Relevant bibliographies by topics / Human interactions

Contents

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

Academic literature on the topic 'Human interactions'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Human interactions"

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Taha, Ahmed E. "The Severe Acute Respiratory Syndrome Coronavirus-2 Pandemic: An Overview to Control Human-wildlife and Human-human Interactions." Journal of Pure and Applied Microbiology 14, no. 2 (June 19, 2020): 1095–98. http://dx.doi.org/10.22207/jpam.14.2.02.

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Oguz, Ozgur S., Wolfgang Rampeltshammer, Sebastian Paillan, and Dirk Wollherr. "An Ontology for Human-Human Interactions and Learning Interaction Behavior Policies." ACM Transactions on Human-Robot Interaction 8, no. 3 (August 29, 2019): 1–26. http://dx.doi.org/10.1145/3326539.

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C., Rosell, and F. Llimona. "Human–wildlife interactions." Animal Biodiversity and Conservation 35, no. 2 (December 2012): 219–20. http://dx.doi.org/10.32800/abc.2012.35.0219.

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219Animal Biodiversity and Conservation 35.2 (2012)© 2012 Museu de Ciències Naturals de BarcelonaISSN: 1578–665XRosell, C. & Llimona, F., 2012. Human–wildlife interactions. Animal Biodiversity and Conservation, 35.2: 219–220. The nature of wildlife management throughout the world is changing. The increase in the world’s human population has been accompanied by a rapid expansion of agricultural and urban areas and infrastructures, especially road and railway networks. Worldwide, wildlife habitats are being transformed and fragmented by human activities, and the behavior of several species has changed as a result of human activities. Some species have adapted easily to urban or peri–urban habitats and take advantage of the new resources available. These data provide the context for why human–wildlife interactions are increasing. At the 30th International Union of Game Biologists Congress held in Barcelona in early September 2011, in addition to two plenary presentations, 52 authors from 12 different countries and three continents presented 15 papers in the Interactions of Humans and Wildlife Session, three of which are included in this volume. To some extent, all the papers reflected the inherent difficulty in solving the complex problems caused either by rapidly increasing species that begin to inhabit urban and agricultural areas in numbers not seen previously (e.g. coyo-tes, Canis latrans, inhabiting big cities; wild boar, Sus scrofa, across western Europe; wood pigeons, Columba palumbus, in France), or species whose populations are threatened by human activities (e.g., Eurasian Lynx, Lynx lynx, in the Czech Republic). Some papers addressed the contentious issue of predator control (e.g., gamebirds in Great Britain), while others presented data regarding how human activities influenced animal behavior (e.g., pink footed geese, Anser brachyrhynchus; and red deer, Cervus elaphus, in Germany). The papers presented at the congress show how human activities affect the distributions and dynamics of wildlife populations and also change the behavior of some species. Wildlife causes social and economic con-flicts by damaging agricultural and forest resources, bringing about traffic collisions, and creating problems for residents in urban areas; while many are increasingly distant from nature and may not accept the presence of wildlife others may actively encourage the presence of wild animals. The first paper in this volume, by Cahill et al. (2012), analyzes the management challenges of the increasing abundance of wild boar in the peri–urban area of Barcelona. This conflict has arisen in other large cities in Europe and elsewhere. The presence of the species causes problems for many residents, to such an extent that it is considered a pest in these areas. Wild boar habituation has not only been facilitated by population expansion, but also by the attitudes of some citizens who encourage their presence by direct feeding. This leads to wild boar behavior modification and also promotes an increase in the fertility rate of habituated females, which are significantly heavier than non–habituated females. Public attitudes regarding the species and harvesting methods (at present most specimens are removed by live capture and subsequently sacrificed) are highlighted as one of the key factors in the management of the conflict. The second paper provides an example of how the distribution of irrigated croplands influences wild boar roadkills in NW Spain (Colino–Rabanal et al., 2012). By modeling the spatial distribution of wild boar collisions with vehicles and using generalized additive models based on GIS, the authors show that the number of roadkills is higher in maize croplands than in forested areas. This factor is the main explanatory variable in the model. The paper provides an excellent example of how the synergies of diverse human elements in the landscape (maize croplands and roads in this case) affect the location and dimensions of these types of conflicts. The third and final paper, by Belotti et al. (2012), addresses the effects of tourism on Eurasian lynx movements and prey usage at Šumava National Park in the Czech Republic. The monitoring of 5 GPS–collared lynxes and analyses of data regarding habitat features suggests that human disturbance (proximity of roads and tourist trails) can modify the presence of lynxes during the day close to the site where they have hidden a prey item, such as an ungulate, that can provide them with food for several days. In such cases, adequate management of tourism development must involve a commitment to species conservation. The analyses and understanding of all these phenomena and the design of successful wildlife management strategies and techniques used to mitigate the conflicts require a good knowledge base that considers informa-tion both about wildlife and human attitudes. The papers presented stress the importance of spatial analyses of the interactions and their relationship with landscape features and the location of human activities. Species distribution and abundance are related to important habitat variables such as provision of shelter, food, comfor-table spaces, and an appropriate climate. Therefore, it is essential to analyze these data adequately to predict where conflicts are most likely to arise and to design successful mitigation strategies. The second key factor for adequate management of human–wildlife interactions is to monitor system change. An analysis of the variety of data on population dynamics, hunting, wildlife collisions, and wildlife presence in urban areas would provide a basis for adaptive management. In this respect, in the plenary session, Steve Redpath mentioned the importance of the wildlife biologist’s attitude when interpreting and drawing conclusions from recorded data and stressed the importance of conducting clear, relevant, and transparent science for participants involved in the management decision process, which often involves a high number of stakeholders. All of the papers addressing the problems associated with human wildlife interactions were characterized by a common theme. Regardless of the specific nature of the problem, the public was generally divided on how the problem should be addressed. A particularly sensitive theme was that of population control methods, especially when conflicts are located in peri–urban areas. Several presenters acknowledged that public participation was necessary if a solution was to be reached. Some suggested, as have other authors (Heydon et al., 2010), that a legislative framework may be needed to reconcile human and wildlife interests. However, each problem that was presented appeared to involve multiple stakeholders with different opinions. Solving these kinds of problems is not trivial. Social factors strongly influence perceptions of human–wildlife conflicts but the methods used to mitigate these conflicts often take into account technical aspects but not people’s attitudes. A new, more innovative and interdisciplinary approach to mitigation is needed to allow us 'to move from conflict towards coexistence' (Dickman, 2010). Other authors also mentioned the importance of planning interventions that optimize the participation of experts, policy makers, and affected communities and include the explicit, systematic, and participatory evaluation of the costs and benefits of alternative interventions (Treves et al., 2009). One technique that has been used to solve problems like these is termed Structured Decision Making (SDM). This technique was developed by the U.S. Geological Survey and the U.S. Fish and Wildlife Service. As described by Runge et al. (2009), the process is 'a formal application of common sense for situations too complex for the informal use of common sense', and provides a rational framework and techniques to aid in prescriptive decision making. Fundamentally, the process entails defining a problem, deciding upon the objectives, considering the alternative actions and the consequences for each, using the available science to develop a model (the plan), and then making the decision how to implement (Runge et al., 2009). Although complex, SDM uses a facilitator to guide stakeholders through the process to reach a mutually agreed–upon plan of action. It is clear that human–wildlife interactions are inherently complex because many stakeholders are usually involved. A rational approach that incorporates all interested parties would seem to be a productive way of solving these kinds of problems
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Golding, Ray M. "Quantifying Human Interactions." International Journal of Environmental, Cultural, Economic, and Social Sustainability: Annual Review 3, no. 5 (2007): 137–42. http://dx.doi.org/10.18848/1832-2077/cgp/v03i05/54400.

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Kurokawa, K. "Modeling Human Interactions." IEEE Potentials 16, no. 2 (1997): 26–28. http://dx.doi.org/10.1109/mp.1997.581387.

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Blackshaw, JK. "Human-Livestock Interactions." Australian Veterinary Journal 76, no. 12 (December 1998): 827. http://dx.doi.org/10.1111/j.1751-0813.1998.tb12340.x.

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Hemsworth, P. H., and J. L. Barnett. "Human-Animal Interactions." Veterinary Clinics of North America: Food Animal Practice 3, no. 2 (July 1987): 339–56. http://dx.doi.org/10.1016/s0749-0720(15)31156-7.

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Tyler, Neil. "Human Robot Interactions." New Electronics 51, no. 22 (December 10, 2019): 12–14. http://dx.doi.org/10.12968/s0047-9624(22)61505-0.

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Kobayashi, Hill Hiroki. "Research in Human-Computer-Biosphere Interaction." Leonardo 48, no. 2 (April 2015): 186–87. http://dx.doi.org/10.1162/leon_a_00982.

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Currently human-computer interaction (HCI) is primarily focused on human-centric interactions. However, people experience many non-human-centric interactions every day. Interactions with nature can reinforce the importance of our relationship with nature. This paper presents the author’s vision of human-computer-biosphere interaction (HCBI) to facilitate non-human-centric interaction with the goal of moving society towards environmental sustainability.
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Stergiou, Alexandros, and Ronald Poppe. "Analyzing human–human interactions: A survey." Computer Vision and Image Understanding 188 (November 2019): 102799. http://dx.doi.org/10.1016/j.cviu.2019.102799.

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Dissertations / Theses on the topic "Human interactions"

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Vogt, David. "Learning Continuous Human-Robot Interactions from Human-Human Demonstrations." Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2018. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-233262.

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In der vorliegenden Dissertation wurde ein datengetriebenes Verfahren zum maschinellen Lernen von Mensch-Roboter Interaktionen auf Basis von Mensch-Mensch Demonstrationen entwickelt. Während einer Trainingsphase werden Bewegungen zweier Interakteure mittels Motion Capture erfasst und in einem Zwei-Personen Interaktionsmodell gelernt. Zur Laufzeit wird das Modell sowohl zur Erkennung von Bewegungen des menschlichen Interaktionspartners als auch zur Generierung angepasster Roboterbewegungen eingesetzt. Die Leistungsfähigkeit des Ansatzes wird in drei komplexen Anwendungen evaluiert, die jeweils kontinuierliche Bewegungskoordination zwischen Mensch und Roboter erfordern. Das Ergebnis der Dissertation ist ein Lernverfahren, das intuitive, zielgerichtete und sichere Kollaboration mit Robotern ermöglicht.
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Frazer, Ann L. (Ann Louise) 1977. "Modeling human-spacesuit interactions." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/82761.

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Smith, Mary Kathryn. "Human coronavirus-receptor interactions /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2008.

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Thesis (Ph.D. in Microbiology) -- University of Colorado Denver, 2008.
Typescript. Includes bibliographical references (leaves 168-210). Free to UCD Anschutz Medical Campus. Online version available via ProQuest Digital Dissertations;
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Kan, Viirj. "Molecular design interactions : material synthesis for human interaction with fluids." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112539.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 89-99).
[Color illustrations] Figure 0-1. Key elements within a Molecular Design Interactions interaction loop. Be it information embodied within rain, the oceans, a dinner plate, or human tears; the flow of information through fluids provides insights into the biological and chemical states of systems. Yet a large portion of our everyday experience with these systems remain inaccessible to users, designers and engineers whom operate outside the context of chemical disciplines. This thesis introduces a design framework coined Molecular Design Interactions, along with a toolbox of material based input-output devices termed Organic Primitives to facilitate the design of interactions with organic, fluid-based systems. The design methodology utilizes organic compounds from food for the development of color, odor and shape changing information displays. Activated by units of fluid information called droplets, this thesis focuses on pH signals in fluid as a model to demonstrate how molecular scale phenomena can be brought from materials into applications for interaction with a range of organic systems. A design language and vocabulary, drawing from signaling theory and molecular associations, offer designers a method with which to translate sensor-display output into meaningful experience designs for human perception. The design space showcases techniques for how the Organic Primitives can transcend beyond mere input-output devices to achieve higher order complexity. Passive and computational methods are presented to enable designers to control material interface output behaviors. An evaluation of the individual output properties of the sensors-actuators is presented to assess the rate, range, and reversibility of the changes as a function of pH 2-10. Strategies for how the materiality of objects can be augmented using Organic Primitives are investigated through several applications under four contexts: environmental, on-body, food, and interspecies. Molecular Design Interactions offers a process and toolbox to create interfaces between humans and molecules in fluids, across scales, from the nano to the macro systems.
by Viirj Kan.
S.M.
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Midgley, Caroline Ann. "Binocular interactions in human vision." Thesis, Durham University, 1998. http://etheses.dur.ac.uk/4839/.

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Early visual processing is subject to binocular interactions because cells in striate cortex show binocular responses and ocular dominance (Hubel & Weisel, 1968). The work presented in this thesis suggests that these physiological interactions can be revealed in psychophysical experiments using normal human observers. In the region corresponding to the blind spot, where binocular interactions differ from areas of the visual field which are represented by two eyes, monocular contrast sensitivity is increased. This finding can be partially explained by an absence of normal binocular interactions in this location (Chapter 2). A hemianopic patient was studied in an attempt to discover whether the effect in normal observers was mediated by either a mechanism in striate cortex or via a subcortical pathway. However, the results were unable to distinguish between these two explanations (Chapter 3).In a visual search task, no difference in reaction time was observed for targets presented to the region corresponding to the blind spot compared with targets presented to adjacent binocularly represented areas of the visual field. Since performance was unaffected by the monocularity of the region corresponding to the blind, pop-out for orientation may be mediated beyond striate cortex where cells are binocularly balanced (Chapter 5). Further support for this contention was provided by studies of orientation pop-out in central vision which found that dichoptic presentation of stimuli did not affect the degree of pop-out obtained and that in general, visual search for a target based solely on eye of origin is impossible (Chapter 6). However, a task that measured orientation difference sensitivity more directly than the search experiments, found that thresholds were higher for dichoptically presented stimuli. This suggests the involvement of neurons that receive a weighted input from each eye. A model of orientation difference coding can account for the results by assuming that the range of inhibition across which orientation differences are coded is narrower for dichoptic stimuli leading to a greater resolvable orientation difference (Chapter 7).
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Dowen, Sally Elizabeth. "Human papillomavirus / host genetic interactions." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620590.

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Erlandsson, Fredrik. "Human Interactions on Online Social Media : Collecting and Analyzing Social Interaction Networks." Doctoral thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15503.

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Online social media, such as Facebook, Twitter, and LinkedIn, provides users with services that enable them to interact both globally and instantly. The nature of social media interactions follows a constantly growing pattern that requires selection mechanisms to find and analyze interesting data. These interactions on social media can then be modeled into interaction networks, which enable network-based and graph-based methods to model and understand users’ behaviors on social media. These methods could also benefit the field of complex networks in terms of finding initial seeds in the information cascade model. This thesis aims to investigate how to efficiently collect user-generated content and interactions from online social media sites. A novel method for data collection that is using an exploratory research, which includes prototyping, is presented, as part of the research results in this thesis.   Analysis of social data requires data that covers all the interactions in a given domain, which has shown to be difficult to handle in previous work. An additional contribution from the research conducted is that a novel method of crawling that extracts all social interactions from Facebook is presented. Over the period of the last few years, we have collected 280 million posts from public pages on Facebook using this crawling method. The collected posts include 35 billion likes and 5 billion comments from 700 million users. The data collection is the largest research dataset of social interactions on Facebook, enabling further and more accurate research in the area of social network analysis.   With the extracted data, it is possible to illustrate interactions between different users that do not necessarily have to be connected. Methods using the same data to identify and cluster different opinions in online communities have also been developed and evaluated. Furthermore, a proposed method is used and validated for finding appropriate seeds for information cascade analyses, and identification of influential users. Based upon the conducted research, it appears that the data mining approach, association rule learning, can be used successfully in identifying influential users with high accuracy. In addition, the same method can also be used for identifying seeds in an information cascade setting, with no significant difference than other network-based methods. Finally, privacy-related consequences of posting online is an important area for users to consider. Therefore, mitigating privacy risks contributes to a secure environment and methods to protect user privacy are presented.
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Siou, Gérard Paul Serge. "Streptococcus pyogenes interactions with human tonsils." Thesis, University of Newcastle upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.424082.

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Kamatsos, Paraskevas. "Smart Homes : Human interactions and IoT." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-53427.

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This thesis studies the phenomenon of human-system interaction in smart homes as a practice of Internet of Things. The research was conducted through interviews, workshops and observations and followed an interpretive research paradigm of phenomenologically-situated paradigm of HCI and a qualitative research approach. The theories of Phenomenology and Postphenomenology were used to interpret the experiences, beliefs and views of the participants. The empirical findings were processed and a thematic analysis was followed in order to identify the main themes that emerged out of the interviews, workshops and observations. The discussion of the findings showed that the research questions were answered to the grade that the participants of the research design, use and interact with smart homes in a multiple and complex way.
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Stewart, Joanna. "Nuclear mitochondrial interactions in human disease." Thesis, University of Newcastle Upon Tyne, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492095.

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The maintenance of mitochondrial DNA (mtDNA) is dependent on DNA polymerase gamma (poly). The catalytic subunit encoded by the POLG1 gene has become the target for much investigation. Reported mutations result in disrupted mtDNA maintenance leading to a quantitative (depletion) or qualitative (point mutation or deletion) defects of mtDNA. This secondary genetic defect results in disruption of the respiratory chain and impaired oxidative phosphorylation.
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Books on the topic "Human interactions"

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Misiune, Ieva, Daniel Depellegrin, and Lukas Egarter Vigl, eds. Human-Nature Interactions. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01980-7.

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Welford, Mark R., and Robert A. Yarbrough. Human-Environment Interactions. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56032-4.

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Brondízio, Eduardo S., and Emilio F. Moran, eds. Human-Environment Interactions. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-4780-7.

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Association for Computing Machinery. Interactions. New York, NY: Association for Computing Machinery, Inc., 1994.

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Lucy, Wilson, Dickinson Pamela J. 1968-, and Jeandron Jason, eds. Reconstructing human-landscape interactions. Newcastle, UK: Cambridge Scholars Pub., 2007.

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Designing interactions. Cambridge, Mass: MIT Press, 2007.

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Antona, Margherita, and Constantine Stephanidis, eds. Universal Access in Human–Computer Interaction. Designing Novel Interactions. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58703-5.

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Marchant, Rob. East Africa’s Human Environment Interactions. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88987-6.

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Hornecker, Eva, and Luigina Ciolfi. Human-Computer Interactions in Museums. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-02225-8.

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Kolski, Christophe, ed. Human-Computer Interactions in Transport. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2011. http://dx.doi.org/10.1002/9781118601907.

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Book chapters on the topic "Human interactions"

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Kanas, Nick, and Dietrich Manzey. "Human Interactions." In Space Psychology and Psychiatry, 89–134. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6770-9_4.

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Conover, Michael R., and Denise O. Conover. "Human Dimensions." In Human–Wildlife Interactions, 445–78. 2nd ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9780429401404-14.

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Anderson, Patricia K. "Human–Bird Interactions." In The Welfare of Domestic Fowl and Other Captive Birds, 17–51. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3650-6_2.

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Géczy, Peter, Noriaki Izumi, Shotaro Akaho, and Kôiti Hasida. "Human–Web Interactions." In Emergent Web Intelligence: Advanced Information Retrieval, 199–232. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-074-8_8.

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Harris, Frances. "Human-Environment Interactions." In Global Environmental Issues, 1–18. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119950981.ch1.

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Mann, Danny. "Human-Machine Interactions." In Agriculture Automation and Control, 387–414. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70400-1_15.

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Boulangeat, Isabelle, Sandrine Allain, Emilie Crouzat, Sabine Girard, Céline Granjou, Clara Poirier, Jean François Ruault, Yoan Paillet, and Isabelle Arpin. "From Human-Nature Dualism Towards More Integration in Socio-ecosystems Studies." In Human-Nature Interactions, 37–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01980-7_4.

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Significance StatementIn the management of natural resources and biodiversity, humans and nature have traditionally been considered as two distinct systems, one controlling the other. The concept of socio-ecosystems allows a more integrated approach, in which humans and nature are recognized as interdependent. However, this new perspective does not necessarily eliminate a distinction between humans and nature, or even a hierarchy of humans over nature. This chapter aims to raise awareness of the potential human–nature dualism in socio-ecosystem approaches. Other research fields have adopted different approaches regarding human–nature integration versus dualism, offering a window on the advantages and limitations of various positions. We also discuss how methodological choices are important to translate human–nature integration or dichotomy depending on the study aim.
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Conover, Michael R., and Denise O. Conover. "Threats to Human Safety." In Human–Wildlife Interactions, 29–72. 2nd ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9780429401404-2.

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Welford, Mark R., and Robert A. Yarbrough. "Introduction." In Human-Environment Interactions, 1–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56032-4_1.

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Welford, Mark R., and Robert A. Yarbrough. "Climate." In Human-Environment Interactions, 17–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56032-4_2.

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Conference papers on the topic "Human interactions"

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Han, Ji, Gopika Ajaykumar, Ze Li, and Chien-Ming Huang. "Structuring Human-Robot Interactions via Interaction Conventions." In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, 2020. http://dx.doi.org/10.1109/ro-man47096.2020.9223468.

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Ong, Kai Wei, Gerald Seet, Siang Kok Sim, William Teoh, Kean Hee Lim, Ai Nee Yow, and Soon Chiang Low. "A Testbed for Human-Robot Interactions." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57171.

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This paper describes the design and implementation of a testbed for facilitating the study of human-robot interactions (HRI). HRI has long been a part of robotics research, where humans were typically required to guide the robot task in progress and to ensure safe operation. The current state of human interaction with robots, versus simple “machines” (e.g. in manufacturing automation) is quite different. This called for the need to look into different interaction roles between humans and robots. Robots differ from simple machines in that they are mobile, some may be autonomous and hence not as predictable in their actions. To facilitate the research in this domain, the aim is to develop an easy to use and safe front-end human-robot system for human users to interact with physical mobile robots. This testbed provides different types of system configurations (i.e. one human to one robot, one human to multiple robots, etc.) and interfaces for conducting experiments under different HRI scenarios.
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Burns, Brian, and Biswanath Samanta. "Human Identification for Human-Robot Interactions." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-38496.

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In co-robotics applications, the robots must identify human partners and recognize their status in dynamic interactions for enhanced acceptance and effectiveness as socially interactive agents. Using the data from depth cameras, people can be identified from a person’s skeletal information. This paper presents the implementation of a human identification algorithm using a depth camera (Carmine from PrimeSense), an open-source middleware (NITE from OpenNI) with the Java-based Processing language and an Arduino microcontroller. This implementation and communication sets a framework for future applications of human-robot interactions. Based on the movements of the individual in the depth sensor’s field of view, the program can be set to track a human skeleton or the closest pixel in the image. Joint locations in the tracked human can be isolated for specific usage by the program. Joints include the head, torso, shoulders, elbows, hands, knees and feet. Logic and calibration techniques were used to create systems such as a facial tracking pan and tilt servomotor mechanism. The control system presented here sets groundwork for future implementation into student built animatronic figures and mobile robot platforms such as Turtlebot.
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Lagerstedt, Erik, and Serge Thill. "Benchmarks for evaluating human-robot interaction: lessons learned from human-animal interactions." In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, 2020. http://dx.doi.org/10.1109/ro-man47096.2020.9223347.

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Wilcock, Graham, and Kristiina Jokinen. "WikiTalk human-robot interactions." In the 15th ACM. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2522848.2531753.

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Plouznikoff, Alexandre, Nicolas Plouznikoff, Jean-Marc Robert, and Michel Desmarais. "Enhancing human-machine interactions." In the SIGCHI conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1124772.1124828.

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Porfirio, David, Evan Fisher, Allison Sauppé, Aws Albarghouthi, and Bilge Mutlu. "Bodystorming Human-Robot Interactions." In UIST '19: The 32nd Annual ACM Symposium on User Interface Software and Technology. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3332165.3347957.

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Zgonnikov, Arkady, Serge Thill, Philipp Beckerle, and Catholijn M. Jonker. "Modeling Human Behavior in Human-Robot Interactions." In 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2022. http://dx.doi.org/10.1109/hri53351.2022.9889403.

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Wyeth, Peta, Mitchell McEwan, Paul Roe, and Ian MacColl. "Expressive interactions." In the 23rd Australian Computer-Human Interaction Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2071536.2071585.

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Mercuri, Ilenia. "Social Engineering and Human-Robot Interactions' Risks." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002199.

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Abstract:
Modern robotics seems to have taken root from the theories of Isaac Asimov, in 1941. One area of research that has become increasingly popular in recent decades is the study of artificial intelligence or A.I., which aims to use machines to solve problems that, according to current opinion, require intelligence. This is related to the study on “Social Robots”. Social Robots are created in order to interact with human beings; they have been designed and programmed to engage with people by leveraging a "human" aspect and various interaction channels, such as speech or non-verbal communication. They therefore readily solicit social responsiveness in people who often attribute human qualities to the robot. Social robots exploit the human propensity for anthropomorphism, and humans tend to trust them more and more. Several issues could arise due to this kind of trust and to the ability of “superintelligence” to "self-evolve", which could lead to the violation of the purposes for which it was designed by humans, becoming a risk to human security and privacy. This kind of threat concerns social engineering, a set of techniques used to convince users to perform a series of actions that allow cybercriminals to gain access to the victims' resources. The Human Factor is the weakest ring of the security chain, and the social engineers exploit Human-Robots Interaction to persuade an individual to provide private information.An important research area that has shown interesting results for the knowledge of the possibility of human interaction with robots is "cyberpsychology". This paper aims to provide insights into how the interaction with social robots could be exploited by humans not only in a positive way but also by using the same techniques of social engineering borrowed from "bad actors" or hackers, to achieve malevolent and harmful purposes for man himself. A series of experiments and interesting research results will be shown as examples. In particular, about the ability of robots to gather personal information and display emotions during the interaction with human beings. Is it possible for social robots to feel and show emotions, and human beings could empathize with them? A broad area of research, which goes by the name of "affective computing", aims to design machines that are able to recognize human emotions and consistently respond to them. The aim is to apply human-human interaction models to human-machine interaction. There is a fine line that separates the opinions of those who argue that, in the future, machines with artificial intelligence could be a valuable aid to humans and those who believe that they represent a huge risk that could endanger human protection systems and safety. It is necessary to examine in depth this new field of cybersecurity to analyze the best path to protect our future. Are social robots a real danger? Keywords: Human Factor, Cybersecurity, Cyberpsychology, Social Engineering Attacks, Human-Robot Interaction, Robotics, Malicious Artificial Intelligence, Affective Computing, Cyber Threats
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Reports on the topic "Human interactions"

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Bernard, Michael Lewis, Dereck H. Hart, Stephen J. Verzi, Matthew R. Glickman, Paul R. Wolfenbarger, and Patrick Gordon Xavier. Simulating human behavior for national security human interactions. Office of Scientific and Technical Information (OSTI), January 2007. http://dx.doi.org/10.2172/900422.

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Perry, Marcus B., Patrick J. Vincent, and Jeremy D. Jordan. Human Predictive Reasoning for Group Interactions. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada535335.

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Fu, Runshan, Ginger Zhe Jin, and Meng Liu. Human-Algorithm Interactions: Evidence from Zillow.com. Cambridge, MA: National Bureau of Economic Research, March 2022. http://dx.doi.org/10.3386/w29880.

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Hodgson, Ernest. Human Metabolism and Interactions of Deployment-Related Chemicals. Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada401660.

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Komen, Stephen J. van. Functional Interactions of Human Rad54 with the Rad51 Recombinase. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada407443.

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Hoelz, Derek J., and Linda H. Malkas. The Regulator Interactions of p21 and PCNA in Human Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada400020.

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Tucker, Stephenson. LDRD Final Report: Improving Human/System Interactions in Systems-of-Systems. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/1142061.

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Hoelz, Derek, and Linda Malkas. The Regulatory Interactions of p21 and PCNA in Human Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada408176.

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Breazael, Cynthia, and Brian Scassellati. Infant-Like Social Interactions Between a Robot and a Human Caregiver. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada450357.

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Chan, Huei-Mei. Mechanism of Abnormal Cell-Extracellular Matrix Interactions in Human Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada367382.

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