The resulting frequent patterns are used to generate temporal association rules which convey the semantic information contained in the scene . Our overall aim is to generate rules that govern the dynamics of the scene and perform anomaly detection . We apply the proposed approach on two publicly available complex traffic datasets and demonstrate considerable improvements over the existing techniques . 


Summary: Uses temporal mining technique event recognition in dynamic scenes Temporal association rules are then generated from frequent patterns. These association rules help model the sequence cycle. Spatio temporal anomalies are identified and detected in a hierarchical manner.
Gender and ethnicity are both key demographic attributes of human beings and they play a very fundamental and important role in automatic machine based face analysis therefore there has been increasing attention for face based gender and ethnicity classification in recent years . In this paper we present an effective and efficient approach on this issue by combining both boosted local texture and shape features extracted from 3D face models in contrast to the existing ones that only depend on either 2D texture or 3D shape of faces . In order to comprehensively represent the difference between different genders or ethnicities we propose a novel local descriptor namely local circular patterns . LCP improves the widely utilized local binary patterns and its variants by replacing the binary quantization with a clustering based one resulting in higher discriminative power as well as better robustness to noise . Meanwhile the following Adaboost based feature selection finds the most discriminative gender and race related features and assigns them with different weights to highlight their importance in classification which not only further raises the performance but reduces the time and memory cost as well . Experimental results achieved on the FRGC v2.0 and BU 3DFE datasets clearly demonstrate the advantages of the proposed method . 


Summary: We investigate multi modal 2D 3D facial gender and ethnicity classification. We propose a novel local descriptor LCP to capture both 2D and 3D facial clues. LCP is more discriminative and more robust to noise than LBP like features. Combing both modalities improves results in gender and ethnicity classification. We achieve competitive performance compared to state of the art in such tasks.
Multimedia objects can be retrieved using their context that can be for instance the text surrounding them in documents . This text may be either near or far from the searched objects . Our goal in this paper is to study the impact in term of effectiveness of text position relatively to searched objects . The multimedia objects we consider are described in structured documents such as XML ones . The document structure is therefore exploited to provide this text position in documents . Although structural information has been shown to be an effective source of evidence in textual information retrieval only a few works investigated its interest in multimedia retrieval . More precisely the task we are interested in this paper is to retrieve multimedia fragments . Our general approach is built on two steps we first retrieve XML elements containing multimedia objects and we then explore the surrounding information to retrieve relevant multimedia fragments . In both cases we study the impact of the surrounding information using the documents structure . Our work is carried out on images but it can be extended to any other media since the physical content of multimedia objects is not used . We conducted several experiments in the context of the Multimedia track of the INEX evaluation campaign . Results showed that structural evidences are of high interest to tune the importance of textual context for multimedia retrieval . Moreover the proposed approach outperforms state of the art approaches . 


Summary: Multimedia objects retrieved by their textual context. Structural factors help to identify relevant textual parts. Multimedia elements are useful to retrieve multimedia fragments. Specific multimedia retrieval system outperforms classical XML retrieval systems.
This paper proposes a novel approach to recognize object and scene categories in depth images. We introduce a Bag of Words BoW representation in 3D the Selective 3D Spatial Pyramid Matching Kernel 3DSPMK . It starts quantizing 3D local descriptors computed from point clouds to build a vocabulary of 3D visual words. This codebook is used to build the 3DSPMK which starts partitioning a working volume into fine sub volumes and computing a hierarchical weighted sum of histogram intersections of visual words at each level of the 3D pyramid structure. With the aim of increasing both the classification accuracy and the computational efficiency of the kernel we propose two selective hierarchical volume decomposition strategies based on representative and discriminative sub volume selection processes which drastically reduce the pyramid to consider. Results on different RGBD datasets show that our approaches obtain state of the art results for both object recognition and scene categorization. 


Summary: We introduce the 3DSPMK for object and scene recognition in depth images. Our model repeatedly subdivides a cube inscribed in the point cloud. Then a weighted sum of histogram of visual word occurrences is computed. Results on publicly available benchmarks have been reported.
Researchers have recently been performing region of interest detection in such applications as object recognition object segmentation and adaptive coding . In this paper a novel region of interest detection model that is based on visually salient regions is introduced by utilizing the frequency and space domain features in very high resolution remote sensing images . First the frequency domain features that are based on a multi scale spectrum residual algorithm are extracted to yield intensity features . Next we extract the color and orientation features by generating space dynamic pyramids . Then spectral features are obtained by analyzing spectral information content . In addition a multi scale feature fusion method is proposed to generate a saliency map . Finally the detected visual saliency regions are described using adaptive threshold segmentation . Compared with existing models our model eliminates the background information effectively and highlights the salient detected results with well defined boundaries and shapes . Moreover an experimental evaluation indicates promising results from our model with respect to the accuracy of detection results . 


Summary: Novel multi scale frequency analysis is proposed for intensity feature analysis. Multi scale analysis is introduced to extract color and orientation features. Spectral information content is proposed for spectral feature analysis. Propose a novel weight multi scale feature fusion method Synthesize frequency and space domain features in proposed algorithm frame.
We propose a method to produce near laser scan quality 3 D face models of a freely moving user with a low cost low resolution range sensor in real time . Our approach does not require any prior knowledge about the geometry of a face and can produce faithful geometric models of any star shaped object . We use a cylindrical representation which enables us to efficiently process the 3 D mesh by applying 2 D filters . We use the first frame as a reference and incrementally build the model by registering each subsequent cloud of 3 D points to the reference using the ICP algorithm implemented on a GPU . The registered point clouds are merged into a single image through a cylindrical representation . The noise from the sensor and from the pose estimation error is removed with a temporal integration and a spatial smoothing of the successively incremented model . To validate our approach we quantitatively compare our model to laser scans and show comparable accuracy . This paper extends the method presented in . 


Summary: We infer a very accurate 3 D face model for a freely moving user from a single depth camera. Using unwrapped cylindrical 2 D images enables us to use simple 2 D image processing algorithms to process the 3 D information. We use a combination of spatial smoothing and temporal integration for noise removal. A robust rejection method produces reliable results in the presence of facial expression changes and partial occlusions. Our system runs online and in real time.