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Journal articles on the topic 'Machine Interaction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Prepin, Ken, and Arnaud Revel. "Human–machine interaction as a model of machine–machine interaction: how to make machines interact as humans do." Advanced Robotics 21, no. 15 (2007): 1709–23. http://dx.doi.org/10.1163/156855307782506192.

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2

Spillard, Samuel, Christopher J. Turner, and Konstantinos Meichanetzidis. "Machine learning entanglement freedom." International Journal of Quantum Information 16, no. 08 (2018): 1840002. http://dx.doi.org/10.1142/s0219749918400026.

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Quantum many-body systems realize many different phases of matter characterized by their exotic emergent phenomena. While some simple versions of these properties can occur in systems of free fermions, their occurrence generally implies that the physics is dictated by an interacting Hamiltonian. The interaction distance has been successfully used to quantify the effect of interactions in a variety of states of matter via the entanglement spectrum [C. J. Turner, K. Meichanetzidis, Z. Papic and J. K. Pachos, Nat. Commun. 8 (2017) 14926, Phys. Rev. B 97 (2018) 125104]. The computation of the inte

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3

Beyls, Peter. "Intimate machine interaction." Visual Computer 2, no. 3 (1986): 152–58. http://dx.doi.org/10.1007/bf01900325.

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4

Hong, Fuxing, Dongbo Huang, and Ge Chen. "Interaction-Aware Factorization Machines for Recommender Systems." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3804–11. http://dx.doi.org/10.1609/aaai.v33i01.33013804.

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Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named Interaction-aware Factorization Machine (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the feature a

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5

Liu, Conghui. "Human-Machine Trust Interaction." International Journal of Dependable and Trustworthy Information Systems 1, no. 4 (2010): 61–74. http://dx.doi.org/10.4018/jdtis.2010100104.

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Improving user’s trust appropriately could help in designing an intelligent system and make it work effectively, especially with the fast growth of Web-base technology. This chapter introduces the solutions of improving user’s trust in human-machine interaction (HMI), especially for electronic commerce (e-commerce). The author firstly reviews the concept of trust and the main factors that affects the appropriateness of user’s trust in human-machine interaction, such as the properties of machine systems, the properties of human, and context. On the basis of these, the author further discusses t

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6

Coe, J. P. "Machine Learning Configuration Interaction." Journal of Chemical Theory and Computation 14, no. 11 (2018): 5739–49. http://dx.doi.org/10.1021/acs.jctc.8b00849.

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7

Haqqu, Rizca, and Salwa Nur Rohmah. "Interaction Process Between Humans and ChatGPT in the Context of Interpersonal Communication." Jurnal Ilmiah LISKI (Lingkar Studi Komunikasi) 10, no. 1 (2024): 23. http://dx.doi.org/10.25124/liski.v10i1.7216.

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This study examines human interaction with artificial intelligence technology, focusing on the implementation of ChatGPT, a chatbot developed by OpenAI. Through the Human-Machine Communication (HMC) approach, the research describes human-like attributes in ChatGPT, exploring emotional responses and utility in educational, professional, and personal contexts. Qualitative research methods with triangulation techniques were used for a holistic understanding, involving interviews, observations, and document analysis. The results indicate that ChatGPT can provide adaptive responses, adjusting langu

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8

Hoc, Jean-Michel. "From human – machine interaction to human – machine cooperation." Ergonomics 43, no. 7 (2000): 833–43. http://dx.doi.org/10.1080/001401300409044.

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Zhu, Chaoyang. "Hidden Markov Model Deep Learning Architecture for Virtual Reality Assessment to Compute Human–Machine Interaction-Based Optimization Model." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 01–13. http://dx.doi.org/10.17762/ijritcc.v11i7.7736.

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Virtual Reality (VR) is a technology that immerses users in a simulated, computer-generated environment. It creates a sense of presence, allowing individuals to interact with and experience virtual worlds. Human-Machine Interaction (HMI) refers to the communication and interaction between humans and machines. Optimization plays a crucial role in Virtual Reality (VR) and Human-Machine Interaction (HMI) to enhance the overall user experience and system performance. This paper proposed an architecture of the Hidden Markov Model with Grey Relational Analysis (GRA) integrated with Salp Swarm Algori

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Lindvall, Martin, Jesper Molin, and Jonas Löwgren. "From machine learning to machine teaching." Interactions 25, no. 6 (2018): 52–57. http://dx.doi.org/10.1145/3282860.

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11

Singh, Ahdil, and Vikas Kumar. "Neuralink: Spearheading the Point of Interaction among Brain and Machine." International Journal of Science and Research (IJSR) 12, no. 9 (2023): 1263–66. http://dx.doi.org/10.21275/sr23910185406.

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12

V., Dr Suma. "COMPUTER VISION FOR HUMAN-MACHINE INTERACTION-REVIEW." Journal of Trends in Computer Science and Smart Technology 2019, no. 02 (2019): 131–39. http://dx.doi.org/10.36548/jtcsst.2019.2.006.

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The paper is a review on the computer vision that is helpful in the interaction between the human and the machines. The computer vision that is termed as the subfield of the artificial intelligence and the machine learning is capable of training the computer to visualize, interpret and respond back to the visual world in a similar way as the human vision does. Nowadays the computer vision has found its application in broader areas such as the heath care, safety security, surveillance etc. due to the progress, developments and latest innovations in the artificial intelligence, deep learning and

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13

Sam Ge, Shuzhi, Yaozhang Pan, and Abdullah Al Mamun. "Methodologies on Brain-Machine Interaction." IFAC Proceedings Volumes 41, no. 2 (2008): 13779–84. http://dx.doi.org/10.3182/20080706-5-kr-1001.02333.

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14

King, Raymond E. "Handbook of human-machine interaction." Aviation, Space, and Environmental Medicine 83, no. 8 (2012): 811. http://dx.doi.org/10.3357/asem.3232.2012.

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15

Okura, Michiko. "Interface for Human-machine Interaction." TRENDS IN THE SCIENCES 10, no. 8 (2005): 52–55. http://dx.doi.org/10.5363/tits.10.8_52.

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16

Gouvrit, Florence. "Empathy and Human-Machine Interaction." International Journal of Synthetic Emotions 4, no. 2 (2013): 8–21. http://dx.doi.org/10.4018/ijse.2013070102.

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This paper presents the framework of the author’s practice and research exploring empathy and human-machine interaction in projects involving robotic art and video installations and performance. The works investigate emotions and embodiment, presence and absence, relationships and loss, and ways to implicate these ideas in encounters between technology-based artwork and the viewer.

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17

DEL R. MILLÁN, JOSÉ, PIERRE W. FERREZ, FERRAN GALÁN, EILEEN LEW, and RICARDO CHAVARRIAGA. "NON-INVASIVE BRAIN-MACHINE INTERACTION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 05 (2008): 959–72. http://dx.doi.org/10.1142/s0218001408006600.

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The promise of Brain-Computer Interfaces (BCI) technology is to augment human capabilities by enabling interaction with computers through a conscious and spontaneous modulation of the brainwaves after a short training period. Indeed, by analyzing brain electrical activity online, several groups have designed brain-actuated devices that provide alternative channels for communication, entertainment and control. Thus, a person can write messages using a virtual keyboard on a computer screen and also browse the internet. Alternatively, subjects can operate simple computer games, or brain games, an

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18

NEWELL, CHRISTOPHER, ALISTAIR D. N. EDWARDS, and PAUL CAIRNS. "‘Liveness’ in human-machine interaction." International Journal of Performance Arts and Digital Media 7, no. 2 (2011): 221–37. http://dx.doi.org/10.1386/padm.7.2.221_1.

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19

Lemprière, Sarah. "Brain–machine interaction improves mobility." Nature Reviews Neurology 15, no. 12 (2019): 685. http://dx.doi.org/10.1038/s41582-019-0285-y.

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20

Crowley, James L. "Vision for man-machine interaction." Robotics and Autonomous Systems 19, no. 3-4 (1997): 347–58. http://dx.doi.org/10.1016/s0921-8890(96)00061-9.

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21

Frisk, Henrik. "Aesthetics, Interaction and Machine Improvisation." Organised Sound 25, no. 1 (2020): 33–40. http://dx.doi.org/10.1017/s135577181900044x.

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Departing from the artistic research project Goodbye Intuition (GI) hosted by the Norwegian Academy of Music in Oslo, this article discusses the aesthetics of improvising with machines. Playing with a system such as the one described in this article, with limited intelligence and no real cognitive skills, will obviously reveal the weaknesses of the system, but it will also convey part of the preconditions and aesthetic frameworks that the human improviser brings to the table. If we want the autonomous system to have the same kind of freedom we commonly value in human players’ improvisational p

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22

Morimoto, Carlos Hitoshi, Flávio Coutinho, Jefferson Silva, Silvia Ghirotti, and Thiago Santos. "Computer Vision based Machine Interaction." Journal on Interactive Systems 2, no. 2 (2011): 1. http://dx.doi.org/10.5753/jis.2011.580.

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This paper introduces the Laboratory of Technologies for Interaction(LaTIn) and briefly describes its current main projects. The mainfocus of LaTInhas been developing new ways of human-machineinteraction using computer vision techniques. The projects arecathegorized according to the distance between the human user and themachine being operated. For close distances, appropriate forinteraction with desktop computers for example, we have developed eye-gazebased interfaces. We have also built hand and body gestures interfacesappropriate for kiosks and virtual reality settings and, for largedistanc

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23

Amershi, Saleema, James Fogarty, Ashish Kapoor, and Desney Tan. "Effective End-User Interaction with Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1529–32. http://dx.doi.org/10.1609/aaai.v25i1.7964.

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End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. This work presents three explorations in designing for effective end-user interaction with machine learning in CueFlik, a system developed to support Web image search. These explorations demonstrate th

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24

Chen, Shuxian, Zongqiang Ren, Xikai Yu, and Ao Huang. "A Dynamic Model of Evolutionary Knowledge and Capabilities Based on Human-Machine Interaction in Smart Manufactures." Computational Intelligence and Neuroscience 2022 (April 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/8584888.

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The increasing use of smart machines and devices is not only changing production principles but also reshaping the value of cocreation logic. The interaction between human and smart machine is the enabler of generating augmented intelligence. A system dynamics model is abstracted from smart manufacturing practices to represent the evolutionary processes of inertia, capability, and reliability induced by human-machine interaction. Human-machine interaction is conceptualized into two dimensions: technical and cognitive interaction. Simulation experiments illustrate how the improvement of human-m

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25

Sudhoff, S. D., D. C. Aliprantis, B. T. Kuhn, and P. L. Chapman. "An induction machine model for predicting inverter-machine interaction." IEEE Transactions on Energy Conversion 17, no. 2 (2002): 203–10. http://dx.doi.org/10.1109/tec.2002.1009469.

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26

Sudhoff, S. D., D. C. Aliprantis, B. T. Kuhn, and P. L. Chapman. "An Induction Machine Model for Predicting Inverter-Machine Interaction." IEEE Power Engineering Review 22, no. 3 (2002): 55–56. http://dx.doi.org/10.1109/mper.2002.4312051.

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27

Modi, Nandini, and Jaiteg Singh. "Role of Eye Tracking in Human Computer Interaction." ECS Transactions 107, no. 1 (2022): 8211–18. http://dx.doi.org/10.1149/10701.8211ecst.

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With the invention of computers arises the need of an interface for users and interacting with a computer has become a natural practice. For all the opportunities a machine can bring, it is now a limiting factor for humans and their interaction with machines. This has given rise to a significant amount of research in the area of human computer interaction to make it more intuitive, simpler, and efficient. Human interaction with computers is no longer confined to printers and keyboards. Traditional input devices give way to natural inputs like voice, gestures, and visual computing using eye tra

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28

Andrist, Sean, Dan Bohus, Bilge Mutlu, and David Schlangen. "Turn-Taking and Coordination in Human-Machine Interaction." AI Magazine 37, no. 4 (2017): 5–6. http://dx.doi.org/10.1609/aimag.v37i4.2700.

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This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination

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29

Komarova, V., J. Lonska, V. Tumalavičius, and A. Krasko. "Artificial sociality in the human-machine interaction." RUDN Journal of Sociology 21, no. 2 (2021): 377–90. http://dx.doi.org/10.22363/2313-2272-2021-21-2-377-390.

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The article aims at clarifying the concept artificial sociality in the human-machine interaction by answering the question whether artificial sociality is a prerequisite or a result of this interaction. The authors conducted a logical analysis of the definitions of sociality and artificial sociality as presented in the scientific literature, and conducted an empirical study of artificial sociality in the human-machine interaction with three methods - comparison of means, correlation analysis and discriminant analysis. All three methods were used in the analysis of the same data: indicators of

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30

Stanley, Jeff, Ozgur Eris, and Monika Lohani. "A Conceptual Framework for Machine Self-Presentation and Trust." International Journal of Humanized Computing and Communication 2, no. 1 (2021): 20–45. http://dx.doi.org/10.35708/hcc1869-148366.

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Increasingly, researchers are creating machines with humanlike social behaviors to elicit desired human responses such as trust and engagement, but a systematic characterization and categorization of such behaviors and their demonstrated effects is missing. This paper proposes a taxonomy of machine behavior based on what has been experimented with and documented in the literature to date. We argue that self-presentation theory, a psychosocial model of human interaction, provides a principled framework to structure existing knowledge in this domain and guide future research and development. We

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Siddique, AH, T. Shamsi, and M. Hasan. "Human Machine Interaction (HMI) in Offshore Drilling - oil rig workers’ opinion about their interaction with machines." International Journal of Occupational Safety and Health 11, no. 3 (2021): 181–91. http://dx.doi.org/10.3126/ijosh.v11i3.39812.

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Introduction: There are huge numbers of drilling platforms in the world and once the worker on those platforms meet with an accident, the situation could be very serious. The consequence of this could be environmental, economic and in some cases fatal. Middle East, being one of the oil rich regions hence some of the largest operator works here. Companies here own various types of jack up rigs ranging from old generation rigs to the latest cyber-rig. This paper addresses what oil rig workers have to say about their interaction with machines, and how Human Machine Interaction (HMI) in Offshore D

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32

Xu, Jiandong, Jiong Pan, Tianrui Cui, Sheng Zhang, Yi Yang, and Tian-Ling Ren. "Recent Progress of Tactile and Force Sensors for Human–Machine Interaction." Sensors 23, no. 4 (2023): 1868. http://dx.doi.org/10.3390/s23041868.

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Human–Machine Interface (HMI) plays a key role in the interaction between people and machines, which allows people to easily and intuitively control the machine and immersively experience the virtual world of the meta-universe by virtual reality/augmented reality (VR/AR) technology. Currently, wearable skin-integrated tactile and force sensors are widely used in immersive human–machine interactions due to their ultra-thin, ultra-soft, conformal characteristics. In this paper, the recent progress of tactile and force sensors used in HMI are reviewed, including piezoresistive, capacitive, piezoe

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Sui, Zezhou, Mian Zhou, Zhikun Feng, Angelos Stefanidis, and Nan Jiang. "Language-Led Visual Grounding and Future Possibilities." Electronics 12, no. 14 (2023): 3142. http://dx.doi.org/10.3390/electronics12143142.

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In recent years, with the rapid development of computer vision technology and the popularity of intelligent hardware, as well as the increasing demand for human–machine interaction in intelligent products, visual localization technology can help machines and humans to recognize and locate objects, thereby promoting human–machine interaction and intelligent manufacturing. At the same time, human–machine interaction is constantly evolving and improving, becoming increasingly intelligent, humanized, and efficient. In this article, a new visual localization model is proposed, and a language valida

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Animesh, Kumar, and Dr Srikanth V. "Enhancing Healthcare through Human-Robot Interaction using AI and Machine Learning." International Journal of Research Publication and Reviews 5, no. 3 (2024): 184–90. http://dx.doi.org/10.55248/gengpi.5.0324.0831.

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35

Alonso-García, María, Ana García-Sánchez, Paula Jaén-Moreno, and Manuel Fernández-Rubio. "Performance Analysis of Urban Cleaning Devices Using Human–Machine Interaction Method." Sustainability 13, no. 11 (2021): 5846. http://dx.doi.org/10.3390/su13115846.

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Presently, several jobs require the collaboration of humans and machines to perform different services and tasks. The ease and intuitiveness of the worker when using each machine will not only improve the worker’s experience but also improve the company’s productivity and the satisfaction that all users have. Specifically, electromechanical devices used to provide cleaning services require complex interactions. These interactions determine the usability and performance of devices. Therefore, devices must have appropriate ergonomic arrangements for human–machine interactions. Otherwise, the des

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36

Ma, Xue Liang, and Li Min Yu. "Study on the Feedback Information of Man-Machine Interface." Applied Mechanics and Materials 235 (November 2012): 340–44. http://dx.doi.org/10.4028/www.scientific.net/amm.235.340.

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This paper synthesizes the human-computer interaction and feedback from two aspects of the theory of in-depth research and analysis, reveals the interactive human-machine interfaces and inner relationship: human-computer interaction is a person and" contains the computer machines" effect relationship between scene depicts; and the human-machine interface is to achieve human-computer interaction forms and methods; at the same time, the system presents a new product development new thinking - interactive guide design. The design of the man-machine interface and real significance and related meth

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37

Feng, Wei, Bin Yao, BinQiang Chen, DongSheng Zhang, XiangLei Zhang, and ZhiHuang Shen. "Modeling and Simulation of Process-Machine Interaction in Grinding of Cemented Carbide Indexable Inserts." Shock and Vibration 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/508181.

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Interaction of process and machine in grinding of hard and brittle materials such as cemented carbide may cause dynamic instability of the machining process resulting in machining errors and a decrease in productivity. Commonly, the process and machine tools were dealt with separately, which does not take into consideration the mutual interaction between the two subsystems and thus cannot represent the real cutting operations. This paper proposes a method of modeling and simulation to understand well the process-machine interaction in grinding process of cemented carbide indexable inserts. Fir

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38

Huang, Rui. "A Man-machine Interaction System Based on the Advanced RISC Machines." Journal of Applied Sciences 13, no. 12 (2013): 2246–51. http://dx.doi.org/10.3923/jas.2013.2246.2251.

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39

Abdullah, Syahid, Wisnu Ananta Kusuma, and Sony Hartono Wijaya. "Sequence-based prediction of protein-protein interaction using autocorrelation features and machine learning." Jurnal Teknologi dan Sistem Komputer 10, no. 1 (2022): 1–11. http://dx.doi.org/10.14710/jtsiskom.2021.13984.

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Protein-protein interaction (PPI) can define a protein's function by knowing the protein's position in a complex network of protein interactions. The number of PPIs that have been identified is relatively small. Therefore, several studies were conducted to predict PPI using protein sequence information. This research compares the performance of three autocorrelation methods: Moran, Geary, and Moreau-Broto, in extracting protein sequence features to predict PPI. The results of the three extractions are then applied to three machine learning algorithms, namely k-Nearest Neighbor (KNN), Random Fo

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40

Li, Jie. "Exploring a Human-Machine Interaction Method." International Journal of High School Research 2, no. 3 (2020): 1–6. http://dx.doi.org/10.36838/v2i3.1.

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41

Alm, Torbjorn, Jens Alfredson, and Kjell Ohlsson. "Simulator-based human-machine interaction design." International Journal of Vehicle Systems Modelling and Testing 4, no. 1/2 (2009): 1. http://dx.doi.org/10.1504/ijvsmt.2009.029174.

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42

de Wit, Paulus A. J. M., and Roberto Moraes Cruz. "Learning from AF447: Human-machine interaction." Safety Science 112 (February 2019): 48–56. http://dx.doi.org/10.1016/j.ssci.2018.10.009.

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Roth, E. M., K. B. Bennett, and D. D. Woods. "Human interaction with an “intelligent” machine." International Journal of Man-Machine Studies 27, no. 5-6 (1987): 479–525. http://dx.doi.org/10.1016/s0020-7373(87)80012-3.

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Lu, Jia-ni, Hua Qian, Ai-ping Xiao, and Miao-wen Shi. "Human-machine Interaction Based on Voice." AASRI Procedia 3 (2012): 583–88. http://dx.doi.org/10.1016/j.aasri.2012.11.092.

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45

Cacciabue, P. C. "Understanding and modelling man-machine interaction." Nuclear Engineering and Design 165, no. 3 (1996): 351–58. http://dx.doi.org/10.1016/0029-5493(96)01206-x.

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Alukaidey, K. A. S., and E. H. T. El-Shirbeeny. "Interaction factors in synchronous machine modelling." Mathematical and Computer Modelling 11 (1988): 969–74. http://dx.doi.org/10.1016/0895-7177(88)90637-1.

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47

Hollnagel, Erik. "The reliability of man-machine interaction." Reliability Engineering & System Safety 38, no. 1-2 (1992): 81–89. http://dx.doi.org/10.1016/0951-8320(92)90108-w.

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Harris, C. C., and S. M. Khandrika. "Flotation machine design: impeller—stator interaction." Powder Technology 43, no. 3 (1985): 273–78. http://dx.doi.org/10.1016/0032-5910(85)80008-5.

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Dunne, B. J., and R. G. Jahn. "Experiments in Remote Human/Machine Interaction." EXPLORE 3, no. 3 (2007): 272. http://dx.doi.org/10.1016/j.explore.2007.03.025.

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Stephens, Keri, Anastazja Harris, Amanda Hughes, et al. "Human-AI Teaming During an Ongoing Disaster: How Scripts Around Training and Feedback Reveal this is a Form of Human-Machine Communication." Human-Machine Communication 6 (July 1, 2023): 65–85. http://dx.doi.org/10.30658/hmc.6.5.

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Humans play an integral role in identifying important information from social media during disasters. While human annotation of social media data to train machine learning models is often viewed as human-computer interaction, this study interrogates the ontological boundary between such interaction and human-machine communication. We conducted multiple interviews with participants who both labeled data to train machine learning models and corrected machine-inferred data labels. Findings reveal three themes: scripts invoked to manage decision-making, contextual scripts, and scripts around perce

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