varunr14/newsummary huggingface dataset:
Named entity recognition is mostly formalized as a sequence labeling problem in which segments of named entities are represented by label sequences . Although a considerable effort has been made to investigate sophisticated features that encode textual characteristics of named entities little attention has been paid to segment representations for multi token named entities . In this paper we investigate the effects of different SRs on NER tasks and propose a feature generation method using multiple SRs . The proposed method allows a model to exploit not only highly discriminative features of complex SRs but also robust features of simple SRs against the data sparseness problem . Since it incorporates different SRs as feature functions of Conditional Random Fields we can use the well established procedure for training . In addition the tagging speed of a model integrating multiple SRs can be accelerated equivalent to that of a model using only the most complex SR of the integrated model . Experimental results demonstrate that incorporating multiple SRs into a single model improves the performance and the stability of NER . We also provide the detailed analysis of the results . 


Summary: Different segmentation representations SRs cause little difference in performance. Different SRs result in quite different outputs. Incorporation of different SRs is beneficial to NER task. We proposed a new feature generation method that uses multiple SRs. The proposed method improves the performance and the stability of NER.
Surface defects are important factors of surface quality of industrial products . Most of the traditional machine vision based methods for surface defect recognition have some shortcomings such as low detection rate of defects and high rate of false alarms . Different types of defects have special information at some directions and scales of their images while the traditional methods of feature extraction such as Wavelet transform are unable to get the information at all directions . In this study Shearlet transform is introduced to provide efficient multi scale directional representation and a general framework has been developed to analyze and represent surface defect images with anisotropic information . The metal surface images captured from production lines are decomposed into multiple directional subbands with Shearlet transform and features are extracted from all subbands and combined into a high dimensional feature vector . Kernel Locality Preserving Projection is applied to the dimension reduction of the feature vector . The proposed method is tested with the surface images captured from different production lines and the results show that the classification rates of surface defects of continuous casting slabs hot rolled steels and aluminum sheets are 94.35 95.75 and 92.5 respectively . 


Summary: We developed a method called DST KLPP which is effective in classification of surface defects of different metals. DST KLPP was tested with samples of three typical metals including slabs hot rolled steels and aluminum sheets. DST KLPP provides higher classification rates than other methods including Wavelet Curvelet and Contourlet transform. DST KLPP can recognize defects from complex backgrounds efficiently. DST KLPP can recognize tiny defects from low contrast images availably.
Knowledge organization and bibliometrics have traditionally been seen as separate subfields of library and information science but bibliometric techniques make it possible to identify candidate terms for thesauri and to organize knowledge by relating scientific papers and authors to each other and thereby indicating kinds of relatedness and semantic distance . It is therefore important to view bibliometric techniques as a family of approaches to KO in order to illustrate their relative strengths and weaknesses . The subfield of bibliometrics concerned with citation analysis forms a distinct approach to KO which is characterized by its social historical and dynamic nature its close dependence on scholarly literature and its explicit kind of literary warrant . The two main methods co citation analysis and bibliographic coupling represent different things and thus neither can be considered superior for all purposes . The main difference between traditional knowledge organization systems and maps based on citation analysis is that the first group represents intellectual KOSs whereas the second represents social KOSs . For this reason bibliometric maps can not be expected ever to be fully equivalent to scholarly taxonomies but they are along with other forms of KOSs valuable tools for assisting users to orient themselves to the information ecology . Like other KOSs citation based maps can not be neutral but will always be based on researchers decisions which tend to favor certain interests and views at the expense of others . 


Summary: Citation analytic methods as approaches to knowledge organization. Semantic relations between citing and cited documents. The distinction between intellectual and social organizations of knowledge. Citation based maps as social organizations. Positivistic versus pragmatic approaches in bibliometrics.
In this paper we present our solution to the 300 Faces in the Wild Facial Landmark Localization Challenge . We demonstrate how to achieve very competitive localization performance with a simple deep learning based system . Human study is conducted to show that the accuracy of our system has been very close to human performance . We discuss how this finding would affect our future direction to improve our system . 


Summary: We show how to achieve state of the art facial landmark localization by CNN. The system s performance is improved by deeper network. We show our system s performance is close to human.
The estimation of query model is an important task in language modeling approaches to information retrieval . The ideal estimation is expected to be not only effective in terms of high mean retrieval performance over all queries but also stable in terms of low variance of retrieval performance across different queries . In practice however improving effectiveness can sacrifice stability and vice versa . In this paper we propose to study this tradeoff from a new perspective i.e . the bias variance tradeoff which is a fundamental theory in statistics . We formulate the notion of bias variance regarding retrieval performance and estimation quality of query models . We then investigate several estimated query models by analyzing when and why the bias variance tradeoff will occur and how the bias and variance can be reduced simultaneously . A series of experiments on four TREC collections have been conducted to systematically evaluate our bias variance analysis . Our approach and results will potentially form an analysis framework and a novel evaluation strategy for query language modeling . 


Summary: We study the retrieval effectiveness stability tradeoff in query model estimation. This tradeoff is investigated through a novel angle i.e. bias variance tradeoff. We formulate the performance bias variance and estimation bias variance. We investigate various query estimation methods using bias variance analysis. Experiments have been conducted to verify hypotheses on bias variance analysis.
In this work we deal with the problem of high level event detection in video . Specifically we study the challenging problems of i learning to detect video events from solely a textual description of the event without using any positive video examples and ii additionally exploiting very few positive training samples together with a small number of related videos . For learning only from an event s textual description we first identify a general learning framework and then study the impact of different design choices for various stages of this framework . For additionally learning from example videos when true positive training samples are scarce we employ an extension of the Support Vector Machine that allows us to exploit related event videos by automatically introducing different weights for subsets of the videos in the overall training set . Experimental evaluations performed on the large scale TRECVID MED 2014 video dataset provide insight on the effectiveness of the proposed methods . 


Summary: We deal with the challenging problem of high level event detection in video. We build event detectors based solely on textual descriptions of the event classes. We also learn event detectors from very few positive and related training samples. We present results and comparisons on a large scale TRECVID MED video dataset.
Social networking sites enable user to personalize their contents and functions . This feature has been assumed as causing positive effects on the use of online information services through enhancing user satisfaction . However unlike other online information services due to the results of personalization in a certain situation SNS users can not help using the SNS even though they feel dissatisfaction on using it . SNSs are different from other information services in the sense that they create and sustain their own value based on the number of participating members . In SNSs personalization reflected by updates and maintenance of profile pages results in such participation . This study hypothesizes that personalization influences on the continued use of SNSs through two factors switching cost and satisfaction . Web based survey was conducted with the samples of 677 SNS users from six universities in the US . In person interviews were conducted with 25 university students to elicit their thoughts on the SNSs . Quantitative analysis employed by testing the proposed model with five hypotheses through a structural equation modeling technique . The transcribed interview data was analyzed following the constant comparative technique . The main findings indicate that as expected the personalization increases its switching cost as well as satisfaction which results in further use of SNSs . These findings suggest that it is necessary to consider both extrinsic and intrinsic factors of user perceptions when adding personalization features on SNSs . 


Summary: Personalization in SNS increases its switching cost and satisfaction. Switching cost and satisfaction affect user continuance of SNSs. SNS users cannot easily switch to other SNS because of time and efforts invested. Social exchange theory provides a useful framework and proves its feasibility. A new insight on research concerning participatory networks such as SNSs.
Given a surveillance video of a moving person we present a novel method of estimating layout of a cluttered indoor scene . We propose an idea that trajectories of a moving person can be used to generate features to segment an indoor scene into different areas of interest . We assume a static uncalibrated camera . Using pixel level color and perspective cues of the scene each pixel is assigned to a particular class either a sitting place the ground floor or the static background areas like walls and ceiling . The pixel level cues are locally integrated along global topological order of classes such as sitting objects and background areas are above ground floor into a conditional random field by an ordering constraint . The proposed method yields very accurate segmentation results on challenging real world scenes . We focus on videos with people walking in the scene and show the effectiveness of our approach through quantitative and qualitative results . The proposed estimation method shows better estimation results as compared to the state of the art scene layout estimation methods . We are able to correctly segment 90.3 of background 89.4 of sitting areas and 74.7 of the ground floor . 


Summary: Scene layout estimation using both trajectory data and image features No assumptions are considered about the structure of the scene. Efficient estimation of the scene structure in the presence of scene clutter We show that using line segments we obtain better surface normal. Our data and segmentation results will be publicly available for comparison.
Some human detection or tracking algorithms output a low dimensional representation of the human body such as a bounding box . Even though this representation is enough for some tasks a more accurate and detailed point wise representation of the human body is more useful for pose estimation and action recognition . The refinement process can produce a point wise mask of the human body from its low dimensional representation . In this paper we tackle the problem of refining low dimensional human shapes using RGB D data with a novel and accurate method for this purpose . This algorithm combines low level cues such as shape and color and high level observations such as the estimated ground plane in a multi layer graph cut framework . In our algorithm shape prior information is learned by training a classifier . Unlike some existing work our method does not utilize or carry features from the internal steps of the methods which provide the bounding box so our method can work on the outputs of any similar shape providers . Extensive experiments demonstrate that the proposed technique significantly outperforms other suitable methods . Moreover a previously published refinement method is extended by incorporating more generic cues to serve this purpose . 


Summary: A novel and accurate method to refine low dimensional human shape using RGB D data is proposed. Uses of multiple modalities do not carry any features from the shape provider. Combines low and high level observations jointly in multi layer graph structure Extensive experiments showed that it outperforms compared suitable algorithms. Also an existing method is extended by fusing more generic cues for this purpose.
Research in natural language processing has increasingly focused on normalizing Twitter messages . Currently while different well defined approaches have been proposed for the English language the problem remains far from being solved for other languages such as Malay . Thus in this paper we propose an approach to normalize the Malay Twitter messages based on corpus driven analysis . An architecture for Malay Tweet normalization is presented which comprises seven main modules enhanced tokenization In Vocabulary detection specialized dictionary query repeated letter elimination abbreviation adjusting English word translation and de tokenization . A parallel Tweet dataset consisting of 9000 Malay Tweets is used in the development and testing stages . To measure the performance of the system an evaluation is carried out . The result is promising whereby we score 0.83 in BLEU against the baseline BLEU which scores 0.46 . To compare the accuracy of the architecture with other statistical approaches an SMT like normalization system is implemented trained and evaluated with an identical parallel dataset . The experimental results demonstrate that we achieve higher accuracy by the normalization system which is designed based on the features of Malay Tweets compared to the SMT like system . 


Summary: To observe features of Malay Tweets three distinct corpus based analyses are done. A rule based architecture is developed based on results of the analyses. The architecture consists of seven distinct modules in a pipeline structure. Experimental results indicate high accuracy in term of BLEU score. The architecture outperforms SMT like normalization approach.
This paper describes the use of Wikipedia as a rich knowledge source for a question answering system . We suggest multiple answer matching modules based on different types of semi structured knowledge sources of Wikipedia including article content infoboxes article structure category structure and definitions . These semi structured knowledge sources each have their unique strengths in finding answers for specific question types such as infoboxes for factoid questions category structure for list questions and definitions for descriptive questions . The answers extracted from multiple modules are merged using an answer merging strategy that reflects the specialized nature of the answer matching modules . Through an experiment our system showed promising results with a precision of 87.1 a recall of 52.7 and an F measure of 65.6 all of which are much higher than the results of a simple text analysis based system . 


Summary: The use of Wikipedia as a knowledge source for question answering system. Wikipedia article content structure infobox category and definition are used. Each knowledge source has its unique strength for certain question types. Answer merging strategy for multiple answer matching modules.
Nowadays with so many surveillance cameras having been installed the market demand for intelligent violence detection is continuously growing while it is still a challenging topic in research area. Therefore we attempt to make some improvements of existing violence detectors. The primary contributions of this paper are two fold. Firstly a novel feature extraction method named Oriented VIolent Flows OViF which takes full advantage of the motion magnitude change information in statistical motion orientations is proposed for practical violence detection in videos. The comparison of OViF and baseline approaches on two public databases demonstrates the efficiency of the proposed method. Secondly feature combination and multi classifier combination strategies are adopted and excellent results are obtained. Experimental results show that using combined features with AdaBoost Linear SVM achieves improved performance over the state of the art on the Violent Flows benchmark. 


Summary: A novel feature named OViF for violence detection. OViF makes full use of the orientation information of optical flows compared to ViF. We use AdaBoost as a feature selector and Linear SVM as a classifier. Experiments show that ViF OViF obtain the state of the art violence detection performance when using AdaBoost with SVM.
Extensible Markup Language documents are associated with time in two ways XML documents evolve over time and XML documents contain temporal information . The efficient management of the temporal and multi versioned XML documents requires optimized use of storage and efficient processing of complex historical queries . This paper provides a comparative analysis of the various schemes available to efficiently store and query the temporal and multi versioned XML documents based on temporal change management versioning and querying support . Firstly the paper studies the multi versioning control schemes to detect manage and query change in dynamic XML documents . Secondly it describes the storage structures used to efficiently store and retrieve XML documents . Thirdly it provides a comparative analysis of the various commercial tools based on change management versioning collaborative editing and validation support . Finally the paper presents some future research and development directions for the multi versioned XML documents . 


Summary: XML documents contain temporal information and evolve over time. Our work explores change detection versioning and querying support in XML documents. It describes the storage structures to efficiently store and retrieve XML documents. It explains the change management features in some commercial tools. It highlights several open research issues for multi versioned XML documents.
Collaborative information retrieval involves retrieval settings in which a group of users collaborates to satisfy the same underlying need . One core issue of collaborative IR models involves either supporting collaboration with adapted tools or developing IR models for a multiple user context and providing a ranked list of documents adapted for each collaborator . In this paper we introduce the first document ranking model supporting collaboration between two users characterized by roles relying on different domain expertise levels . Specifically we propose a two step ranking model we first compute a document relevance score taking into consideration domain expertise based roles . We introduce specificity and novelty factors into language model smoothing and then we assign via an Expectation Maximization algorithm documents to the best suited collaborator . Our experiments employ a simulation based framework of collaborative information retrieval and show the significant effectiveness of our model at different search levels . 


Summary: A collaborative ranking model involving users with different domain expertise levels. EM based learning method for document allocation. Simulation based evaluation with effective results at session and user roles levels.
This paper introduces an adaptive visual tracking method that combines the adaptive appearance model and the optimization capability of the Markov decision process. Most tracking algorithms are limited due to variations in object appearance from changes in illumination viewing angle object scale and object shape. This paper is motivated by the fact that tracking performance degradation is caused not only by changes in object appearance but also by the inflexible controls of tracker parameters. To the best of our knowledge optimization of tracker parameters has not been thoroughly investigated even though it critically influences tracking performance. The challenge is to equip an adaptive tracking algorithm with an optimization capability for a more flexible and robust appearance model. In this paper the Markov decision process which has been applied successfully in many dynamic systems is employed to optimize an adaptive appearance model based tracking algorithm. The adaptive visual tracking is formulated as a Markov decision process based dynamic parameter optimization problem with uncertain and incomplete information. The high computation requirements of the Markov decision process formulation are solved by the proposed prioritized Q learning approach. We carried out extensive experiments using realistic video sets and achieved very encouraging and competitive results. 


Summary: We use an MDP formulation for optimal adaptation of tracking algorithms. We optimize the tracker control parameters using prioritized Q learning. The proposed prioritized Q learning approach is based on sensitivity analysis. The performance of our method is superior to other approaches. The proposed method can balance tracking accuracy and speed.
Image classification is to assign a category of an image and image annotation is to describe individual components of an image by using some annotation terms . These two learning tasks are strongly related . The main contribution of this paper is to propose a new discriminative and sparse topic model for image classification and annotation by combining visual annotation and label information from a set of training images . The essential features of DSTM different from existing approaches are that the label information is enforced in the generation of both visual words and annotation terms such that each generative latent topic corresponds to a category the zero mean Laplace distribution is employed to give a sparse representation of images in visual words and annotation terms such that relevant words and terms are associated with latent topics . Experimental results demonstrate that the proposed method provides the discrimination ability in classification and annotation and its performance is better than the other testing methods for LabelMe UIUC NUS WIDE and PascalVOC07 images . 


Summary: The label information is enforced in the generation of visual and annotation terms. The zero mean Laplace distribution is added to a topic generative process. The sparse image representation is helpful to learn a training model. A series of experiments on four data sets demonstrate the performance of DSTM.