varunr14/newsummary huggingface dataset:
The equilibrium and socially optimal balking strategies are investigated for unobservable and observable single server classical retrial queues . There is no waiting space in front of the server . If an arriving customer finds the server idle he occupies the server immediately and leaves the system after service . Otherwise if the server is found busy the customer decides whether or not to enter a retrial pool with infinite capacity and becomes a repeated customer based on observation of the system and the reward cost structure imposed on the system . Accordingly two cases with respect to different levels of information are studied and the corresponding Nash equilibrium and social optimization balking strategies for all customers are derived . Finally we compare the equilibrium and optimal behavior regarding these two information levels through numerical examples . 


Summary: Study the equilibrium strategic behavior and social optimization in the M M 1 retrial queues. Two different levels of information provided to arriving customers have been investigated extensively. Nash equilibrium and the equilibrium social benefit for all customers are derived. The effect of the information levels is illustrated by extensive numerical examples. It provides insight into the optimal design and control of the retrial queueing systems.
We propose and apply a novel approach for modeling special day effects to predict electricity demand in Korea . Notably we model special day effects on an hourly rather than a daily basis . Hourly specified predictor variables are implemented in the regression model with a seasonal autoregressive moving average type error structure in order to efficiently reflect the special day effects . The interaction terms between the hour of day effects and the hourly based special day effects are also included to capture the unique intraday patterns of special days more accurately . The multiplicative SARMA mechanism is employed in order to identify the double seasonal cycles namely the intraday effect and the intraweek effect . The forecast results of the suggested model are evaluated by comparing them with those of various benchmark models for the following year . The empirical results indicate that the suggested model outperforms the benchmark models for both special and non special day predictions . 


Summary: We model special day effects to predict hourly electricity demand in Korea. The regression model with hourly special day effect and the double SARMA error is suggested. The hourly special day effects are more important than the daily ones. The suggested model is effective during both the special day prediction and the non special day prediction.
Analyzing and modeling users online search behaviors when conducting exploratory search tasks could be instrumental in discovering search behavior patterns that can then be leveraged to assist users in reaching their search task goals . We propose a framework for evaluating exploratory search based on implicit features and user search action sequences extracted from the transactional log data to model different aspects of exploratory search namely uncertainty creativity exploration and knowledge discovery . We show the effectiveness of the proposed framework by demonstrating how it can be used to understand and evaluate user search performance and thereby make meaningful recommendations to improve the overall search performance of users . We used data collected from a user study consisting of 18 users conducting an exploratory search task for two sessions with two different topics in the experimental analysis . With this analysis we show that we can effectively model their behavior using implicit features to predict the user s future performance level with above 70 accuracy in most cases . Further using simulations we demonstrate that our search process based recommendations improve the search performance of low performing users over time and validate these findings using both qualitative and quantitative approaches . 


Summary: We propose a framework for evaluating exploratory search using implicit features. User search action sequences are also used to find behavior patterns. Show effectiveness with above 70 prediction accuracy for user search performance. Provide search process based recommendation to assist underperforming users. Demonstrate recommendation effectiveness both qualitatively and quantitatively.
Emission trading schemes such as the European Union Emissions Trading System attempt to reconcile economic efficiency with ecological efficiency by creating financial incentives for companies to invest in climate friendly innovations . Using real options methodology we demonstrate that under uncertainty economic and ecological efficiency continue to be mutually exclusive . This problem is even worse if a climate friendly project depends on investing in of a whole supply chain . We model a sequential bargaining game in a supply chain where the parties negotiate over implementation of a carbon dioxide saving investment project . We show that the outcome of their bargaining is not economically efficient and even less ecologically efficient . Furthermore we show that a supply chain becomes less economically efficient and less ecologically efficient with every additional chain link . Finally we make recommendations for how managers or politicians can improve the situation and thereby increase economic as well as ecological efficiency and thus also the eco efficiency of supply chains . 


Summary: We analyze the timing decision of a company which can invest in a CO2 saving project. Doing this we use a real options game theoretic model. Total emission of the supply chain increases with carbon price uncertainty. Furthermore inefficiency increases with the length of the supply chain. Cooperation and vertical integration improve ecologic as well as economic efficiency.
Cross language plagiarism detection aims to detect plagiarised fragments of text among documents in different languages . In this paper we perform a systematic examination of Cross language Knowledge Graph Analysis an approach that represents text fragments using knowledge graphs as a language independent content model . We analyse the contributions to cross language plagiarism detection of the different aspects covered by knowledge graphs word sense disambiguation vocabulary expansion and representation by similarities with a collection of concepts . In addition we study both the relevance of concepts and their relations when detecting plagiarism . Finally as a key component of the knowledge graph construction we present a new weighting scheme of relations between concepts based on distributed representations of concepts . Experimental results in Spanish English and German English plagiarism detection show state of the art performance and provide interesting insights on the use of knowledge graphs . 


Summary: Study of the impact of the implicit aspects of knowledge graphs for cross language plagiarism detection. We present a new weighting scheme for relations between concepts based on distributed representations of concepts. We obtain state of the art performance compared to several state of the art models.
Aspect level sentiment analysis is important for numerous opinion mining and market analysis applications . In this paper we study the problem of identifying and rating review aspects which is the fundamental task in aspect level sentiment analysis . Previous review aspect analysis methods seldom consider entity or rating but only 2 tuples i.e . head and modifier pair e.g . in the phrase nice room room is the head and nice is the modifier . To solve this problem we novelly present a Quad tuple Probability Latent Semantic Analysis which incorporates entity and its rating together with the 2 tuples into the PLSA model . Specifically QPLSA not only generates fine granularity aspects but also captures the correlations between words and ratings . We also develop two novel prediction approaches the Quad tuple Prediction and the Expectation Prediction . For evaluation systematic experiments show that Quad tuple PLSA outperforms 2 tuple PLSA significantly on both aspect identification and aspect rating prediction for publication datasets . Moreover for aspect rating prediction QPLSA shows significant superiority over state of the art baseline methods . Besides the Quad tuple Prediction and the Expectation Prediction also show their strong ability in aspect rating on different datasets . 


Summary: We propose two novel aspect rating prediction approaches i.e. Quad tuple prediction and Expectation prediction. We analyze and investigate the performance of the proposed aspect rating prediction methods in contrast with Local Prediction and Global Prediction. We experimentally inspect the influence of aspect rating variance for different rating prediction approaches.
Word sense ambiguity has been identified as a cause of poor precision in information retrieval systems . Word sense disambiguation and discrimination methods have been defined to help systems choose which documents should be retrieved in relation to an ambiguous query . However the only approaches that show a genuine benefit for word sense discrimination or disambiguation in IR are generally supervised ones . In this paper we propose a new unsupervised method that uses word sense discrimination in IR . The method we develop is based on spectral clustering and reorders an initially retrieved document list by boosting documents that are semantically similar to the target query . For several TREC ad hoc collections we show that our method is useful in the case of queries which contain ambiguous terms . We are interested in improving the level of precision after 5 10 and 30 retrieved documents respectively . We show that precision can be improved by 8 above current state of the art baselines . We also focus on poor performing queries . 


Summary: We propose a new unsupervised method that uses word sense discrimination in IR. The re ranking method is based on spectral clustering. The effectiveness over queries with ambiguous terms is proved on TREC corpora. Our interest regards improving the precision after 5 10 and 30 retrieved documents. The method improves results for queries with poor baseline performance.
Video summarization aims at producing a compact version of a full length video while preserving the significant content of the original video . Movie summarization condenses a full length movie into a summary that still retains the most significant and interesting content of the original movie . In the past several movie summarization systems have been proposed to generate a movie summary based on low level video features such as color motion texture etc . However a generic summary which is common to everyone and is produced based only on low level video features will not satisfy every user . As users preferences for the summary differ vastly for the same movie there is a need for a personalized movie summarization system nowadays . To address this demand this paper proposes a novel system to generate semantically meaningful video summaries for the same movie which are tailored to the preferences and interests of a user . For a given movie shots and scenes are automatically detected and their high level features are semi automatically annotated . Preferences over high level movie features are explicitly collected from the user using a query interface . The user preferences are generated by means of a stored query . Movie summaries are generated at shot level and scene level where shots or scenes are selected for summary skim based on the similarity measured between shots and scenes and the user s preferences . The proposed movie summarization system is evaluated subjectively using a sample of 20 subjects with eight movies in the English language . The quality of the generated summaries is assessed by informativeness enjoyability relevance and acceptance metrics and Quality of Perception measures . Further the usability of the proposed summarization system is subjectively evaluated by conducting a questionnaire survey . The experimental results on the performance of the proposed movie summarization approach show the potential of the proposed system . 


Summary: A novel personalized semantic movie summarization approach is proposed. User s subjective preferences are exploited for personalized movie summarization. An extensive user study demonstrates the advantages of the proposed system. A subjective study shows the users diverse behavior on the system usability. Proof of concept prototype of the proposed movie summarization system is provided.
Considering the inherent connection between supplier selection and inventory management in supply chain networks this article presents a multi period inventory lot sizing model for a single product in a serial supply chain where raw materials are purchased from multiple suppliers at the first stage and external demand occurs at the last stage . The demand is known and may change from period to period . The stages of this production distribution serial structure correspond to inventory locations . The first two stages stand for storage areas for raw materials and finished products in a manufacturing facility and the remaining stages symbolize distribution centers or warehouses that take the product closer to customers . The problem is modeled as a time expanded transshipment network which is defined by the nodes and arcs that can be reached by feasible material flows . A mixed integer nonlinear programming model is developed to determine an optimal inventory policy that coordinates the transfer of materials between consecutive stages of the supply chain from period to period while properly placing purchasing orders to selected suppliers and satisfying customer demand on time . The proposed model minimizes the total variable cost including purchasing production inventory and transportation costs . The model can be linearized for certain types of cost structures . In addition two continuous and concave approximations of the transportation cost function are provided to simplify the model and reduce its computational time . 


Summary: This article provides an inventory model for a serial supply chain with multiple suppliers and time varying demand. The model minimizes the total cost of purchasing production inventory setup and holding and transportation. Considering lead times necessary feasibility conditions regarding the demand at the last stage are established. Actual transportation costs are modeled by exact piecewise linear functions. Our results show that inventory setup and holding costs can affect supplier selection and order lot sizing decisions.
In social networks identifying influential nodes is essential to control the social networks . Identifying influential nodes has been among one of the most intensively studies of analyzing the structure of networks . There are a multitude of evaluation indicators of node importance in social networks such as degree betweenness and cumulative nomination and so on . But most of the indicators only reveal one characteristic of the node . In fact in social networks node importance is not affected by a single factor but is affected by a number of factors . Therefore the paper puts forward a relatively comprehensive and effective method of evaluation node importance in social networks by using the multi objective decision method . Firstly we select several different representative indicators given a certain weight . We regard each node as a solution and different indicators of each node as the solution properties . Then through calculating the closeness degree of each node to the ideal solution we obtain evaluation indicator of node importance in social networks . Finally we verify the effectiveness of the proposed method experimentally on a few actual social networks . 


Summary: Based on the theories of comprehensive decision making a multiattribute ranking method is presented. The proposed method is superior to other methods through contrast experiments. The theories of comprehensive decision making are utilized to detect important nodes in social networks. This method can be used in different types and different scales of networks.
In order to successfully apply opinion mining to the large amounts of user generated content produced every day we need robust models that can handle the noisy input well yet can easily be adapted to a new domain or language . We here focus on opinion mining for YouTube by modeling classifiers that predict the type of a comment and its polarity while distinguishing whether the polarity is directed towards the product or video proposing a robust shallow syntactic structure that adapts well when tested across domains and evaluating the effectiveness on the proposed structure on two languages English and Italian . We rely on tree kernels to automatically extract and learn features with better generalization power than traditionally used bag of word models . Our extensive empirical evaluation shows that STRUCT outperforms the bag of words model both within the same domain it is particularly useful when tested across domains especially when little training data is available and the proposed structure is also effective in a lower resource language scenario where only less accurate linguistic processing tools are available . 


Summary: We designed the first model for effectively carrying out opinion mining on YouTube comments. We propose kernel methods applied to a robust shallow syntactic structure which improves accuracy for both languages. Our approach greatly outperforms other basic models on cross domain settings. We created a YouTube corpus in Italian and English and made it available for the research community. Comments must be classified in subcategories to make opinion mining effective on YouTube.
This paper highlights the subject of integrated projects planning in contemporary IS departments and presents a multi period multi project selection and assignment approach to assist the departments in handling continuous project based IS requests . The MPPA features a model to optimize the selection and assignment of IS projects . In the scope of multi project multi period planning the model innovatively considers the losses due to the accumulated postponement of a previously unselected IS request and the expected delay of ongoing projects when inserting a new project request . The MPPA also features an event based decisional process for cumulative selection and assignment on a multi period basis . Due to the complex and contextual nature of data in this paper a computerized system is implemented for aiding the execution of the model and the process . The paper reports on an industrial case for a demonstration of the proposed work . Finally the paper compares the MPPA with related work to summarize the value and role it may play in the IPP context . 


Summary: The paper is an inter disciplinary research of applying OR in projects management. Address a sustainability issue of handling continuous IS project demands in contemporary IT departments. Features mixed integer programming model to optimize the selection and assignment of projects on a multi period multi project basis. It innovatively considers the losses due to unselecting or delaying projects in each time of selection and assignment. It implements a computerized solution that automatically enables the sustainability of continuous and cumulative selections and assignments.
Apart from the well known weaknesses of the standard Malmquist productivity index related to infeasibility and not accounting for slacks already addressed in the literature we identify a new and significant drawback of the Malmquist Luenberger index decomposition that questions its validity as an empirical tool for environmental productivity measurement associated with the production of bad outputs . In particular we show that the usual interpretation of the technical change component in terms of production frontier shifts can be inconsistent with its numerical value thereby resulting in an erroneous interpretation of this component that passes on to the index itself . We illustrate this issue with a simple numerical example . Finally we propose a solution for this inconsistency issue based on incorporating a new postulate for the technology related to the production of bad outputs . 


Summary: We show that the Malmquist Luenberger ML index may yield inconsistent results. The ML index can signal declining productivity when productivity is increasing. We discuss this issue within the standard decomposition of the ML index. We use a simple example and give cautionary advice to practitioners. We propose a solution for solving the inconsistency through a new postulate.
This paper presents a Web intelligence portal that captures and aggregates news and social media coverage about Game of Thrones an American drama television series created for the HBO television network based on George R.R . Martin s series of fantasy novels . The system collects content from the Web sites of Anglo American news media as well as from four social media platforms Twitter Facebook Google and YouTube . An interactive dashboard with trend charts and synchronized visual analytics components not only shows how often Game of Thrones events and characters are being mentioned by journalists and viewers but also provides a real time account of concepts that are being associated with the unfolding storyline and each new episode . Positive or negative sentiment is computed automatically which sheds light on the perception of actors and new plot elements . 


Summary: Westeros Sentinel a visual analytics dashboard for Game of Thrones. Extraction of affective and factual knowledge from news and social media coverage. Emotional categories from semantic knowledge bases. Automated annotation services for contextualized information spaces. Interactive visualizations to explore context features.
Complex applications such as big data analytics involve different forms of coupling relationships that reflect interactions between factors related to technical business and environmental aspects . There are diverse forms of couplings embedded in poor structured and ill structured data . Such couplings are ubiquitous implicit and or explicit objective and or subjective heterogeneous and or homogeneous presenting complexities to existing learning systems in statistics mathematics and computer sciences such as typical dependency association and correlation relationships . Modeling and learning such couplings thus is fundamental but challenging . This paper discusses the concept of coupling learning focusing on the involvement of coupling relationships in learning systems . Coupling learning has great potential for building a deep understanding of the essence of business problems and handling challenges that have not been addressed well by existing learning theories and tools . This argument is verified by several case studies on coupling learning including handling coupling in recommender systems incorporating couplings into coupled clustering coupling document clustering coupled recommender algorithms and coupled behavior analysis for groups . 


Summary: The concept of coupling and major coupling relationships. Coupling layers and forms appearing in complex data and applications. Modeling couplings measuring couplings and the curse of couplings. A new theoretical framework for the next generation recommender systems. Case studies of learning couplings in data mining and recommendation.