varunr14/newsummary huggingface dataset:
Cloud computing combines established computing technologies and outsourcing advantages into a new ICT paradigm that is generally expected to foster productivity and economic growth . However despite a series of studies on the drivers of cloud adoption evidence of its economic effects is lacking possibly because many of the datasets on cloud computing are of insufficient size and often lack a time dimension as well as precise definitions of cloud computing thus making them unsuitable for rigorous quantitative analysis . To overcome these limitations we propose a proxy variable for cloud computing usage cloud adaptiveness based on survey panel data from European firms . Observations based on a descriptive analysis suggest three important aspects for further research . First cloud studies should be conducted at the industry level as cloud computing adaptiveness differs widely across industry sectors . Second it is important to know what firms do with cloud computing to understand the economic mechanisms and effects triggered by this innovation . And third cloud adaptiveness is potentially correlated to a firm s position in the supply chain and thus the type of output it produces as well as the market in which it operates . Our indicator can be employed to further analyze the effects of cloud computing in the context of firm heterogeneity . 


Summary: We propose a measure for cloud computing using existing datasets. Our empirical findings map out an agenda for future research. Cloud studies should be at the industry level. Researchers need to figure out what firms actually do with cloud computing. Studying cloud usage across the value chain helps understanding the mechanisms.
The attributes of vehicle routing problems are additional characteristics or constraints that aim to better take into account the specificities of real applications . The variants thus formed are supported by a well developed literature including a large variety of heuristics . This article first reviews the main classes of attributes providing a survey of heuristics and meta heuristics for Multi Attribute Vehicle Routing Problems . It then takes a closer look at the concepts of 64 remarkable meta heuristics selected objectively for their outstanding performance on 15 classic MAVRP with different attributes . This cross analysis leads to the identification of winning strategies in designing effective heuristics for MAVRP . This is an important step in the development of general and efficient solution methods for dealing with the large range of vehicle routing variants . 


Summary: We identify classify and analyze 15 multi attribute vehicle routing problems. We analyze in detail 64 of the most efficient heuristics for these problems. We identify winning strategies for designing effective heuristics for MAVRP s.
The rapid growth of information in the digital world especially on the web calls for automated methods of organizing the digital information for convenient access and efficient information retrieval . Topic modeling is a branch of machine learning and probabilistic graphical modeling that helps in arranging the web pages according to their topical structure . The topic distribution over a set of documents and the affinity of a document toward a specific topic can be revealed using topic modeling . Topic modeling algorithms are typically computationally expensive due to their iterative nature . Recent research efforts have attempted to parallelize specific topic models and are successful in their attempts . These parallel algorithms however have tightly coupled parallel processes which require frequent synchronization and are also tightly coupled with the underlying topic model which is used for inferring the topic hierarchy . In this paper we propose a parallel algorithm to infer topic hierarchies from a large scale document corpus . A key feature of the proposed algorithm is that it exploits coarse grained parallelism and the components running in parallel need not synchronize after every iteration thus the algorithm lends itself to be implemented on a geographically dispersed set of processing elements interconnected through a network . The parallel algorithm realizes a speed up of 53.5 on a 32 node cluster of dual core workstations and at the same time achieving approximately the same likelihood or predictive accuracy as that of the sequential algorithm with respect to the performance of Information Retrieval tasks . 


Summary: We propose a novel parallel Algorithm for inferring topic hierarchies using HLDA. We use loosely coupled parallel tasks that do not require frequent synchronization. The parallel Algorithm is well suited to be run on distributed computing systems. The proposed Algorithm achieves a predictive accuracy on par with that of HLDA. The parallel Algorithm exhibits a near linear speed up and scales well.
We live in the Information Age where most of the personal business and administrative data are collected and managed electronically . However poor data quality may affect the effectiveness of knowledge discovery processes thus making the development of the data improvement steps a significant concern . In this paper we propose the Multidimensional Robust Data Quality Analysis a domain independent technique aimed to improve data quality by evaluating the effectiveness of a black box cleansing function . Here the proposed approach has been realized through model checking techniques and then applied on a weakly structured dataset describing the working careers of millions of people . Our experimental outcomes show the effectiveness of our model based approach for data quality as they provide a fine grained analysis of both the source dataset and the cleansing procedures enabling domain experts to identify the most relevant quality issues as well as the action points for improving the cleansing activities . Finally an anonymized version of the dataset and the analysis results have been made publicly available to the community . 


Summary: MRDQA a model based approach for supporting the Data Quality task on KDD. Evaluation of quality requirements of weakly structured data via model checking. A fine grained quality analysis of the cleansing procedures effectiveness. Automatic identification of error patterns and interactive visualisation. Experiments done on a real scenario making data publicly available.
In this paper we focus on applying sentiment analysis to resources from online art collections by exploiting as information source tags intended as textual traces that visitors leave to comment artworks on social platforms . We present a framework where methods and tools from a set of disciplines ranging from Semantic and Social Web to Natural Language Processing provide us the building blocks for creating a semantic social space to organize artworks according to an ontology of emotions . The ontology is inspired by the Plutchik s circumplex model a well founded psychological model of human emotions . Users can be involved in the creation of the emotional space through a graphical interactive interface . The development of such semantic space enables new ways of accessing and exploring art collections . The affective categorization model and the emotion detection output are encoded into W3C ontology languages . This gives us the twofold advantage to enable tractable reasoning on detected emotions and related artworks and to foster the interoperability and integration of tools developed in the Semantic Web and Linked Data community . The proposal has been evaluated against a real word case study a dataset of tagged multimedia artworks from the ArsMeteo Italian online collection and validated through a user study . 


Summary: Emotions evoked by artworks are captured by emotion analysis of social tags. An OWL 2 ontology based on Plutchik s circumplex model of emotions is developed. Ontologies linked data and affective lexicons are combined in a novel framework. Emotion driven organization and access to online art collections is enabled. The proposal is applied to a real dataset of tagged multimedia artworks.
The emerging research area of opinion mining deals with computational methods in order to find extract and systematically analyze people s opinions attitudes and emotions towards certain topics . While providing interesting market research information the user generated content existing on the Web 2.0 presents numerous challenges regarding systematic analysis the differences and unique characteristics of the various social media channels being one of them . This article reports on the determination of such particularities and deduces their impact on text preprocessing and opinion mining algorithms . The effectiveness of different algorithms is evaluated in order to determine their applicability to the various social media channels . Our research shows that text preprocessing algorithms are mandatory for mining opinions on the Web 2.0 and that part of these algorithms are sensitive to errors and mistakes contained in the user generated content . 


Summary: We carry out an empirical analysis to determine characteristics of social media channels. User generated content is noisy and contains mistakes emoticons etc. We evaluate text preprocessing algorithms regarding user generated content. Discussion of improvements to opinion mining process.
This article introduces and solves a new rich routing problem integrated with practical operational constraints . The problem examined calls for the determination of the optimal routes for a vehicle fleet to satisfy a mix of two different request types . Firstly vehicles must transport three dimensional rectangular and stackable boxes from a depot to a set of predetermined customers . In addition vehicles must also transfer products between pairs of pick up and delivery locations . Service of both request types is subject to hard time window constraints . In addition feasible palletization patterns must be identified for the transported products . A practical application of the problem arises in the transportation systems of chain stores where vehicles replenish the retail points by delivering products stored at a central depot while they are also responsible for transferring stock between pairs of the retailer network . To solve this very complex combinatorial optimization problem our major objective was to develop an efficient methodology whose required computational effort is kept within reasonable limits . To this end we propose a local search based framework for optimizing vehicle routes in which feasible loading arrangements are identified via a simple structured packing heuristic . The algorithmic framework is enhanced with various memory components which store and retrieve useful information gathered through the search process in order to avoid any duplicate unnecessary calculations . The proposed solution approach is assessed on newly introduced benchmark instances . 


Summary: New vehicle routing model with operational constraints. Routes satisfy a blend of plain delivery and pick up and delivery requests. Feasible pallet packing patterns must be identified for the transported goods. Local search framework equipped with appropriate memory components. Results are reported on new benchmark instances derived from well known routing problems.
Electronic word of mouth communication is an important force in building a digital marketplace . The study of eWOM has implications for how to build an online community through social media design web communication and knowledge exchange . Innovative use of eWOM has significant benefits especially for start up firms . We focus on how users on the web communicate value related to online products . It is the premise of this paper that generating emotional value in social media and networking sites is critical for the survival of new e service ventures . Hence by introducing a formal value theory as a coding scheme we report a study on E value in SMNS by analyzing how a Swedish start up industrial design company attempted to build a global presence by creating followers on the web . The aim of the study was to investigate how the company s website design and communication can affect eWOM over time . This was done by capturing a series of emoticon and value expressions generated by community members from three different e communication campaigns with changing website content hence giving different stimuli to viewers . Those members who expressed emotional value often incorporating emoticons displayed both shorter verbal expressions and reaction time . These value expressions we suggest are important aspects of eWOM and need to be actively taken into account . The study has implications for information management strategies through using eWOM . 


Summary: The paper is one of the few papers to explore the role of emotion in eWOM. It includes a practical study of a business using eWOM and the data is analyzed. It provides recommendations on how to use emotion in eWOM to encourage participation and improve sales.
This paper presents a genetic algorithm for solving the resource constrained project scheduling problem . The innovative component of the algorithm is the use of a magnet based crossover operator that can preserve up to two contiguous parts from the receiver and one contiguous part from the donator genotype . For this purpose a number of genes in the receiver genotype absorb one another to have the same order and contiguity they have in the donator genotype . The ability of maintaining up to three contiguous parts from two parents distinguishes this crossover operator from the powerful and famous two point crossover operator which can maintain only two contiguous parts both from the same parent . Comparing the performance of the new procedure with that of other procedures indicates its effectiveness and competence . 


Summary: A new precedence based crossover operator has been developed based on extensive research on related work. The operator can be applied to a variety of permutation problems. The procedure strikes a balance between diversification and intensification. The computational experiments show the effectiveness of the procedure. The operator is versatile the procedure is robust and the results are very promising.
In this paper we propose a linguistically motivated query expansion framework that recognizes and encodes significant query constituents characterizing query intent in order to improve retrieval performance . Concepts of Interest are recognized as the core concepts that represent the gist of the search goal whilst the remaining query constituents which serve to specify the search goal and complete the query structure are classified as descriptive relational or structural . Acknowledging the need to form semantically associated base pairs for the purpose of extracting related potential expansion concepts an algorithm which capitalizes on syntactical dependencies to capture relationships between adjacent and non adjacent query concepts is proposed . Lastly a robust weighting scheme that duly emphasizes the importance of query constituents based on their linguistic role within the expanded query is presented . We demonstrate improvements in retrieval effectiveness in terms of increased mean average precision garnered by the proposed linguistic based query expansion framework through experimentation on the TREC ad hoc test collections . 


Summary: Linguistically motivated query expansion framework. Classification of query constituents within four concept types descriptive relational structural and concepts of interest. Algorithm capitalizing on syntactical dependencies to capture relationships between adjacent and non adjacent query concepts. Weighting scheme emphasizing the importance of query constituents based on their linguistic role.
Automatic document summarization using citations is based on summarizing what others explicitly say about the document by extracting a summary from text around the citations . While this technique works quite well for summarizing the impact of scientific articles other genres of documents as well as other types of summaries require different approaches . In this paper we introduce a new family of methods that we developed for legal documents summarization to generate catchphrases for legal cases . Our methods use both incoming and outgoing citations and we show how citances can be combined with other elements of cited and citing documents including the full text of the target document and catchphrases of cited and citing cases . On a legal summarization corpus our methods outperform competitive baselines . The combination of full text sentences and catchphrases from cited and citing cases is particularly successful . We also apply and evaluate the methods on scientific paper summarization where they perform at the level of state of the art techniques . Our family of citation based summarization methods is powerful and flexible enough to target successfully a range of different domains and summarization tasks . 


Summary: We extend citation based summarization techniques to use both cited and citing documents. Our method combines citation text with other elements of cited and citing documents such as the full text and catchphrases. We investigate using citations to extract information from the target document. We apply these techniques on the domain of legal judgements a new area for citations based summarization. On a legal summarization corpus our methods outperform competitive baselines.
Urban legends are a genre of modern folklore consisting of stories about rare and exceptional events just plausible enough to be believed which tend to propagate inexorably across communities . In our view while urban legends represent a form of sticky deceptive text they are marked by a tension between the credible and incredible . They should be credible like a news article and incredible like a fairy tale to go viral . In particular we will focus on the idea that urban legends should mimic the details of news to be credible while they should be emotional and readable like a fairy tale to be catchy and memorable . Using NLP tools we will provide a quantitative analysis of these prototypical characteristics . We also lay out some machine learning experiments showing that it is possible to recognize an urban legend using just these simple features . 


Summary: Urban legends are viral deceptive texts in between credible and incredible. To be credible they mimic news articles while being incredible like a fairy tale. High level features who where when of news emotional readable of fairy tales. Quantitative analysis and machine learning experiments for recognizing urban legends.
Online review mining has been used to help manufacturers and service providers improve their products and services and to provide valuable support for consumer decision making . Product aspect extraction is fundamental to online review mining . This research is aimed to improve the performance of aspect extraction from online consumer reviews . To this end we augment a frequency based extraction method with PMI IR which utilizes web search in measuring the semantic similarity between aspect candidates and target entities . In addition we extend RCut an algorithm originally developed for text classification to learn the threshold for selecting candidate aspects . Experiment results with Chinese online reviews show that our proposed method not only outperforms the state of the art frequency based method for aspect extraction but also generalizes across different product domains and various data sizes . 


Summary: Augment frequency based aspect extraction with web based similarity measure. Improve the efficiency of aspect extraction methods that use PMI IR measures. Demonstrate the generality of the proposed method across various products.
This paper presents a generalized weighted vertex p center model that represents uncertain nodal weights and edge lengths using prescribed intervals or ranges . The objective of the robust WVPC model is to locate p facilities on a given set of candidate sites so as to minimize worst case deviation in maximum weighted distance from the optimal solution . The RWVPC model is well suited for locating urgent relief distribution centers in an emergency logistics system responding to quick onset natural disasters in which precise estimates of relief demands from affected areas and travel times between URDCs and affected areas are not available . To reduce the computational complexity of solving the model this work proposes a theorem that facilitates identification of the worst case scenario for a given set of facility locations . Since the problem is NP hard a heuristic framework is developed to efficiently obtain robust solutions . Then a specific implementation of the framework based on simulated annealing is developed to conduct numerical experiments . Experimental results show that the proposed heuristic is effective and efficient in obtaining robust solutions . We also examine the impact of the degree of data uncertainty on the selected performance measures and the tradeoff between solution quality and robustness . Additionally this work applies the proposed RWVPC model to a real world instance based on a massive earthquake that hit central Taiwan on September 21 1999 . 


Summary: Weighted p center model is proposed for locating urgent relief distribution centers. Uncertain relief demands and delivery times are represented by fixed intervals. The objective is to optimize worst case performances. Property of worst case scenarios is identified and applied in the algorithm. The proposed model is applied to a real world case based on a massive earthquake.
Asset allocation among diverse financial markets is essential for investors especially under situations such as the financial crisis of 2008 . Portfolio optimization is the most developed method to examine the optimal decision for asset allocation . We employ the hidden Markov model to identify regimes in varied financial markets a regime switching model gives multiple distributions and this information can convert the static mean variance model into an optimization problem under uncertainty which is the case for unobservable market regimes . We construct a stochastic program to optimize portfolios under the regime switching framework and use scenario generation to mathematically formulate the optimization problem . In addition we build a simple example for a pension fund and examine the behavior of the optimal solution over time by using a rolling horizon simulation . We conclude that the regime information helps portfolios avoid risk during left tail events . 


Summary: We applied the hidden Markov model for stock bond and commodity markets. HMM identified two extreme regimes and two transition regimes. We employed the stochastic programming under regime switching framework. Optimized portfolio performed well in crash periods by reducing the risky assets.