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Статті в журналах з теми "Low attention"
Mäki-Marttunen, Verónica, Natasha Pickard, Anne-Kristin Solbakk, Keith H. Ogawa, Robert T. Knight, and Kaisa M. Hartikainen. "Low attentional engagement makes attention network activity susceptible to emotional interference." NeuroReport 25, no. 13 (September 2014): 1038–43. http://dx.doi.org/10.1097/wnr.0000000000000223.
Повний текст джерелаGuarnera, Maria, and Antonella D’Amico. "Training of Attention in Children With Low Arithmetical Achievement." Europe’s Journal of Psychology 10, no. 2 (May 28, 2014): 277–90. http://dx.doi.org/10.5964/ejop.v10i2.744.
Повний текст джерелаMillichap, J. Gordon. "Low Birth Weight and Attention Deficit Disorder." Pediatric Neurology Briefs 21, no. 3 (March 1, 2007): 21. http://dx.doi.org/10.15844/pedneurbriefs-21-3-8.
Повний текст джерелаGasparini, Francesca. "Low-quality image enhancement using visual attention." Optical Engineering 46, no. 4 (April 1, 2007): 040502. http://dx.doi.org/10.1117/1.2721764.
Повний текст джерелаBach Jensen, Morten, and Anna Lund Jepsen. "Low attention advertising processing in B2B markets." Journal of Business & Industrial Marketing 22, no. 5 (August 7, 2007): 342–48. http://dx.doi.org/10.1108/08858620710773477.
Повний текст джерелаYang, Howard, Peng Sun, Charles Chubb, and George Sperling. "Complex Attention Filters for Low Contrast Items." Journal of Vision 16, no. 12 (September 1, 2016): 681. http://dx.doi.org/10.1167/16.12.681.
Повний текст джерелаDu, Wenchao, Hu Chen, Peixi Liao, Hongyu Yang, Ge Wang, and Yi Zhang. "Visual Attention Network for Low-Dose CT." IEEE Signal Processing Letters 26, no. 8 (August 2019): 1152–56. http://dx.doi.org/10.1109/lsp.2019.2922851.
Повний текст джерелаSantoso, Irene, Malcolm J. Wright, Giang Trinh, and Mark Avis. "Mind the attention gap: how does digital advertising impact choice under low attention?" European Journal of Marketing 56, no. 2 (December 31, 2021): 442–66. http://dx.doi.org/10.1108/ejm-01-2021-0031.
Повний текст джерелаSokhadze, E. M., B. Hillard, M. Eng, A. S. El-Baz, A. Tasman, and L. Sears. "ELECTROENCEPHALOGRAPHIC BIOFEEDBACK IMPROVES FOCUSED ATTENTION IN ATTENTION DEFICIT/HYPERACTIVITY DISORDER." Bulletin of Siberian Medicine 12, no. 2 (April 28, 2013): 182–94. http://dx.doi.org/10.20538/1682-0363-2013-2-182-194.
Повний текст джерелаScheel, Jennifer F., Karin Schielke, Stefan Lautenbacher, Sabine Aust, Simone Kremer, and Jörg Wolstein. "Low-Dose Alcohol Effects on Attention in Adolescents." Zeitschrift für Neuropsychologie 24, no. 2 (January 2013): 103–11. http://dx.doi.org/10.1024/1016-264x/a000094.
Повний текст джерелаДисертації з теми "Low attention"
Haycock, Anna Cornelia. "Attention-deficit / hyperactivity disorder and low birth weight." Thesis, University of Limpopo, 2004. http://hdl.handle.net/10386/2045.
Повний текст джерелаNiklasson, Lucas. "Low Intensity Natural Sounds and Pink Noise’s Effect on Attention." Thesis, Umeå universitet, Institutionen för psykologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-169713.
Повний текст джерелаBackground noise and how it influences attention in humans is researched in various ways and forms. Usually it has been done by using music and silence to compare the effects on a primary cognitive task. Since music is an artificial sound created with the intention to draw attention to it, the present study sought to determine if background noise cause differences in reaction time whether it was artificial noise or natural noise (such as the sound of a stream of water compared to pink noise). The two background noises were compared through a visual oddball paradigm measuring reaction time on a sample (N = 30) whose mean age was 29 years (M = 29.70, SD = 7,82). The paired t-test confirmed the hypothesis. Therefore, this study concludes that pink noise creates longer reactions compared to natural sounds when presented as background noise.
Park, Gewn hi. "Vagal influence on selective attention under high and low perceptual load." Columbus, Ohio : Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1245438999.
Повний текст джерелаGao, Fei Ph D. Massachusetts Institute of Technology. "Modeling human attention and performance in automated environments with low task loading." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106592.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 211-225).
Automation has the benefit of reducing human operators' workload. By leveraging the power of computers and information technology, the work of human operators is becoming easier. However, when the workload is too low but the human is required to be present either by regulation or due to limitations of automation, human performance can be negatively affected. Negative consequences such as distraction, mind wandering, and inattention have been reported across many high risk settings including unmanned aerial vehicle operation, process control plant supervision, train engineers, and anesthesiologists. Because of the move towards more automated systems in the future, a better understanding is needed to enable intervention and mitigation of possible negative impacts. The objectives of this research are to systematically investigate the attention and performance of human operators when they interact with automated systems under low task load, build a dynamic model and use it to facilitate system design. A systems-based framework, called the Boredom Influence Diagram, was proposed to better understand the relationships between the various influences and outcomes of low task loading. A System Dynamics model, named the Performance and Attention with Low-task-loading (PAL) Model, was built based on this framework. The PAL model captures the dynamic changes of task load, attention, and performance over time in long duration low task loading automated environments. In order to evaluate the replication and prediction capability of the model, three dynamic hypotheses were proposed and tested using data from three experiments. The first hypothesis stated that attention decreases under low task load. This was supported by comparing model outputs with data from an experiment of target searching using unmanned vehicles. Building on Hypothesis 1, the second and third hypotheses examined the impact of decreased attention on performance in responding to an emergency event. Hypothesis 2 was examined by comparing model outputs with data from an experiment of accident response in nuclear power plant monitoring. Results showed that performance is worse with lower attention levels. Hypothesis 3 was tested by comparing model outputs with data from an experiment of defensive target tracking. The results showed that the impact of decreased attention on performance was larger when the task was difficult. The process of testing these three hypotheses shows that the PAL model is a generalized theory that could explain behaviors under low task load in different supervisory control settings. Finally, benefits, limitations, generalizability and applications of the PAL model were evaluated. Further research is needed to improve and extend the PAL model, investigate individual differences to facilitate personnel selection, and develop system and task designs to mitigate negative consequences.
by Fei Gao.
Ph. D. in Engineering Systems
Botting, Nicola Fay. "Psychological and educational outcome of Very Low Birthweight children at 12yrs." Thesis, University of Liverpool, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266195.
Повний текст джерелаLarsson, Joakim. "Using gaze aware regions in eye tracking calibration for users with low-attention span." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-216672.
Повний текст джерелаÖgonstyrning har under en längre tid möjliggjort interaktion för användare. Dock är det fortfarande många utmaningar för att göra interaktionen lätt för användare med intellektuella funktionsnedsättningar. Framförallt när det kommer till inställningar för ögonstyrning, där kalibrering har visat sig vara viktigt för att ge en noggrann uppskattning vart användarna fokuserar. Denna rapport presenterar en studie där tre modifierade versioner av ett kalibreringsgränsnitt för ögonstyrning har blivit designat och utvärderat av nio deltagare med låg fokuseringsförmåga. Dessa gränssnitt använde regioner som var medvetna när en användare tittade inom dom, så kallade blickmedvetna regioner, och varierade i vilken hastighet ett stimuli rörde sig och hur snabbt regionerna runt ett stimuli växte. Data samlades in för varje gränssnitt om interaktionen med de blickmedvetna regionerna, tiden för att genomföra kalibreringen, antal avklarade kalibreringspunkter och avståndet mellan användarnas blick och stimuli. Ingen statistisk signifikans hittades mellan de modifierade gränssnitten mellan tidseffektivitet, effektivitet och noggrannhet. Däremot indikerades en mer tidseffektiv och effektiv kalibreringsmetod, utan minskad noggrannhet, genom användningen av ett stimuli som rör sig snabbare med blickmedvetna regioner som växer. Dessutom skulle kalibreringsprocessen kunna förbättras om enbart engagemang med skärmen används genom smooth-pursuit kalibrering
Ward, John Jason. "Measurement of the photon structure function with special attention to the low-X region." Thesis, University College London (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338768.
Повний текст джерелаLease, Cynthia Ann. "Differential Family Characteristics of High and Low Aggressive Children with Attention Deficit Hyperactivity Disorder." W&M ScholarWorks, 1989. https://scholarworks.wm.edu/etd/1539625550.
Повний текст джерелаKaranouh-Schuler, Eran James. "COGNITIVE EFFECTS OF COFFEE CONSUMPTION IN INDIVIDUALS WITH LOW VERSUS HIGH SLEEP QUALITY." Kent State University Honors College / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ksuhonors1399309032.
Повний текст джерелаOhlinger, Christina M. "The Effect of Active Workstation Use on Measures of Cognition, Attention and Motor Skill." Miami University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=miami1247679643.
Повний текст джерелаКниги з теми "Low attention"
Wallace, Lewis J. A study of the housing needs of the low and moderate income elderly with special attention to the reverse mortgage program. [Columbia, S.C.?]: South Carolina Commission on Aging, 1993.
Знайти повний текст джерелаSims, Wendy L. The effect of high versus low teacher affect and passive versus active student activity during music listening on preschool children's attention, piece preference, time spent listening and piece recognition. [S.l: s.n.], 1985.
Знайти повний текст джерелаLatham, Peter S. Attention deficit disorder and the law. 2nd ed. Washington, D.C: JKL Communications, 2000.
Знайти повний текст джерелаAttention!: Repair industry. Springfield, Ill.]: Illinois Environmental Protection Agency, 2004.
Знайти повний текст джерелаBrinkerhoff, Shirley. Attention-deficit/hyperactivity disorder. Broomall, PA: Mason Crest, 2015.
Знайти повний текст джерелаLatham, Peter S. Attention deficit disorder and the law: A guide for advocates. Washington, D.C: JKL Communications, 1992.
Знайти повний текст джерелаThe law of attention: Nada yoga and the way of inner vigilance. Rochester, Vt: Inner Traditions, 2010.
Знайти повний текст джерелаMichaël, Salim. The law of attention: Nada yoga and the way of inner vigilance. Rochester, Vt: Inner Traditions, 2010.
Знайти повний текст джерелаFernández, Rafael Velasco. El niño hiperquinético: Los síndromes de disfunción cerebral. 3rd ed. México: Trillas, 1990.
Знайти повний текст джерелаEl pequeño gran libro de los juegos: 101 actividades divertidas y fáciles para que los niños aprendan a concentrarse. Barcelona: Oniro, 2007.
Знайти повний текст джерелаЧастини книг з теми "Low attention"
Freeman, Elliot, Jon Driver, and Dov Sagi. "Psychophysical Measurement of Attentional Modulation in Low-Level Vision Using the Lateral-Interactions Paradigm." In Visual Attention Mechanisms, 25–39. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0111-4_3.
Повний текст джерелаWestelius, Carl-Johan, Hans Knutsson, and Gösta Granlund. "Low Level Focus of Attention Mechanisms." In Vision as Process, 179–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03113-1_13.
Повний текст джерелаWendzel, Steffen, and Jörg Keller. "Low-Attention Forwarding for Mobile Network Covert Channels." In Communications and Multimedia Security, 122–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24712-5_10.
Повний текст джерелаBækgaard, Per, Michael Kai Petersen, and Jakob Eg Larsen. "Assessing Levels of Attention Using Low Cost Eye Tracking." In Lecture Notes in Computer Science, 409–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40250-5_39.
Повний текст джерелаJiang, Hang, Song Wu, Dehong He, and Guoqiang Xiao. "Natural Image Matting with Low-Level Feature Attention Guidance." In Knowledge Science, Engineering and Management, 550–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10989-8_44.
Повний текст джерелаShang, Xiaoke, Jingjie Shang, Long Ma, Shaomin Zhang, and Nai Ding. "Attention Guided Retinex Architecture Search for Robust Low-light Image Enhancement." In Artificial Intelligence, 444–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93046-2_38.
Повний текст джерелаMa, Li, and Qian Wang. "Low-Light Image Enhancement Combining U-Net and Self-attention Mechanism." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 769–80. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89698-0_79.
Повний текст джерелаRitter, Walter, Guido Kempter, Isabella Hämmerle, and Andreas Wohlgenannt. "Automatic Low-Level Overlays on Presentations to Support Regaining an Audience’s Attention." In Human-Computer Interaction. Theories, Methods, and Human Issues, 429–40. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91238-7_35.
Повний текст джерелаWu, Jing, Hongxu Hou, Zhipeng Shen, Jian Du, and Jinting Li. "Adapting Attention-Based Neural Network to Low-Resource Mongolian-Chinese Machine Translation." In Natural Language Understanding and Intelligent Applications, 470–80. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50496-4_39.
Повний текст джерелаMeng, Ziyi, Jiawei Ma, and Xin Yuan. "End-to-End Low Cost Compressive Spectral Imaging with Spatial-Spectral Self-Attention." In Computer Vision – ECCV 2020, 187–204. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58592-1_12.
Повний текст джерелаТези доповідей конференцій з теми "Low attention"
Huang, Qingfu, Zhichao Lian, and Qianmu Li. "Attention Based Adversarial Attacks with Low Perturbations." In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2022. http://dx.doi.org/10.1109/icme52920.2022.9859848.
Повний текст джерелаStentiford, Fred W. M. "Visual attention: low-level and high-level viewpoints." In SPIE Photonics Europe, edited by Peter Schelkens, Touradj Ebrahimi, Gabriel Cristóbal, Frédéric Truchetet, and Pasi Saarikko. SPIE, 2012. http://dx.doi.org/10.1117/12.923511.
Повний текст джерелаWu, Chun-Ying, Jin-Jang Leou, and Chen Hsuan-Ying. "Visual attention region determination using low-level features." In 2009 IEEE International Symposium on Circuits and Systems - ISCAS 2009. IEEE, 2009. http://dx.doi.org/10.1109/iscas.2009.5118478.
Повний текст джерелаZhang, Cheng, Qingsen Yan, Yu Zhu, Xianjun Li, Jinqiu Sun, and Yanning Zhang. "Attention-Based Network For Low-Light Image Enhancement." In 2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2020. http://dx.doi.org/10.1109/icme46284.2020.9102774.
Повний текст джерелаLin, Lan, Shisong Tan, and Feifei Long. "Low-parameter hybrid attention model based image classification." In International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), edited by Pavel Loskot. SPIE, 2022. http://dx.doi.org/10.1117/12.2660001.
Повний текст джерелаAtoum, Yousef, Mao Ye, Liu Ren, Ying Tai, and Xiaoming Liu. "Color-wise Attention Network for Low-light Image Enhancement." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00261.
Повний текст джерелаLi, Hongliang, Guanghui Liu, and KingNgi Ngan. "Learn to segment attention object from low DoF image." In 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010. IEEE, 2010. http://dx.doi.org/10.1109/iscas.2010.5536977.
Повний текст джерелаRay, Avik, Yilin Shen, and Hongxia Jin. "Fast Domain Adaptation of Semantic Parsers via Paraphrase Attention." In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-6111.
Повний текст джерелаPagliari, Daniele Jahier, Matteo Ansaldi, Enrico Macii, and Massimo Poncino. "CNN-Based Camera-less User Attention Detection for Smartphone Power Management." In 2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED). IEEE, 2019. http://dx.doi.org/10.1109/islped.2019.8824982.
Повний текст джерелаFiedler, Jens, and Graham Ashcroft. "A LOW-MACH, LOW-REYNOLDS PRECONDITIONING SCHEME WITH PARTICULAR ATTENTION TO VISCOUS TIME-STEPPING." In VII European Congress on Computational Methods in Applied Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2016. http://dx.doi.org/10.7712/100016.2337.6066.
Повний текст джерелаЗвіти організацій з теми "Low attention"
Penman, Olivia, Andrew Sheridan, Nic Badcock, Georgia Horsburgh, and Carmela Pestell. Could local sleep explain the occurrence of attentional lapses in primary school-aged children? A scoping review protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0074.
Повний текст джерелаKaffenberger, Michelle, and Marla Spivack. System Coherence for Learning: Applications of the RISE Education Systems Framework. Research on Improving Systems of Education (RISE), January 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/086.
Повний текст джерелаMcKay, Tasseli, Megan Comfort, Justin Landwehr, Erin Kennedy, and Oliver Williams. Partner Violence After Reentry from Prison: Putting the Problem in Context. RTI Press, March 2020. http://dx.doi.org/10.3768/rtipress.2020.pb.0022.2004.
Повний текст джерелаRonak, Paul, and Rashmi. Is educational wellbeing associated with grade repetition and school dropout rates among Indian students? Evidence from a panel study. Verlag der Österreichischen Akademie der Wissenschaften, August 2021. http://dx.doi.org/10.1553/populationyearbook2021.res5.2.
Повний текст джерелаChambers-Ju, Christopher, Amanda Beatty, and Rezanti Putri Pramana. Exploring the Politics of Expertise:The Indonesian Teachers’ Union and Education Policy, 2005-2020. Research on Improving Systems of Education (RISE), July 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/101.
Повний текст джерелаObiakor, Thelma, and Kirsty Newman. Education and Employability: The Critical Role of Foundational Skills. Research on Improving Systems of Education (RISE), November 2022. http://dx.doi.org/10.35489/bsg-rise-ri_2022/048.
Повний текст джерелаSmit, Timo, Sofia Sacks Ferrari, and Jaïr van der Lijn. Trends in Multilateral Peace Operations, 2019. Stockholm International Peace Research Institute, May 2020. http://dx.doi.org/10.55163/ixjs4170.
Повний текст джерелаTian, Nan, Diego Lopes da Silva, and Xiao Liang. Using Taxation to Fund Military Spending. Stockholm International Peace Research Institute, January 2023. http://dx.doi.org/10.55163/xlej7426.
Повний текст джерелаMarcellino, Massimiliano, and Dalibor Stevanovic. The demand and supply of information about inflation. CIRANO, November 2022. http://dx.doi.org/10.54932/djgr5759.
Повний текст джерелаBeach, Rachel, and Vanessa van den Boogaard. Tax and Governance in the Context of Scarce Revenues: Inefficient Tax Collection and its Implications in Rural West Africa. Institute of Development Studies (IDS), February 2022. http://dx.doi.org/10.19088/ictd.2022.005.
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