varunr14/newsummary huggingface dataset:
In this paper we study the problem of Face Recognition when using Single Sensor Multi Wavelength imaging systems that operate in the Short Wave Infrared band . The contributions of our work are four fold First a SWIR database is collected when using our developed SSMW system under the following scenarios i.e . Multi Wavelength multi pose images were captured when the camera was focused at either 1150 1350 or 1550nm . Second an automated quality based score level fusion scheme is proposed for the classification of input MW images . Third a weighted quality based score level fusion scheme is proposed for the automated classification of full frontal vs. nonfrontal face images . Fourth a set of experiments is performed indicating that our proposed algorithms for the classification of multiwavelength images and FF vs. NFF face images are beneficial when designing different steps of multi spectral face recognition systems including face detection eye detection and face recognition . We also determined that when our SWIR based system is focused at 1350nm the identification performance increases compared to focusing the camera at any of the other SWIR wavelengths available . This outcome is particularly important for unconstrained FR scenarios where imaging at 1550nm at long distances and when operating at night time environments is preferable over different SWIR wavelengths . 


Summary: Developed SSMW system in SWIR band can acquire a series of images in short duration. Proposed an automated quality score fusion scheme for classification of MW images. Proposed an automated method for classification of frontal vs non frontal face images. Proposed algorithms are beneficial when designing face recognition systems.
Cross Lingual Link Discovery is a new problem in Information Retrieval . The aim is to automatically identify meaningful and relevant hypertext links between documents in different languages . This is particularly helpful in knowledge discovery if a multi lingual knowledge base is sparse in one language or another or the topical coverage in each language is different such is the case with Wikipedia . Techniques for identifying new and topically relevant cross lingual links are a current topic of interest at NTCIR where the CrossLink task has been running since the 2011 NTCIR 9 . This paper presents the evaluation framework for benchmarking algorithms for cross lingual link discovery evaluated in the context of NTCIR 9 . This framework includes topics document collections assessments metrics and a toolkit for pooling assessment and evaluation . The assessments are further divided into two separate sets manual assessments performed by human assessors and automatic assessments based on links extracted from Wikipedia itself . Using this framework we show that manual assessment is more robust than automatic assessment in the context of cross lingual link discovery . 


Summary: We present an evaluation framework for the study of cross lingual link discovery. All aspects of this evaluation framework are detailed. The effectiveness of various cross lingual link discovery systems is discussed. Top approaches for realisation of English to Chinese link discovery are compared.
Current semantic video analysis systems are usually hierarchical and consist of some levels to overcome semantic gaps between low level features and high level concepts . In these systems some features descriptors objects or concepts are extracted in each level and therefore total computational complexity of such systems is huge . In this paper we present a new general framework to impose attention control on a video analysis system using Q learning . Thus our proposed framework restructures a given system dynamically to direct attention to the blocks extracting the most informative features concepts and reduces computational complexity of the system . In other words the proposed framework directs flow of processing actively using a learning attention control method . The proposed framework is evaluated for event detection in broadcast soccer videos using limited numbers of training samples . Experiments show that the proposed framework is able to learn how to direct attention to informative features concepts and restructure the initial structure of the system dynamically to reach the final goal with less computational complexity . 


Summary: A framework is proposed to restructure an initial video analysis system dynamically. The initial structure of the system must be hierarchical containing some units. The framework imposes a learning based dynamic feature selection method on each unit. The system learns how to direct attention to the informative units to achieve a goal. The framework is tested for goal and card event detection in broadcast soccer videos.
This paper presents an accurate and efficient eye detection method using the discriminatory Haar features and a new efficient support vector machine . The DHFs are extracted by applying a discriminating feature extraction method to the 2D Haar wavelet transform . The DFE method is capable of extracting multiple discriminatory features for two class problems based on two novel measure vectors and a new criterion in the whitened principal component analysis space . The eSVM significantly improves the computational efficiency upon the conventional SVM for eye detection without sacrificing the generalization performance . Experiments on the Face Recognition Grand Challenge database and the BioID face database show that the DHFs exhibit promising classification capability for eye detection problem the eSVM runs much faster than the conventional SVM and the proposed eye detection method achieves near real time eye detection speed and better eye detection performance than some state of the art eye detection methods . 


Summary: A discriminating feature extraction DFE method for two class problems is proposed. The DFE method is applied to derive the discriminatory Haar features DHFs for eye detection. An efficient support vector machine eSVM is proposed to improve the efficiency of the SVM. An accurate and efficient eye detection method is presented using the DHFs and the eSVM.
In daily life humans demonstrate an amazing ability to remember images they see on magazines commercials TV web pages etc . but automatic prediction of intrinsic memorability of images using computer vision and machine learning techniques has only been investigated very recently . Our goal in this article is to explore the role of visual attention and image semantics in understanding image memorability . In particular we present an attention driven spatial pooling strategy and show that considering image features from the salient parts of images improves the results of the previous models . We also investigate different semantic properties of images by carrying out an analysis of a diverse set of recently proposed semantic features which encode meta level object categories scene attributes and invoked feelings . We show that these features which are automatically extracted from images provide memorability predictions as nearly accurate as those derived from human annotations . Moreover our combined model yields results superior to those of state of the art fully automatic models . 


Summary: We examine the role of visual attention and image semantics in understanding image memorability. We propose an attention driven spatial pooling strategy for image memorability. Considering image features from the salient parts of images improves the results of the previous models. We also investigate different semantic properties of images. Combining attention driven pooling with semantic features yields state of the art results.
We consider the problem of searching posts in microblog environments . We frame this microblog post search problem as a late data fusion problem . Previous work on data fusion has mainly focused on aggregating document lists based on retrieval status values or ranks of documents without fully utilizing temporal features of the set of documents being fused . Additionally previous work on data fusion has often worked on the assumption that only documents that are highly ranked in many of the lists are likely to be of relevance . We propose BurstFuseX a fusion model that not only utilizes a microblog post s ranking information but also exploits its publication time . BurstFuseX builds on an existing fusion method and rewards posts that are published in or near a burst of posts that are highly ranked in many of the lists being aggregated . We experimentally verify the effectiveness of the proposed late data fusion algorithm and demonstrate that in terms of mean average precision it significantly outperforms the standard state of the art fusion approaches as well as burst or time sensitive retrieval methods . 


Summary: We tackle the problem of microblog search differently by data fusion. We propose to integrate temporal characteristics into data fusion. We propose a novel and effective probabilistic data fusion model for microblog search.
Retrieval systems with non deterministic output are widely used in information retrieval . Common examples include sampling approximation algorithms or interactive user input . The effectiveness of such systems differs not just for different topics but also for different instances of the system . The inherent variance presents a dilemma What is the best way to measure the effectiveness of a non deterministic IR system Existing approaches to IR evaluation do not consider this problem or the potential impact on statistical significance . In this paper we explore how such variance can affect system comparisons and propose an evaluation framework and methodologies capable of doing this comparison . Using the context of distributed information retrieval as a case study for our investigation we show that the approaches provide a consistent and reliable methodology to compare the effectiveness of a non deterministic system with a deterministic or another non deterministic system . In addition we present a statistical best practice that can be used to safely show how a non deterministic IR system has equivalent effectiveness to another IR system and how to avoid the common pitfall of misusing a lack of significance as a proof that two systems have equivalent effectiveness . 


Summary: We propose methods to compare non deterministic IR systems. We show pitfalls in using standard significance tests to compare such systems. We verify the applicability of proposed methods using simulations and a case study. We show how to compare a non deterministic IR system for equivalent effectiveness.
Social media websites such as YouTube and Flicker are currently gaining in popularity . A large volume of information is generated by online users and how to appropriately provide personalized content is becoming more challenging . Traditional recommendation models are overly dependent on preference ratings and often suffer from the problem of data sparsity . Recent research has attempted to integrate sentiment analysis results of online affective texts into recommendation models however these studies are still limited . The one class collaborative filtering method is more applicable in the social media scenario yet it is insufficient for item recommendation . In this study we develop a novel sentiment aware social media recommendation framework referred to as SA OCCF in order to tackle the above challenges . We leverage inferred sentiment feedback information and OCCF models to improve recommendation performance . We conduct comprehensive experiments on a real social media web site to verify the effectiveness of the proposed framework and methods . The results show that the proposed methods are effective in improving the performance of the baseline OCCF methods . 


Summary: We propose a sentiment aware social media recommendation framework. An ensemble learning based method is proposed to classify sentiments from affective texts. We conduct comprehensive experiments to verify the effectiveness of the proposed methods.
Bibliographic collections in traditional libraries often compile records from distributed sources where variable criteria have been applied to the normalization of the data . Furthermore the source records often follow classical standards such as MARC21 where a strict normalization of author names is not enforced . The identification of equivalent records in large catalogues is therefore required for example when migrating the data to new repositories which apply modern specifications for cataloguing such as the FRBR and RDA standards . An open source tool has been implemented to assist authority control in bibliographic catalogues when external features are not available for the disambiguation of creator names . This tool is based on similarity measures between the variants of author names combined with a parser which interprets the dates and periods associated with the creator . An efficient data structure has been used to accelerate the identification of variants . The algorithms employed and the attribute grammar are described in detail and their implementation is distributed as an open source resource to allow for an easier uptake . 


Summary: A tool assisting authority control in large bibliographic collections. An efficient data structure accelerates the identification of name variants. The space search is pruned using a parser of complex temporal expressions. The algorithm and the parser s grammar are distributed as open source resources.
In the web environment most of the queries issued by users are implicit by nature . Inferring the different temporal intents of this type of query enhances the overall temporal part of the web search results . Previous works tackling this problem usually focused on news queries where the retrieval of the most recent results related to the query are usually sufficient to meet the user s information needs . However few works have studied the importance of time in queries such as Philip Seymour Hoffman where the results may require no recency at all . In this work we focus on this type of queries named time sensitive queries where the results are preferably from a diversified time span not necessarily the most recent one . Unlike related work we follow a content based approach to identify the most important time periods of the query and integrate time into a re ranking model to boost the retrieval of documents whose contents match the query time period . For that purpose we define a linear combination of topical and temporal scores which reflects the relevance of any web document both in the topical and temporal dimensions thus contributing to improve the effectiveness of the ranked results across different types of queries . Our approach relies on a novel temporal similarity measure that is capable of determining the most important dates for a query while filtering out the non relevant ones . Through extensive experimental evaluation over web corpora we show that our model offers promising results compared to baseline approaches . As a result of our investigation we publicly provide a set of web services and a web search interface so that the system can be graphically explored by the research community . 


Summary: We propose a novel temporal re ranking algorithm. We devise and provide new datasets for time sensitive evaluation purposes. We conduct comparative experiments including algorithms with a temporal focus . We investigate the effectiveness of GRank by running a crowdsourcing experiment. We build a prototype system that can be tested by the research community.
One of the major reasons why people find music so enjoyable is its emotional impact . Creating emotion based playlists is a natural way of organizing music . The usability of online music streaming services could be greatly improved by developing emotion based access methods and automatic music emotion recognition is the most quick and feasible way of achieving it . When resorting to music for emotional regulation purposes users are interested in the MER method to predict their induced or felt emotion . The progress of MER in this area is impeded by the absence of publicly accessible ground truth data on musically induced emotion . Also there is no consensus on the question which emotional model best fits the demands of the users and can provide an unambiguous linguistic framework to describe musical emotions . In this paper we address these problems by creating a sizeable publicly available dataset of 400 musical excerpts from four genres annotated with induced emotion . We collected the data using an online game with a purpose Emotify which attracted a big and varied sample of participants . We employed a nine item domain specific emotional model GEMS . In this paper we analyze the collected data and report agreement of participants on different categories of GEMS . We also analyze influence of extra musical factors on induced emotion . We suggest that modifications in GEMS model are necessary . 


Summary: We collected ground truth data on induced musical emotion for 400 musical excerpts. We designed an online game with a purpose to attract a big number of participants. We analyzed inter rater agreement on emotional terms from GEMS model. We found that mood gender and liking or disliking the music influence induced emotion. We suggested improvements to GEMS scale.
Social media is playing a growing role in elections world wide . Thus automatically analyzing electoral tweets has applications in understanding how public sentiment is shaped tracking public sentiment and polarization with respect to candidates and issues understanding the impact of tweets from various entities etc . Here for the first time we automatically annotate a set of 2012 US presidential election tweets for a number of attributes pertaining to sentiment emotion purpose and style by crowdsourcing . Overall more than 100 000 crowdsourced responses were obtained for 13 questions on emotions style and purpose . Additionally we show through an analysis of these annotations that purpose even though correlated with emotions is significantly different . Finally we describe how we developed automatic classifiers using features from state of the art sentiment analysis systems to predict emotion and purpose labels respectively in new unseen tweets . These experiments establish baseline results for automatic systems on this new data . 


Summary: We automatically compile a dataset of 2012 US presidential election tweets. We annotate the tweets for sentiment emotion style and purpose. We show that the tweets convey negative emotions twice as often as positive. We describe two automatic systems that predict emotion and purpose in tweets.
Information filtering has been a major task of study in the field of information retrieval for a long time focusing on filtering well formed documents such as news articles . Recently more interest was directed towards applying filtering tasks to user generated content such as microblogs . Several earlier studies investigated microblog filtering for focused topics . Another vital filtering scenario in microblogs targets the detection of posts that are relevant to long standing broad and dynamic topics i.e . topics spanning several subtopics that change over time . This type of filtering in microblogs is essential for many applications such as social studies on large events and news tracking of temporal topics . In this paper we introduce an adaptive microblog filtering task that focuses on tracking topics of broad and dynamic nature . We propose an entirely unsupervised approach that adapts to new aspects of the topic to retrieve relevant microblogs . We evaluated our filtering approach using 6 broad topics each tested on 4 different time periods over 4 months . Experimental results showed that on average our approach achieved 84 increase in recall relative to the baseline approach while maintaining an acceptable precision that showed a drop of about 8 . Our filtering method is currently implemented on TweetMogaz a news portal generated from tweets . The website compiles the stream of Arabic tweets and detects the relevant tweets to different regions in the Middle East to be presented in the form of comprehensive reports that include top stories and news in each region . 


Summary: Broad topics on Twitter are highly dynamic. Boolean filtering retrieve high precision but limited number of tweets. A proposed adaptive filtering achieved 84 gain in recall with slight drop in prec. Proposed method showed robustness over time across domains and query formulations. Our method is currently adopted in a live service that follows news from Twitter.
Recommender systems are filters which suggest items or information that might be interesting to users . These systems analyze the past behavior of a user build her profile that stores information about her interests and exploit that profile to find potentially interesting items . The main limitation of this approach is that it may provide accurate but likely obvious suggestions since recommended items are similar to those the user already knows . In this paper we investigate this issue known as overspecialization or serendipity problem by proposing a strategy that fosters the suggestion of surprisingly interesting items the user might not have otherwise discovered . The proposed strategy enriches a graph based recommendation algorithm with background knowledge that allows the system to deeply understand the items it deals with . The hypothesis is that the infused knowledge could help to discover hidden correlations among items that go beyond simple feature similarity and therefore promote non obvious suggestions . Two evaluations are performed to validate this hypothesis an in vitro experiment on a subset of the hetrec2011 movielens 2k dataset and a preliminary user study . Those evaluations show that the proposed strategy actually promotes non obvious suggestions by narrowing the accuracy loss . 


Summary: We design a Knowledge Infusion KI process for providing systems with background knowledge. We design a KI based recommendation algorithm for providing serendipitous recommendations. An in vitro evaluation shows the effectiveness of the proposed approach. We collected implicit emotional feedback on serendipitous recommendations. Results show that serendipity is moderately correlated with surprise and happiness.
In this paper we develop a supply contract for a two echelon manufacturer retailer supply chain with a bidirectional option which may be exercised as either a call option or a put option . Under the bidirectional option contract we derive closed form expressions for the retailer s optimal order strategies including the initial order strategy and the option purchasing strategy with a general demand distribution . We also analytically examine the feedback effects of the bidirectional option on the retailer s initial order strategy . In addition taking a chain wide perspective we explore how the bidirectional option contract should be set to attain supply chain coordination . 


Summary: We model the supply chain that deals with products having high demand uncertainty. We characterize the optimal order strategies with bidirectional options provision. We examine the feedback effects that come from bidirectional options provision. We develop the bidirectional option contracts to coordinate the supply chain.