Готові списки джерел за темами / Time on task

Добірка наукової літератури з теми "Time on task"

Автор: Grafiati
Опубліковано: 4 червня 2021
Оновлено: 11 лютого 2022

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Статті в журналах з теми "Time on task"

1

Chan, Alan H. S., and Errol R. Hoffmann. "Subjective Estimation of Task Time and Task Difficulty of Simple Movement Tasks." Journal of Motor Behavior 49, no. 2 (September 3, 2016): 185–99. http://dx.doi.org/10.1080/00222895.2016.1169984.

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2

Tsang, Pamela S. "Display/Control Integrality and Time-Sharing Performance." Proceedings of the Human Factors Society Annual Meeting 30, no. 5 (September 1986): 445–49. http://dx.doi.org/10.1177/154193128603000507.

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Time-sharing performance was investigated as a function of the display and response integrality of the time-shared tasks. A manual step-tracking task was time-shared with a Stroop task that could be responded to manually or by speech. A secondary task technique was employed to manipulate the resource allocation between the two tasks. Display integrality was manipulated by: (1) contingent processing of the different dimensions of the Stroop task, and (2) the “objectness” of the dual task display. Response integrality was manipulated by the number of responses required of the dual task and the response modality of the Stroop task. A prevalent resource competition effect between the manual responses of the two tasks were observed, supporting the concept of multiple resources. Results were also in concordance with Kahneman's object file model of attention; demonstrating that irrelevant elements within an object were difficult to ignore. The findings demonstrated the interactive effects of resource competition and task integrality on time-sharing performance.
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3

Wang, Endong, Fan Ni, Jicheng Chen, Hongwei Wang, and Yihan Li. "Cache-Aware Cooperative Task Mapping in Multi-core Real-Time Systems." International Journal of Information and Electronics Engineering 6, no. 2 (2016): 72–78. http://dx.doi.org/10.18178/ijiee.2016.6.2.598.

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4

Rączka, Krzysztof. "Zadaniowy czas pracy." Praca i Zabezpieczenie Społeczne 2019, no. 2 (February 20, 2019): 18–21. http://dx.doi.org/10.33226/0032-6186.2019.2.4.

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5

Nayeem, Razia V., Tal Oron-Gilad, and P. A. Hancock. "Operators' Time Perception Under Stress." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 4 (October 2007): 151–55. http://dx.doi.org/10.1177/154193120705100402.

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Time perception is extremely important to the understanding, design and use of complex military systems. This experiment focused on differences in time estimation, navigation performance, and monitoring tasks. In a between-subjects experiment, participants navigated through a ground scenario while monitoring a screen and listening to white noise at either 55dBA or 85dBA. Performance data was collected throughout the task for both the navigation and monitoring tasks. Participants also completed the NASA-TLX and the DSSQ-S. Statistical analyses showed that the noise condition did not significantly affect workload, monitoring abilities, task completion and time estimates for the dual task. However, the noise did affect subjective state questionnaires. These results suggest that the dual task was not demanding enough and the stress was not adequate to push participants out of the comfort range and experience a performance decrement.
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6

Karweit, Nancy. "Time-on-Task: The Second Time Around." NASSP Bulletin 72, no. 505 (February 1988): 31–39. http://dx.doi.org/10.1177/019263658807250507.

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7

Lei, Zhenyang, Xiangdong Lei, and Jun Long. "Memory-Aware Scheduling Parallel Real-Time Tasks for Multicore Systems." International Journal of Software Engineering and Knowledge Engineering 31, no. 04 (April 2021): 613–34. http://dx.doi.org/10.1142/s0218194021400106.

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Shared resources on the multicore chip, such as main memory, are increasingly becoming a point of contention. Traditional real-time task scheduling policies focus on solely on the CPU, and do not take in account memory access and cache effects. In this paper, we propose parallel real-time tasks scheduling (PRTTS) policy on multicore platforms. Each set of tasks is represented as a directed acyclic graph (DAG). The priorities of tasks are assigned according to task periods Rate Monotonic (RM). Each task is composed of three phases. The first phase is read memory stage, the second phase is execution phase and the third phase is write memory phase. The tasks use locks and critical sections to protect data access. The global scheduler maintains the task pool in which tasks are ready to be executed which can run on any core. PRTTS scheduling policy consists of two levels: the first level scheduling schedules ready real-time tasks in the task pool to cores, and the second level scheduling schedules real-time tasks on cores. Tasks can preempt the core on running tasks of low priority. The priorities of tasks which want to access memory are dynamically increased above all tasks that do not access memory. When the data accessed by a task is in the cache, the priority of the task is raised to the highest priority, and the task is scheduled immediately to preempt the core on running the task not accessing memory. After accessing memory, the priority of these tasks is restored to the original priority and these tasks are pended, the preempted task continues to run on the core. This paper analyzes the schedulability of PRTTS scheduling policy. We derive an upper-bound on the worst-case response-time for parallel real-time tasks. A series of extensive simulation experiments have been performed to evaluate the performance of proposed PRTTS scheduling policy. The results of simulation experiment show that PRTTS scheduling policy offers better performance in terms of core utilization and schedulability rate of tasks.
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Khatami, Mostafa, and Amir Salehipour. "Coupled task scheduling with time-dependent processing times." Journal of Scheduling 24, no. 2 (January 21, 2021): 223–36. http://dx.doi.org/10.1007/s10951-020-00675-2.

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9

Bisson, Nicolas, and Simon Grondin. "Time Estimates of Internet Surfing and Video Gaming." Timing & Time Perception 1, no. 1 (2013): 39–64. http://dx.doi.org/10.1163/22134468-00002002.

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Time estimation researchers have long been interested in the effects exerted by the time estimation paradigms, the task’s target duration and the nature of the task on the precision and variability of time estimates. Only a few studies though have focused on how these results can be applied to daily life tasks. Hence, the present study aims at exploring these effects with two different tasks: surfing the Internet and playing a video game. Results show that the time estimation paradigms, target duration and task effects normally observed in short and simple tasks can be extended to daily life tasks. Of relevance, the video gaming task was significantly underestimated compared to the Internet surfing task. These findings are discussed in the light of the current time estimation literature.
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10

Mahan, Robert P. "Time-on-Task Effects within a Probabilistic Task Environment." Perceptual and Motor Skills 72, no. 1 (February 1991): 307–14. http://dx.doi.org/10.2466/pms.1991.72.1.307.

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Дисертації з теми "Time on task"

1

Buckman, Noam (Noam M. ). "Decentralized task allocation for dynamic, time-sensitive tasks." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120195.

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Анотація:
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 103-110).
In time-sensitive and dynamic missions, autonomous vehicles must respond quickly to new information and objectives. In the case of dynamic task allocation, a team of agents are presented with a new, unknown task that must be allocated with their original allocations. This is exacerbated further in decentralized settings where agents are limited to utilizing local information during the allocation process. This thesis presents a fully decentralized, dynamic task allocation algorithm that extends the Consensus-Based Bundle Algorithm (CBBA) to allow for allocating new tasks. Whereas static CBBA requires a full resetting of previous allocations, CBBA with Partial Replanning (CBBA-PR) enables the agents to only partially reset their allocations to efficiently and quickly allocate a new task. By varying the number of existing tasks that are reset during replan, the team can trade-off convergence speed with amount of coordination. By specifically choosing the lowest bid tasks for resetting, CBBA-PR is shown to converge linearly with the number of tasks reset and the network diameter of the team. In addition, limited replanning methods are presented for scenarios without sufficient replanning time. These include a single reset bidding procedure for agents at capacity, a no-replanning heuristic that can identify scenarios that does not require replanning, and a subteam formation algorithm for reducing the network diameter. Finally, this thesis describes hardware and simulation experiments used to explore the effects of ad-hoc, decentralized communication on consensus algorithms and to validate the performance of CBBA-PR.
by Noam Buckman.
S.M.
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2

Khaluf, Yara [Verfasser]. "Task allocation in robot swarms for time-constrained tasks / Yara Khaluf." Paderborn : Universitätsbibliothek, 2014. http://d-nb.info/1050192877/34.

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3

Fergusson, Janel. "Time judgments in dual-task conditions." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27745.

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Every day we complete a number of tasks which require us to accurately time events, from estimating how long it will take us to drive to work in the morning to steeping our afternoon tea for the correct duration. Although timing is very important in our everyday lives, we know relatively little about how we process time information. Many models have been proposed to account for human timing, with the most prominent are the attentional gate model (AGM) and the multiple resources model. The AGM and the multiple resources model make many similar predictions about human timing, and it is often difficult to discriminate between the two. Toward this goal, the present research focused on a situation in which the two models make opposing predictions, that is, conditions which require participants two carry out two tasks concurrently with both of them requiring time-related processing.. Three experiments are reported, in which subjects were asked to estimate various shorter or longer intervals while concurrently carrying out a task that either required processing of time-related information or non-time based information. Results of all three studies seem more supportive of the multiple resources model of timing, rather than the AGM.
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4

Manolache, Sorin. "Schedulability analysis of real-time systems with stochastic task execution times." Licentiate thesis, Linköping University, Linköping University, ESLAB - Embedded Systems Laboratory, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5730.

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Systems controlled by embedded computers become indispensable in our lives and can be found in avionics, automotive industry, home appliances, medicine, telecommunication industry, mecatronics, space industry, etc. Fast, accurate and flexible performance estimation tools giving feedback to the designer in every design phase are a vital part of a design process capable to produce high quality designs of such embedded systems.

In the past decade, the limitations of models considering fixed task execution times have been acknowledged for large application classes within soft real-time systems. A more realistic model considers the tasks having varying execution times with given probability distributions. No restriction has been imposed in this thesis on the particular type of these functions. Considering such a model, with specified task execution time probability distribution functions, an important performance indicator of the system is the expected deadline miss ratio of tasks or task graphs.

This thesis proposes two approaches for obtaining this indicator in an analytic way. The first is an exact one while the second approach provides an approximate solution trading accuracy for analysis speed. While the first approach can efficiently be applied to monoprocessor systems, it can handle only very small multi-processor applications because of complexity reasons. The second approach, however, can successfully handle realistic multiprocessor applications. Experiments show the efficiency of the proposed techniques.


Report code: LiU-Tek-Lic-2002:58.
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5

Marion, Curtis George. "The task-resource allocation model : a perspective on task attributes, motivation and the dedication of time and effort to tasks /." The Ohio State University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487779120906091.

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6

Krauth, Elfriede Irene. "Real-time planning support : a task-technology fit perspective = Real-time planningsondersteuning : de afstemming tussen taak en technologie /." Rotterdam : Erasmus Universiteit, 2008. http://opac.nebis.ch/cgi-bin/showAbstract.pl?u20=97890589221932.

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7

Mudrova, Lenka. "Task scheduling and merging in space and time." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7872/.

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Every day, robots are being deployed in more challenging environments, where they are required to perform complex tasks. In order to achieve these tasks, robots rely on intelligent deliberation algorithms. In this thesis, we study two deliberation approaches – task scheduling and task planning. We extend these approaches in order to not only deal with temporal and spatial constraints imposed by the environment, but also exploit them to be more efficient than the state-of-the-art approaches. Our first main contribution is a scheduler that exploits a heuristic based on Allen’s interval algebra to prune the search space to be traversed by a mixed integer program. We empirically show that the proposed scheduler outperforms the state of the art by at least one order of magnitude. Furthermore, the scheduler has been deployed on several mobile robots in long-term autonomy scenarios. Our second main contribution is the POPMERX algorithm, which is based on merging of partially ordered temporal plans. POPMERX first reasons with the spatial and temporal structure of separately generated plans. Then, it merges these plans into a single final plan, while optimising the makespan of the merged plan. We empirically show that POPMERX produces better plans that the-state-ofthe- art planners on temporal domains with time windows.
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8

Mshololo, Felix Hlanganani Engelbert. "The time management task of the school principal." Thesis, University of Zululand, 2014. http://hdl.handle.net/10530/1408.

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A thesis submitted to the Faculty of Education in partial fulfilment of the requirements for the Degree of Master of Education in the Department of Social Science Education at the University of Zululand, South Africa, 2014
Effective time utilisation leads to greater freedom to use time as one would like to: to do more of the things one has always wanted to do; in short to enjoy life more. Like in any other business organisation, time for planning, organising, leading and controlling is managed carefully. The principal needs time to perform all the above mentioned tasks. In this study, the author accentuates that it is disconcerting that from time to time, there are schools that do not even have a time table when the schools open at the beginning of the year. It would be interesting to know how much time might be wasted as a result of a lack of planning, lack of priorities, paperwork and reading, meetings, unclear objectives, procrastination, lack of delegation, incompetent subordinates and many other time wasters. The purpose of the study was to determine the school principal’s understanding of the time management task and explore how effective school principals manage time in performing their different tasks. Fifty principals from Phumelela and Umbumbulu Circuits, under the Umlazi District completed a survey questionnaire and semi structured interviews were conducted. The researcher was able to determine the pattern of time usage. The findings revealed that factors hampering time management include among other things meetings that principals have to attend, departmental expectations, visitors, the organisational stress, administrative obligations and inability to differentiate between urgent and important matters to attend to hampers the principal’s time management tasks. The study among other things recommended that the provincial education departments need to provide appropriate training for school governing bodies that a well organised and goal directed system should operate to control school visitors in the schools’ interest.
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9

Incera, Burkert Sara. "THE TIME COURSE OF A BILINGUAL STROOP TASK." Cleveland State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=csu1393608029.

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Ard, Michael Colin. "On the origin of a response time underadditivity by means of cross-modal task switching, or the redundancy of operations in the configuration of task sets for cross-modal shifts." Diss., [La Jolla] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3366481.

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Анотація:
Thesis (Ph. D.)--University of California, San Diego, 2009.
Title from first page of PDF file (viewed August 20, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 178-184).
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Книги з теми "Time on task"

1

Peg, Ballard, ed. Task time: The sound of "t". [Chanhassen, Minn.]: Child's World, Inc., 2000.

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2

The critical legal studies movement: Another time, a greater task. London: Verso, 2015.

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3

Shukla, Shridhar B. Real-time execution control of task-level data-flow graphs using a compile-time approach. Monterey, Calif: Naval Postgraduate School, 1992.

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4

Shaeffer, Blanca A. Refining a task-execution time prediction model for use in MSHN. Monterey, Calif: Naval Postgraduate School, 2000.

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5

McTarnaghan, Roy E. On task, on time: The development of Florida Gulf Coast University. Fort Myers, Fla: Florida Gulf Coast University, 2003.

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6

Brown, Gerald J. Dynamic task scheduling for the Knowledge Worker System. Champaign, IL: U.S. Army Construction Engineering Research Laboratory, 1996.

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7

Task Force on Learning Technologies. A time to sow: Report from the Task Force on Learning technologies. Toronto, Ont: Council of Ontario Universities, 2000.

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8

Guha, Krishnendu, Sangeet Saha, and Amlan Chakrabarti. Self Aware Security for Real Time Task Schedules in Reconfigurable Hardware Platforms. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79701-0.

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9

Cochran, J. Wesley. Time management handbook for librarians. New York: Greenwood Press, 1992.

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10

Heidegger, Martin. Basic writings: From Being and time (1927) to The task of thinking (1964). New York: Harper Perennial Modern Thought, 2008.

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Частини книг з теми "Time on task"

1

Wolter, Katinka. "Task Completion Time." In Stochastic Models for Fault Tolerance, 13–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11257-7_2.

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2

Buttazzo, Giorgio C. "Aperiodic Task Scheduling." In Hard Real-Time Computing Systems, 53–78. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-0676-1_3.

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Buttazzo, Giorgio C. "Periodic Task Scheduling." In Hard Real-Time Computing Systems, 79–118. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-0676-1_4.

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4

Tang, Yue, Nan Guan, and Wang Yi. "Real-Time Task Models." In Handbook of Real-Time Computing, 1–19. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-4585-87-3_29-1.

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Stankovic, John A., Marco Spuri, Krithi Ramamritham, and Giorgio C. Buttazzo. "Aperiodic Task Scheduling." In Deadline Scheduling for Real-Time Systems, 169–96. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5535-3_8.

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MacGregor, Donald. "Time Pressure and Task Adaptation." In Time Pressure and Stress in Human Judgment and Decision Making, 73–82. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4757-6846-6_5.

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Rusu, Vlad. "Verifying periodic task-control systems." In Hybrid and Real-Time Systems, 63–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0014713.

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Xia, Jiali, Zhonghua Cao, Wenting Zhu, and Wenle Wang. "A Real-Time Task Scheduling Strategy Supporting Compensatory Task." In Communications in Computer and Information Science, 543–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31965-5_63.

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Verster, Joris C., Thomas M. Tzschentke, Kieran O’Malley, Francis C. Colpaert, Bart Ellenbroek, Bart Ellenbroek, R. Hamish McAllister-Williams, et al. "Five-Choice Serial Reaction Time Task." In Encyclopedia of Psychopharmacology, 537. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_485.

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Galton, Maurice. "Time: Allocated, Institutional and Task Oriented." In International Handbook of Educational Research in the Asia-Pacific Region, 781–92. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-3368-7_54.

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Тези доповідей конференцій з теми "Time on task"

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Combi, C., and G. Pozzi. "Task Scheduling for a TemporalWorkflow Management System." In Thirteenth International Symposium on Temporal Representation and Reasoning (TIME'06). IEEE, 2006. http://dx.doi.org/10.1109/time.2006.26.

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Chen, Jian-Jia. "Task Set Synthesis with Cost Minimization for Sporadic Real-Time Tasks." In 2013 IEEE 34th Real-Time Systems Symposium (RTSS). IEEE, 2013. http://dx.doi.org/10.1109/rtss.2013.42.

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Sun, Jinghao, Nan Guan, Yang Wang, Qingqiang He, and Wang Yi. "Real-Time Scheduling and Analysis of OpenMP Task Systems with Tied Tasks." In 2017 IEEE Real-Time Systems Symposium (RTSS). IEEE, 2017. http://dx.doi.org/10.1109/rtss.2017.00016.

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4

Baruah, Haritsa, and Sharma. "On-line scheduling to maximize task completions." In Proceedings Real-Time Systems Symposium. IEEE Comput. Soc. Press, 1994. http://dx.doi.org/10.1109/real.1994.342713.

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Chen, Jian-Jia, Tei-Wei Kuo, Chia-Lin Yang, and Ku-Jei King. "Energy-Efficient Real-Time Task Scheduling with Task Rejection." In Design, Automation & Test in Europe Conference. IEEE, 2007. http://dx.doi.org/10.1109/date.2007.364535.

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Bertout, Antoine, Julien Forget, and Richard Olejnik. "Minimizing a real-time task set through Task Clustering." In the 22nd International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2659787.2659820.

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Reif, Stefan, and Wolfgang Schroder-Preikschat. "Precisely Timed Task Execution." In 2020 IEEE 23rd International Symposium on Real-Time Distributed Computing (ISORC). IEEE, 2020. http://dx.doi.org/10.1109/isorc49007.2020.00012.

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Chen, Ya-Shu, Li-Pin Chang, Tei-Wei Kuo, and Aloysius K. Mok. "Real-time task scheduling anomaly." In the 2005 ACM symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1066677.1066881.

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Henderson, Sarah. "Genre, task, topic and time." In the 6th ACM SIGCHI New Zealand chapter's international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1073943.1073957.

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10

Guan, Nan, Chuancai Gu, Martin Stigge, Qingxu Deng, and Wang Yi. "Approximate Response Time Analysis of Real-Time Task Graphs." In 2014 IEEE Real-Time Systems Symposium (RTSS). IEEE, 2014. http://dx.doi.org/10.1109/rtss.2014.20.

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Звіти організацій з теми "Time on task"

1

Scott, Harry D., and Jr. Joint Task Force Headquarters -- Time For Permanency? Fort Belvoir, VA: Defense Technical Information Center, February 1997. http://dx.doi.org/10.21236/ada326623.

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2

Nelson, W. T., Merry M. Roe, Robert S. Bolia, and Rebecca M. Morley. Assessing Simulator Sickness in a See-Through HMD: Effects of Time Delay, Time on Task, and Task Complexity. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada430344.

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3

Fisher, Donna, Paul Fortier, David Hughes, and Mayford Roark. DBSSG Predictable Real-Time Information Systems Task Group. Fort Belvoir, VA: Defense Technical Information Center, February 1995. http://dx.doi.org/10.21236/ada291950.

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4

Swaminathan, Vishnu, and Krishnendu Chakrabarty. Real-Time Task Scheduling for Energy-Aware Embedded Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada439593.

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5

Bakhtiari, Sasan. Task 1. Monitoring real time materials degradation. NRC extended In-situ and real-time Monitoring. Office of Scientific and Technical Information (OSTI), March 2012. http://dx.doi.org/10.2172/1252705.

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6

Sousa, Joao P., and David Garlan. Beyond Desktop Management: Scaling Task Management in Space and Time. Fort Belvoir, VA: Defense Technical Information Center, August 2004. http://dx.doi.org/10.21236/ada457874.

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7

Clayton, Alan B. A Standing Joint Task Force: It is Time for a Virtual Solution. Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada400932.

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8

Huang, Hui-Min. Hierarchical real-time control task decomposition for a coal mining automation project. Gaithersburg, MD: National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.90-4271.

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9

Borger, Mark W. VAXELN Experimentation: Programming a Real-Time Periodic Task Dispatcher Using VAXELN Ada 1.1. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada200612.

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10

Zhang, Ying, and Krishnendu Chakrabarty. Task Feasibility Analysis and Dynamic Voltage Scaling in Fault-Tolerant Real-Time Embedded Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada439598.

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