Relevant bibliographies by topics / Hard/Soft data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Hard/Soft data'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Hard/Soft data"

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Aronson, J. "Hard data, soft data." BMJ 344, mar28 1 (2012): e1692-e1692. http://dx.doi.org/10.1136/bmj.e1692.

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Cowell, H. R. "Hard decisions from soft data." Journal of Bone & Joint Surgery 72, no. 10 (1990): 1441. http://dx.doi.org/10.2106/00004623-199072100-00002.

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Cudell, J. R., E. Martynov, O. Selyugin, and A. Lengyel. "The hard pomeron in soft data." Physics Letters B 587, no. 1-2 (2004): 78–86. http://dx.doi.org/10.1016/j.physletb.2004.02.058.

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Burkhardt, Boyd R. "Capsular Contracture: Hard Breasts, Soft Data." Clinics in Plastic Surgery 15, no. 4 (1988): 521–32. http://dx.doi.org/10.1016/s0094-1298(20)31387-0.

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Cudell, J. R., A. Lengyel, E. Martynov, and O. V. Selyugin. "Soft data and the hard pomeron." Nuclear Physics B - Proceedings Supplements 152, no. 1 (2006): 79–82. http://dx.doi.org/10.1016/j.nuclphysbps.2005.08.015.

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Sansom, Robert S., and Matthew A. Wills. "Differences between hard and soft phylogenetic data." Proceedings of the Royal Society B: Biological Sciences 284, no. 1869 (2017): 20172150. http://dx.doi.org/10.1098/rspb.2017.2150.

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When building the tree of life, variability of phylogenetic signal is often accounted for by partitioning gene sequences and testing for differences. The same considerations, however, are rarely applied to morphological data, potentially undermining its use in evolutionary contexts. Here, we apply partition heterogeneity tests to 59 animal datasets to demonstrate that significant differences exist between the phylogenetic signal conveyed by ‘hard’ and ‘soft’ characters (bones, teeth and shells versus myology, integument etc). Furthermore, the morphological partitions differ significantly in their consistency relative to independent molecular trees. The observed morphological differences correspond with missing data biases, and as such their existence presents a problem not only for phylogeny reconstruction, but also for interpretations of fossil data. Evolutionary inferences drawn from clades in which hard, readily fossilizable characters are relatively less consistent and different from other morphology (mammals, bivalves) may be less secure. More secure inferences might be drawn from the fossil record of clades that exhibit fewer differences, or exhibit more consistent hard characters (fishes, birds). In all cases, it will be necessary to consider the impact of missing data on empirical data, and the differences that exist between morphological modules.
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Seymour, Daniel. "Soft Data – Hard Data: The Painful Art of Fence‐Sitting." Journal of Consumer Marketing 6, no. 2 (1989): 25–32. http://dx.doi.org/10.1108/eum0000000002544.

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Khaleghi, Bahador, and Fakhreddine Karray. "Random set theoretic soft/hard data fusion framework." IEEE Transactions on Aerospace and Electronic Systems 50, no. 4 (2014): 3068–81. http://dx.doi.org/10.1109/taes.2014.120621.

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Sullivan, Karen. "It’s hard being soft." Mental Lexicon 7, no. 3 (2012): 306–25. http://dx.doi.org/10.1075/ml.7.3.03sul.

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This study compares antonymous relations between lemmata (such as soft/softer/softest and hard/harder/hardest), words (soft and hard) and word senses (for example, the sense of soft indicating a yielding surface and that of hard describing an unyielding surface). In agglomerative cluster analyses of data from the British National Corpus, specific antonymous adjective senses are found to cluster more tightly and neatly than either antonymous words or lemmata. Moreover, when pairs such as soft and hard co-occur in the corpus, the co-occurring senses are typically instances of antonymous senses that cluster together. This evidence from clustering and co-occurrence suggests that antonymy operates primarily at the level of the word sense, rather than the word or the lemma.
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Seymour, Daniel. "COMMENTARY SOFT DATA—HARD DATA: THE PAINFUL ART OF FENCE‐SITTING." Journal of Services Marketing 2, no. 4 (1988): 41–48. http://dx.doi.org/10.1108/eb024741.

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Dissertations / Theses on the topic "Hard/Soft data"

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Khaleghi, Bahador. "Distributed Random Set Theoretic Soft/Hard Data Fusion." Thesis, 2012. http://hdl.handle.net/10012/6842.

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Research on multisensor data fusion aims at providing the enabling technology to combine information from several sources in order to form a unifi ed picture. The literature work on fusion of conventional data provided by non-human (hard) sensors is vast and well-established. In comparison to conventional fusion systems where input data are generated by calibrated electronic sensor systems with well-defi ned characteristics, research on soft data fusion considers combining human-based data expressed preferably in unconstrained natural language form. Fusion of soft and hard data is even more challenging, yet necessary in some applications, and has received little attention in the past. Due to being a rather new area of research, soft/hard data fusion is still in a edging stage with even its challenging problems yet to be adequately de fined and explored. This dissertation develops a framework to enable fusion of both soft and hard data with the Random Set (RS) theory as the underlying mathematical foundation. Random set theory is an emerging theory within the data fusion community that, due to its powerful representational and computational capabilities, is gaining more and more attention among the data fusion researchers. Motivated by the unique characteristics of the random set theory and the main challenge of soft/hard data fusion systems, i.e. the need for a unifying framework capable of processing both unconventional soft data and conventional hard data, this dissertation argues in favor of a random set theoretic approach as the first step towards realizing a soft/hard data fusion framework. Several challenging problems related to soft/hard fusion systems are addressed in the proposed framework. First, an extension of the well-known Kalman lter within random set theory, called Kalman evidential filter (KEF), is adopted as a common data processing framework for both soft and hard data. Second, a novel ontology (syntax+semantics) is developed to allow for modeling soft (human-generated) data assuming target tracking as the application. Third, as soft/hard data fusion is mostly aimed at large networks of information processing, a new approach is proposed to enable distributed estimation of soft, as well as hard data, addressing the scalability requirement of such fusion systems. Fourth, a method for modeling trust in the human agents is developed, which enables the fusion system to protect itself from erroneous/misleading soft data through discounting such data on-the-fly. Fifth, leveraging the recent developments in the RS theoretic data fusion literature a novel soft data association algorithm is developed and deployed to extend the proposed target tracking framework into multi-target tracking case. Finally, the multi-target tracking framework is complemented by introducing a distributed classi fication approach applicable to target classes described with soft human-generated data. In addition, this dissertation presents a novel data-centric taxonomy of data fusion methodologies. In particular, several categories of fusion algorithms have been identifi ed and discussed based on the data-related challenging aspect(s) addressed. It is intended to provide the reader with a generic and comprehensive view of the contemporary data fusion literature, which could also serve as a reference for data fusion practitioners by providing them with conducive design guidelines, in terms of algorithm choice, regarding the specifi c data-related challenges expected in a given application.
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Long, Yuan. "Data Assimilation for Spatial Temporal Simulations Using Localized Particle Filtering." 2016. http://scholarworks.gsu.edu/cs_diss/115.

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As sensor data becomes more and more available, there is an increasing interest in assimilating real time sensor data into spatial temporal simulations to achieve more accurate simulation or prediction results. Particle Filters (PFs), also known as Sequential Monte Carlo methods, hold great promise in this area as they use Bayesian inference and stochastic sampling techniques to recursively estimate the states of dynamic systems from some given observations. However, PFs face major challenges to work effectively for complex spatial temporal simulations due to the high dimensional state space of the simulation models, which typically cover large areas and have a large number of spatially dependent state variables. As the state space dimension increases, the number of particles must increase exponentially in order to converge to the true system state. The purpose of this dissertation work is to develop localized particle filtering to support PFs-based data assimilation for large-scale spatial temporal simulations. We develop a spatially dependent particle-filtering framework that breaks the system state and observation data into sub-regions and then carries out localized particle filtering based on these spatial regions. The developed framework exploits the spatial locality property of system state and observation data, and employs the divide-and-conquer principle to reduce state dimension and data complexity. Within this framework, we propose a two-level automated spatial partitioning method to provide optimized and balanced spatial partitions with less boundary sensors. We also consider different types of data to effectively support data assimilation for spatial temporal simulations. These data include both hard data, which are measurements from physical devices, and soft data, which are information from messages, reports, and social network. The developed framework and methods are applied to large-scale wildfire spread simulations and achieved improved results. Furthermore, we compare the proposed framework to existing particle filtering based data assimilation frameworks and evaluate the performance for each of them.
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Goede, Roelien. "A framework for the explicit use of specific systems thinking methodologies in data-driven decision support system development." Thesis, 2005. http://hdl.handle.net/2263/24606.

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Data-driven decision support systems, such as data warehouses, are extremely costly to develop. Forty one per cent of data warehouse development practitioners have experienced project failures. These projects were either completed after exceeding budget and time limits, or not at all. Some influential data warehousing authors advocate user involvement as a solution, while others focus on technical factors to improve data warehouse success. This study proposes a framework for data warehousing success based on systems thinking methodology. Systems thinking implies a holistic approach to problem solving. A system is a set of interrelated elements. A systems approach represents a broad view, taking all aspects into account and concentrating on interactions between different parts of the problem. This study investigates the practices of data warehousing professionals from a systems thinking point of view, before proposing a framework for the explicit use of specific systems thinking methodologies in data warehouse development. Interpretive case study research is used to investigate practices of data warehousing professionals in three different organisations. Pattern matching is used to analyse collected data. This is done by mapping practices to different systems thinking perspectives. However, the theory component of the thesis is not a description of current data warehousing practices from a systems thinking point of view, as in typical interpretive research. The theory component relates more to critical research in that it is meant to change data warehousing practices towards specific systems thinking methodologies. The proposed framework incorporates three sources of information. These are a literature study on systems thinking philosophy, methodology and practice; a literature study on data warehousing and data warehousing success factors; and the results of case studies on current practices of data warehousing professionals analysed from a systems thinking perspective. The framework gives a methodological foundation for a holistic approach to data warehousing with maximum user involvement. It views a data warehouse as a system with typical systems characteristics, such as specified objectives relating to the organisation’s objectives, an environment, available resources, specified components and effective management.<br>Thesis (PhD (Information Technology))--University of Pretoria, 2006.<br>Informatics<br>unrestricted
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Books on the topic "Hard/Soft data"

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International Conference on English Language Research on Computerized Corpora (8th 1987 Helsinki, Finland). Corpus linguistics, hard and soft: Proceedings of the Eighth International Conference on English Language Research on Computerized Corpora. Rodopi, 1988.

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Bettin Lattes, Gianfranco, and Marco Bontempi, eds. Generazione Erasmus? Firenze University Press, 2008. http://dx.doi.org/10.36253/978-88-8453-802-4.

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The Erasmus programme is one of the outstanding Community initiatives, even if it is spoken little of outside the world of the university. This book, one of the first devoted to the subject, analyses the virtuous effects that the programme has had on the university system, the geography of student flows, and the motivations and propositions of those who have taken part in it. The reports of the students indicate the Erasmus as a 'bubble of experience' and the book explores these inner experiences through a sociological approach, illustrating the vast potential in terms of the moulding of a 'homo novus Europaeus'. The data gathered prompt a reflection on the redefinition of the role of the student when he or she directly experiences the comparison with a context different and distant from that of origin, to which he or she is nevertheless destined to return. From this perspective, the Erasmus experience assumes the significance of a sort of temporary upheaval of status open to forms of 'experimentation of identity'.
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Golding, Johnny, Martin Reinhart, and Mattia Paganelli. Data Loam: Sometimes Hard, Usually Soft. on the Future of Knowledge Systems. de Gruyter GmbH, Walter, 2020.

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Data Loam: Sometimes Hard, Usually Soft. on the Future of Knowledge Systems. de Gruyter GmbH, Walter, 2020.

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Ihalainen, Ossi, and Merja Kyoto. CORPUS LINGUISTICS, HARD AND SOFT. Proceedings of the Eighth International Conference on English Language Research on Computerized Corpora. (Language and Computers). Editions Rodopi, 1988.

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Walsh, Bruce, and Michael Lynch. Using Molecular Data to Detect Selection: Signatures from Recent Single Events. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0009.

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Different types and phases of a selective sweep (hard, soft, partial, polygenic) generate different patterns of departures from neutrality, and hence require different tests. It is thus not surprising that a large number of tests have been proposed that use sequence information to detect ongoing, or very-recently completed, episodes of selection. This chapter critically reviews over 50 such tests, which use information on allele-frequency change, linkage disequilibrium patterns, spatial allele-frequency patterns, site-frequency spectrum data, allele-frequency spectrum data, and haplotype structure. This chapter discusses the domain of applicability for each test, and their strengths and weaknesses. Finally, this chapter examines application of these methods in the search for recent, or ongoing, selection in humans and for genes involved in the domestication process in plants and animals.
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Holt, Robin, and Mike Zundel. Using fiction in organization and management research. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198796978.003.0003.

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Robin Holt and Mike Zundel describe their use of another unconventional source of data—a television fictional crime series. They argue that the boundaries between ‘soft fiction’ and ‘hard fact’ are blurred, and that fictional accounts can generate insights into aspects of organizational and social life more effectively than conventional methods. The relationship between fiction and social science can be understood in four ways: fictional research, fiction as inspiration, fiction as data, and fiction as research. Their approach is illustrated with an analysis of the cult television crime series The Wire, which is based on the drugs trade in Baltimore, involving the gangs, police, social workers, churches, local authorities, and wider community. The Wire can be seen as a rich ethnography, illustrating how fiction can illuminate individual, group, and organizational phenomena including emotions, hopes, fears, and conflicts, and the wider social condition, highlighting the institutional constraints on individual behaviour.
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Houle, Christian. Does Inequality Harm Economic Development and Democracy? Edited by Carol Lancaster and Nicolas van de Walle. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199845156.013.4.

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This article examines whether economic inequality undermines economic development and democracy in the long run. After reviewing the literature on the effect of inequality on economic development and democracy, it considers three approaches that have been put forward to explain why inequality harms the economy and democracy: (1) the political economy approach, (2) the social unrest approach, and (3) the credit market imperfections approach. A complete data set on inequality is generated using three measures of inequality: the capital share data set of Ortega and Rodriguez (2006), the Gini coefficients data set of Solt (2009), and the income Gini coefficients of the “Estimated Household Income Inequality” (EHII) data set, developed by the University of Texas Inequality Project (UTIP). The article then tests the relationship between inequality and democracy using dynamic probit models.
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Hood, Christopher, and Rozana Himaz. UK Fiscal Squeezes over a Century. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198779612.003.0002.

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This chapter draws on historical statistics reporting financial outcomes for spending, taxation, debt, and deficit for the UK over a century to (a) identify quantitatively and compare the main fiscal squeeze episodes (i.e. major revenue increases, spending cuts, or both) in terms of type (soft squeezes and hard squeezes, spending squeezes, and revenue squeezes), depth, and length; (b) compare these periods of austerity against measures of fiscal consolidation in terms of deficit reduction; and (c) identify economic and financial conditions before and after the various squeezes. It explores the extent to which the identification of squeeze episodes and their classification is sensitive to which thresholds are set and what data sources are used. The chapter identifies major changes over time that emerge from this analysis over the changing depth and types of squeeze.
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Stoneman, Paul, Eleonora Bartoloni, and Maurizio Baussola. The Microeconomics of Product Innovation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198816676.001.0001.

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The prime objective of this book is the use microeconomic analysis to guide and provide insight into the generation and adoption of new products. Taking an approach that uses minimal formal mathematics, the volume initially addresses questions of definitions, sources, and extent of product innovation, differentiating between goods and services; hard and soft innovations; horizontal and vertical innovations; original, new to market, and new to firm innovations. The sources of product innovations (e.g. R&amp;D, design, and creativity) are explored empirically, and the extent of such innovations is then pursued using survey and other data. Three chapters are devoted to the theoretical analysis of the demand for and supply of new products and to the determination of firms’ decisions to undertake product innovation. Later chapters encompass empirical evidence on the determination of the extent of product innovation, the diffusion of such innovation, the impact of product innovation on firm performance, price measurement, and welfare, while the final chapter addresses policy issues.
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Book chapters on the topic "Hard/Soft data"

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Hornik, Kurt, and Walter Böhm. "Hard and Soft Euclidean Consensus Partitions." In Data Analysis, Machine Learning and Applications. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78246-9_18.

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Meier, Andreas. "Rundgang Big Data Analytics – Hard & Soft Data Mining." In Big Data Analytics. Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32236-6_1.

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Kozera, Ryszard, and Artur Wiliński. "Fitting Dense and Sparse Reduced Data." In Advances in Soft and Hard Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03314-9_1.

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Bobulski, Janusz, and Mariusz Kubanek. "Design of the BLINDS System for Processing and Analysis of Big Data - A Pre-processing Data Analysis Module." In Advances in Soft and Hard Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03314-9_12.

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Rogoza, Walery, and Ann Ishchenko. "Algorithmic Decomposition of Tasks with a Large Amount of Data." In Advances in Soft and Hard Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03314-9_21.

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Karpio, Krzysztof, Piotr Łukasiewicz, and Rafik Nafkha. "Regression Technique for Electricity Load Modeling and Outlined Data Points Explanation." In Advances in Soft and Hard Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03314-9_5.

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Hodzic, Migdat. "Soft to Hard Data Transformation Using Uncertainty Balance Principle." In Lecture Notes in Networks and Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71321-2_69.

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Konys, Agnieszka. "Ontology-Based Approaches to Big Data Analytics." In Hard and Soft Computing for Artificial Intelligence, Multimedia and Security. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48429-7_32.

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Klasa, Tomasz, and Imed El Fray. "Data Scheme Conversion Proposal for Information Security Monitoring Systems." In Hard and Soft Computing for Artificial Intelligence, Multimedia and Security. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48429-7_15.

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Holimchayachotikul, Pongsak, and Wimalin Laosiritaworn. "Data Mining for CNC Machine Adjustment Decision in Hard Disk Drive Arm Manufacturing: Empirical Study." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10430-5_35.

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Conference papers on the topic "Hard/Soft data"

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Tyagi, Gaurav, Nilesh Patel, and Ishwar Sethi. "Soft-Hard Clustering for Multiview Data." In 2015 IEEE International Conference on Information Reuse and Integration (IRI). IEEE, 2015. http://dx.doi.org/10.1109/iri.2015.77.

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Yi Du, Jihong Yan, and Ting Zhang. "The MPS application of integrating soft data and hard data." In 2014 9th International Conference on Computer Science & Education (ICCSE). IEEE, 2014. http://dx.doi.org/10.1109/iccse.2014.6926545.

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Wang, Yanlong, Ting Zhang, and Jinhua Liu. "A 3D Porosity Simulation Method Using Soft Data and Hard Data." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5367018.

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Wickramarathne, T. L., K. Premaratne, M. N. Murthi, M. Scheutz, S. Kubler, and M. Pravia. "Belief theoretic methods for soft and hard data fusion." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946964.

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Rimland, Jeffrey C., Dan Coughlin, David L. Hall, and Jacob L. Graham. "Advances in data representation for hard/soft information fusion." In SPIE Defense, Security, and Sensing, edited by Jerome J. Braun. SPIE, 2012. http://dx.doi.org/10.1117/12.919208.

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Zhang, Ting, and Yi Du. "Notice of Retraction: An information prediction method integrating soft data with hard data." In 2010 2nd International Conference on Mechanical and Electronics Engineering (ICMEE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icmee.2010.5558607.

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Khaleghi, Bahador, and Fakhreddine Karray. "Distributed random set theoretic soft-hard data fusion: Target tracking application." In 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2011. http://dx.doi.org/10.1109/issnip.2011.6146559.

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Falcon, Rafael, Rami Abielmona, Sean Billings, Alex Plachkov, and Hussein Abbass. "Risk management with hard-soft data fusion in maritime domain awareness." In 2014 Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA). IEEE, 2014. http://dx.doi.org/10.1109/cisda.2014.7035641.

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Sawicki, Aleksander, Zdenek Slanina, and Arturas Linkel. "Compensation of hard- and soft-iron distortions is magnetometer measurement data." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2017, edited by Ryszard S. Romaniuk and Maciej Linczuk. SPIE, 2017. http://dx.doi.org/10.1117/12.2280794.

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Aktarakci, H. K. "Integrated Interpretation of Hard and Soft Data via Geostatistical Markov-Bayes Simulation." In 67th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2005. http://dx.doi.org/10.3997/2214-4609-pdb.1.p157.

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