varunr14/newsummary huggingface dataset:
Under circumstances of increasing environmental pressures from markets and regulators focal companies in supply chains have recognized the importance of greening their supply chain through green supplier development programs . Various studies have started to explore the inter relationships between green supply chain management and supplier performance . Much of this performance can be achieved only with suppliers involvement in green supplier development programs . But the literature focusing on green supplier development programs and supplier involvement propensity is very limited . In addition formal tools and models for focal companies to evaluate these inter relationships especially considering propensity of suppliers involvement are even rarer . To help address this gap in the literature we introduce a grey analytical network process based model to identify green supplier development programs that will effectively improve suppliers performance . We further comprehensively evaluate green supplier development programs with explicit consideration of suppliers involvement propensity levels . A real world example is introduced to demonstrate the effectiveness of the model . We end with a discussion of managerial implications and present some directions for further research . 


Summary: We develop a grey analytical network process based model for green supplier development programs selection. The model is applied to a leading manufacturer in the China s pivot irrigation equipment industry. We conclude that suppliers performance evaluation and involvement propensity should be simultaneously considered.
General graph random walk has been successfully applied in multi document summarization but it has some limitations to process documents by this way . In this paper we propose a novel hypergraph based vertex reinforced random walk framework for multi document summarization . The framework first exploits the Hierarchical Dirichlet Process topic model to learn a word topic probability distribution in sentences . Then the hypergraph is used to capture both cluster relationship based on the word topic probability distribution and pairwise similarity among sentences . Finally a time variant random walk algorithm for hypergraphs is developed to rank sentences which ensures sentence diversity by vertex reinforcement in summaries . Experimental results on the public available dataset demonstrate the effectiveness of our framework . 


Summary: We propose a novel hybrid method to capture group relation of sentences. We cluster sentences with a KL divergence based on word topic distribution. We proposed a vertex reinforcement random walk process in a hypergraph model. The process simultaneously consider the query similarity the centrality and the diversity of sentences. We implement our framework and verify improvement over appropriate baselines.
Query suggestion is generally an integrated part of web search engines . In this study we first redefine and reduce the query suggestion problem as comparison of queries . We then propose a general modular framework for query suggestion algorithm development . We also develop new query suggestion algorithms which are used in our proposed framework exploiting query session and user features . As a case study we use query logs of a real educational search engine that targets K 12 students in Turkey . We also exploit educational features in our query suggestion algorithms . We test our framework and algorithms over a set of queries by an experiment and demonstrate a 66 90 statistically significant increase in relevance of query suggestions compared to a baseline method . 


Summary: Hightlights Query suggestion QS problem is reduced to comparison of queries problem. A modular and practical framework is suggested for development of QS algorithms. Breadth First Search graph traversal method is better for query log traversing. Combining QS algorithms improves the performance. Suggested new QS algorithms achieved 66 90 performance increase.
Applying text mining techniques to legal issues has been an emerging research topic in recent years . Although a few previous studies focused on assisting professionals in the retrieval of related legal documents to our knowledge no previous studies could provide relevant statutes to the general public using problem statements . In this work we design a text mining based method the three phase prediction algorithm which allows the general public to use everyday vocabulary to describe their problems and find pertinent statutes for their cases . The experimental results indicate that our approach can help the general public who are not familiar with professional legal terms to acquire relevant statutes more accurately and effectively . 


Summary: In the legal domain this method for statute prediction is a new research topic. We predict relevant statutes for the problem described by everyday vocabulary. The gap between lay terms and legal terms was remedied without using a synopsis. Employing the Normalized Google Distance SVM and Apriori algorithms into TPP. The result shows the performance of TPP is accurately and effectively.
Summary writing is a process for creating a short version of a source text . It can be used as a measure of understanding . As grading students summaries is a very time consuming task computer assisted assessment can help teachers perform the grading more effectively . Several techniques such as BLEU ROUGE N gram co occurrence Latent Semantic Analysis LSA Ngram and LSA ERB have been proposed to support the automatic assessment of students summaries . Since these techniques are more suitable for long texts their performance is not satisfactory for the evaluation of short summaries . This paper proposes a specialized method that works well in assessing short summaries . Our proposed method integrates the semantic relations between words and their syntactic composition . As a result the proposed method is able to obtain high accuracy and improve the performance compared with the current techniques . Experiments have displayed that it is to be preferred over the existing techniques . A summary evaluation system based on the proposed method has also been developed . 


Summary: We proposed a specialized method that works well in assessing short summaries. It integrates the semantic relations between words and their syntactic composition. We experimentally examine the influence of the combination of semantic and syntactic information on short summary assessment. Experiments have displayed that it is to be preferred over the existing techniques. We have developed the method as an intelligent tool to grade students summaries.
Nowadays opinion mining systems play a strategic role in different areas such as Marketing Decision Support Systems or Policy Support . Since the arrival of the Web 2.0 more and more textual documents containing information that express opinions or comments in different languages are available . Given the proven importance of such documents the use of effective multilingual opinion mining systems has become of high importance to different fields . This paper presents the experiments carried out with the objective to develop a multilingual sentiment analysis system . We present initial evaluations of methods and resources performed in two international evaluation campaigns for English and for Spanish . After our participation in both competitions additional experiments were carried out with the aim of improving the performance of both Spanish and English systems by using multilingual machine translated data . Based on our evaluations we show that the use of hybrid features and multilingual machine translated data can help to better distinguish relevant features for sentiment classification and thus increase the precision of sentiment analysis systems . 


Summary: We study different strategies to classify sentiment from tweets using supervised learning with hybrid features. We experiment with English and Spanish data and compare against benchmark competitions. We employ machine translated data from other languages for training. We show that the use of multilingual data improves the sentiment classification accuracy.
Background Our methodology describes a human activity recognition framework based on feature extraction and feature selection techniques where a set of time statistical and frequency domain features taken from 3 dimensional accelerometer sensors are extracted . This framework specifically focuses on activity recognition using on body accelerometer sensors . We present a novel interactive knowledge discovery tool for accelerometry in human activity recognition and study the sensitivity to the feature extraction parametrization . Results The implemented framework achieved encouraging results in human activity recognition . We have implemented a new set of features extracted from wearable sensors that are ambitious from a computational point of view and able to ensure high classification results comparable with the state of the art wearable systems . A feature selection framework is developed in order to improve the clustering accuracy and reduce computational complexity . The software OpenSignals was used for signal acquisition and signal processing algorithms were developed in Python Programming Language and Orange Software . Several clustering methods such as K Means Affinity Propagation Mean Shift and Spectral Clustering were applied . The K means methodology presented promising accuracy results for person dependent and independent cases with 99.29 and 88.57 respectively . Conclusions The presented study performs two different tests in intra and inter subject context and a set of 180 features is implemented which are easily selected to classify different activities . The implemented algorithm does not stipulate a priori any value for time window or its overlap percentage of the signal but performs a search to find the best parameters that define the specific data . A clustering metric based on the construction of the data confusion matrix is also proposed . The main contribution of this work is the design of a novel gesture recognition system based solely on data from a single 3 dimensional accelerometer . 


Summary: The presented study performs two different tests in intra and inter subject context. A set of 180 features is implemented to be selected based on clustering performance. Our algorithm searches for the best feature extraction parameter. A new clustering metric based on the construction of the confusion matrix is proposed. A novel gesture recognition system based on data from a single 3 dimensional accelerometer.
We propose two novel language models to improve the performance of sentence retrieval in Question Answering class based language model and trained trigger language model . As the search in sentence retrieval is conducted over smaller segments of text than in document retrieval the problems of data sparsity and exact matching become more critical . Different techniques such as the translation model are also proposed to overcome the word mismatch problem . Our class based and trained trigger language models however use different approaches to this aim and are shown to outperform the exiting models . The class model uses word clustering algorithm to capture term relationships . In this model we assume a relation between the terms that belong to the same clusters as a result they can be substituted when searching for relevant sentences . The trigger model captures pairs of trigger and target words while training on a large corpus . The model considers a relation between a question and a sentence if a trigger word appears in the question and the sentence contains the corresponding target word . For both proposed models we introduce different notions of co occurrence to find word relations . In addition we study the impact of corpus size and domain on the models . Our experiments on TREC QA collection verify that the proposed model significantly improves the sentence retrieval performance compared to the state of the art translation model . While the translation model based on mutual information has 0.3927 Mean Average Precision the class model achieves 0.4174 MAP and the trigger model enhances the performance to 0.4381 . 


Summary: We introduce two novel LM based models to relax the exact matching assumption in IR. The class based model clusters words to provide a coarse grained word representation. The trigger model captures pairs of trigger and target words to find word relationships. Different types of word co occurrence and triggering are studied within the models. We further studied the combination of both models to achieve the best result.
In this paper a new homomorphic image watermarking method implementing the Singular Value Decomposition algorithm is presented . The idea of the proposed method is based on embedding the watermark with the SVD algorithm in the reflectance component after applying the homomorphic transform . The reflectance component contains most of the image features but with low energy and hence watermarks embedded in this component will be invisible . A block by block implementation of the proposed method is also introduced . The watermark embedding on a block by block basis makes the watermark more robust to attacks . A comparison study between the proposed method and the traditional SVD watermarking method is presented in the presence of attacks . The proposed method is more robust to various attacks . The embedding of chaotic encrypted watermarks is also investigated in this paper to increase the level of security . 


Summary: Information embedding and retrieval in digital images. Digital Watermarking. Image processing.
We study a problem of tactical planning in a divergent supply chain . It involves decisions regarding production inventory internal transportation sales and distribution to customers . The problem is motivated by the context of a company in the speciality oils industry . The overall objective at tactical level is to maximize contribution and in order to achieve this the planning has been divided into two separate problems . The first problem concerns sales where the final sales and distribution planning is decentralized to individual sellers . The second problem concerns production transportation and inventory planning through refineries hubs and depots and is managed centrally with the aim of minimizing costs . Due to this decoupling the solution of the two problems needs to be coordinated in order to achieve the overall objective . In the company this is pursued through an internal price system aiming at giving the sellers the incentives needed to align their decisions with the overall objective . We propose and discuss linear programming models for the decoupled and integrated planning problems . We present numerical examples to illustrate potential effects of integration and coordination and discuss the advantages and disadvantages of the integrated over the decoupled approach . While the total contribution is higher in the integrated approach it has also been found that the sellers contribution can be considerably lower . Therefore we also suggest contribution sharing rules to achieve a solution where both the company and the sellers attain a better outcome under the integrated planning . 


Summary: We propose linear models for the supply chain of a speciality oils company. We consider operation and sales decisions in decoupled and integrated approaches. The integrated outperforms the decoupled planning for the company. The sellers may get worse premiums in the integrated solution. Company and sellers are better off by reallocating the integrated contribution.
Question Answering systems are developed to answer human questions . In this paper we have proposed a framework for answering definitional and factoid questions enriched by machine learning and evolutionary methods and integrated in a web based QA system . Our main purpose is to build new features by combining state of the art features with arithmetic operators . To accomplish this goal we have presented a Genetic Programming based approach . The exact GP duty is to find the most promising formulas made by a set of features and operators which can accurately rank paragraphs sentences and words . We have also developed a QA system in order to test the new features . The input of our system is texts of documents retrieved by a search engine . To answer definitional questions our system performs paragraph ranking and returns the most related paragraph . Moreover in order to answer factoid questions the system evaluates sentences of the filtered paragraphs ranked by the previous module of our framework . After this phase the system extracts one or more words from the ranked sentences based on a set of hand made patterns and ranks them to find the final answer . We have used Text Retrieval Conference QA track questions web data and AQUAINT and AQUAINT 2 datasets for training and testing our system . Results show that the learned features can perform a better ranking in comparison with other evaluation formulas . 


Summary: A new framework for answering definitional and factoid questions. Producing new features by combining effective features with arithmetic operators. Genetic Programming GP algorithm has been employed for feature learning. Three discriminant based methods have been used for learning features weights.
XML is a pervasive technology for representing and accessing semi structured data . XPath is the standard language for navigational queries on XML documents and there is a growing demand for its efficient processing . In order to increase the efficiency in executing four navigational XML query primitives namely descendants ancestors children and parent we introduce a new paradigm where traditional approaches based on the efficient traversing of nodes and edges to reconstruct the requested subtrees are replaced by a brand new one based on basic set operations which allow us to directly return the desired subtree avoiding to create it passing through nodes and edges . Our solution stems from the NEsted SeTs for Object hieRarchies formal model which makes use of set inclusion relations for representing and providing access to hierarchical data . We define in memory efficient data structures to implement NESTOR we develop algorithms to perform the descendants ancestors children and parent query primitives and we study their computational complexity . We conduct an extensive experimental evaluation by using several datasets digital archives INEX 2009 Wikipedia collection and two widely used synthetic datasets . We show that NESTOR based data structures and query primitives consistently outperform state of the art solutions for XPath processing at execution time and they are competitive in terms of both memory occupation and pre processing time . 


Summary: Set based access to XML data. Improved XPath primitive operations. Up to eight orders of magnitude speed up.
In recent years there has been a rapid growth of user generated data in collaborative tagging systems due to the prevailing of Web 2.0 communities . To effectively assist users to find their desired resources it is critical to understand user behaviors and preferences . Tag based profile techniques which model users and resources by a vector of relevant tags are widely employed in folksonomy based systems . This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles . However conventional measurements neglect the sentiment aspect of user generated tags . In fact tags can be very emotional and subjective as users usually express their perceptions and feelings about the resources by tags . Therefore it is necessary to take sentiment relevance into account into measurements . In this paper we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment based information for personalized search by user profiles and resource profiles . In this framework content based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment based personalized ranking . To the best of our knowledge this is the first work of integrating sentiment information to address the problem of the personalized tag based search in collaborative tagging systems . Moreover we compare the proposed sentiment based personalized search with baselines in the experiments the results of which have verified the effectiveness of the proposed framework . In addition we study the influences by popular sentiment dictionaries and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy . 


Summary: We present a framework SenticRank to incorporate sentiment for personalized search. Content based and collaborative sentiment ranking methods are proposed. We compare the proposed sentiment based search with baselines experimentally. We study the influence of sentiment corpora by using some sentiment dictionaries. Sentiment based information can significantly improve performance in folksonomy.
This paper surveys the academic OR analytics literature describing research into the laws and rules of sports and sporting competitions . The literature is divided into post hoc analyses and proposals for future changes and is also divided into laws rules of sports themselves and rules organisation of tournaments or competitions . The survey outlines a large number of studies covering 21 sports in many parts of the world . The analytical approaches most commonly used are found to be various forms of regression analysis and simulation . Issues highlighted by this survey include the different views of what constitutes fairness and the frequency with which changes produce unintended consequences . 


Summary: Reviews OR analysis of the effects of changes to the rules and laws of sports. Also reviews OR analysis of proposed future changes. Cites 67 references involving 21 different sports.
As demand for mobile broadband services continues to explode mobile wireless networks must expand greatly their capacities . This paper describes and quantifies the economic and technical challenges associated with deepening wireless networks to meet this growing demand . Methods of capacity expansion divide into three general categories the deployment of more radio spectrum more intensive geographic reuse of spectrum and increasing the throughput capacity of each MHz of spectrum within a given geographic area . The paper describes these several basic methods to deepen mobile wireless capacity . It goes on to measure the contribution of each of these methods to historical capacity growth within U.S. networks . The paper then describes the capabilities of 4G LTE wireless technology and further innovations off of it to further improve network capacity . These capacity expansion capabilities of LTE Advanced along with traditional spectrum reuse are quantified and compared to forecasts of future demand to evaluate the ability of U.S. networks to match future demand . Without significantly increasing current spectrum allocations by 560MHz over the 2014 2022 period the presented model suggests that U.S. wireless capacity expansion will be inadequate to accommodate expected demand growth . This conclusion is in contrast to claims that the U.S. faces no spectrum shortage . 


Summary: The three basic tools for deepening the capacity of mobile wireless networks are described. Historical growth in spectrum quantity geographic reuse and throughput efficiency is reported. The capability of 4G LTE and LTE Advanced to further improve network capacity is projected. These projected capacity improvements are compared with forecasts of future U.S. mobile demand. U.S. networks will require an additional 560MHz of spectrum by 2022 to meet forecasted demand.