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1

Kanis, Ira B. "Task-Oriented Evaluation." Science Activities: Classroom Projects and Curriculum Ideas 29, no. 3 (September 1992): 26–30. http://dx.doi.org/10.1080/00368121.1992.10113035.

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2

KOSUGE, Kazuhiro, Jun ISHIKAWA, Katsuhisa FURUTA, Kazuo HARIKI, and Masaru SAKAI. "Task-Oriented Control of Single-Master Multi-Slave Manipulator System." Transactions of the Society of Instrument and Control Engineers 30, no. 7 (1994): 793–801. http://dx.doi.org/10.9746/sicetr1965.30.793.

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3

Wall, Victor D., Gloria J. Galanes, and SueBeth Love. "Small, Task-Oriented Groups." Small Group Behavior 18, no. 1 (February 1987): 31–55. http://dx.doi.org/10.1177/104649648701800102.

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4

Schweighofer, Nicolas, Younggeun Choi, Carolee Winstein, and James Gordon. "Task-Oriented Rehabilitation Robotics." American Journal of Physical Medicine & Rehabilitation 91 (November 2012): S270—S279. http://dx.doi.org/10.1097/phm.0b013e31826bcd42.

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5

Wang, Shouhong. "Object-oriented task analysis." Information & Management 29, no. 6 (December 1995): 331–41. http://dx.doi.org/10.1016/0378-7206(95)00036-x.

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6

Rossi, Carol A., and Thomas S. Tullis. "A TASK-ORIENTED PROTOTYPING TOOL." ACM SIGCHI Bulletin 19, no. 3 (January 1988): 75–77. http://dx.doi.org/10.1145/49108.1046357.

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7

GUERRERO, PABLO, JAVIER RUIZ-DEL-SOLAR, MIGUEL ROMERO, and SERGIO ANGULO. "TASK-ORIENTED PROBABILISTIC ACTIVE VISION." International Journal of Humanoid Robotics 07, no. 03 (September 2010): 451–76. http://dx.doi.org/10.1142/s0219843610002179.

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In this work, an explicitly task-oriented approach to the active vision problem is presented. The system tries to reduce the most relevant components of the uncertainty in the world model, for the task the robot is currently performing. It is task oriented in the sense that it explicitly considers a task-specific value function. As test-bed for the presented active vision approach, we selected a robot soccer attention problem: goal-covering by a goalie player. The proposed system is compared with information-based approaches. Experimental results show that it surpasses them in the tested application. We conclude that, when the goal is not the uncertainty reduction itself, the minimization of the belief entropy is not a useful optimality criterion, and that for such cases, task-oriented optimality criteria are better suited.
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8

Agrawal, Pankaj, and B. Pradhan. "Task-oriented maximally entangled states." Journal of Physics A: Mathematical and Theoretical 43, no. 23 (May 17, 2010): 235302. http://dx.doi.org/10.1088/1751-8113/43/23/235302.

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9

Pigott, M. T. "The task‐oriented lab report." Physics Teacher 25, no. 8 (November 1987): 490–91. http://dx.doi.org/10.1119/1.2342341.

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10

El-Khoury, Sahar, Ravin de Souza, and Aude Billard. "On computing task-oriented grasps." Robotics and Autonomous Systems 66 (April 2015): 145–58. http://dx.doi.org/10.1016/j.robot.2014.11.016.

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11

Zonnenshain, Avigdor, and Avi Harel. "5.2.1 Task-oriented System Engineering." INCOSE International Symposium 19, no. 1 (July 2009): 775–87. http://dx.doi.org/10.1002/j.2334-5837.2009.tb00982.x.

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12

Xiang, Lu, Junnan Zhu, Yang Zhao, Yu Zhou, and Chengqing Zong. "Robust Cross-lingual Task-oriented Dialogue." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (November 30, 2021): 1–24. http://dx.doi.org/10.1145/3457571.

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Cross-lingual dialogue systems are increasingly important in e-commerce and customer service due to the rapid progress of globalization. In real-world system deployment, machine translation (MT) services are often used before and after the dialogue system to bridge different languages. However, noises and errors introduced in the MT process will result in the dialogue system's low robustness, making the system's performance far from satisfactory. In this article, we propose a novel MT-oriented noise enhanced framework that exploits multi-granularity MT noises and injects such noises into the dialogue system to improve the dialogue system's robustness. Specifically, we first design a method to automatically construct multi-granularity MT-oriented noises and multi-granularity adversarial examples, which contain abundant noise knowledge oriented to MT. Then, we propose two strategies to incorporate the noise knowledge: (i) Utterance-level adversarial learning and (ii) Knowledge-level guided method. The former adopts adversarial learning to learn a perturbation-invariant encoder, guiding the dialogue system to learn noise-independent hidden representations. The latter explicitly incorporates the multi-granularity noises, which contain the noise tokens and their possible correct forms, into the training and inference process, thus improving the dialogue system's robustness. Experimental results on three dialogue models, two dialogue datasets, and two language pairs have shown that the proposed framework significantly improves the performance of the cross-lingual dialogue system.
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13

Zhou, Chuan Sheng. "Research on Task-Oriented Application Design." Applied Mechanics and Materials 263-266 (December 2012): 1482–86. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1482.

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Alone with “The Internet of Things” development and expanding in the areas of life, it also brings some big challenges to the traditional software application design and development; especially with the corresponding technologies and strategies of the internet of things enhancement and improvement, and further more with some new equipments and technologies appended or changed in the existing environment, it needs rapidly and easily add some new functionalities to the existing working software applications. But the traditional application design and development still thinking of solution from designers and developers and not from the business point of view, this results in the traditional software applications and its scalability cannot be easily and rapidly satisfy to business requirements. Here, by research on XML, software bus, software component and task-oriented technologies, it illustrates a new way for the software application design and development and try to use task-oriented technology to improve the software application flexibility and scalability to satisfy to enterprise business changes.
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14

Li, Runde, Jinshan Pan, Min He, Zechao Li, and Jinhui Tang. "Task-Oriented Network for Image Dehazing." IEEE Transactions on Image Processing 29 (2020): 6523–34. http://dx.doi.org/10.1109/tip.2020.2991509.

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15

Andreas, Jacob, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, et al. "Task-Oriented Dialogue as Dataflow Synthesis." Transactions of the Association for Computational Linguistics 8 (September 2020): 556–71. http://dx.doi.org/10.1162/tacl_a_00333.

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We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends this graph. Programs include metacomputation operators for reference and revision that reuse dataflow fragments from previous turns. Our graph-based state enables the expression and manipulation of complex user intents, and explicit metacomputation makes these intents easier for learned models to predict. We introduce a new dataset, SMCalFlow, featuring complex dialogues about events, weather, places, and people. Experiments show that dataflow graphs and metacomputation substantially improve representability and predictability in these natural dialogues. Additional experiments on the MultiWOZ dataset show that our dataflow representation enables an otherwise off-the-shelf sequence-to-sequence model to match the best existing task-specific state tracking model. The SMCalFlow dataset, code for replicating experiments, and a public leaderboard are available at https://www.microsoft.com/en-us/research/project/dataflow-based-dialogue-semantic-machines .
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16

Lerner, R., E. Rivlin, and I. Shimshoni. "Landmark Selection for Task-Oriented Navigation." IEEE Transactions on Robotics 23, no. 3 (June 2007): 494–505. http://dx.doi.org/10.1109/tro.2007.895070.

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17

Sills, Matthew A. "Integrated Robotics vs. Task-Oriented Automation." Journal of Biomolecular Screening 2, no. 3 (April 1997): 137–38. http://dx.doi.org/10.1177/108705719700200302.

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18

Leroux, Alain, H??l??ne Pinet, and Sylvie Nadeau. "Task-Oriented Intervention in Chronic Stroke." American Journal of Physical Medicine & Rehabilitation 85, no. 10 (October 2006): 820–30. http://dx.doi.org/10.1097/01.phm.0000233179.64769.8c.

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19

Luz, Nuno, Nuno Silva, and Paulo Novais. "A survey of task-oriented crowdsourcing." Artificial Intelligence Review 44, no. 2 (August 20, 2014): 187–213. http://dx.doi.org/10.1007/s10462-014-9423-5.

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20

Wanderley, Gregory Moro Puppi, Cesar Augusto Tacla, Jean-Paul A. Barthès, and Emerson Cabrera Paraiso. "Knowledge discovery in task-oriented dialogue." Expert Systems with Applications 42, no. 20 (November 2015): 6807–18. http://dx.doi.org/10.1016/j.eswa.2015.05.005.

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21

Yang, Fan, and Peter A. Heeman. "Initiative conflicts in task-oriented dialogue." Computer Speech & Language 24, no. 2 (April 2010): 175–89. http://dx.doi.org/10.1016/j.csl.2009.04.003.

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22

Zou, Xiaochun, Xinbo Zhao, Jian Wang, and Yongjia Yang. "Learning to Model Task-Oriented Attention." Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2381451.

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For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene with a particular task. Models of saliency can be used to predict fixation locations, but a large body of previous saliency models focused on free-viewing task. They are based on bottom-up computation that does not consider task-oriented image semantics and often does not match actual eye movements. To address this problem, we collected eye tracking data of 11 subjects when they performed some particular search task in 1307 images and annotation data of 2,511 segmented objects with fine contours and 8 semantic attributes. Using this database as training and testing examples, we learn a model of saliency based on bottom-up image features and target position feature. Experimental results demonstrate the importance of the target information in the prediction of task-oriented visual attention.
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23

Xue, Tianfan, Baian Chen, Jiajun Wu, Donglai Wei, and William T. Freeman. "Video Enhancement with Task-Oriented Flow." International Journal of Computer Vision 127, no. 8 (February 12, 2019): 1106–25. http://dx.doi.org/10.1007/s11263-018-01144-2.

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24

Higgs, Karyn, Joseph P. Magliano, Eduardo Vidal-Abarca, Tomas Martínez, and Danielle S. McNamara. "Bridging Skill and Task-Oriented Reading." Discourse Processes 54, no. 1 (October 22, 2015): 19–39. http://dx.doi.org/10.1080/0163853x.2015.1100572.

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25

Whittington, CD, and LM Campbell. "Task‐Oriented Learning on the Web." Innovations in Education & Training International 36, no. 1 (February 1999): 26–33. http://dx.doi.org/10.1080/1355800990360105.

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26

Peters, Heather Tanksley, and Stephen J. Page. "Task-Oriented Rehabilitation Program for Stroke." JAMA 316, no. 1 (July 5, 2016): 101. http://dx.doi.org/10.1001/jama.2016.5016.

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27

Lee, John R., and Andrew B. Williams. "Behavior development through task oriented discourse." Computer Animation and Virtual Worlds 15, no. 34 (June 16, 2004): 327–37. http://dx.doi.org/10.1002/cav.36.

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28

Kosuge, Kazuhiro, Tomotaka Itoh, and Toshio Fukuda. "Task-oriented Control of Scaled Telemanipulation System." IEEJ Transactions on Electronics, Information and Systems 115, no. 10 (1995): 1121–27. http://dx.doi.org/10.1541/ieejeiss1987.115.10_1121.

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29

Hernández-Orallo, José. "Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement." Artificial Intelligence Review 48, no. 3 (August 19, 2016): 397–447. http://dx.doi.org/10.1007/s10462-016-9505-7.

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30

TAKAKU, Masao. "User Task is Important: Towards User Task Oriented Design and Development." Joho Chishiki Gakkaishi 28, no. 5 (March 31, 2019): 363–66. http://dx.doi.org/10.2964/jsik_2019_015.

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31

Zheliazkova, Irina, and R. Kolev. "Task results processing for the needs of task-oriented design environments." Computers & Education 51, no. 1 (August 2008): 86–96. http://dx.doi.org/10.1016/j.compedu.2007.04.009.

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32

Linyao, Q., D. Zhiqiang, and Z. Qing. "A TASK-ORIENTED DISASTER INFORMATION CORRELATION METHOD." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (July 10, 2015): 169–76. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-169-2015.

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With the rapid development of sensor networks and Earth observation technology, a large quantity of disaster-related data is available, such as remotely sensed data, historic data, case data, simulated data, and disaster products. However, the efficiency of current data management and service systems has become increasingly difficult due to the task variety and heterogeneous data. For emergency task-oriented applications, the data searches primarily rely on artificial experience based on simple metadata indices, the high time consumption and low accuracy of which cannot satisfy the speed and veracity requirements for disaster products. In this paper, a task-oriented correlation method is proposed for efficient disaster data management and intelligent service with the objectives of 1) putting forward disaster task ontology and data ontology to unify the different semantics of multi-source information, 2) identifying the semantic mapping from emergency tasks to multiple data sources on the basis of uniform description in 1), and 3) linking task-related data automatically and calculating the correlation between each data set and a certain task. The method goes beyond traditional static management of disaster data and establishes a basis for intelligent retrieval and active dissemination of disaster information. The case study presented in this paper illustrates the use of the method on an example flood emergency relief task.
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33

CHEN, Ting-wei, Shan-jie ZHOU, and Ming-da QIN. "Task classification method oriented to cloud computing." Journal of Computer Applications 32, no. 10 (May 22, 2013): 2719–23. http://dx.doi.org/10.3724/sp.j.1087.2012.02719.

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34

Li, Tao, Wenzhe Dong, Aiqun Hu, and Jinguang Han. "Task-Oriented Network Abnormal Behavior Detection Method." Security and Communication Networks 2022 (June 30, 2022): 1–13. http://dx.doi.org/10.1155/2022/3105291.

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Since network systems have become increasingly large and complex, the limitations of traditional abnormal packet detection have gradually emerged. The existing detection methods mainly rely on the recognition of packet features, which lack the association of specific applications and result in hysteresis and inaccurate judgement. In this paper, a task-oriented abnormal packet behavior detection method is proposed, which creatively collects action identifications during the execution of network tasks and inserts security labels into communication packets. Specifically, this paper defines the network tasks as a collection of state and action sequences to achieve the fine-grained division of the execution of network tasks, performs Hash value matching based on random communication string and action identification sequence for packet authentication, and proposes a mechanism of action identification sequence matching and abnormal behavior decision-making based on a finite state machine, according to the fine-grained monitoring of task execution action sequence. Furthermore, to verify the validity of the anomaly detection method proposed in this paper, a prototype based on the FTP communication platform is constructed, on which the simulation experiments, including the DDOS attack and backdoor attack, are conducted. The experimental results show that the proposed task-oriented abnormal behavior detection method can effectively intercept network malicious data packets and realize the active security defense for network systems.
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35

Khizhnikova, A. E., A. S. Klochkov, A. A. Zimin, A. M. Kotov-Smolenskiy, N. A. Suponeva, and M. A. Piradov. "Hand Motor Asymmetry in Task Oriented Movement." Human Physiology 47, no. 8 (December 2021): 815–20. http://dx.doi.org/10.1134/s0362119721080065.

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36

Salmani Nodoushan, Mohammad Ali. "A Framework for Task-Oriented Language Instruction." i-manager's Journal on School Educational Technology 3 (February 15, 2008): 5–16. http://dx.doi.org/10.26634/jsch.3.3.726.

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37

Mu, Yan Hua. "Task-Oriented Architecture for a Humanoid Robot." Applied Mechanics and Materials 40-41 (November 2010): 228–34. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.228.

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A humanoid robot is developed to perform various kinds of tasks in human daily environments. In this paper, three advanced capabilities which are indispensable for a humanoid robot to perform tasks, namely, natural human-humanoid robot interaction based on spoken dialogue and vision, vision-based navigation in complex and dynamic environments, object grasp and manipulation with hand-eye coordination, are discussed firstly. Then, a biologically-inspired system structure for a humanoid robot is presented. Based on this system structure, a task-oriented layered architecture for a humanoid robot is proposed, and the architectures of the three advanced capabilities are presented, respectively.
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38

ZHANG, Yuru. "Task Oriented Method for Designing Haptic Devices." Journal of Mechanical Engineering 51, no. 13 (2015): 212. http://dx.doi.org/10.3901/jme.2015.13.212.

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39

Wang, Tong. "Research on SoS-oriented Task Allocation Algorithm." Journal of Information and Computational Science 12, no. 10 (July 1, 2015): 3809–17. http://dx.doi.org/10.12733/jics20106037.

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40

Kim, Yochan, and Wan C. Yoon. "Generating Task-Oriented Interactions of Service Robots." IEEE Transactions on Systems, Man, and Cybernetics: Systems 44, no. 8 (August 2014): 981–94. http://dx.doi.org/10.1109/tsmc.2014.2298214.

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41

Miura, Jun, and Katsushi Ikeuchi. "Task-Oriented Generation of Visual Sensing Strategies." Journal of the Robotics Society of Japan 14, no. 4 (1996): 574–85. http://dx.doi.org/10.7210/jrsj.14.574.

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42

Jarboe, Susan C., and Hal R. Witteman. "Intragroup Conflict Management in Task-Oriented Groups." Small Group Research 27, no. 2 (May 1996): 316–38. http://dx.doi.org/10.1177/1046496496272007.

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43

LI, ZHENGGUO, CHEONG BOON SOH, and XINHE XU. "Task-oriented design of hybrid dynamic systems." International Journal of Systems Science 28, no. 6 (June 1997): 595–610. http://dx.doi.org/10.1080/00207729708929421.

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44

Ulich, Eberhard, Matthias Rauterberg, Thomas Moll, Thomas Greutmann, and Oliver Strohm. "Task orientation and user‐oriented dialog design." International Journal of Human-Computer Interaction 3, no. 2 (January 1991): 117–44. http://dx.doi.org/10.1080/10447319109526001.

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45

Rowe, Veronica T., and Marsha Neville. "Task Oriented Training and Evaluation at Home." OTJR: Occupation, Participation and Health 38, no. 1 (August 31, 2017): 46–55. http://dx.doi.org/10.1177/1539449217727120.

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46

Sherwood, Arthur Lloyd, and Concetta A. DePaolo. "Task and Relationship-Oriented Trust In Leaders." Journal of Leadership & Organizational Studies 12, no. 2 (December 2005): 65–81. http://dx.doi.org/10.1177/107179190501200206.

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47

Tan, K. C., Y. J. Chen, K. K. Tan, and T. H. Lee. "Task-Oriented Developmental Learning for Humanoid Robots." IEEE Transactions on Industrial Electronics 52, no. 3 (June 2005): 906–14. http://dx.doi.org/10.1109/tie.2005.847577.

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48

Yin, Carol. "A task-oriented taxonomy of visual completion." Behavioral and Brain Sciences 21, no. 6 (December 1998): 780–81. http://dx.doi.org/10.1017/s0140525x98581757.

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Differences and similarities between modal and amodal completions can only be understood by considering the goals of visual completion: unity, shape, and perceptual quality. Pessoa et al. cannot reject representational accounts of vision because of flaws with isomorphic representations of perceptual quality: representations and processes for perceptual quality (modal completion) and most likely dissociable from those for unity and shape (nonmodal completions).
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49

Yeh, Chung-Hsing, Robert J. Willis, Hepu Deng, and Hongqi Pan. "Task oriented weighting in multi-criteria analysis." European Journal of Operational Research 119, no. 1 (November 1999): 130–46. http://dx.doi.org/10.1016/s0377-2217(98)90353-8.

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50

Janiszowski, K. B. "Task Oriented Identification of the Process Model." IFAC Proceedings Volumes 18, no. 11 (September 1985): 513–19. http://dx.doi.org/10.1016/s1474-6670(17)60176-9.

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