Academic literature on the topic 'Rich prior knowledge'
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Journal articles on the topic "Rich prior knowledge"
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Dehbi, Y., L. Klingbeil, and L. Plümer. "UAV MISSION PLANNING FOR AUTOMATIC EXPLORATION AND SEMANTIC MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 521–26. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-521-2020.
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Abstract. Unmanned Aerial Vehicles (UAVs) are used for the inspection of areas which are otherwise difficult to access. Autonomous monitoring and navigation requires a background knowledge on the surroundings of the vehicle. Most mission planing systems assume collision-free pre-defined paths and do not tolerate a GPS signal outage. Our approach makes weaker assumptions. This paper introduces a mission planing platform allowing for the integration of environmental prior knowledge such as 3D building and terrain models. This prior knowledge is integrated to pre-compute an octomap for collision detection. The semantically rich building models are used to specify semantic user queries such as roof or facade inspection. A reasoning process paves the way for semantic mission planing of hidden and a-priori unknown objects. Subsequent scene interpretation is performed by an incremental parsing process.
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Papoušek, Jan, Radek Pelánek, and Vít Stanislav. "Adaptive Geography Practice Data Set." Journal of Learning Analytics 3, no. 2 (September 17, 2016): 317–21. http://dx.doi.org/10.18608/jla.2016.32.17.
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We present a data set on student learning of geography facts in an open online system slepemapy.cz. The data set has simple format with intuitive interpretation. At the same time it offers rich possibilities for modelling and analysis, for example of prior knowledge, forgetting or response times.
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Schreiber, Jacob M., and William S. Noble. "Finding the optimal Bayesian network given a constraint graph." PeerJ Computer Science 3 (July 3, 2017): e122. http://dx.doi.org/10.7717/peerj-cs.122.
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Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen variables. Fortunately, domain knowledge can frequently be exploited to achieve dramatic computational savings, and in many cases domain knowledge can even make structure learning tractable. Several methods have previously been described for representing this type of structural prior knowledge, including global orderings, super-structures, and constraint rules. While super-structures and constraint rules are flexible in terms of what prior knowledge they can encode, they achieve savings in memory and computational time simply by avoiding considering invalid graphs. We introduce the concept of a “constraint graph” as an intuitive method for incorporating rich prior knowledge into the structure learning task. We describe how this graph can be used to reduce the memory cost and computational time required to find the optimal graph subject to the encoded constraints, beyond merely eliminating invalid graphs. In particular, we show that a constraint graph can break the structure learning task into independent subproblems even in the presence of cyclic prior knowledge. These subproblems are well suited to being solved in parallel on a single machine or distributed across many machines without excessive communication cost.
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Lee, Maria. "Winner of the SLS Annual Conference Best Paper Prize 2016: Knowledge and landscape in wind energy planning." Legal Studies 37, no. 1 (March 2017): 3–24. http://dx.doi.org/10.1111/lest.12156.
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‘Landscape’ is relatively underexplored in legal scholarship, notwithstanding its occasional centrality to legal analysis, and the ways in which law contributes to the shaping of landscape. Wind energy is an especially rich area for the exploration of landscape; not only do wind farms consistently raise concerns about landscape, but the existence of climate change complicates the response to local concern. Most of the legal literature on ‘knowledge’ focuses on the ways in which different ‘expert’ knowledges find their way into, and then shape, legal processes and decisions. This article is more concerned with the ways in which the planning system receives different knowledge claims, and validates some of them. The discussion turns around four tentative categories of knowledge claim: expert or technical knowledge claims; lay (or sometimes local) knowledge claims; prior institutional knowledge claims; and professional planning knowledge claims. Knowledge of the complex, dynamic and plural idea of landscape in any particular case is constructed in the process of decision making and reason giving through a careful layering of these different sorts of knowledge claim.
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Hsu, Chia-Yu, Wenwen Li, and Sizhe Wang. "Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection." Remote Sensing 13, no. 11 (May 28, 2021): 2116. http://dx.doi.org/10.3390/rs13112116.
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This paper introduces a new GeoAI solution to support automated mapping of global craters on the Mars surface. Traditional crater detection algorithms suffer from the limitation of working only in a semiautomated or multi-stage manner, and most were developed to handle a specific dataset in a small subarea of Mars’ surface, hindering their transferability for global crater detection. As an alternative, we propose a GeoAI solution based on deep learning to tackle this problem effectively. Three innovative features are integrated into our object detection pipeline: (1) a feature pyramid network is leveraged to generate feature maps with rich semantics across multiple object scales; (2) prior geospatial knowledge based on the Hough transform is integrated to enable more accurate localization of potential craters; and (3) a scale-aware classifier is adopted to increase the prediction accuracy of both large and small crater instances. The results show that the proposed strategies bring a significant increase in crater detection performance than the popular Faster R-CNN model. The integration of geospatial domain knowledge into the data-driven analytics moves GeoAI research up to the next level to enable knowledge-driven GeoAI. This research can be applied to a wide variety of object detection and image analysis tasks.
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Peterson, A. Townsend, Adolfo G. Navarro-Sigüenza, and Enrique Martínez-Meyer. "Digital Accessible Knowledge and well-inventoried sites for birds in Mexico: baseline sites for measuring faunistic change." PeerJ 4 (September 7, 2016): e2362. http://dx.doi.org/10.7717/peerj.2362.
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BackgroundFaunal change is a basic and fundamental element in ecology, biogeography, and conservation biology, yet vanishingly few detailed studies have documented such changes rigorously over decadal time scales. This study responds to that gap in knowledge, providing a detailed analysis of Digital Accessible Knowledge of the birds of Mexico, designed to marshal DAK to identify sites that were sampled and inventoried rigorously prior to the beginning of major global climate change (1980).MethodsWe accumulated DAK records for Mexican birds from all relevant online biodiversity data portals. After extensive cleaning steps, we calculated completeness indices for each 0.05° pixel across the country; we also detected ‘hotspots’ of sampling, and calculated completeness indices for these broader areas as well. Sites were designated as well-sampled if they had completeness indices above 80% and >200 associated DAK records.ResultsWe identified 100 individual pixels and 20 broader ‘hotspots’ of sampling that were demonstrably well-inventoried prior to 1980. These sites are catalogued and documented to promote and enable resurvey efforts that can document events of avifaunal change (and non-change) across the country on decadal time scales.ConclusionsDevelopment of repeated surveys for many sites across Mexico, and particularly for sites for which historical surveys document their avifaunas prior to major climate change processes, would pay rich rewards in information about distributional dynamics of Mexican birds.
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Tsai, Chen-Tse, and Dan Roth. "Concept Grounding to Multiple Knowledge Bases via Indirect Supervision." Transactions of the Association for Computational Linguistics 4 (December 2016): 141–54. http://dx.doi.org/10.1162/tacl_a_00089.
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We consider the problem of disambiguating concept mentions appearing in documents and grounding them in multiple knowledge bases, where each knowledge base addresses some aspects of the domain. This problem poses a few additional challenges beyond those addressed in the popular Wikification problem. Key among them is that most knowledge bases do not contain the rich textual and structural information Wikipedia does; consequently, the main supervision signal used to train Wikification rankers does not exist anymore. In this work we develop an algorithmic approach that, by carefully examining the relations between various related knowledge bases, generates an indirect supervision signal it uses to train a ranking model that accurately chooses knowledge base entries for a given mention; moreover, it also induces prior knowledge that can be used to support a global coherent mapping of all the concepts in a given document to the knowledge bases. Using the biomedical domain as our application, we show that our indirectly supervised ranking model outperforms other unsupervised baselines and that the quality of this indirect supervision scheme is very close to a supervised model. We also show that considering multiple knowledge bases together has an advantage over grounding concepts to each knowledge base individually.
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Calderon, Maria Apolonia, Daniel E. Chand, and Daniel P. Hawes. "Final Lines of Defense: Explaining Policy Advocacy by Immigrant-Serving Organizations." Nonprofit Policy Forum 12, no. 2 (March 8, 2021): 285–310. http://dx.doi.org/10.1515/npf-2020-0023.
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Abstract Nonprofit scholars have developed a rich literature on nonprofit advocacy. While the literature is rich, however, gaps remain in our collective knowledge, especially regarding specific sectors of nonprofit human service organizations. Here, we apply existing theory on advocacy by human service organizations to an important subset of the nonprofit community, that being immigrant-serving organizations (ISOs). Most prior research on nonprofit advocacy has not focused on politically polarized issues, such as contemporary immigration policy. Using a nationwide survey of ISOs, we find that unlike other types of human service organizations, the majority of ISOs do engage in at least some forms of policy advocacy. However, those that report using the H-election status on their Form 990s are significantly more likely to engage in advocacy and do so to a wide variety of policymakers, including legislators, chief executives, and even local law enforcement agencies. H-election groups are also more likely to perceive their advocacy activities as effective. These findings add to the evolving knowledge on when and how human service groups seek policy change for marginalized groups.
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Zhang, Zhenyu, Xiaobo Shu, Bowen Yu, Tingwen Liu, Jiapeng Zhao, Quangang Li, and Li Guo. "Distilling Knowledge from Well-Informed Soft Labels for Neural Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9620–27. http://dx.doi.org/10.1609/aaai.v34i05.6509.
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Extracting relations from plain text is an important task with wide application. Most existing methods formulate it as a supervised problem and utilize one-hot hard labels as the sole target in training, neglecting the rich semantic information among relations. In this paper, we aim to explore the supervision with soft labels in relation extraction, which makes it possible to integrate prior knowledge. Specifically, a bipartite graph is first devised to discover type constraints between entities and relations based on the entire corpus. Then, we combine such type constraints with neural networks to achieve a knowledgeable model. Furthermore, this model is regarded as teacher to generate well-informed soft labels and guide the optimization of a student network via knowledge distillation. Besides, a multi-aspect attention mechanism is introduced to help student mine latent information from text. In this way, the enhanced student inherits the dark knowledge (e.g., type constraints and relevance among relations) from teacher, and directly serves the testing scenarios without any extra constraints. We conduct extensive experiments on the TACRED and SemEval datasets, the experimental results justify the effectiveness of our approach.
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Broadhead, Geoffrey D., and Allen W. Burton. "The Legacy of Early Adapted Physical Activity Research." Adapted Physical Activity Quarterly 13, no. 2 (April 1996): 116–26. http://dx.doi.org/10.1123/apaq.13.2.116.
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In this opinion paper, we pose the question whether the current generation of scholars have taken advantage of the rich legacy of early adapted physical activity (APA) research. We believe that this legacy often has been ignored, even though it holds many treasures waiting to be rediscovered. We begin with a brief description of the knowledge base in APA prior to 1980, then evaluate the present recognition of past research contributions. Finally, we recommend how students, professionals, and researchers might be encouraged to take advantage of the vast body of literature in APA and related fields.
Dissertations / Theses on the topic "Rich prior knowledge"
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Felt, Paul L. "Facilitating Corpus Annotation by Improving Annotation Aggregation." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5678.
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Annotated text corpora facilitate the linguistic investigation of language as well as the automation of natural language processing (NLP) tasks. NLP tasks include problems such as spam email detection, grammatical analysis, and identifying mentions of people, places, and events in text. However, constructing high quality annotated corpora can be expensive. Cost can be reduced by employing low-cost internet workers in a practice known as crowdsourcing, but the resulting annotations are often inaccurate, decreasing the usefulness of a corpus. This inaccuracy is typically mitigated by collecting multiple redundant judgments and aggregating them (e.g., via majority vote) to produce high quality consensus answers. We improve the quality of consensus labels inferred from imperfect annotations in a number of ways. We show that transfer learning can be used to derive benefit from out-dated annotations which would typically be discarded. We show that, contrary to popular preference, annotation aggregation models that take a generative data modeling approach tend to outperform those that take a condition approach. We leverage this insight to develop csLDA, a novel annotation aggregation model that improves on the state of the art for a variety of annotation tasks. When data does not permit generative data modeling, we identify a conditional data modeling approach based on vector-space text representations that achieves state-of-the-art results on several unusual semantic annotation tasks. Finally, we identify a family of models capable of aggregating annotation data containing heterogenous annotation types such as label frequencies and labeled features. We present a multiannotator active learning algorithm for this model family that jointly selects an annotator, data items, and annotation type.
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Jonschkowski, Rico [Verfasser], Oliver [Akademischer Betreuer] Brock, Oliver [Gutachter] Brock, George [Gutachter] Konidaris, and Marc [Gutachter] Toussaint. "Learning robotic perception through prior knowledge / Rico Jonschkowski ; Gutachter: Oliver Brock, George Konidaris, Marc Toussaint ; Betreuer: Oliver Brock." Berlin : Technische Universität Berlin, 2018. http://d-nb.info/1160593299/34.
Full textBooks on the topic "Rich prior knowledge"
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Dallal, Ahmad S. Islam without Europe. University of North Carolina Press, 2018. http://dx.doi.org/10.5149/northcarolina/9781469641409.001.0001.
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Replete with a cast of giants in Islamic thought and philosophy, Ahmad S. Dallal’s pathbreaking intellectual history of the eighteenth-century Muslim world challenges stale views of this period as one of decline, stagnation, and the engendering of a widespread fundamentalism. Far from being moribund, Dallal argues, the eighteenth century--prior to systematic European encounters--was one of the most fertile eras in Islamic thought. Across vast Islamic territories, Dallal charts in rich detail not only how intellectuals rethought and reorganized religious knowledge but also the reception and impact of their ideas. From the banks of the Ganges to the shores of the Atlantic, commoners and elites alike embraced the appeals of Muslim thinkers who, while preserving classical styles of learning, advocated for general participation by Muslims in the definition of Islam. Dallal also uncovers the regional origins of most reform projects, showing how ideologies were forged in particular sociopolitical contexts. Reformists’ ventures were in large part successful--up until the beginnings of European colonization of the Muslim world. By the nineteenth century, the encounter with Europe changed Islamic discursive culture in significant ways into one that was largely articulated in reaction to the radical challenges of colonialism.
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Audi, Robert. Seeing, Knowing, and Doing. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197503508.001.0001.
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This book provides an overall theory of perception and an account of knowledge and justification concerning the physical, the abstract, and the normative. It has the rigor appropriate for professionals but explains its main points using concrete examples. It accounts for two important aspects of perception on which philosophers have said too little: its relevance to a priori knowledge—traditionally conceived as independent of perception—and its role in human action. Overall, the book provides a full-scale account of perception, presents a theory of the a priori, and explains how perception guides action. It also clarifies the relation between action and practical reasoning; the notion of rational action; and the relation between propositional and practical knowledge. Part One develops a theory of perception as experiential, representational, and causally connected with its objects: as a discriminative response to those objects, embodying phenomenally distinctive elements; and as yielding rich information that underlies human knowledge. Part Two presents a theory of self-evidence and the a priori. The theory is perceptualist in explicating the apprehension of a priori truths by articulating its parallels to perception. The theory unifies empirical and a priori knowledge by clarifying their reliable connections with their objects—connections many have thought impossible for a priori knowledge as about the abstract. Part Three explores how perception guides action; the relation between knowing how and knowing that; the nature of reasons for action; the role of inference in determining action; and the overall conditions for rational action.
Book chapters on the topic "Rich prior knowledge"
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Tavazzi, Erica, Camille L. Gerard, Olivier Michielin, Alexandre Wicky, Roberto Gatta, and Michel A. Cuendet. "A Process Mining Approach to Statistical Analysis: Application to a Real-World Advanced Melanoma Dataset." In Lecture Notes in Business Information Processing, 291–304. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72693-5_22.
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AbstractThanks to its ability to offer a time-oriented perspective on the clinical events that define the patient’s path of care, Process Mining (PM) is assuming an emerging role in clinical data analytics. PM’s ability to exploit time-series data and to build processes without any a priori knowledge suggests interesting synergies with the most common statistical analyses in healthcare, in particular survival analysis. In this work we demonstrate contributions of our process-oriented approach in analyzing a real-world retrospective dataset of patients treated for advanced melanoma at the Lausanne University Hospital. Addressing the clinical questions raised by our oncologists, we integrated PM in almost all the steps of a common statistical analysis. We show: (1) how PM can be leveraged to improve the quality of the data (data cleaning/pre-processing), (2) how PM can provide efficient data visualizations that support and/or suggest clinical hypotheses, also allowing to check the consistency between real and expected processes (descriptive statistics), and (3) how PM can assist in querying or re-expressing the data in terms of pre-defined reference workflows for testing survival differences among sub-cohorts (statistical inference). We exploit a rich set of PM tools for querying the event logs, inspecting the processes using statistical hypothesis testing, and performing conformance checking analyses to identify patterns in patient clinical paths and study the effects of different treatment sequences in our cohort.
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Hohwy, Jakob. "Quick’n’Lean or Slow and Rich?" In Andy Clark and His Critics, 191–205. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190662813.003.0015.
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Andy Clark’s exciting work on predictive processing provides the umbrella under which his hugely influential previous work on embodied and extended cognition seeks a unified home. This chapter argues that in fact predictive processing harbours internalist, inferentialist and epistemic tenets that cannot leave embodied and extended cognition unchanged. Predictive processing cannot do the work Clark requires of it without relying on rich, preconstructive internal representations of the world, nor without engaging in paradigmatically rational integration of prior knowledge and new sensory input. Hence, next to Clark’s image of fluid “uncertainty surfing” is an equally valid image of more emaciated and plodding world-modelling. Rather than underpinning orthodox embodied and extended approches, predictive processing therefore presents an opportunity for a potentially fruitful new synthesis of cognitivist and embodied approaches to cognition.
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Clinton, Virginia, Jennifer L. Cooper, Joseph E. Michaelis, Martha W. Alibali, and Mitchell J. Nathan. "How Revisions to Mathematical Visuals Affect Cognition." In Eye-Tracking Technology Applications in Educational Research, 195–218. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1005-5.ch010.
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Mathematics curricula are frequently rich with visuals, but these visuals are often not designed for optimal use of students' limited cognitive resources. The authors of this study revised the visuals in a mathematics lesson based on instructional design principles. The purpose of this study is to examine the effects of these revised visuals on students' cognitive load, cognitive processing, learning, and interest. Middle-school students (N = 62) read a lesson on early algebra with original or revised visuals while their eye movements were recorded. Students in the low prior knowledge group had less cognitive load and cognitive processing with the revised lesson than the original lesson. However, the reverse was true for students in the middle prior knowledge group. There were no effects of the revisions on learning. The findings are discussed in the context of the expertise reversal effect as well as the cognitive theory of multimedia learning and cognitive load theory.
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Butcher, Kirsten R., Sebastian de la Chica, Faisal Ahmad, Qianyi Gu, Tamara Sumner, and James H. Martin. "Conceptual Customization for Learning with Multimedia." In Cognitive Effects of Multimedia Learning, 260–87. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-158-2.ch014.
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This chapter discusses an emerging theme in supporting effective multimedia learning: developing scalable, cognitively-grounded tools that customize learning interactions for individual students. We discuss the theoretical foundation for expected benefits of customization and current approaches in educational technology that leverage a learner’s prior knowledge. We then describe the development of a customized tool for science learning, called CliCk, that uses automatic techniques to create knowledge models that can be fed into cognitively-informed pedagogical tools. CliCk leverages existing multimedia resources in educational digital libraries for two purposes: (a) to generate rich representations of domain content relevant for learner modeling that are easily scaled to new domains and disciplines, and (b) to serve as a repository of instructional resources that support customized pedagogical interactions. The potential of the CliCk system is discussed, along with initial comparisons of knowledge models created by CliCk and human experts. Finally, the chapter discusses the remaining challenges and relevant future extensions for effective customization tools in educational technology.
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Berg, Christopher. "Introduction." In The Classical Guitar Companion, 1–8. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190051105.003.0001.
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This chapter lays out the scope and intended use of The Classical Guitar Companion. It explores how guitarists and teachers can create unique and distinctive curricula of study for themselves or their students to help them acquire and develop fundamental techniques and foundational knowledge. Included are practice guidelines applicable to each chapter of the book. Because all new learning requires a finely honed foundation of prior knowledge and skill, the introduction presents advice that will help guitarists transform their foundational work into advanced technique and a deeper understanding of the instrument. This work will help guitarists meet the artistic and technical demands of advanced guitar literature. The introduction also explores those best served by the book: those studying the classical guitar (whether novice or advanced); guitarists wishing to fill gaps in their background; new guitar teachers looking for guidance with curricula; and guitarists interested in the rich pedagogical heritage of the instrument.
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Orr, David W. "Late-Night Thoughts about Democracy in the Long Emergency." In Down to the Wire. Oxford University Press, 2009. http://dx.doi.org/10.1093/oso/9780195393538.003.0008.
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Democracy, winston churchill once famously said, is the worst form of government except for all of the others ever tried. The Greeks, from whom we inherited the idea of self-government, after all, couldn’t manage it for long and fell victim to the political vices of greed, hubris, imperial overreach, and ruinous wars. In modern times it is possible, historian Walter Prescott Webb once wrote, that the upsurge of democracy in the early modern era was largely the result of the abundance of resources resulting from the discovery of the New World rather than from any general human improvement. His point was that the larger per capita ratios of land, minerals, and natural resources after 1492 reduced the pressures on governments and populations under conditions of scarcity and otherwise diverted peoples’ energies to the tasks of getting rich and getting on in the New World, the effect of which was to make us a more agreeable and more manageable lot. The ratios of resources to people, however, are now about what they were prior to the “discovery” of the New World, and the due bill for the long binge of fossil fuel–powered modernization is said to be in the mail. In a more crowded and hotter world, perhaps democracy will be “just a moment in history,” as Robert Kaplan (1997) once put it, a casualty of the failure to manage growing complexity and scarcity. Many other forces also work against democracy. Vice President Al Gore, for one, argues that decades of television and nonstop exposure to advertising have eroded our capacity for the reasoned judgment necessary for democracy and that this is a large factor in the tide of irrationality that has recently flooded our politics. Susan Jacoby, similarly, believes that we live in a “new age of unreason,” that America is “ill with a powerful mutant strain of intertwined ignorance, anti-rationalism, and anti-intellectualism,” and that Americans are “living through an overarching crisis of memory and knowledge involving everything about the way we learn and think” (2008, pp. xx, 309).
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Barreto, Amílcar Antonio. "Rational Politics." In The Politics of Language in Puerto Rico, 83–95. University Press of Florida, 2020. http://dx.doi.org/10.5744/florida/9781683401131.003.0007.
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This chapter focuses on the PPD’s decision-making logic when it surprised many by enacting a controversial law. They did this with clear knowledge that their party could suffer in the following elections. The decision in 1991 to support unilingualism seems to violate fundamental assumptions of rational behavior by political parties. Vote maximization, developed by Anthony Downs, is one of the foremost assumptions in the study of political parties and the PPD’s actions prior to the 1990s illustrate that it complied with these expectations. Indeed, the PPD leadership sidelined attempts to repeal the 1902 official bilingualism law precisely because their opponents would accuse them of moving towards independence – an electorally unpopular stance. The party’s decision to move ahead with proposal calls into question the applicability of rational choice models to Puerto Rico.
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O’Reilly, Dr Daragh, Dr Gretchen Larsen, and Dr Krzysztof Kubacki. "Music Consumption." In Music, Markets and Consumption. Goodfellow Publishers, 2013. http://dx.doi.org/10.23912/978-1-908999-52-8-2250.
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Music can be heard everywhere, infiltrating our everyday existence. Not only does one choose to listen to music across a range of situations, times and spaces; one is also exposed to music in innumerable day-to-day situations – on public transport, from a passing car, through advertisements. Even prior to the technological advances which have revolutionized the way music is acquired, purchased and used (Elberse 2010; Simun 2009), Merriam noted that ‘the importance of music, as judged by the sheer ubiquity of its presence, is enormous... There is probably no other human cultural activity which is so all-pervasive and which reaches into, shapes and often controls so much of human behaviour’ (1964: 218). Technological, social and cultural trends have only served to deepen and diversify the ways in which one listens to, or engages with, music. The marketing and consumer behaviour perspective on music engagement has focused primarily on experiential aspects. Interest in the consumption of music arose on the back of the experiential turn in consumer research, and the associated interest in aesthetic products (e.g. Holbrook and Hirschman 1982). Music is a rich and complex symbolic, social and political product (Larsen et al., 2010), the experience of which can be distinguished from the consumption of other kinds of products. For example, music is the only product which is primarily auditory (Larsen and Lawson, 2010); consumption does not alter its recorded form and it can be consumed actively or passively, with or without ownership, in private and in public (Lacher and Mizerski, 1994). As a result, most of our knowledge about the consumption of music has concentrated on the emotional and aesthetic reasons for listening to music (e.g. Cherian and Jones, 1991; Kellaris and Kent, 1993; Lacher and Mizerski, 1994; North and Hargreaves, 1997; Chien et al., 2007; Lonsdale and North, 2011); the relationship between music and identity, particularly the use of music as a ‘badge of identity’ (e.g. Holbrook, 1986; DeNora, 1999; North and Hargreaves, 1999; Shankar, 2000; Goulding et al., 2002; Negus and Velazquez, 2002; Nuttall, 2009) and the symbolic function of music (Hogg and Banister, 2000; Larsen et al., 2009, 2010). There is a broader question, underlying this body of knowledge, which remains unexamined. That is: What does it mean to frame music engagement as consumption and music listeners as consumers, and what are the consequences of doing so for our understanding of music consumption? As conceptualized by Holbrook and Anand (1990) and Lacher and Mizerski (1994), music consumption is the act of listening to a piece of music. Listening to music is, without a doubt, one of the most significant aspects of the act of consuming music; however, it does not entirely capture all that is involved. For example, talking and reading about music are also important activities in consuming music (Larsen et al., 2009). If, in addition, one also acknowledges that the music product can be an artist, venue and associated paraphernalia (see Chapter 3), then the consumption of the music product must necessarily go beyond listening. Finally, this conceptualization does not help us to identify or understand how the experience of engaging with music differs if one does it as an audience member, as a fan, or as a consumer. Thus, a clearer understanding is needed of what one means by consumption in the context of music.
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Aryal, Jagannath, and Didier Josselin. "Environmental Object Recognition in a Natural Image." In Geospatial Intelligence, 1353–71. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8054-6.ch059.
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Natural images, which are filled with intriguing stimuli of spatial objects, represent our cognition and are rich in spatial information. Accurate extraction of spatial objects is challenging due to the associated spatial and spectral complexities in object recognition. In this paper, the authors tackle the problem of spatial object extraction in a GEOgraphic Object Based Image Analysis framework taking psychological and mathematical complexities into account. In doing so, the authors experimented with human and GEOBIA based recognition and segmentation in an image of an area of natural importance, the Ventoux Mountain, France. Focus was given to scales, color, and texture properties at multiple levels in delineating the candidate spatial objects from the natural image. Such objects along with the original image were provided to the human subjects in two stages and three different groups of samples. The results of two stages were collated and analyzed. The analysis showed that there exist different ways to comprehend the geographical objects according to priori knowledge.
Conference papers on the topic "Rich prior knowledge"
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Cheng, Jun, Fuxiang Wu, Yanling Tian, Lei Wang, and Dapeng Tao. "RiFeGAN: Rich Feature Generation for Text-to-Image Synthesis From Prior Knowledge." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.01092.
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Su, Hang, Jun Zhu, Yinpeng Dong, and Bo Zhang. "Forecast the Plausible Paths in Crowd Scenes." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/386.
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Forecasting the future plausible paths of pedestrians in crowd scenes is of wide applications, but it still remains as a challenging task due to the complexities and uncertainties of crowd motions. To address these issues, we propose to explore the inherent crowd dynamics via a social-aware recurrent Gaussian process model, which facilitates the path prediction by taking advantages of the interplay between the rich prior knowledge and motion uncertainties. Specifically, we derive a social-aware LSTM to explore the crowd dynamic, resulting in a hidden feature embedding the rich prior in massive data. Afterwards, we integrate the descriptor into deep Gaussian processes with motion uncertainties appropriately harnessed. Crowd motion forecasting is implemented by regressing relative motion against the current positions, yielding the predicted paths based on a functional object associated with a distribution. Extensive experiments on public datasets demonstrate that our method obtains the state-of-the-art performance in both structured and unstructured scenes by exploring the complex and uncertain motion patterns, even if the occlusion is serious or the observed trajectories are noisy.
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Xu, Huapeng, Guilin Qi, Jingjing Li, Meng Wang, Kang Xu, and Huan Gao. "Fine-grained Image Classification by Visual-Semantic Embedding." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/145.
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This paper investigates a challenging problem,which is known as fine-grained image classification(FGIC). Different from conventional computer visionproblems, FGIC suffers from the large intraclassdiversities and subtle inter-class differences.Existing FGIC approaches are limited to exploreonly the visual information embedded in the images.In this paper, we present a novel approachwhich can use handy prior knowledge from eitherstructured knowledge bases or unstructured text tofacilitate FGIC. Specifically, we propose a visual-semanticembedding model which explores semanticembedding from knowledge bases and text, andfurther trains a novel end-to-end CNN frameworkto linearly map image features to a rich semanticembedding space. Experimental results on a challenginglarge-scale UCSD Bird-200-2011 datasetverify that our approach outperforms several state-of-the-art methods with significant advances.
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Eberhardt, Alan W., and Joel H. Dobbs. "An Interdisciplinary Capstone Experience Involving Engineering and Business Students and a Manufacturing Rotation." In ASME 2013 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/sbc2013-14163.
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The Department of Biomedical Engineering (BME) at the University of Alabama at Birmingham (UAB) has a rich history of teaching critical and integrative thinking and design skills throughout the curriculum, culminating in a year-long senior capstone design experience. The capstone includes clinical rotations and shadowing in the early stages of the design sequence — participating medical faculty enhance student exposure to biomedical device ideation, which promotes the virtues of team-based experiential learning activities that teach critical thinking and integrate new knowledge with prior learning.
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Xiao, Chunyang, Marc Dymetman, and Claire Gardent. "Symbolic Priors for RNN-based Semantic Parsing." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/585.
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Seq2seq models based on Recurrent Neural Networks (RNNs) have recently received a lot of attention in the domain of Semantic Parsing. While in principle they can be trained directly on pairs (natural language utterances, logical forms), their performance is limited by the amount of available data. To alleviate this problem, we propose to exploit various sources of prior knowledge: the well-formedness of the logical forms is modeled by a weighted context-free grammar; the likelihood that certain entities present in the input utterance are also present in the logical form is modeled by weighted finite-state automata. The grammar and automata are combined together through an efficient intersection algorithm to form a soft guide (“background”) to the RNN.We test our method on an extension of the Overnight dataset and show that it not only strongly improves over an RNN baseline, but also outperforms non-RNN models based on rich sets of hand-crafted features.
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Li, Jundong, Harsh Dani, Xia Hu, and Huan Liu. "Radar: Residual Analysis for Anomaly Detection in Attributed Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/299.
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Attributed networks are pervasive in different domains, ranging from social networks, gene regulatory networks to financial transaction networks. This kind of rich network representation presents challenges for anomaly detection due to the heterogeneity of two data representations. A vast majority of existing algorithms assume certain properties of anomalies are given a prior. Since various types of anomalies in real-world attributed networks co-exist, the assumption that priori knowledge regarding anomalies is available does not hold. In this paper, we investigate the problem of anomaly detection in attributed networks generally from a residual analysis perspective, which has been shown to be effective in traditional anomaly detection problems. However, it is a non-trivial task in attributed networks as interactions among instances complicate the residual modeling process. Methodologically, we propose a learning framework to characterize the residuals of attribute information and its coherence with network information for anomaly detection. By learning and analyzing the residuals, we detect anomalies whose behaviors are singularly different from the majority. Experiments on real datasets show the effectiveness and generality of the proposed framework.
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Meschian, Moe, Andy Duncan, Matt Yarmuch, and Fred Myschuk. "An Investigation on Microstructural Evolution of X70 Steel Pipe During Hot Induction Bending." In 2018 12th International Pipeline Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/ipc2018-78018.
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It is generally accepted that hot induction bending (HIB) results in a decrease in strength and an increase in fracture toughness in bend area, heat affected zone (HAZ) and weld metal (WM). As the result, Post bend heat treatment (PBHT) is not considered to be a requirement and could be waived for saving money and time. This research work raises the concern that factual verification of proper microstructure and no localized brittle zone is vitally necessary prior to waving PBHT. Evaluation of the steel microstructure and mechanical properties as the result of various pipe chemistries during pipe bending has been verified in this experimental work. It is emphasized that knowledge and control of prior steel pipe chemistry, control of temperature, cooling rate and bending speed assures the reliability and repeatability of induction bends, especially in critical environments such as low temperature application. In the present work, qualitative and quantitative microstructural analysis, hardness and impact test performed and evaluated on samples from X70 line pipe with 3 different steel chemistries. The samples prepared from different locations on body, weld and HAZ in the as received and as bent condition. It was found that the final microstructure and mechanical properties in the as bent condition is dependent on the chemistry, steel cleanliness and microstructural uniformity. It was observed that small localized brittle zone with traces of upper Bainite and Martensite islands could be transformed in the microstructure with rich chemistry containing non-homogenous central segregation. It is concluded that factual verification of proper microstructure with no localized hard zone is required prior to waving PBHT.
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Yu, Mengshi, Jian Liu, Yufeng Chen, Jinan Xu, and Yujie Zhang. "Cross-Domain Slot Filling as Machine Reading Comprehension." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/550.
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With task-oriented dialogue systems being widely applied in everyday life, slot filling, the essential component of task-oriented dialogue systems, is required to be quickly adapted to new domains that contain domain-specific slots with few or no training data. Previous methods for slot filling usually adopt sequence labeling framework, which, however, often has limited ability when dealing with the domain-specific slots. In this paper, we take a new perspective on cross-domain slot filling by framing it as a machine reading comprehension (MRC) problem. Our approach firstly transforms slot names into well-designed queries, which contain rich informative prior knowledge and are very helpful for the detection of domain-specific slots. In addition, we utilize the large-scale MRC dataset for pre-training, which further alleviates the data scarcity problem. Experimental results on SNIPS and ATIS datasets show that our approach consistently outperforms the existing state-of-the-art methods by a large margin.
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Nguyen, Raymond, Antoine Jacques, Vincent Jaffrezic, Yann Bigno, Amr Mohamed Serry, Hasan Zakaria, Owais Ameer Khan, Omar Jadallah, and Benoit Brouard. "Piloting the 1st Well-Test-Logging in the Middle East, Paving the Way to Low-Cost Dynamic Reservoir Characterization and Well Value Optimization." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206177-ms.
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Abstract The development of carbonate reservoirs of a giant field, Offshore Abu Dhabi, requires long horizontal wells to maximize productivity, but at the risk of unwanted gas and water channeling through its inherent heterogeneities. Conformance can be enhanced with dedicated segmented completions (blank sections, Inflow Control Device, Autonomous Inflow Control Device, etc.) or selective acid stimulation (diverter, Limited Entry Liner), which are increasingly implemented to extend well life, and eventually well value. If these technologies have matured, success depends heavily on the quality of the formation knowledge prior to completion. As of today, conventional logs provide the basic ground, but they lack dynamic information, whereas production logging results are obtained too late, when the well is already completed. Initially introduced for the optimization of unconventional well completions (see Jacques et al, URTEC 2019), the Well Testing Logging (WTLog) offers the advantage to record a log of mobility, at the end of drilling the openhole, enabling a favorable timing to influence adapted completion and stimulation design. Contrasted viscosity brines are sequentially circulated through the drill pipes at a constant rate and back-produced from the casing at constant pressure. The fluids interface travels in the drain from the TD to the casing shoe, and the measurement of the differential formation seepage is interpreted into an injectivity profile. Combined with rate fall-off phase analysis, permeability and skin logs are derived. Lasting a few hours and realized with conventional rig equipment (such as cement pumps, coriolis flowmeters, Managed Pressure Drilling system), it is a nonintrusive, safe, and ultimately low-cost operation. Forward, it can replace costly logging, when aimed at characterizing heterogeneities. Within a year, the two first WTLog pilots of the Middle East were successfully designed and carried out. They targeted two appraisal wells in distinct undeveloped reservoirs (Cretaceous and Upper Jurassic formations) which benefited from rich acquisition programs (Image log, Production log) to benchmark and qualify this technology. After an explanation of the technology principles, this paper describes the design, operations, and results of these pilots. It then focuses on the petrophysical consolidation of the matrix/fracture characterization. It concludes by sharing the learnings and offers insight to what extent it is a promising technology to be applied in Middle East carbonate reservoir developments.
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Leitão, Teresa E., Tiago Martins, Maria José Henriques, and J. P. Lobo-Ferreira. "Large Scale Soil-Aquifer-Treatment (SAT-MAR) Physical Model Experiments to Improve Water Quality." In 11th International Conference “Environmental Engineering”. VGTU Technika, 2020. http://dx.doi.org/10.3846/enviro.2020.762.
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The effluents from agriculture practices usually contain several contaminants creating an environmental concern to downgradient water bodies. The use of SAT systems to improve the effluents water quality, during the transport of infiltrated water through the unsaturated and saturated zones, can bring a solution for water reclamation, water reuse, and overall as a water resources management tool. The research was carried out under MARSOL project were SAT experiments were executed in a physical (sandbox) model. These experiments aimed to contribute solving the problem of removing rice field contaminants from water, using a soil-aquifer prototype basin to treat water prior to its discharge in Melides lagoon, Portugal. The sandbox model was divided into three sections to test the adsorption and biodegradation capacity of three soil profiles, two of them including soil mixtures of sand with vegetal compost with different layouts. In each section, two tracer experiments were performed with spiked fertilizer and hydrocarbons. To analyse the tracer’s behaviour, monitoring devices were installed in three piezometers for continuous in situ readings of pH, T, EC, ORP and water level, besides water sampling hand-pump for chemical analysis. The results obtained in the experiments gave useful knowledge necessary to build an in situ facility.