varunr14/newsummary huggingface dataset:
Most existing search engines focus on document retrieval . However information needs are certainly not limited to finding relevant documents . Instead a user may want to find relevant entities such as persons and organizations . In this paper we study the problem of related entity finding . Our goal is to rank entities based on their relevance to a structured query which specifies an input entity the type of related entities and the relation between the input and related entities . We first discuss a general probabilistic framework derive six possible retrieval models to rank the related entities and then compare these models both analytically and empirically . To further improve performance we study the problem of feedback in the context of related entity finding . Specifically we propose a mixture model based feedback method that can utilize the pseudo feedback entities to estimate an enriched model for the relation between the input and related entities . Experimental results over two standard TREC collections show that the derived relation generation model combined with a relation feedback method performs better than other models . 


Summary: We derive six generative models for related entity finding. We conduct both analytical and empirical studies to compare these six models. We propose a novel entity relation based feedback method to improve performance.
Recently a video representation based on dense trajectories has been shown to outperform other human action recognition methods on several benchmark datasets . The trajectories capture the motion characteristics of different moving objects in space and temporal dimensions . In dense trajectories points are sampled at uniform intervals in space and time and then tracked using a dense optical flow field over a fixed length of L frames spread overlapping over the entire video . However among these base trajectories a few may continue for longer than duration L capturing motion characteristics of objects that may be more valuable than the information from the base trajectories . Thus we propose a technique that searches for trajectories with a longer duration and refer to these as ordered trajectories . Experimental results show that ordered trajectories perform much better than the base trajectories both standalone and when combined . Moreover the uniform sampling of dense trajectories does not discriminate objects of interest from the background or other objects . Consequently a lot of information is accumulated which actually may not be useful . This can especially escalate when there is more data due to an increase in the number of action classes . We observe that our proposed trajectories remove some background clutter too . We use a Bag of Words framework to conduct experiments on the benchmark HMDB51 UCF50 and UCF101 datasets containing the largest number of action classes to date . Further we also evaluate three state of the art feature encoding techniques to study their performance on a common platform . 


Summary: A technique that captures information of objects with longer duration. A feature selection like approach that delivers better performance than several trajectory variants. Removal of a large number of trajectories related to background noise. We apply our technique on action datasets HMDB51 UCF50 and UCF101 containing largest number of classes till date.
In this paper a novel and effective lip based biometric identification approach with the Discrete Hidden Markov Model Kernel is developed . Lips are described by shape features on two different grid layouts rectangular and polar . These features are then specifically modeled by a DHMMK and learnt by a support vector machine classifier . Our experiments are carried out in a ten fold cross validation fashion on three different datasets GPDS ULPGC Face Dataset PIE Face Dataset and RaFD Face Dataset . Results show that our approach has achieved an average classification accuracy of 99.8 97.13 and 98.10 using only two training images per class on these three datasets respectively . Our comparative studies further show that the DHMMK achieved a 53 improvement against the baseline HMM approach . The comparative ROC curves also confirm the efficacy of the proposed lip contour based biometrics learned by DHMMK . We also show that the performance of linear and RBF SVM is comparable under the frame work of DHMMK . 


Summary: We model a lip biometric approach based on shape information. This system is working with static lip on three public datasets. We develop a kernel based Hidden Markov Model DHMMK . The use of DHMMK obtains discriminative information. The use of DHMMK on lip gets a robust approach for identification.
The emergence of large scale human action datasets poses a challenge to efficient action labeling. Hand labeling large scale datasets is tedious and time consuming thus a more efficient labeling method would be beneficial. One possible solution is to make use of the knowledge of a known dataset to aid the labeling of a new dataset. To this end we propose a new transfer learning method for cross dataset human action recognition. Our method aims at learning generalized feature representation for effective cross dataset classification. We propose a novel dual many to one encoder architecture to extract generalized features by mapping raw features from source and target datasets to the same feature space. Benefiting from the favorable property of the proposed many to one encoder cross dataset action data are encouraged to possess identical encoded features if the actions share the same class labels. Experiments on pairs of benchmark human action datasets achieved state of the art accuracy proving the efficacy of the proposed method. 


Summary: Proposed a new transfer learning method for cross dataset action recognition. A new dual many to one encoder method for feature extraction across action datasets. Achieved over 10 increase in recognition accuracy over recent work.
Textual entailment is a task for which the application of supervised learning mechanisms has received considerable attention as driven by successive Recognizing Data Entailment data challenges . We developed a linguistic analysis framework in which a number of similarity dissimilarity features are extracted for each entailment pair in a data set and various classifier methods are evaluated based on the instance data derived from the extracted features . The focus of the paper is to compare and contrast the performance of single and ensemble based learning algorithms for a number of data sets . We showed that there is some benefit to the use of ensemble approaches but based on the extracted features Na ve Bayes proved to be the strongest learning mechanism . Only one ensemble approach demonstrated a slight improvement over the technique of Na ve Bayes . 


Summary: Feature extraction for the processing of supervised entailment classification. Comparison of various ensemble methods to single learning approaches. Consideration of a novel heterogeneous homogeneous ensemble combination for the problem area.
Nowadays using increasingly granular data from real time location information and detailed demographics to consumers generated content on the social networking sites businesses are starting to offer precise location based product recommendation services through mobile devices . Based on the technology acceptance model this paper develops a theoretical model to examine the adoption intention of active SNS users toward location based recommendation agents . The research model was tested by using the Partial Least Squares technique.The results show that perceived usefulness perceived control and perceived institutional assurance are important in developing adoption intention . Perceived effort saving special treatment and social benefit have influences on the adoption intention through the mediating effect of perceived usefulness . Perceived accuracy has direct influence on adoption intention . 


Summary: We develop a model to examine the adoption of location based recommendation agents. The theoretical model is developed based on the technology acceptance model. Perceived usefulness control and institutional assurance are driving factors. Perceived effort special treatment social benefit and accuracy are antecedents. Perceived usefulness is a significant mediator.
Arabic is a widely spoken language but few mining tools have been developed to process Arabic text . This paper examines the crime domain in the Arabic language using text mining techniques . The development and application of a Crime Profiling System is presented . The system is able to extract meaningful information in this case the type of crime location and nationality from Arabic language crime news reports . The system has two unique attributes firstly information extraction that depends on local grammar and secondly dictionaries that can be automatically generated . It is shown that the CPS improves the quality of the data through reduction where only meaningful information is retained . Moreover the Self Organising Map approach is adopted in order to perform the clustering of the crime reports based on crime type . This clustering technique is improved because only refined data containing meaningful keywords extracted through the information extraction process are inputted into it i.e . the data are cleansed by removing noise . The proposed system is validated through experiments using a corpus collated from different sources it was not used during system development . Precision recall and F measure are used to evaluate the performance of the proposed information extraction approach . Also comparisons are conducted with other systems . In order to evaluate the clustering performance three parameters are used data size loading time and quantization error . 


Summary: Text mining system for extraction of information related to crime from Arabic texts. Local grammar used to extract information and build dictionaries automatically. Visualisation of clustering enhances ability to analyse crime information in corpora.
Wide field of view panoramic videos have recently become popular due to the availability of high resolution displays. These panoramic videos are generated by stitching video frames captured from a panoramic video acquisition system typically comprising of multiple video cameras arranged on a static or mobile platform. A mobile panoramic video acquisition system may suffer from global mechanical vibrations as well as independent inter camera vibrations resulting in a jittery panoramic video. While existing stabilization schemes generally tackle single camera vibrations they do not account for these inter camera vibrations. In this paper we propose a video stabilization technique for multi camera panoramic videos under the consideration that independent jitter may be exhibited by content of each camera. The proposed method comprises of three steps the first step removes the global jitter in the video by estimating collective motion and subsequently removing the high frequency component from it. The second step removes the independent i.e. local jitter of each camera by estimating motion of each camera content separately. Pixels that are located in the overlapping regions of panoramic video are contributed by neighboring cameras therefore the estimated camera motion for these pixels is weighted using the blend masks generated by the stitching process. The final step applies local geometric warping to the stitched frames and removes any residual jitter induced due to parallax. Experimental results prove that proposed scheme performs better than existing panoramic stabilization schemes. 


Summary: Stabilization of panoramic and stabilization of single camera videos are separate problems. Panoramic videos suffer from global and inter camera vibrations. Blend masks are useful for dealing with inter camera vibrations. Our survey suggests that viewers prefer this scheme over prior works.
Much of the valuable information in supporting decision making processes originates in text based documents . Although these documents can be effectively searched and ranked by modern search engines actionable knowledge need to be extracted and transformed in a structured form before being used in a decision process . In this paper we describe how the discovery of semantic information embedded in natural language documents can be viewed as an optimization problem aimed at assigning a sequence of labels to a set of interdependent variables . Dependencies among variables are efficiently modeled through Conditional Random Fields an indirected graphical model able to represent the distribution of labels given a set of observations . The Markov property of these models prevent them to take into account long range dependencies among variables which are indeed relevant in Natural Language Processing . In order to overcome this limitation we propose an inference method based on Integer Programming formulation of the problem where long distance dependencies are included through non deterministic soft constraints . 


Summary: Named Entity Recognition is addressed by constraining inference in CRF. An two phases integer linear programming approach is proposed. Complex relationships among labels are automatically extracted from data. Extracted relationships are introduced as soft constraints in the ILP formulation. The proposed method significantly outperforms the state of the art approach.
IDR QR which is an incremental dimension reduction algorithm based on linear discriminant analysis and QR decomposition has been successfully employed for feature extraction and incremental learning . IDR QR can update the discriminant vectors with light computation when new training samples are inserted into the training data set . However IDR QR has two limitations 1 IDR QR can only process new samples one instance after another even if a chunk of training samples is available at a time and 2 the approximate trick is used in IDR QR . Then there exists a gap in performance between incremental and batch IDR QR solutions . To address the problems of IDR QR in this paper we propose a new chunk IDR method which is capable of processing multiple data instances at a time and can accurately update the discriminant vectors when new data items are added dynamically . Experiments on some real databases demonstrate the effectiveness of the proposed algorithm over the original one . 


Summary: We propose a new implementation of the batch IDR QR method. Our new implementation is theoretically equivalent to the original one but is more efficient. Based on our new implementation of batch IDR QR we propose the chunk IDR method. Chunk IDR is capable of processing multiple data instances at a time. Chunk IDR can accurately update the discriminant vectors when new data items are added dynamically.
Disaster Management is a diffused area of knowledge . It has many complex features interconnecting the physical and the social views of the world . Many international and national bodies create knowledge models to allow knowledge sharing and effective DM activities . But these are often narrow in focus and deal with specified disaster types . We analyze thirty such models to uncover that many DM activities are actually common even when the events vary . We then create a unified view of DM in the form of a metamodel . We apply a metamodelling process to ensure that this metamodel is complete and consistent . We validate it and present a representational layer to unify and share knowledge as well as combine and match different DM activities according to different disaster situations . 


Summary: Disaster management knowledge is dispersed and requires a centralised and better access. We present metamodelling a way of unifying DM knowledge. We develop our DM Metamodel DMM. We validate theoretically and illustrate how it would apply in representing DM knowledge used in two recent disasters.
Automatic face alignment is a fundamental step in facial image analysis . However this problem continues to be challenging due to the large variability of expression illumination occlusion pose and detection drift in the real world face images . In this paper we present a multi view multi scale and multi component cascade shape regression model for robust face alignment . Firstly face view is estimated according to the deformable facial parts for learning view specified CSR which can decrease the shape variance alleviate the drift of face detection and accelerate shape convergence . Secondly multi scale HoG features are used as the shape index features to incorporate local structure information implicitly and a multi scale optimization strategy is adopted to avoid trapping in local optimum . Finally a component based shape refinement process is developed to further improve the performance of face alignment . Extensive experiments on the IBUG dataset and the 300 W challenge dataset demonstrate the superiority of the proposed method over the state of the art methods . 


Summary: We investigate how face detection affects face alignment. We improve the CSR model by multi view multi scale and multi component strategies. We obtain impressive results on the IBUG and 300 W challenge datasets.
Multi document discourse parsing aims to automatically identify the relations among textual spans from different texts on the same topic . Recently with the growing amount of information and the emergence of new technologies that deal with many sources of information more precise and efficient parsing techniques are required . The most relevant theory to multi document relationship Cross document Structure Theory has been used for parsing purposes before though the results had not been satisfactory . CST has received many critics because of its subjectivity which may lead to low annotation agreement and consequently to poor parsing performance . In this work we propose a refinement of the original CST which consists in formalizing the relationship definitions pruning and combining some relations based on their meaning and organizing the relations in a hierarchical structure . The hypothesis for this refinement is that it will lead to better agreement in the annotation and consequently to better parsing results . For this aim it was built an annotated corpus according to this refinement and it was observed an improvement in the annotation agreement . Based on this corpus a parser was developed using machine learning techniques and hand crafted rules . Specifically hierarchical techniques were used to capture the hierarchical organization of the relations according to the proposed refinement of CST . These two approaches were used to identify the relations among texts spans and to generate multi document annotation structure . Results outperformed other CST parsers showing the adequacy of the proposed refinement in the theory . 


Summary: CST was refined by formalizing pruning and organizing relations. Refinements improved the annotation agreement in CSTNews corpus. A parser was built based on the refined version of CST. The results obtained by the parser outperformed previous CST based parsers.
Feature correspondence lays the foundation for many tasks in computer vision and pattern recognition . In this paper the directed structural model is utilized to represent the feature set and the correspondence problem is then formulated as the structural model matching . Compared with the undirected structural model the proposed directed model provides more discriminating ability and invariance against rotation and scale transformations . Finally the recently proposed convex concave relaxation procedure is generalized to approximately solve the problem . Extensive experiments on synthetic and real data witness the effectiveness of the proposed method . 


Summary: A directed structural model is proposed for feature correspondence. It provides more discriminating ability than commonly used undirected model. The feature correspondence is casted as a directed structural matching problem. The convex concave relaxation procedure CCRP is generalized to solve the problem.
A user study of aNobii was conducted with an aim to exploring possible criteria for evaluating social navigational tools . A set of measures designed to capture various aspects of the benefits provided by the tools was proposed . To test the applicability of these measures a within subject experimental design was adopted where fifty regular aNobii users searched alternately with three book finding tools browsing friends bookshelves similar bookshelves and books by known authors . Other than the self report user experience and search result measures the choice set model was used as a novel framework for navigational effectiveness . Further analyses were conducted to explore whether three aspects of reader preference preference insight preference diversity and reading involvement might influence the performance of the tools . Some major findings are as follows . While the author browsing function was shown to be most efficient browsing friends bookshelves was shown to generate more interesting and informative browsing experiences . Three evaluative dimensions were derived from our study search experience search efficiency and result quality . The disagreement of these measures shows a need for a multi faceted evaluative framework for these exploration based navigational tools . Furthermore interaction effects on performance were found between users preference characteristics and tools . While users with high preference insight relied more heavily on author browsing to obtain more accurate results highly involved readers tended percentage wise to examine and select more titles when browsing friends bookshelves . 


Summary: A user study of the book finding tools on aNobii was conducted. A set of performance measures were tested on these exploratory tools. Browsing friends bookshelves was more conducive to novelty and serendipity. Users preference structure shown to impact on the performance of the tools.