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Journal articles on the topic 'Human interactions'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

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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3

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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4

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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8

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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Kieson, Emily. "Horse-Human Communication: The Roles of Language and Communication in the Context of Horse-Human Interactions." International Journal of Zoology and Animal Biology 5, no. 6 (2022): 1–10. http://dx.doi.org/10.23880/izab-16000414.

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Horses have played an important role in human history and the techniques and strategies with which we interact with them is based on concepts of operant conditioning with emphasis on negative and positive reinforcement. The human-horse interactions in training are primarily based on the desires and goals of the human with the recognition that proper response to horse behaviors should be considered in order to effectively achieve the desired training goal and minimize stress. When considering the concepts of language and communication, horse owners need to consider the ethological communication strategies of horses and the role they play in traditional horse-human interactions. By including principles of interspecies communication, mutual development of language, and pro-social behaviors, it may be possible to involve horses in the decision-making processes in which they are so often involved.

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Priyanayana, S., B. Jayasekara, and R. Gopura. "Adapting concept of human-human multimodal interaction in human-robot applications." Bolgoda Plains 2, no. 2 (December 2022): 18–20. http://dx.doi.org/10.31705/bprm.v2(2).2022.4.

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Human communication is multimodal in nature. In a normal environment, people use to interact with other humans and with the environment using more than one modality or medium of communication. They speak, use gestures and look at things to interact with nature and other humans. By listening to the different voice tones, looking at face gazes, and arm movements people understand communication cues. A discussion with two people will be in vocal communication, hand gestures, head gestures, and facial cues, etc. [1]. If textbook definition is considered synergistic use of these interaction methods is known as multimodal interaction [2]. For example, , a wheelchair user might instruct the smart wheelchair or the assistant to go forward, as shown in Fig. 1(a). However, with a hand gesture shown in the figure, he or she might want to go slowly. In the same way as of Fig. 1(b), a person might give someone a direction with a vocal command ‘that way’ and gesture the direction with his or her hand. In most Human-Robot Interaction (HRI) developments, there is an assumption that human interactions are unimodal. This forces the researchers to ignore the information other modalities carry with them. Therefore, it would provide an additional dimension for interpretation of human robot interactions. This article provides a concise description of how to adapt the concept of multimodal interaction in human-robot applications.

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13

Mitman, Gregg. "Population Dynamics, Human Interactions." Ecology 72, no. 5 (October 1991): 1908–9. http://dx.doi.org/10.2307/1940995.

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Rowan, Andrew N. "Human Interactions With Wildlife." Anthrozoös 6, no. 2 (June 1993): 70–71. http://dx.doi.org/10.2752/089279393787002286.

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Weller, Peter F., and Kaiser Lim. "Human eosinophil-lymphocyte interactions." Memórias do Instituto Oswaldo Cruz 92, suppl 2 (December 1997): 173–82. http://dx.doi.org/10.1590/s0074-02761997000800023.

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Blythe, Mark, and Mark Jones. "Human computer (sexual) interactions." Interactions 11, no. 5 (September 2004): 75–76. http://dx.doi.org/10.1145/1015530.1015570.

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Ramesh, Kashi S., Stephanie H. Pincus, and Ross E. Rocklin. "Human lymphocyte-eosinophil interactions." Cellular Immunology 92, no. 2 (May 1985): 366–75. http://dx.doi.org/10.1016/0008-8749(85)90018-8.

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Brayda, Luca, and Ryad Chellali. "Measuring Human-Robots Interactions." International Journal of Social Robotics 4, no. 3 (May 3, 2012): 219–21. http://dx.doi.org/10.1007/s12369-012-0150-2.

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ENCK, P., and T. FRIELING. "Human gut-brain interactions." Neurogastroenterology & Motility 5, no. 2 (June 28, 2008): 77–87. http://dx.doi.org/10.1111/j.1365-2982.1993.tb00111.x.

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Anderson, Richard, and Jon Kolko. "Interactions." Interactions 16, no. 5 (September 2009): 5. http://dx.doi.org/10.1145/1572626.1572627.

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Anderson, Richard, and Jon Kolko. "Interactions." Interactions 17, no. 2 (March 2010): 5. http://dx.doi.org/10.1145/1699775.1699776.

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Anderson, Richard, and Jon Kolko. "Interactions." Interactions 16, no. 6 (November 2009): 5. http://dx.doi.org/10.1145/1620693.1620694.

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Anderson, Richard, and Jon Kolko. "Interactions." Interactions 17, no. 5 (September 2010): 5. http://dx.doi.org/10.1145/1836216.1836217.

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Anderson, Richard, and Jon Kolko. "Interactions." Interactions 16, no. 4 (July 2009): 5. http://dx.doi.org/10.1145/1551986.1551987.

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Anderson, Richard, and Jon Kolko. "Interactions." Interactions 16, no. 2 (March 2009): 5. http://dx.doi.org/10.1145/1487632.1487633.

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Anderson, Richard, and Jon Kolko. "Interactions." Interactions 17, no. 1 (January 2010): 5. http://dx.doi.org/10.1145/1649475.1649476.

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Anderson, Richard, and Jon Kolko. "Interactions." Interactions 15, no. 4 (July 2008): 5. http://dx.doi.org/10.1145/1374489.1374490.

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Atzmueller, Martin. "Behavioral Link Analytics on Heterogeneous Human Interaction Networks." International Journal of Transdisciplinary Artificial Intelligence 2, no. 1 (August 1, 2020): 26–48. http://dx.doi.org/10.35708/tai1869-126248.

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For designing and modeling Artificial Intelligence (AI) systems in the area of human-machine interaction, suitable approaches for user modeling are important in order to both capture user characteristics. Using multimodal data, this can be performed from various perspectives. Specifically, for modeling user interactions in human interaction networks, appropriate approaches for capturing those interactions, as well as to analyze them in order to extract meaningful patterns are important. Specifically, for modeling user behavior for the respective AI systems, we can make use of diverse heterogeneous data sources. This paper investigates face-to-face as well as socio-spatial interaction networks for modeling user interactions from three perspectives: We analyze preferences and perceptions of human social interactions in relation to the interactions observed using wearable sensors, i. e., face-to-face as well as socio-spatial interactions fo the respective actors. For that, we investigate the correspondence of according networks, in order to identify conformance, exceptions, and anomalies. The analysis is performed on a real-world dataset capturing networks of proximity interactions coupled with self-report questionnaires about preferences and perception of those interactions. The different networks, and according perspectives then provide different options for user modeling and integration into AI systems modeling such user behavior.

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Carroll, John M., Sooyeon Lee, Madison Reddie, Jordan Beck, and Mary Beth Rosson. "Human-Computer Synergies in Prosthetic Interactions." Interaction Design and Architecture(s), no. 44 (May 10, 2020): 29–52. http://dx.doi.org/10.55612/s-5002-044-002.

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Remote sighted assistance provides prosthetic support to people with visual impairments (PVI) through internet-mediated conversational interactions. In these interactions, PVI broadcast live video to remotely-located, sighted people who engage in speech interactions with PVI to create prosthetic support. These interactions can be quite nuanced, creative, and effective. In this paper, we present a design investigation of remote sighted assistance (RSA) in which computer vision capabilities are integrated into the prosthetic interaction, supporting the human participants in various ways. Our study involved creating design scenarios to identify and concretize future possibilities in order to articulate and analyze design rationale for these scenarios, that is to say, strengths and challenges of RSA integrated with CV. We discuss implications for the design of the next generation of remote sighted assistance.

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Clavel, Chloé. "Surprise and human-agent interactions." Expressing and Describing Surprise 13, no. 2 (December 30, 2015): 461–77. http://dx.doi.org/10.1075/rcl.13.2.08cla.

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Affective Computing aims at improving the naturalness of human-computer interactions by integrating the socio-emotional component in the interaction. The use of embodied conversational agents (ECAs) – virtual characters interacting with humans – is a key answer to this issue. On the one hand, the ECA has to take into account the human emotional behaviours and social attitudes. On the other hand, the ECA has to display socio-emotional behaviours with relevance. In this paper, we provide an overview of computational methods used for user’s socio-emotional behaviour analysis and of human-agent interaction strategies by questioning the ambivalent status of surprise. We focus on the computational models and on the methods we use to detect user’s emotion through language and speech processing and present a study investigating the role of surprise in the ECA’s answer.

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Shiomi, Masahiro, Hidenobu Sumioka, and Hiroshi Ishiguro. "Special Issue on Human-Robot Interaction in Close Distance." Journal of Robotics and Mechatronics 32, no. 1 (February 20, 2020): 7. http://dx.doi.org/10.20965/jrm.2020.p0007.

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As social robot research is advancing, the interaction distance between people and robots is decreasing. Indeed, although we were once required to maintain a certain physical distance from traditional industrial robots for safety, we can now interact with social robots in such a close distance that we can touch them. The physical existence of social robots will be essential to realize natural and acceptable interactions with people in daily environments. Because social robots function in our daily environments, we must design scenarios where robots interact closely with humans by considering various viewpoints. Interactions that involve touching robots influence the changes in the behavior of a person strongly. Therefore, robotics researchers and developers need to design such scenarios carefully. Based on these considerations, this special issue focuses on close human-robot interactions. This special issue on “Human-Robot Interaction in Close Distance” includes a review paper and 11 other interesting papers covering various topics such as social touch interactions, non-verbal behavior design for touch interactions, child-robot interactions including physical contact, conversations with physical interactions, motion copying systems, and mobile human-robot interactions. We thank all the authors and reviewers of the papers and hope this special issue will help readers better understand human-robot interaction in close distance.

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Alarcon, Gene M., August Capiola, Izz Aldin Hamdan, Michael A. Lee, and Sarah A. Jessup. "Differential biases in human-human versus human-robot interactions." Applied Ergonomics 106 (January 2023): 103858. http://dx.doi.org/10.1016/j.apergo.2022.103858.

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Sandrock, Kirstin, Ingrid Bartsch, Susanne Bläser, Anja Busse, Eileen Busse, and Barbara Zieger. "Characterization of human septin interactions." Biological Chemistry 392, no. 8-9 (August 1, 2011): 751–61. http://dx.doi.org/10.1515/bc.2011.081.

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Abstract Septins constitute a group of GTP binding proteins that assemble into homo- and hetero-oligomeric complexes and filaments. These higher order septin structures are thought to function like scaffolds and/or diffusion barriers serving as spatial localizers for many proteins with key roles in cell polarity and cell cycle progression. In this study, we extensively characterized septin interaction partners using yeast two-hybrid and three-hybrid systems in addition to precipitation analyses in platelets. As a result, we identified human hetero-trimeric septin complexes on a large scale, which had been only postulated in the past. In addition, we illustrated roles of SEPT9 that might contribute to hetero-trimeric septin complex formation. SEPT9 can substitute for septins of the SEPT2 group and partially for SEPT7. Mutagenic analyses revealed that mutation of a potential phosphorylation site in SEPT7 (Y318) regulates the interaction with other septins. We identified several septin-septin interactions in platelets suggesting a regulatory role of diverse septin complexes in platelet function.

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Itzhaki, Zohar. "Domain-Domain Interactions Underlying Herpesvirus-Human Protein-Protein Interaction Networks." PLoS ONE 6, no. 7 (July 7, 2011): e21724. http://dx.doi.org/10.1371/journal.pone.0021724.

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Oom, S. P., A. J. Hester, D. A. Elston, and C. J. Legg. "Spatial interaction models: from human geography to plant-herhivore interactions." Oikos 98, no. 1 (July 2002): 65–74. http://dx.doi.org/10.1034/j.1600-0706.2002.980107.x.

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Ibrahim, Filzani Illia, Dasimah Omar, and Nik Hanita Nik Mohamad. "Human Interaction In Urban Open Spaces." Environment-Behaviour Proceedings Journal 4, no. 10 (March 1, 2019): 188. http://dx.doi.org/10.21834/e-bpj.v4i10.1590.

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The aim of this paper is to evaluate the multi-dimensional human interaction experienced in the open spaces and develop the ranking of human interaction in relation to the typological of open spaces. The analysis in this paper addresses human-human interaction and human-nature interaction in five selected open spaces of Shah Alam, Selangor, Malaysia. The findings show that all four research domains namely socio-demographic domain, the human-human interactions in open spaces domain, the human-nature interactions in open spaces domain and perceived benefits domain significantly influence the human interactions in the Shah Alam open spaces area.Keywords: open spaces; sustainability; human interaction; landscapeeISSN: 2398-4287 © 2019. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v4i10.1590

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Meyer, Joachim, Chris Miller, Peter Hancock, Ewart J. de Visser, and Michael Dorneich. "Politeness in Machine-Human and Human-Human Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 279–83. http://dx.doi.org/10.1177/1541931213601064.

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Computers communicate with humans in ways that increasingly resemble interactions between humans. Nuances in expression and responses to human behavior become more sophisticated, and they approach those of human-human interaction. The question arises whether we want systems eventually to behave like humans, or whether systems should, even when much more developed, still adhere to rules that are different from the rules governing interpersonal communication. The panel addresses this issue from various perspectives, eventually aiming to gain some insights into the question of the direction to which the development of machine-human communication and the etiquette implemented in the systems should move.

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Wright, Alex. "FEATUREPrimal interactions." Interactions 15, no. 1 (January 2008): 11–12. http://dx.doi.org/10.1145/1330526.1330532.

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Bardzell, Jeffrey, and Shaowen Bardzell. "FEATUREIntimate interactions." Interactions 15, no. 5 (September 2008): 11–15. http://dx.doi.org/10.1145/1390085.1400111.

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Hayman, Luke. "Redesigning Interactions." Interactions 21, no. 1 (January 2014): 6–7. http://dx.doi.org/10.1145/2556463.

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Greenberg, Saul, Nicolai Marquardt, Till Ballendat, Rob Diaz-Marino, and Miaosen Wang. "Proxemic interactions." Interactions 18, no. 1 (January 2011): 42–50. http://dx.doi.org/10.1145/1897239.1897250.

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Hourcade, Juan Pablo, and Natasha E. Bullock-Rest. "Universal interactions." Interactions 18, no. 2 (March 2011): 76–79. http://dx.doi.org/10.1145/1925820.1925837.

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Anderson, Richard, and Jon Kolko. "WELCOME Interactions." Interactions 17, no. 6 (November 2010): 5. http://dx.doi.org/10.1145/1865245.1865246.

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Anderson, Richard, and Jon Kolko. "WELCOME Interactions." Interactions 15, no. 6 (November 2008): 5. http://dx.doi.org/10.1145/1409040.1409041.

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Anderson, Richard, and Jon Kolko. "WELCOME Interactions." Interactions 16, no. 1 (January 2009): 5. http://dx.doi.org/10.1145/1456202.1456203.

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Anderson, Richard, and Jon Kolko. "WELCOME Interactions." Interactions 16, no. 3 (May 2009): 5. http://dx.doi.org/10.1145/1516016.1516017.

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Warren, William H. "Collective Motion in Human Crowds." Current Directions in Psychological Science 27, no. 4 (July 11, 2018): 232–40. http://dx.doi.org/10.1177/0963721417746743.

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The balletic motion of bird flocks, fish schools, and human crowds is believed to emerge from local interactions between individuals in a process of self-organization. The key to explaining such collective behavior thus lies in understanding these local interactions. After decades of theoretical modeling, experiments using virtual crowds and analysis of real crowd data are enabling us to decipher the “rules of engagement” governing these interactions. On the basis of such results, my students and I built a dynamical model of how a pedestrian aligns his or her motion with that of a neighbor and how these binary interactions are combined within a neighborhood of interaction. Computer simulations of the model generate coherent motion at the global level and reproduce individual trajectories at the local level. This approach has yielded the first experiment-driven, bottom-up model of collective motion, providing a basis for understanding more complex patterns of crowd behavior in both everyday and emergency situations.

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Ullah, Sami. "A Review on Human-Mammals Interaction and Conflict." Current Research in Agriculture and Farming 3, no. 5 (October 28, 2022): 1–10. http://dx.doi.org/10.18782/2582-7146.175.

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Interactions between humans and mammals are a defining experience of human life. Humans have utilized mammals and their products for thousands of years. The spread of new inhabited places, accompanying facilities, and residential areas, which vary greatly depending on the species' original habitat, has destroyed large ecosystems. Mammals are used for various reasons including food, and medicine. Urbanization, industrialization, and hunting have damaged the mammalian landscape. These activities are the primary causes of mammal’s extinction. Mammals have adapted to new habitats and diets, but some species have failed to adopt, and they are rapidly declining. However, in an increasingly urbanized and resource-constrained world, we need to learn how to manage the risks from wildlife in new ways, and to understand how to maximize the diverse benefits that living with wildlife can bring. Ethnomammalogy is the study of human knowledge of mammals and this field of research helps with mammalian conservation efforts. Habitat loss is a global issue that has got a lot of attention in the last two decades. Human actions have had a significant impact on the chemical, biological and physical structure of the Earth's land and water.

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Reid, Tahira, and James Gibert. "Inclusion in human–machine interactions." Science 375, no. 6577 (January 14, 2022): 149–50. http://dx.doi.org/10.1126/science.abf2618.

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Zhu, Haibin, and Ming Hou. "Role-Based Human-Computer Interactions." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 2 (April 2011): 37–57. http://dx.doi.org/10.4018/jcini.2011040103.

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With increased understanding of cognitive informatics and the advance of computer technologies, it is becoming clear that human-computer interaction (HCI) is an interaction between two kinds of intelligences, i.e., natural intelligence and artificial intelligence. This paper attempts to clarify interaction-related terminologies through step-by-step definitions, and discusses the nature of HCI, arguing that shared models are the most important aspect of HCI. This paper also proposes that a role-based interaction can be taken as an appropriate shared model for HCI, i.e., Role-Based HCI.

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