varunr14/newsummary huggingface dataset:
Robust portfolios reduce the uncertainty in portfolio performance . In particular the worst case optimization approach is based on the Markowitz model and form portfolios that are more robust compared to mean variance portfolios . However since the robust formulation finds a different portfolio from the optimal mean variance portfolio the two portfolios may have dissimilar levels of factor exposure . In most cases investors need a portfolio that is not only robust but also has a desired level of dependency on factor movement for managing the total portfolio risk . Therefore we introduce new robust formulations that allow investors to control the factor exposure of portfolios . Empirical analysis shows that the robust portfolios from the proposed formulations are more robust than the classical mean variance approach with comparable levels of exposure on fundamental factors . 


Summary: We introduce the robustness measure and its properties. We propose new robust formulations that allow investors to control the factor exposure of portfolios. The optimal portfolio of our model is not only robust but also has a desired factor exposure.
Natural earthquake disasters are unprecedented incidents which take many lives as a consequence and cause major damages to lifeline infrastructures . Various agencies in a country are responsible for reducing such adverse impacts within specific budgets . These responsibilities range from before to after the incident targeting one of the main phases of disaster management . Use of OR in disaster management and coordination of its phases has been mostly ignored and highly recommended in former reviews . This paper presents a formulation to coordinate three main agencies and proposes a heuristic approach to solve the different introduced sub problems . The results show an improvement of 7.5 24 when the agencies are coordinated . entire population of sub region r in region l vulnerable population ratio of sub region r in region l vulnerable population of sub region r in region l improvement ratio of sub region r in region l budget for investment in the building renovation sector existing emergency supplies in region k inventory cost of one unit of humanitarian goods additional level of humanitarian goods level of relief supplies sent from sub region kto sub region rl budget for investment in the emergency response sector travel time from sub region k to sub region rl survival function which describes the efficiency of goods mobility full cost of retrofitting link failure probability of link retrofitting ratio of link budget for investment in the transportation sector 


Summary: We model three different agencies involved in disaster management through OR. We coordinate agencies responsible for different phases of disaster management. Coordination indicates an average improvement of 7.5 24 in the death toll. Through multi agent optimization connections between the agencies are observed. Efficiency of relief operations is dependent on transportation network reliability.
Temporal aspects have been receiving a great deal of interest in Information Retrieval and related fields . Although previous studies have proposed designed and implemented temporal aware systems and solutions understanding of people s temporal information searching behaviour is still limited . This paper reports the findings of a user study that explored temporal information searching behaviour and strategies in a laboratory setting . Information needs were grouped into three temporal classes to systematically study their characteristics . The main findings of our experiment are as follows . It is intuitive for people to augment topical keywords with temporal expressions such as history recent or future as a tactic of temporal search . However such queries produce mixed results and the success of query reformulations appears to depend on topics to a large extent . Search engine interfaces should detect temporal information needs to trigger the display of temporal search options . Finding a relevant Wikipedia page or similar summary page is a popular starting point of past information needs . Current search engines do a good job for information needs related to recent events but more work is needed for past and future tasks . Participants found it most difficult to find future information . Searching for domain experts was a key tactic in Future search and file types of relevant documents are different from other temporal classes . Overall the comparison of search across temporal classes indicated that Future search was the most difficult and the least successful followed by the search for the Past and then for Recency information . This paper discusses the implications of these findings on the design of future temporal IR systems . 


Summary: Temporal information searching behaviour and strategies were investigated. Searching patterns were identified for past recency and future search tasks. Implications for the development of temporal IR systems are discussed.
The paper reports on some of the results of a research project into how changes in digital behaviour and services impacts on concepts of trust and authority held by researchers in the sciences and social sciences in the UK and the USA . Interviews were used in conjunction with a group of focus groups to establish the form and topic of questions put to a larger international sample in an online questionnaire . The results of these 87 interviews were analysed to determine whether or not attitudes have indeed changed in terms of sources of information used citation behaviour in choosing references and in dissemination practices . It was found that there was marked continuity in attitudes though an increased emphasis on personal judgement over established and new metrics . Journals were more highly respected than other sources and still the vehicle for formal scholarly communication . The interviews confirmed that though an open access model did not in most cases lead to mistrust of a journal a substantial number of researchers were worried about the approaches from what are called predatory OA journals . Established researchers did not on the whole use social media in their professional lives but a question about outreach revealed that it was recognised as effective in reaching a wider audience . There was a remarkable similarity in practice across research attitudes in all the disciplines covered and in both the countries where interviews were held . 


Summary: Digital transition had resulted in changes in researcher behaviour. It is now easier for scientists to discover and disseminate research. The way scientists exercise trust has not changed. Metrics are less important than experience and personal recommendation.
Statistical process control and maintenance planning have long been treated as two separate problems . The interdependence between these two activities has not been adequately addressed in the literature despite their apparent connections . Information obtained in the course of statistical process control signals the need for possible maintenance actions and thus affects the preventive maintenance schedules . Preventive maintenance actions can prevent a production process from further deterioration and improve product quality in conjunction with statistical process control . This paper presents an integrated model for the joint optimization of statistical process control and preventive maintenance . The proposed model is developed for a production process that deteriorates according to a discrete time Markov chain . It is assumed that preventive maintenance is imperfect and both preventive and corrective maintenance are instantaneous . The formulation of the deterioration process with maintenance interventions formulated as a Markov chain provides a breakthrough in designing an efficient solution algorithm and obtaining analytical results . A numerical example is used to illustrate the proposed integrated statistical process control and preventive maintenance policies . Sensitivity analysis is conducted to analyze the impact of model parameters on optimal policies . Sensitivity analysis further indicates the interrelationship between statistical process control and maintenance actions . Numerical results indicate that potential cost savings can be achieved from the proposed integrated policies . 


Summary: We developed a model for the joint optimization of process control and maintenance. Information from the control charts is used to facilitate maintenance decisions. Process control and maintenance procedures are inter dependent. Potential cost savings can be obtained from joint SPC and maintenance policies.
Search task difficulty has been attracting much research attention in recent years mostly regarding its relationship with searchers behaviors and the prediction of task difficulty from search behaviors . However it remains unknown what makes searchers feel the difficulty . A study consisting of 48 undergraduate students was conducted to explore this question . Each participant was given 4 search tasks that were carefully designed following a task classification scheme . Questionnaires were used to elicit participants ratings on task difficulty and why they gave those ratings . Based on the collected difficulty reasons a coding scheme was developed which covered various aspects of task user and user task interaction . Difficulty reasons were then categorized following this scheme . Results showed that searchers reported some common reasons leading to task difficulty in different tasks but most of the difficulty reasons varied across tasks . In addition task difficulty had some common reasons between searchers with low and high levels of topic knowledge although there were also differences in top task difficulty reasons between high and low knowledge users . These findings further our understanding of search task difficulty the relationship between task difficulty and task type and that between task difficulty and knowledge level . The findings can also be helpful with designing tasks for information search experiments and have implications on search system design both in general and for personalization based on task type and searchers knowledge . 


Summary: Development of a search task difficulty reason scheme. Relationship between task difficulty and task type common and different reasons across task types. Relationship between task difficulty and user knowledge common and different reasons among different knowledge groups. Implications for general and personalized information retrieval system design.
Vendors of Business Intelligence software have recently started extending their systems by features from social software . The generated reports may include profiles of report authors and later be supplemented by information about users who accessed the report user evaluations of the report or other social cues . With these features users can support each other in discovering and filtering valuable information in the context of BI . Users who consider reusing an existing report that was not designed by or for them can now not only peruse the report content but also take the social cues into consideration . We analyze which report features influence their perception of report usefulness . Our analysis is based on the elaboration likelihood model which assumes that information recipients are either influenced by the quality of information or peripheral cues . We conduct an experiment with knowledge workers from different companies . The results confirm most hypotheses derived from ELM in the context of BI reports but we also find a deviation from the basic ELM expectations . We find that even people who are able and motivated to scrutinize the report content use community cues to decide on report usefulness in addition to report quality considerations . 


Summary: We analyze which social software features influence the usefulness of BI reports. We test theory based hypotheses in an experiment with knowledge workers. Users of BI reports are mostly influenced by the report s argument quality. Personal characteristics also influence the users perception of report usefulness.
Query auto completion models recommend possible queries to web search users when they start typing a query prefix . Most of today s QAC models rank candidate queries by popularity and in doing so they tend to follow a strict query matching policy when counting the queries . That is they ignore the contributions from so called homologous queries queries with the same terms but ordered differently or queries that expand the original query . Importantly homologous queries often express a remarkably similar search intent . Moreover today s QAC approaches often ignore semantically related terms . We argue that users are prone to combine semantically related terms when generating queries . We propose a learning to rank based QAC approach where for the first time features derived from homologous queries and semantically related terms are introduced . In particular we consider the observed and predicted popularity of homologous queries for a query candidate and the semantic relatedness of pairs of terms inside a query and pairs of queries inside a session . We quantify the improvement of the proposed new features using two large scale real world query logs and show that the mean reciprocal rank and the success rate can be improved by up to 9 over state of the art QAC models . 


Summary: We propose a learning to rank based query auto completion model L2R QAC that exploits contributions from so called homologous queries for a QAC candidate in which two kinds of homologous queries are taken into account. We propose semantic features for QAC using the semantic relatedness of terms inside a query candidate and of pairs of terms from a candidate and from queries previously submitted in the same session. We analyze the effectiveness of our L2R QAC model with newly added features and find that it significantly outperforms state of the art QAC models either based on learning to rank or on popularity.
With the rise of Web 2.0 platforms personal opinions such as reviews ratings recommendations and other forms of user generated content have fueled interest in sentiment classification in both academia and industry . In order to enhance the performance of sentiment classification ensemble methods have been investigated by previous research and proven to be effective theoretically and empirically . We advance this line of research by proposing an enhanced Random Subspace method POS RS for sentiment classification based on part of speech analysis . Unlike existing Random Subspace methods using a single subspace rate to control the diversity of base learners POS RS employs two important parameters i.e . content lexicon subspace rate and function lexicon subspace rate to control the balance between the accuracy and diversity of base learners . Ten publicly available sentiment datasets were investigated to verify the effectiveness of proposed method . Empirical results reveal that POS RS achieves the best performance through reducing bias and variance simultaneously compared to the base learner i.e . Support Vector Machine . These results illustrate that POS RS can be used as a viable method for sentiment classification and has the potential of being successfully applied to other text classification problems . 


Summary: The rise of social media has fueled interest in sentiment classification. POS RS is proposed for sentiment analysis based on part of speech analysis. Ten public datasets were investigated to verify the effectiveness of POS RS. Experimental results reveal POS RS can be used as a viable method.
Clusterwise regression consists of finding a number of regression functions each approximating a subset of the data . In this paper a new approach for solving the clusterwise linear regression problems is proposed based on a nonsmooth nonconvex formulation . We present an algorithm for minimizing this nonsmooth nonconvex function . This algorithm incrementally divides the whole data set into groups which can be easily approximated by one linear regression function . A special procedure is introduced to generate a good starting point for solving global optimization problems at each iteration of the incremental algorithm . Such an approach allows one to find global or near global solution to the problem when the data sets are sufficiently dense . The algorithm is compared with the multistart Sp th algorithm on several publicly available data sets for regression analysis . 


Summary: We develop an incremental algorithm to solve clusterwise linear regression problems. The algorithm gradually computes clusters and linear regression functions. Two special procedures to construct initial solutions are proposed. The algorithm finds global or near global minimizers of the overall fit function.
In this paper we consider a latent Markov process governing the intensity rate of a Poisson process model for software failures . The latent process enables us to infer performance of the debugging operations over time and allows us to deal with the imperfect debugging scenario . We develop the Bayesian inference for the model and also introduce a method to infer the unknown dimension of the Markov process . We illustrate the implementation of our model and the Bayesian approach by using actual software failure data . 


Summary: A new model for software reliability is proposed. Proposed model enables us to infer performance of the debugging process. Bayesian inference is developed. An approach is introduced to assess dimension of the Markov process.
This work presents a content based semantics and image retrieval system for semantically categorized hierarchical image databases . Each module is designed with an aim to develop a system that works closer to human perception . Images are mapped to a multidimensional feature space where images belonging a semantic are clustered and indexed to acquire its efficient representation . This helps in handling the existing variability or heterogeneity within this semantic . Adaptive combinations of the obtained depictions are utilized by the branch selection and pruning algorithms to identify some closer semantics and select only a part of the large hierarchical search space for actual search . So obtained search space is finally used to retrieve desired semantics and similar images corresponding to them . The system is evaluated in terms of accuracy of the retrieved semantics and precision recall curves . Experiments show promising semantics and image retrieval results on hierarchical image databases . The results reported with non hierarchical but categorized image databases further prove the efficacy of the proposed system . 


Summary: Proposes automatic semantics and image retrieval system for hierarchical databases. System uses 1 3 search space to retrieve semantics and finally similar images. 77 semantic retrieval accuracy on ImageNet. Uses 4 space to retrieve images. System reports precision of 0.78 @ 20 and 0.67 @ 100 images on categorized WANG. The study explores adequacy of visual signatures set used to represent a semantic.
Websites can learn what their users do on their pages to provide better content and services to those users . A website can easily find out where a user has been but in order to find out what content is consumed and how it was consumed at a sub page level prior work has proposed client side tracking to record cursor activity which is useful for computing the relevance for search results or determining user attention on a page . While recording cursor interactions can be done without disturbing the user the overhead of recording the cursor trail and transmitting this data over the network can be substantial . In our work we investigate methods to compress cursor data taking advantage of the fact that not every cursor coordinate has equal value to the website developer . We evaluate 5 lossless and 5 lossy compression algorithms over two datasets reporting results about client side performance space savings and how well a lossy algorithm can replicate the original cursor trail . The results show that different compression techniques may be suitable for different goals LZW offers reasonable lossless compression but lossy algorithms such as piecewise linear interpolation and distance thresholding offer better client side performance and bandwidth reduction . 


Summary: We study 10 techniques to compress users cursor interactions on the Web. A systematic evaluation of both lossy and lossless algorithms is performed. We found that different compression techniques excel depending on the objective. LZW and piecewise linear interpolation balance well accuracy and efficiency.
Community question answering services that enable users to ask and answer questions have become popular on the internet . However lots of new questions usually can not be resolved by appropriate answerers effectively . To address this question routing task in this paper we treat it as a ranking problem and rank the potential answerers by the probability that they are able to solve the given new question . We utilize tensor model and topic model simultaneously to extract latent semantic relations among asker question and answerer . Then we propose a learning procedure based on the above models to get optimal ranking of answerers for new questions by optimizing the multi class AUC . Experimental results on two real world CQA datasets show that the proposed method is able to predict appropriate answerers for new questions and outperforms other state of the art approaches . 


Summary: We formally described the answerer ranking problem in CQA service. A novel model with tensor decomposition is proposed to solve the problem. The parameters of the above model are learned by maximizing the multi class AUC. Introducing the asker dimension improves the answerer ranking performance. Our approach outperforms the previous methods on two real world CQA datasets.
Media sharing applications such as Flickr and Panoramio contain a large amount of pictures related to real life events . For this reason the development of effective methods to retrieve these pictures is important but still a challenging task . Recognizing this importance and to improve the retrieval effectiveness of tag based event retrieval systems we propose a new method to extract a set of geographical tag features from raw geo spatial profiles of user tags . The main idea is to use these features to select the best expansion terms in a machine learning based query expansion approach . Specifically we apply rigorous statistical exploratory analysis of spatial point patterns to extract the geo spatial features . We use the features both to summarize the spatial characteristics of the spatial distribution of a single term and to determine the similarity between the spatial profiles of two terms i.e . term to term spatial similarity . To further improve our approach we investigate the effect of combining our geo spatial features with temporal features on choosing the expansion terms . To evaluate our method we perform several experiments including well known feature analyzes . Such analyzes show how much our proposed geo spatial features contribute to improve the overall retrieval performance . The results from our experiments demonstrate the effectiveness and viability of our method . 


Summary: We have developed a new approach that effectively retrieves event based images. We have proposed a rigorous technique to extract spatial features from image tags. We have developed a method for summarizing spatial distributions of single tags. We have developed new techniques for spatial relatedness between two tag terms. Our spatio temporal IR method improves the retrieval performance significantly.