varunr14/newsummary huggingface dataset:
With constant growth in size of analyzable data ranking of academic entities is becoming an attention grabbing task . For ranking of authors this study considers the author s own contribution as well as the impact of mutual influence of the co authors along with exclusivity in their received citations . The ranking of researchers is influenced by the ranking of their co authors more so if co authors are seniors . Tracking the citations received by an author is also an important factor to measure standing of an author . This study proposes Mutual Influence and Citation Exclusivity Author Rank algorithm . We performed a sequence of experiments to calculate the MuICE Rank . First we calculated Mutual Influence considering three different factors the number of papers the number of citations and the author s appearance as first author . Secondly we computed MuICE incorporating all three factors of MuInf along with the exclusivity in citations received by an author . Empirically it is shown that the proposed methods generate substantial results . 


Summary: A mutual influence and exclusive citations based method for author ranking is proposed. It considers effect of publications citations and publications as first author for authors and co authors. It also considers exclusivity in citations received by an author.
Nowadays the increasing demand for group recommendations can be observed . In this paper we address the problem of recommendation performance for groups of users . We focus on the performance of very Top N recommendations which are important when recommending the long lasting items . To improve existing group recommenders we propose a mixed hybrid recommender for groups combining content based and collaborative strategies . The principle of proposed group recommender is to generate content and collaborative recommendations for each user apply an aggregation strategy to solve the group conflict preferences for the content and collaborative sets separately and finally reorder the collaborative candidates based on the content based ones . It is based on an idea that candidates recommended by both recommendation strategies at the same time are presumably more appropriate for the group than the candidates recommended by individual strategies . The evaluation is performed by several experiments in the multimedia domain . Both online and offline experiments were performed in order to compare real users satisfaction to the standard group recommenders and also to compare performance of proposed approach to the state of the art recommenders based on the MovieLens dataset . Finally we experimented with the proposed hybrid recommender to generate the recommendation for a group of size one . Obtained results support our hypothesis that proposed mixed hybrid approach improves the precision of the recommendation for groups of users and for the single user recommendation respectively on very Top N recommended items . 


Summary: Novel group hybrid method combining collaborative and content based recommendation. Proposed method improves the quality of recommended items ordering. Proposed method increases the recommendation precision for very Top N results. Applicable for single user as well as group recommendation.
To reduce labor intensive and costly order picking activities many distribution centers are subdivided into a forward area and a reserve area . The former is a small area where most popular stock keeping units can conveniently be picked and the latter is applied for replenishing the forward area and storing SKUs that are not assigned to the forward area at all . Clearly reducing SKUs stored in forward area enables a more compact forward area but requires a more frequent replenishment . To tackle this basic trade off different versions of forward reserve problems determine the SKUs to be stored in forward area the space allocated to each SKU and the overall size of the forward area . As previous research mainly focuses on simplified problem versions where the forward area can continuously be subdivided we investigate discrete forward reserve problems . Important subproblems are defined and computation complexity is investigated . Furthermore we experimentally analyze the model gaps between the different fluid models and their discrete counterparts . 


Summary: Existing literature mainly focuses on fluid models . We formulate discrete versions of basic and extended forward reserve problems. We discuss their computational complexity and present suitable solution procedures. We show the basic allocation problem to be solvable in polynomial time. We propose and computationally examine a very effective repair heuristic .
This study proposes an efficient exact algorithm for the precedence constrained single machine scheduling problem to minimize total job completion cost where machine idle time is forbidden . The proposed algorithm is based on the SSDP method and is an extension of the authors previous algorithms for the problem without precedence constraints . In this method a lower bound is computed by solving a Lagrangian relaxation of the original problem via dynamic programming and then it is improved successively by adding constraints to the relaxation until the gap between the lower and upper bounds vanishes . Numerical experiments will show that the algorithm can solve all instances with up to 50 jobs of the precedence constrained total weighted tardiness and total weighted earliness tardiness problems and most instances with 100 jobs of the former problem . 


Summary: The precedence constrained single machine scheduling problem is considered. An efficient exact algorithm is proposed based on our previous studies. The SSDP Successive Sublimation Dynamic Programming method is employed. Instances of the total weighted tardiness problem with up to 100 jobs are solved.
Semantic similarity assessment between concepts is an important task in many language related applications . In the past several approaches to assess similarity by evaluating the knowledge modeled in an ontology have been proposed . However there are some limitations such as the facts of relying on predefined ontologies and fitting non dynamic domains in the existing measures . Wikipedia provides a very large domain independent encyclopedic repository and semantic network for computing semantic similarity of concepts with more coverage than usual ontologies . In this paper we propose some novel feature based similarity assessment methods that are fully dependent on Wikipedia and can avoid most of the limitations and drawbacks introduced above . To implement similarity assessment based on feature by making use of Wikipedia firstly a formal representation of Wikipedia concepts is presented . We then give a framework for feature based similarity based on the formal representation of Wikipedia concepts . Lastly we investigate several feature based approaches to semantic similarity measures resulting from instantiations of the framework . The evaluation based on several widely used benchmarks and a benchmark developed in ourselves sustains the intuitions with respect to human judgements . Overall several methods proposed in this paper have good human correlation and constitute some effective ways of determining similarity between Wikipedia concepts . 


Summary: A formal representation of Wikipedia concepts is presented. A framework for feature based similarity is proposed. Some novel feature based approaches to semantic similarity measures are presented. Results show that several proposed methods have good human correlation.
This paper presents a novel solution heuristic to the General Lotsizing and Scheduling Problem for Parallel production Lines . The GLSPPL addresses the problem of simultaneously deciding about the sizes and schedules of production lots on parallel heterogeneous production lines with respect to scarce capacity sequence dependent setup times and deterministic dynamic demand of multiple products . Its objective is to minimize inventory holding sequence dependent setup and production costs . The new heuristic iteratively decomposes the multi line problem into a series of single line problems which are easier to solve . Different approaches for decomposition and for the iteration between a modified multi line master problem and the single line subproblems are proposed . They are compared with an existing solution method for the GLSPPL by means of medium sized and large practical problem instances from different types of industries . The new methods prove to be superior with respect to both solution quality and computation time . 


Summary: A novel solution heuristic for the General Lotsizing and Scheduling Problem for Parallel production Lines is presented. The idea is to iteratively decompose the parallel line problem into a series of single line problems. The new heuristic improves already existing approaches. Large practical applications can now be solved.
Daily deals have emerged in the last three years as a successful form of online advertising . The downside of this success is that users are increasingly overloaded by the many thousands of deals offered each day by dozens of deal providers and aggregators . The challenge is thus offering the right deals to the right users i.e . the relevance ranking of deals . This is the problem we address in our paper . Exploiting the characteristics of deals data we propose a combination of a term and a concept based retrieval model that closes the semantic gap between queries and documents expanding both of them with category information . The method consistently outperforms state of the art methods based on term matching alone and existing approaches for ad classification and ranking . 


Summary: We formalize the problem of retrieving daily deals in the context of Web search. We effectively combine keyword based retrieval with automated classification. Our solution outperforms state of the art query expansion and prior ad ranking work.
This paper proposes a test for whether data are over represented in a given production zone i.e . a subset of a production possibility set which has been estimated using the non parametric Data Envelopment Analysis approach . A binomial test is used that relates the number of observations inside such a zone to a discrete probability weighted relative volume of that zone . A Monte Carlo simulation illustrates the performance of the proposed test statistic and provides good estimation of both facet probabilities and the assumed common inefficiency distribution in a three dimensional input space . Potential applications include tests for whether benchmark units dominate more observations than expected . 


Summary: This paper proposes a test for whether data are over represented in a subset of a DEA estimated production possibility set. The binomial test the number of observations inside a zone to a discrete probability weighted relative volume of that zone. A Monte Carlo simulation illustrates the performance of the proposed test.
In the area of Information Retrieval the task of automatic text summarization usually assumes a static underlying collection of documents disregarding the temporal dimension of each document . However in real world settings collections and individual documents rarely stay unchanged over time . The World Wide Web is a prime example of a collection where information changes both frequently and significantly over time with documents being added modified or just deleted at different times . In this context previous work addressing the summarization of web documents has simply discarded the dynamic nature of the web considering only the latest published version of each individual document . This paper proposes and addresses a new challenge the automatic summarization of changes in dynamic text collections . In standard text summarization retrieval techniques present a summary to the user by capturing the major points expressed in the most recent version of an entire document in a condensed form . In this new task the goal is to obtain a summary that describes the most significant changes made to a document during a given period . In other words the idea is to have a summary of the revisions made to a document over a specific period of time . This paper proposes different approaches to generate summaries using extractive summarization techniques . First individual terms are scored and then this information is used to rank and select sentences to produce the final summary . A system based on Latent Dirichlet Allocation model is used to find the hidden topic structures of changes . The purpose of using the LDA model is to identify separate topics where the changed terms from each topic are likely to carry at least one significant change . The different approaches are then compared with the previous work in this area . A collection of articles from Wikipedia including their revision history is used to evaluate the proposed system . For each article a temporal interval and a reference summary from the article s content are selected manually . The articles and intervals in which a significant event occurred are carefully selected . The summaries produced by each of the approaches are evaluated comparatively to the manual summaries using ROUGE metrics . It is observed that the approach using the LDA model outperforms all the other approaches . Statistical tests reveal that the differences in ROUGE scores for the LDA based approach is statistically significant at 99 over baseline . 


Summary: The summarization of changes addresses a new challenge the automatic summarization of changes in dynamic text collections. Four different approaches are proposed for the summarization of changes. A system based on Latent Dirichlet Allocation model is used to find the hidden topic structures of changes. The approach based on LDA model outperforms all the other approaches. The differences in ROUGE scores for LDA based approach is statistically significant at 99 over baseline.
This paper investigates the contributions of digital infrastructure policies of provincial governments in Canada to the development of broadband networks . Using measurements of broadband network speeds between 2007 and 2011 the paper analyzes potential causes for observed differences in network performance growth across the provinces including geography Internet use intensity platform competition and provincial broadband policies . The analysis suggests provincial policies that employed public sector procurement power to open access to essential facilities and channeled public investments in Internet backbone infrastructure were associated with the emergence of relatively high quality broadband networks . However a weak essential facilities regime and regulatory barriers to entry at the national level limit the scope for decentralized policy solutions . 


Summary: The impact of multilevel broadband policies on network development in Canada. Analyzes variations in the levels and patterns of network performance growth. Federal regulations limited the scope for policy decentralization. Higher rate of progress in provinces that promoted access to essential facilities.
The Sequential Probability Ratio Test control chart is a powerful tool for monitoring manufacturing processes . It is highly suitable for the applications where testing is destructive or very expensive such as the automobile airbags test . This article studies the effect of the Average Sample Number on the chart s performance . A design algorithm is proposed to develop the optimal SPRT chart for monitoring the fraction nonconforming p of Bernoulli processes . By optimizing the ASN and other charting parameters the average detection speed of the SPRT chart is almost doubled . It is also found that the optimal SPRT chart significantly outperforms the optimal np and binomial CUSUM charts in terms of Average Number of Defectives under different combinations of the design specifications . It is observed that the SPRT chart using a relatively smaller ASN and a shorter sampling interval has a higher overall detection effectiveness . 


Summary: This article studies the effect of the Average Sample Number ASN on the performance of the SPRT chart. A design algorithm is proposed to explore the optimal ASN of the SPRT chart for monitoring the fraction nonconforming p. The optimal SPRT chart significantly outperforms the optimal np chart optimal CUSUM chart and basic SPRT chart.
Cluster analysis using multiple representations of data is known as multi view clustering and has attracted much attention in recent years . The major drawback of existing multi view algorithms is that their clustering performance depends heavily on hyperparameters which are difficult to set . In this paper we propose the Multi View Normalized Cuts approach a two step algorithm for multi view clustering . In the first step an initial partitioning is performed using a spectral technique . In the second step a local search procedure is used to refine the initial clustering . MVNC has been evaluated and compared to state of the art multi view clustering approaches using three real world datasets . Experimental results have shown that MVNC significantly outperforms existing algorithms in terms of clustering quality and computational efficiency . In addition to its superior performance MVNC is parameter free which makes it easy to use . 


Summary: A new multi view clustering algorithm is proposed. The proposed MVNC algorithm uses spectral partitioning and local refinement. MVNC is compared to state of the art algorithms using three real world datasets. MVNC significantly outperforms the other algorithms. MVNC is parameter free unlike existing multi view clustering algorithms.
This note proposes an alternative procedure for identifying violated subtour elimination constraints in branch and cut algorithms for elementary shortest path problems . The procedure is also applicable to other routing problems such as variants of travelling salesman or shortest Hamiltonian path problems on directed graphs . The proposed procedure is based on computing the strong components of the support graph . The procedure possesses a better worst case time complexity than the standard way of separating SECs which uses maximum flow algorithms and is easier to implement . 


Summary: Presents alternative procedure for separating subtour elimination constraints. Procedure is based on computing the strong components of the support graph. Procedure has better worst case time complexity than standard way of separating SECs. Moreover procedure is easier to implement. Computational experiments verify practical usefulness of the procedure.
Many problems in data mining involve datasets with multiple views where the feature space consists of multiple feature groups . Previous studies employed view weighting method to find a shared cluster structure underneath different views . However most of these studies applied gradient optimization method to optimize the cluster centroids and feature weights iteratively and made the final partition local optimal . In this work we proposed a novel bi level weighted multi view clustering method with emphasizing fuzzy weighting on both view and feature . Furthermore an efficient global search strategy that combines particle swarm optimization and gradient optimization was proposed to solve the induced non convex loss function . In the experimental analysis the performance of the proposed method was compared with five state of the art weighted clustering algorithms on three real world high dimensional multi view datasets . 


Summary: We propose a bi level fuzzy weighting to discriminate view and feature simultaneously. We develop a hybrid optimization based on particle swarm optimization. We propose a representative based method to determine hyper parameters. Our method is compared with six clustering algorithm on three real world datasets.
In this paper we propose and evaluate the Block Max WAND with Candidate Selection and Preserving Top K Results algorithm or BMW CSP . It is an extension of BMW CS a method previously proposed by us . Although very efficient BMW CS does not guarantee preserving the top k results for a given query . Algorithms that do not preserve the top results may reduce the quality of ranking results in search systems . BMW CSP extends BMW CS to ensure that the top k results will have their rankings preserved . In the experiments we performed for computing the top 10 results the final average time required for processing queries with BMW CSP was lesser than the ones required by the baselines adopted . For instance when computing top 10 results the average time achieved by MBMW the best multi tier baseline we found in the literature was 36.29 ms per query while the average time achieved by BMW CSP was 19.64 ms per query . The price paid by BMW CSP is an extra memory required to store partial scores of documents . As we show in the experiments this price is not prohibitive and in cases where it is acceptable BMW CSP may constitute an excellent alternative query processing method . 


Summary: We present a new query processing method for text search. We extend the BMW CS algorithm to now preserve the top k results proposing BMW CSP. We show through experiments that the method is competitive when compared to baselines.
Object matching is an important task for finding the correspondence between objects in different domains such as documents in different languages and users in different databases . In this paper we propose probabilistic latent variable models that offer many to many matching without correspondence information or similarity measures between different domains . The proposed model assumes that there is an infinite number of latent vectors that are shared by all domains and that each object is generated from one of the latent vectors and a domain specific projection . By inferring the latent vector used for generating each object objects in different domains are clustered according to the vectors that they share . Thus we can realize matching between groups of objects in different domains in an unsupervised manner . We give learning procedures of the proposed model based on a stochastic EM algorithm . We also derive learning procedures in a semi supervised setting where correspondence information for some objects are given . The effectiveness of the proposed models is demonstrated by experiments on synthetic and real data sets . 


Summary: We propose a probabilistic model for matching clusters in different domains without correspondence information. The proposed method can handle data with more than two domains and the number of objects in each domain can be different. We extend the proposed method for a semi supervised setting. We demonstrate that the proposed method achieve better matching performance than existing methods using synthetic and real world data sets.
The Team Orienteering Problem is a particular vehicle routing problem in which the aim is to maximize the profit gained from visiting customers without exceeding a travel cost time limit . This paper proposes a new and fast evaluation process for TOP based on an interval graph model and a Particle Swarm Optimization inspired Algorithm to solve the problem . Experiments conducted on the standard benchmark of TOP clearly show that our algorithm outperforms the existing solving methods . PSOiA reached a relative error of 0.0005 whereas the best known relative error in the literature is 0.0394 . Our algorithm detects all but one of the best known solutions . Moreover a strict improvement was found for one instance of the benchmark and a new set of larger instances was introduced . 


Summary: A new fast optimal split for the team orienteering problem. An effective particle swarm algorithm PSOiA . PSOiA is robust and outperforms the state of the art algorithms in the literature.