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Journal articles on the topic 'Task Scenarios'
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1
Diaper, Dan. "Scenarios and task analysis." Interacting with Computers 14, no. 4 (2002): 379–95. http://dx.doi.org/10.1016/s0953-5438(02)00005-x.
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Diaper, Dan. "Task scenarios and thought." Interacting with Computers 14, no. 5 (2002): 629–38. http://dx.doi.org/10.1016/s0953-5438(02)00062-0.
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Brems, Douglas J. "Risk Estimation for Common Consumer Products." Proceedings of the Human Factors Society Annual Meeting 30, no. 6 (1986): 556–60. http://dx.doi.org/10.1177/154193128603000611.
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This paper reports a study of risk estimation for common consumer products. Subjects estimated injury frequencies and recalled/identified accident scenarios. While performance on the frequency estimation task highlighted a surprising ability to assess relative levels of risk very quickly, the scenario recall task showed severe errors in judgment. In the frequency estimation task, estimates that were made within 2 seconds of category presentation were just as accurate as those made after lengthy analysis. In the scenario recall task, subjects could recall or generate only about 40 percent of the common accident scenarios associated with each product category, and they overestimated their own ability to recall scenarios. When asked about specific scenarios that they had failed to identify, subjects cited both awareness and memory problems. These findings are discussed with reference to a model of risk perception in which the individual uses a readily accessible base of knowledge for assessing risks.
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Zhang, Guoxi, and Robert Feyen. "Hierarchical Task Prioritization Behavior in Two- and Four-Task Scenarios." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 4 (2007): 191–95. http://dx.doi.org/10.1177/154193120705100410.
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This paper describes an empirical study conducted to validate a computational model of dynamic task prioritization based on a framework proposed by Zhang and Feyen (2005). Three key factors in task prioritization were manipulated: processing time, available time, and task valence. Because earlier studies did not investigate how people prioritize tasks when valence and temporal characteristics conflict, this study examined how these conflicts are resolved. 20 subjects completed 54 time-limited task scenarios. Each scenario consisted of two or four concurrent tasks, each assigned a point value for completion. Subjects were instructed to maximize points scored. Results indicated that, although valence was predominant in determining task selection, it failed to explain all instances. Instead, a hierarchy of task prioritization was revealed in which subjects first checked what tasks were doable (e.g., self-efficacy), then applied rules first regarding valence, then temporal characteristics, and then others (e.g., task location).
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Ayedoun, Emmanuel, Yuki Hayashi, and Kazuhisa Seta. "An authoring tool for task-oriented dialogue scenarios design in EFL context." Research and Practice in Technology Enhanced Learning 18 (December 28, 2022): 027. http://dx.doi.org/10.58459/rptel.2023.18027.
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Computer-based conversational environments have been advocated as a promising approach for providing virtual, yet realistic opportunities for communication practice to second language learners. However, the high authoring costs of such environments continue to prevent their widespread diffusion and adoption. Furthermore, there is a limited set of authoring interfaces dedicated to making the creation of dialogue scenarios in the context of language learning easier. In this research, we present a dialogue scenario authoring system that could aid the rapid implementation of desirable dialogue scenarios, lowering the barrier to dialogue scenario authoring for non-programmers or even educators. To that end, we built a pseudo-versatile dialogue scenario authoring interface that enables the automatic generation of services-related dialogue scenarios by leveraging the common underlying structure of services (restaurant, hotel, travel planning, etc.) that appear to share a certain degree of similarity at the task level. Here, we describe the proposed system’s features and present the findings of an experimental evaluation study that suggests the usefulness of our approach to facilitating dialogue scenarios designed by people with no prior experience authoring dialogue systems components. According to an evaluation of the tool by a second language teaching expert, the proposed system might also foster second language teaching and learning by allowing both educators and learners to participate in the design of dialogue scenarios that are adapted to different levels of learners.
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Mazur, Lukasz M., Prithima R. Mosaly, Carlton Moore, et al. "Toward a better understanding of task demands, workload, and performance during physician-computer interactions." Journal of the American Medical Informatics Association 23, no. 6 (2016): 1113–20. http://dx.doi.org/10.1093/jamia/ocw016.
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Abstract Objective To assess the relationship between (1) task demands and workload, (2) task demands and performance, and (3) workload and performance, all during physician-computer interactions in a simulated environment. Methods Two experiments were performed in 2 different electronic medical record (EMR) environments: WebCIS ( n = 12) and Epic ( n = 17). Each participant was instructed to complete a set of prespecified tasks on 3 routine clinical EMR-based scenarios: urinary tract infection (UTI), pneumonia (PN), and heart failure (HF). Task demands were quantified using behavioral responses (click and time analysis). At the end of each scenario, subjective workload was measured using the NASA-Task-Load Index (NASA-TLX). Physiological workload was measured using pupillary dilation and electroencephalography (EEG) data collected throughout the scenarios. Performance was quantified based on the maximum severity of omission errors. Results Data analysis indicated that the PN and HF scenarios were significantly more demanding than the UTI scenario for participants using WebCIS ( P < .01), and that the PN scenario was significantly more demanding than the UTI and HF scenarios for participants using Epic ( P < .01). In both experiments, the regression analysis indicated a significant relationship only between task demands and performance ( P < .01). Discussion Results suggest that task demands as experienced by participants are related to participants' performance. Future work may support the notion that task demands could be used as a quality metric that is likely representative of performance, and perhaps patient outcomes. Conclusion The present study is a reasonable next step in a systematic assessment of how task demands and workload are related to performance in EMR-evolving environments.
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Maltoni, Davide, and Vincenzo Lomonaco. "Continuous learning in single-incremental-task scenarios." Neural Networks 116 (August 2019): 56–73. http://dx.doi.org/10.1016/j.neunet.2019.03.010.
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Moeller, Birte, and Christian Frings. "Distractor-response bindings in dual task scenarios." Visual Cognition 23, no. 4 (2015): 516–31. http://dx.doi.org/10.1080/13506285.2015.1041437.
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B., Siva Rama Krishna, and Sreenivasa Reddy E. "Improved Context Aware PSO Task Scheduling in Cloud Computing." Webology 19, no. 1 (2022): 3709–21. http://dx.doi.org/10.14704/web/v19i1/web19244.
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One of the major advantages of switching to the clouds is the scalability ability of the applications. Contrary to the grids, the ability to scale the cloud resources allows their real-time provisioning so as to meet the application constraints Generation of optimal schedule for given set of tasks and machines. Different experiments show that although having an optimum solution is almost impossible but having a sub-optimal solution using heuristic algorithms seems possible. In this paper, we propose a Context Aware PSO Task Scheduling scheme to analyze various scenarios with different parameters in cloud computing system corresponding to APSO In each scenario, we change one parameter and keep other parameters constant.
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Lv, Bingyu, Xianchang Wang, and Rui Zhang. "A task level fusion autonomous switching mechanism." PLOS ONE 18, no. 11 (2023): e0287791. http://dx.doi.org/10.1371/journal.pone.0287791.
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Positioning technology is an important component of environmental perception. It is also the basis for autonomous decision-making and motion control of firefighting robots. However, some issues such as positioning in indoor scenarios still remain inherent challenges. The positioning accuracy of the fire emergency reaction dispatching (FERD) system is far from adequate to support some applications for firefighting and rescue in indoor scenarios with multiple obstacles. To solve this problem, this paper proposes a fusion module based on the Blackboard architecture. This module aims to improve the positioning accuracy of a single sensor of the unmanned vehicles within the FERD system. To reduce the risk of autonomous decision-making of the unmanned vehicles, this module uses a comprehensive manner of multiple channels to complement or correct the positioning of the firefighting robots. Specifically, this module has been developed to fusion a variety of relevant processes for precise positioning. This process mainly includes six strategies. These strategies are the denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision. These strategies merge with the current scenarios-related parameter data, empirical data on sensor errors, and information to form a series of norms. This paper then proceeds to gain experience data with the confidence degree, error of different sensors, and timeliness of this module by training in an indoor scenario with multiple obstacles. This process is from data of multiple sensors (bottom-level) to control decisions knowledge-based (up-level). This process can obtain globally optimal positioning results. Finally, this paper evaluates the performance of this fusion module for the FERD system. The experimental results show that this fusion module can effectively improve positioning accuracy in an indoor scenario with multiple obstacles. Code is available at https://github.com/lvbingyu-zeze/gopath/tree/master.
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Wang, Mengmei. "Optimizing Multitask Assignment of Internet of Things Devices by Reinforcement Learning in Mobile Crowdsensing Scenes." Security and Communication Networks 2022 (August 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/6202237.
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The objective is to optimize the multitask assignment (MTA) in mobile crowdsensing (MCS) scenarios. From the perspective of reinforcement learning (RL), an Internet of Things (IoT) devices-oriented MTA model is established using MCS, IoT technology, and other related theories. Then, the data collected by the University of Cambridge and the University of St. Andrews are chosen to verify the three MTA algorithms on IoT devices. They are multistage online task assignment (MOTA), average makespan-sensitive online task assignment (AOTA), and water filling (WF). Experiments are designed by considering different algorithms’ MTA time consumption and accuracy in simple and complex task scenarios. The research results manifest that with a constant load or task quantity, the MOTA algorithm takes the shortest time to assign tasks. In simple task scenarios, MOTA is compared with the WF. The MOTA algorithm’s total moving distance is relatively short, and the task completion degree is the highest. AOTA algorithm lends best to complex tasks, with the highest MTA accuracy and the shortest time consumption. Therefore, the research on IoT devices’ MTA optimization based on RL in the MCS scenario provides a certain theoretical basis for subsequent MTA studies.
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Chiossi, Francesco, Robin Welsch, Steeven Villa, Lewis Chuang, and Sven Mayer. "Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience." Big Data and Cognitive Computing 6, no. 2 (2022): 55. http://dx.doi.org/10.3390/bdcc6020055.
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Virtual reality is increasingly used for tasks such as work and education. Thus, rendering scenarios that do not interfere with such goals and deplete user experience are becoming progressively more relevant. We present a physiologically adaptive system that optimizes the virtual environment based on physiological arousal, i.e., electrodermal activity. We investigated the usability of the adaptive system in a simulated social virtual reality scenario. Participants completed an n-back task (primary) and a visual detection (secondary) task. Here, we adapted the visual complexity of the secondary task in the form of the number of non-player characters of the secondary task to accomplish the primary task. We show that an adaptive virtual reality can improve users’ comfort by adapting to physiological arousal regarding the task complexity. Our findings suggest that physiologically adaptive virtual reality systems can improve users’ experience in a wide range of scenarios.
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Hearn, Jeff. "Sexualities, organizations and organization sexualities: Future scenarios and the impact of socio-technologies (a transnational perspective from the global ‘north’)." Organization 21, no. 3 (2014): 400–420. http://dx.doi.org/10.1177/1350508413519764.
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The article opens by briefly reviewing studies of sexuality in and around organizations from the 1970s. These studies showed considerable theoretical, empirical and conceptual development, as in the concept of organization sexuality. Building on this, the article’s first task is to analyse alternative future scenarios for organization sexualities, by way of changing intersections of gender, sexuality and organizational forms. Possible gendered future scenarios are outlined based on, first, gender equality/inequality and, second, gender similarity/difference between women, men and further genders: hyper-patriarchy scenario—men and women becoming more divergent; with greater inequality; late capitalist gender scenario—genders becoming more convergent, with greater inequality; bi-polar scenario—men and women becoming more divergent, with greater equality; postgender scenario—genders becoming more convergent, with greater equality. Somewhat similar scenarios for organization sexualities are elaborated in terms of gender/sexual equality and inequality and sexual/gender similarity and difference: heteropatriarchies scenario—greater sexual/gender difference and greater sexual or sexual/gender inequality; late capitalist sexual scenario—greater sexual/gender similarity and greater sexual or gender/sexual inequality; sexual differentiation scenario—greater sexual/gender difference and greater sexual or sexual/gender equality; sexual blurring scenario—greater sexual/gender similarity and greater sexual or sexual/gender equality. The article’s second task is to addresses the impact of globalizations and transnationalizations, specifically information and communication technologies and other socio-technologies, for future scenarios of organization sexualities. The characteristic affordances of ICTs—technological control, virtual reproducibility, conditional communality, unfinished undecidability—are mapped onto the four scenarios above and the implications outlined.
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Babilon, Sebastian, Janika Lenz, Sebastian Beck, et al. "Task-related Luminance Distributions for Office Lighting Scenarios." Light & Engineering, no. 01-2021 (February 2021): 115–28. http://dx.doi.org/10.33383/2020-073.
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For the design of modern office environments, lighting is a central aspect. With regard to current practice, uniform illumination is most often applied in interiors. In this paper, however, further aspects of a more individual approach are investigated, that deliberately violate the usual demands for uniformity by explicitly considering task-related, emotional and psychological effects of lighting. For this purpose, two independent experiments were conducted in an office mock-up setting exploring the impact of spatially variable, non-uniform light distributions on the users’ illumination preferences for the accomplishment of a given task. In the first experiment, three predefined illumination settings wererated by a group of naïve observers. Although the respective light distributions differed in their spatial characteristics, no significant differences were found in the rating scores. In addition, these variations showed no significant effect on the users’ preferred position of task performance. In the second experiment, though, a clearly significant effect could be reported such that, once the users were granted control over the illumination settings, an explicit demand for locally increased illuminance levels at the position of task performance was observed. Furthermore, high rating scores of perceived lighting adequacy indicate the users’ general satisfaction with the degree of visual assistance provided by such a task-related illumination.
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Waite, Kathryn, Tina Harrison, and Gary Hunter. "Exploring bank website expectations across two task scenarios." Journal of Financial Services Marketing 16, no. 1 (2011): 76–85. http://dx.doi.org/10.1057/fsm.2011.6.
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Cao, Shi, and Yili Liu. "Medical Decision Making Performance in Dual-task Scenarios." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 57, no. 1 (2013): 733–37. http://dx.doi.org/10.1177/1541931213571160.
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Nouri, Farhad, and Dr Mohammadreza Nouri. "ADAPTIVE SIMILARITY-DRIVEN APPROACHES FOR CONTINUAL LEARNING: BRIDGING TASK-AWARE AND TASK-FREE PARADIGMS." International Journal of Advanced Artificial Intelligence Research 2, no. 1 (2025): 1–6. https://doi.org/10.55640/ijaair-v02i01-01.
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Continual learning aims to enable models to learn sequential tasks without forgetting previously acquired knowledge. This paper presents an adaptive similarity-driven framework that bridges the gap between task-aware and task-free paradigms in continual learning. By leveraging similarity metrics to dynamically adjust learning strategies based on incoming data distributions, the proposed approach allows models to maintain performance across tasks without relying on explicit task boundaries. Experimental evaluations on benchmark datasets demonstrate that the adaptive similarity-driven method outperforms traditional task-aware and task-free models in mitigating catastrophic forgetting while preserving scalability. The findings offer a promising direction for developing flexible and efficient continual learning systems adaptable to real-world scenarios.
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Medhi, Jishu K., Pusheng Ren, Mengsha Hu, and Xuhui Chen. "A Deep Multi-Task Learning Approach for Bioelectrical Signal Analysis." Mathematics 11, no. 22 (2023): 4566. http://dx.doi.org/10.3390/math11224566.
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Deep learning is a promising technique for bioelectrical signal analysis, as it can automatically discover hidden features from raw data without substantial domain knowledge. However, training a deep neural network requires a vast amount of labeled samples. Additionally, a well-trained model may be sensitive to the study object, and its performance may deteriorate sharply when transferred to other study objects. We propose a deep multi-task learning approach for bioelectrical signal analysis to address these issues. Explicitly, we define two distinct scenarios, the consistent source-target scenario and the inconsistent source-target scenario based on the motivation and purpose of the tasks. For each scenario, we present methods to decompose the original task and dataset into multiple subtasks and sub-datasets. Correspondingly, we design the generic deep parameter-sharing neural networks to solve the multi-task learning problem and illustrate the details of implementation with one-dimension convolutional neural networks (1D CNN), vanilla recurrent neural networks (RNN), recurrent neural networks with long short-term memory units (LSTM), and recurrent neural networks with gated recurrent units (GRU). In these two scenarios, we conducted extensive experiments on four electrocardiogram (ECG) databases. The results demonstrate the benefits of our approach, showing that our proposed method can improve the accuracy of ECG data analysis (up to 5.2%) in the MIT-BIH arrhythmia database.
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Newby-Clark, Ian R., Michael Ross, Roger Buehler, Derek J. Koehler, and Dale Griffin. "People focus on optimistic scenarios and disregard pessimistic scenarios while predicting task completion times." Journal of Experimental Psychology: Applied 6, no. 3 (2000): 171–82. http://dx.doi.org/10.1037/1076-898x.6.3.171.
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Funkhouser, Kelly, and Frank Drews. "Putting the Brakes on Autonomous Vehicle Control." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (2016): 1859–63. http://dx.doi.org/10.1177/1541931213601424.
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The assimilation of automation in commuter vehicles is rapidly increasing, as too are the concerns with these technologies. Human interaction with autonomous vehicles must be thoroughly researched to understand the quantification and qualification of interactive behaviors with these systems. We developed a study using a high-fidelity driving simulator to mimic probable breakdowns with these systems to better understand the subsequent human responses and to explore the necessary technological requirements to overcome potential problems. 30 participants engaged in a driving scenario switching between manual and autonomous vehicle control. We accounted for individual differences in braking reaction time while simultaneously engaging in a secondary cognitive task during times of autonomous vehicle control. Results show the average RT for baseline scenarios without the cognitive task was 832.1 milliseconds while the average RT for baseline scenarios with the cognitive task was 908.4 milliseconds; a 9.17% significant increase. The average RT for the autonomous scenario was 1357.0 milliseconds; a significant increase of 49.38% over the baseline scenario with the cognitive task that can be attributed to the addition of automation. We found a positive linear correlation of time spent in autonomous control and subsequent braking reaction time. Additionally, cognitive task difficulty, attention allocation, self-reported mental demand, fatigue, and heart rate affect reaction time when cued to take control of the vehicle.
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Taylor, Skye, Katherine Garcia, Jing Chen*, and Bin Hu. "Adaptive Task Allocation Preferences in Different Workload Scenarios in Driving Automation Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (2022): 918–22. http://dx.doi.org/10.1177/1071181322661426.
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Adaptive task allocation is used in many human-machine systems and has been proven to improve operators’ performance with automated systems. However, there has been limited knowledge surrounding the benefits of adaptive task allocation in automated vehicles. In this study, participants were presented with photos and videos depicting driving scenarios of low or high workloads at two levels of automation (SAE Levels 2 and 3). The participants reported which tasks they felt comfortable allocating to themselves or to the driving automation system (DAS) in each driving scenario, as well as whether they would conduct the task allocation manually or have the DAS automatically allocate the tasks. Our results showed that participants preferred conducting manual task allocation and preferred the system to complete more tasks when the perceived workload was high. There was no significant difference between the high and low workload scenarios in terms of whether participants chose to allocate tasks.
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Lacey, Heather P., Angela Fagerlin, George Loewenstein, Dylan M. Smith, Jason Riis, and Peter A. Ubel. "It must be awful for them: Perspective and task context affects ratings for health conditions." Judgment and Decision Making 1, no. 2 (2006): 146–52. http://dx.doi.org/10.1017/s1930297500002357.
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AbstractWhen survey respondents rate the quality of life (QoL) associated with a health condition, they must not only evaluate the health condition itself, but must also interpret the meaning of the rating scale in order to assign a specific value. The way that respondents approach this task depends on subjective interpretations, resulting in inconsistent results across populations and tasks. In particular, patients and non-patients often give very different ratings to health conditions, a discrepancy that raises questions about the objectivity of either groups’ evaluations. In this study, we found that the perspective of the raters (i.e., their own current health relative to the health conditions they rated) influences the way they distinguish between different health states that vary in severity. Consistent with prospect theory, a mild and a severe lung disease scenario were rated quite differently by lung disease patients whose own health falls between the two scenarios, whereas healthy non-patients, whose own health was better than both scenarios, rated the two scenarios as much more similar. In addition, we found that the context of the rating task influences the way participants distinguish between mild and severe scenarios. Both patients and non-patients gave less distinct ratings to the two scenarios when each were presented in isolation than when they were presented alongside other scenarios that provided contextual information about the possible range of severity for lung disease. These results raise continuing concerns about the reliability and validity of subjective QoL ratings, as these ratings are highly sensitive to differences between respondent groups and the particulars of the rating task.
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Lu, Hongtao, Xiang Diao, Yonghui Yuan, Jinfeng Qiao, and Fan Yang. "Research on Task Scenario-oriented Information User Concern Mining Technology." SHS Web of Conferences 165 (2023): 01009. http://dx.doi.org/10.1051/shsconf/202316501009.
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With the rapid evolution of war forms and the profound reform of combat forms, battlefield intelligence information will present complex and diverse characteristics. Faced with a large number of complex intelligence information, it is difficult for users to obtain the key information in the battlefield to make real-time decision aid by using traditional methods. Based on the analysis of user interaction behavior rules of intelligence users of different levels, different identities and different specialties facing different task scenarios and different battle stages, the information that users pay attention to, the way that users want to display information and the way that users want to input information can be obtained. Firstly, the task scene is modeled, and the current task scene can be sensed in real time. Secondly, the user behavior under the current scenario is collected and stored in the user behavior analysis sample database. Finally, the user behavior analysis model is constructed, and the model outputs the content that the current user pays attention to, the desired presentation way and the desired information input way. The mission-scenario-oriented information user concern mining technology is an important component of the mission-scenario-oriented information product adaptive service technology, which can support the collection of operational user behavior and the analysis of behavior rules in the interactive process of operational information. This technology mainly studied user interaction behavior preferences facing different task scenarios, and provides basis for information recommendation and information adaptive presentation.
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Whyte, James, David W. Eccles, and Maria D. Whyte. "Novice nurses’ attention to task-relevant stimuli during practice." Journal of Nursing Education and Practice 13, no. 4 (2022): 7. http://dx.doi.org/10.5430/jnep.v13n4p7.
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Objective: Nurses engaged in practice make split-second decisions based on stimuli perceived in the clinical environment. There has been limited research in nursing on stimuli perception and limited research aimed specifically at directly measuring nurses’ gaze and the subsequent quality of their decisions.Methods: This study used an observational descriptive design to examine nurses’ gaze behaviors as they cared for a simulated patient in three different clinical scenarios. Participants were fitted with eye-tracking goggles that facilitated the recording on video of the focal point of their gaze. The recorded videos were coded to quantify the participants’ areas of focus. For each scenario, visual focus data were compared between participants who successfully resolved the scenarios and those who did not. Results: The results revealed statistically significant differences in areas of focus between successful and unsuccessful participants. While successful participants focused on the patient, unsuccessful participants focused on task-irrelevant environmental cues.Conclusions: The results demonstrate a need for nurse educators to focus their students on the patient, while guiding them to avoid becoming mired in task irrelevant foci and actions.
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Nasrullayeva, Shahina. "THE TASK-BASED APPROACH IN LANGUAGE TEACHING." "Ilm-Fan" Journal of Science 1, no. 1 (2023): 29–33. https://doi.org/10.5281/zenodo.7952351.
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This article explores the principles of the task-based approach in language teaching, which emphasizes communication and real-life situations over rote memorization of grammar rules and vocabulary. Tasks are designed to be meaningful and relevant to learners' needs and interests, and often involve collaboration with others such as planning a trip, conducting interviews, and roleplaying scenarios. Overall, the task-based approach is seen as an effective way to develop communication skills and promote active, student-centered learning.
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Li, Shasha, Xiaodong Bai, and Songjie Wei. "Blockchain-Based Crowdsourcing Framework with Distributed Task Assignment and Solution Verification." Security and Communication Networks 2022 (March 19, 2022): 1–16. http://dx.doi.org/10.1155/2022/9464308.
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Internet backboned crowdsourcing utilizes network-wide resources to solve complicated and large-scale tasks, which are not accomplishable for independent individuals. Existing crowdsourcing platforms are mostly centralized solutions with reliability and trustworthiness fragile to single-point failures on the central servers. The innovation of distributed ledgers as blockchain inspires us to optimize the traditional crowdsourcing procedure with distributed sustainability. We propose a blockchain-based design of the distributed secure crowdsourcing scheme for task distribution and result verification without relying on any third trusted institution. A preference-based task distribution (PTD) mechanism is presented which guarantees the percentage of task distribution and the satisfaction of the chosen workers. Task works are continuously assessed for reputations based on their historical behaviors. Task completion correctness is verified by blockchain consensus in two different scenarios after workers submit their results with reputations. We implement a prototype system based on the Ethereum chain with PTD and solution verification components. With various tasks and scenarios evaluated in the system, the proposed distributed crowdsourcing framework shows system reliability, data security, and scenario feasibility.
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Wang, Qiang, Xingye Han, Weizhen He, and Yongsheng Cheng. "Research on multi-UAV hierarchical task allocation in large-scale scenarios." Journal of Physics: Conference Series 2478, no. 10 (2023): 102023. http://dx.doi.org/10.1088/1742-6596/2478/10/102023.
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Abstract The task allocation problem of multiple UAVs in large-scale scenarios has the characteristics of large amount of computation, poor real-time performance, non-convergence or slow convergence. Therefore, this paper designs a hierarchical task allocation method to solve the task allocation problem when multiple UAVs perform large-scale tasks. The problem is divided into two sub-problems, task clustering and task ranking, by using a hierarchical structure, which effectively reduces the scale of solution. The high-level task clustering is used to determine which UAV performs which tasks, and the clustering algorithm based on auction criteria is used to perform task clustering; the bottom-level task sorting is used to obtain the best task execution sequence for the UAV to perform its corresponding tasks. The ant colony algorithm(ACO) is used to sort tasks. The simulation results show that the proposed algorithm can effectively solve the multi-UAV large-scale task allocation problem, and has good realtime performance and convergence.
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Shi, Minglin, Xiaoqi Zhang, Jia Chen, and Hongju Cheng. "UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios." Applied Sciences 13, no. 8 (2023): 4724. http://dx.doi.org/10.3390/app13084724.
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Natural disasters often have an unpredictable impact on human society and can even cause significant problems, such as damage to communication equipment in disaster areas. In such post-disaster emergency rescue situations, unmanned aerial vehicles (UAVs) are considered an effective tool by virtue of high mobility, easy deployment, and flexible communication. However, the limited size of UAVs leads to bottlenecks in battery capacity and computational power, making it challenging to perform overly complex computational tasks. In this paper, we propose a UAV cluster-assisted task-offloading model for disaster areas, by adopting UAV clusters as aerial mobile edge servers to provide task-offloading services for ground users. In addition, we also propose a deep reinforcement learning-based UAV cluster-assisted task-offloading algorithm (DRL-UCTO). By modeling the energy efficiency optimization problem of the system model as a Markov decision process and jointly optimizing the UAV flight trajectory and task-offloading policy to maximize the reward value, DRL-UCTO can effectively improve the energy use efficiency of UAVs under limited-resource conditions. The simulation results show that the DRL-UCTO algorithm improves the UAV energy efficiency by about 79.6% and 301.1% compared with the DQN and Greedy algorithms, respectively.
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Littleton, Karen, Paul Light, Richard Joiner, David Messer, and Peter Barnes. "Gender, Task Scenarios and Children's Computer‐based Problem Solving." Educational Psychology 18, no. 3 (1998): 327–40. http://dx.doi.org/10.1080/0144341980180306.
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Lilin, Yang, Li Guyue, Guo Tao, Xu Hao, and Hu Aiqun. "Physical-layer secret key generation for dual-task scenarios." China Communications 21, no. 7 (2024): 252–66. http://dx.doi.org/10.23919/jcc.ja.2023-0091.
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Paternò, Fabio. "Commentary on ‘scenarios and task analysis’ by Dan Diaper." Interacting with Computers 14, no. 4 (2002): 407–9. http://dx.doi.org/10.1016/s0953-5438(02)00008-5.
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Carey, Tom. "Commentary on “scenarios and task analysis” by Dan Diaper." Interacting with Computers 14, no. 4 (2002): 411–12. http://dx.doi.org/10.1016/s0953-5438(02)00009-7.
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Morton, Hazel, Nancie Gunson, and Mervyn Jack. "Interactive Language Learning through Speech-Enabled Virtual Scenarios." Advances in Human-Computer Interaction 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/389523.
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This paper describes the evaluation of an educational game designed to give learners of foreign languages the opportunity to practice their spoken language skills. Within the speech interactive Computer-Assisted Language Learning (CALL) program, scenarios are presented in which learners interact with virtual characters in the target language using speech recognition technology. Two types of interactive scenarios with virtual characters are presented as part of the game: the one-to-one scenarios which take the form of practice question and answer scenarios where the learner interacts with one virtual character and the interactive scenario which is an immersive contextualised scenario where the learner interacts with two or more virtual characters within the scene to complete a (task-based) communicative goal. The study presented here compares learners’ subjective attitudes towards the different scenarios. In addition, the study investigates the performance of the speech recognition component in this game. Forty-eight students of English as a Foreign Language (EFL) took part in the evaluation. Results indicate that learners’ subjective ratings for the contextualised interactive scenario are higher than for the one-to-one, practice scenarios. In addition, recognition performance was better for these interactive scenarios.
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Wang, Jing, Peng Yang, Yuansheng Liu, et al. "Research on Improved YOLOv5 for Low-Light Environment Object Detection." Electronics 12, no. 14 (2023): 3089. http://dx.doi.org/10.3390/electronics12143089.
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Object detection in low-light scenarios has been widely acknowledged as a significant research area in the field of computer vision, presenting a challenging task. Aiming at the low detection accuracy of mainstream single-stage object detection models in low-light scenarios, this paper proposes a detection model called DK_YOLOv5 based on YOLOv5, specifically designed for such scenarios. First, a low-light image enhancement algorithm with better results is selected to generate enhanced images that achieve relatively better visual effects and amplify target features. Second, the SPPF layer is improved to an R-SPPF module with faster inference speed and stronger feature expression ability. Next, we replace the C3 module with the C2f module and incorporate an attention mechanism to develop the C2f_SKA module, enabling richer gradient information flow and reducing the impact of noise features. Finally, the model detection head is replaced with a decoupled head suitable for the object detection task in this scenario to improve model performance. Additionally, we expand the Exdark dataset to include low-light data of underground mine scenario targets, named Mine_Exdark. Experimental results demonstrate that the proposed DK_YOLOv5 model achieves higher detection accuracy than other models in low-light scenarios, with an mAP0.5 of 71.9% on the Mine_Exdark dataset, which is 4.4% higher than that of YOLOv5.
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Skiter, I. S. "Comprehensive Assessment of the Chornobyl Exclusion Zone Wildfires Impact on the 100-km Area Around the Chornobyl NPP." Nuclear Power and the Environment 27, no. 2 (2023): 67–76. http://dx.doi.org/10.31717/2311-8253.23.2.7.
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The subject of the study is the New Safe Confinement. Its commissioning is the second stage of the task to transform the Shelter object into an ecologically safe system. Works on the implementation of the third stage require the election of scenarios for the Shelter object transformation into an ecologically safe system. Approaches to the selection of scenarios should be based on the development of a methodology for their comparative analysis. The object of this study is a complex system, and the development of a methodology for assessing scenarios should be based on the consideration of a set of qualitatively heterogeneous factors and indicators. Therefore, the task of creating a methodology for comparative analysis of scenarios requires, in addition to the analysis of technical, technological, economic, and financial indicators that will accompany the Shelter object transformation process, a systematic approach to assessing and comparing the states of the research object, decision-making and management methods. The presented research related to the systematic approach to the formation of a methodology for comparative analysis of scenarios for transforming the Shelter object into an ecologically safe system. Indicators and factors for assessing scenarios were identified and grouped according to environmental safety criteria. A systematic approach to the implementation of the scenario assessment methodology using three classes of methods is proposed. The systematic nature of determining the values of scenarios is realized by the stages of research. At the first stage of the analysis, a methodology for qualitative comparison of scenarios based on peer reviews is proposed. A multi-criteria optimization methodology for parametric detailing of scenario values and decision-making was proposed in the second stage. At the third stage of the systematic approach, an algorithm for constructing multifactor models for determining control parameters was developed.
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Tao, Feifei, Yanling Pi, Meng Zhang, Chi Yuan, and Menghua Deng. "Hidden Danger Association Mining for Water Conservancy Projects Based on Task Scenario-Driven." Water 15, no. 15 (2023): 2814. http://dx.doi.org/10.3390/w15152814.
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With the rapid development of water conservancy engineering and infrastructure construction, there are many safety hazards in the construction process of water conservancy engineering, so it is of great significance to study the potential hazards in the construction process. In this context, this paper proposes a task scenario-based association mining method for hydraulic engineering hidden danger records. By analyzing transaction characteristics, the traditional Apriori algorithm is improved to optimize pruning results and generate hidden danger association rules. The research results of this paper have been successfully applied to the investigation and management of hidden dangers in the Xinmenghe dredging project. Based on the mapping of association rules driven by task scenarios, hidden dangers association rules in specific task scenarios are mined to assist construction safety managers in hidden dangers investigation, which reduces the complexity of the algorithm, reduces the running time of the algorithm and improves the efficiency of the algorithm.
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Zhang, Xue Cheng, Rong Xu, and Qi Xun Liu. "Development of Desktop Virtual Maintenance Training System with Dynamically Configured Scenarios." Advanced Materials Research 798-799 (September 2013): 476–79. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.476.
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This paper represents an engineering method to develop a desktop-based interactive virtual maintenance training system, which could configure scenarios dynamically. The interactions of maintenance training scenarios are constructed by hierarchical Petri net. The maintenance knowledge and procedures of assembly/disassembly in virtual environment are presented by using high level Petri net. Each maintenance task is decomposed into a set of sequential basic action element done through left/right hand or both hands. The Petri net presentation of coordination and execution of each task helps to configure scenarios dynamically. This method is conducive to the interactive representation of task and action scenarios in the virtual maintenance training system.
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Panteleev, E. R., A. A. Mukuchan, M. A. Kuznetzov, and A. L. Alykova. "Method of context-dependent assistance for software user solving an applied task." Vestnik IGEU, no. 5 (December 30, 2020): 64–76. http://dx.doi.org/10.17588/2072-2672.2020.5.064-076.
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When using software to solve the applied tasks, the problem to implement the action in need by means of user interface arises. Partly, this problem is solved by studying reference manuals and consultations with the application developers. However, reference manuals are structured in the context of overall application functionality while difficulties arise in the data state context of the task being solved. Consultations lack this flaw, but they are costly and not always available in time. This fact stimulated the development of computer-based methods of contextual help. Yet the task of development of recommendations for performing the requested operation from the current state of the application data has not been solved so far. The research is aimed to reduce the time to get help by developing a knowledge representation model that determines user actions across the application data context, and a method for deriving model based recommendations. The model of the user action scenario is presented in the form of a colored Petri net. This decision content is based on the analogy between user action scenarios and workflow scenarios, for which Petri nets notation has been successfully used for years. For the topological analysis of the Petri net, the strategy of exhaustive depth-first search was applied. The method of the contextual recommendations is proposed to execute the operation requested by the user based on a scenario model in the form of a colored Petri net. The method novelty is application of topological analysis of the Petri net to construct a set of alternative scenarios for performing the operation, followed by filtering alternatives in the process of stepwise execution of the recommended actions. The suggested method provides context-dependent assistance in just one click. When using traditional reference manuals and files for the purpose, the number of clicks is determined by the number of options available to perform the operation.
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Guo, Yibei, Yijiang Pang, Joseph Lyons, Michael Lewis, Katia Sycara, and Rui Liu. "Trust-Aware Reflective Control for Fault-Resilient Dynamic Task Response in Human–Swarm Cooperation." AI 5, no. 1 (2024): 446–64. http://dx.doi.org/10.3390/ai5010022.
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Due to the complexity of real-world deployments, a robot swarm is required to dynamically respond to tasks such as tracking multiple vehicles and continuously searching for victims. Frequent task assignments eliminate the need for system calibration time, but they also introduce uncertainty from previous tasks, which can undermine swarm performance. Therefore, responding to dynamic tasks presents a significant challenge for a robot swarm compared to handling tasks one at a time. In human–human cooperation, trust plays a crucial role in understanding each other’s performance expectations and adjusting one’s behavior for better cooperation. Taking inspiration from human trust, this paper introduces a trust-aware reflective control method called “Trust-R”. Trust-R, based on a weighted mean subsequence reduced algorithm (WMSR) and human trust modeling, enables a swarm to self-reflect on its performance from a human perspective. It proactively corrects faulty behaviors at an early stage before human intervention, mitigating the negative influence of uncertainty accumulated from dynamic tasks. Three typical task scenarios {Scenario 1: flocking to the assigned destination; Scenario 2: a transition between destinations; and Scenario 3: emergent response} were designed in the real-gravity simulation environment, and a human user study with 145 volunteers was conducted. Trust-R significantly improves both swarm performance and trust in dynamic task scenarios, marking a pivotal step forward in integrating trust dynamics into swarm robotics.
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Breeden, Joseph L., and Maxim Vaskouski. "Predicting economists: Generating scenarios for stress testing future loss reserves." International Journal of Financial Engineering 08, no. 03 (2021): 2142004. http://dx.doi.org/10.1142/s2424786321420044.
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Stress testing under the US Comprehensive Capital Analysis and Review (CCAR) regulations and those of many other countries seeks to assess the full possible financial position of a lender through an economic crisis. The introduction of lifetime loan loss reserves under FASB’s Current Expected Credit Loss (CECL) and IASB’s International Financial Reporting Standards 9 (IFRS 9) rules complicates the task of stress testing, because lenders need to estimate future losses using scenarios that are contingent on the stress testing scenario, but without perfect foresight of the future stress test scenario. This work casts the CECL and IFRS 9 stress testing problem as one of generating future economic scenarios that are consistent with how future economists would create scenarios. To that end, we obtained historic consensus economic scenarios for testing. The results here demonstrate that a second-order Ornstein–Uhlenbeck model fits historic scenarios well and could be used to generate future scenarios that would be a realistic representation of what economists would predict given economic conditions up to that point. This approach was tested for US real gross domestic product (RGDP) and unemployment rate scenarios through the 2009 recession. The RGDP modeling was straight-forward, but we discovered that consensus economic scenarios for unemployment rate appear to be conditional on the phase of the economy.
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Kim, Jung In, Young Jae Lee, Jongkook Heo, et al. "Sample-efficient multi-agent reinforcement learning with masked reconstruction." PLOS ONE 18, no. 9 (2023): e0291545. http://dx.doi.org/10.1371/journal.pone.0291545.
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Deep reinforcement learning (DRL) is a powerful approach that combines reinforcement learning (RL) and deep learning to address complex decision-making problems in high-dimensional environments. Although DRL has been remarkably successful, its low sample efficiency necessitates extensive training times and large amounts of data to learn optimal policies. These limitations are more pronounced in the context of multi-agent reinforcement learning (MARL). To address these limitations, various studies have been conducted to improve DRL. In this study, we propose an approach that combines a masked reconstruction task with QMIX (M-QMIX). By introducing a masked reconstruction task as an auxiliary task, we aim to achieve enhanced sample efficiency—a fundamental limitation of RL in multi-agent systems. Experiments were conducted using the StarCraft II micromanagement benchmark to validate the effectiveness of the proposed method. We used 11 scenarios comprising five easy, three hard, and three very hard scenarios. We particularly focused on using a limited number of time steps for each scenario to demonstrate the improved sample efficiency. Compared to QMIX, the proposed method is superior in eight of the 11 scenarios. These results provide strong evidence that the proposed method is more sample-efficient than QMIX, demonstrating that it effectively addresses the limitations of DRL in multi-agent systems.
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GÜNDÜZALP, Emrullah, Güngör YILDIRIM, and Yetkin TATAR. "Efficient Task Scheduling in Cloud Systems with Adaptive Discrete Chimp Algorithm." Balkan Journal of Electrical and Computer Engineering 10, no. 3 (2022): 328–36. http://dx.doi.org/10.17694/bajece.989467.
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Successful task scheduling is one of the priority actions to increase energy efficiency, commercial earnings, and customer satisfaction in cloud computing. On the other hand, since task scheduling processes are NP-hard problems, it is difficult to talk about an absolute solution, especially in scenarios with large task numbers. For this reason, metaheuristic algorithms are frequently used in solving these problems. This study focuses on the metaheuristic-based solution of optimization of makespan, which is one of the important scheduling problems of cloud computing. The adapted Chimp Optimization Algorithm, with enhanced exploration and exploitation phases, is proposed for the first time to solve these problems. The solutions obtained from this adapted algorithm, which can use different mathematical functions, are discussed comparatively. The proposed solutions are also tested in the CloudSim simulator for different scenarios and they prove their performance in the cloud environment.
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GÜNDÜZALP, Emrullah, Güngör YILDIRIM, and Yetkin TATAR. "Efficient Task Scheduling in Cloud Systems with Adaptive Discrete Chimp Algorithm." Balkan Journal of Electrical and Computer Engineering 10, no. 3 (2022): 328–36. http://dx.doi.org/10.17694/bajece.989467.
Full textAbstract:
Successful task scheduling is one of the priority actions to increase energy efficiency, commercial earnings, and customer satisfaction in cloud computing. On the other hand, since task scheduling processes are NP-hard problems, it is difficult to talk about an absolute solution, especially in scenarios with large task numbers. For this reason, metaheuristic algorithms are frequently used in solving these problems. This study focuses on the metaheuristic-based solution of optimization of makespan, which is one of the important scheduling problems of cloud computing. The adapted Chimp Optimization Algorithm, with enhanced exploration and exploitation phases, is proposed for the first time to solve these problems. The solutions obtained from this adapted algorithm, which can use different mathematical functions, are discussed comparatively. The proposed solutions are also tested in the CloudSim simulator for different scenarios and they prove their performance in the cloud environment.
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Zhang, Guoxi, and Robert G. Feyen. "A Conceptual Framework for Dynamic Prioritization in Multiple-Task Scenarios." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 12 (2005): 1172–75. http://dx.doi.org/10.1177/154193120504901216.
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Prioritizing tasks appropriately is particularly critical when performing multiple tasks concurrently. Although necessary to achieve one's goals or avoid serious consequences, prioritization has not received much attention in the research literature, especially with respect to modeling human performance computationally. A conceptual framework that integrates several motivational theories, empirical studies, and neuroscience research is proposed to guide future studies of dynamic prioritization in multiple-goal contexts. Rooted in control theory, the proposed framework illustrates self-regulation processes in prioritizing tasks and explicitly shows important factors affecting the prioritization process so that empirical results can be integrated into the framework and future studies can be inferred. By illustrating information flow in the self-regulation processes and the brain structures associated with prioritization, the framework should help facilitate development of robust computational models of task prioritization.
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Eryani, Rambu Ayu, and Rahmania Sri Untari. "Feasibility of VR Task Scenarios Using RBS in Sketching Subject." JICTE (Journal of Information and Computer Technology Education) 9, no. 1 (2025): 18–26. https://doi.org/10.21070/jicte.v9i1.1670.
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The limitations of learning media have become an urgency in the Sketching and Illustration subject for designing VR scenario designs for literacy tourism. This study aims to measure the feasibility of validation by media experts and subject matter experts. The method used in this research is the Rule-Based System (RBS) with an explore mode. The validation results from media experts obtained a score of 95%, categorized as highly feasible, while the validation results from subject matter experts obtained a score of 84%, also categorized as highly feasible. These results indicate that the designed task scenario meets feasibility standards in terms of both media and content, making it an effective interactive learning solution. Highlights: High feasibility scores from both media and subject experts. Rule-Based System used for evaluation in explore mode. Effective solution for interactive learning in literacy tourism. Keywords: Virtual Reality, Rule-Based System, Sketch, Vocational High School
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Zhu, Si-feng, Cheng-tai Liu, Hai Zhu, Hao Chen, Rui Qiao, and Xiao-yu Wu. "DRL-based structured task offloading decision in intelligent transportation scenarios." Applied Soft Computing 171 (March 2025): 112770. https://doi.org/10.1016/j.asoc.2025.112770.
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Zhu-Zhou, Fangfang, Roberto Gil-Pita, Joaquín García-Gómez, and Manuel Rosa-Zurera. "Robust Multi-Scenario Speech-Based Emotion Recognition System." Sensors 22, no. 6 (2022): 2343. http://dx.doi.org/10.3390/s22062343.
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Every human being experiences emotions daily, e.g., joy, sadness, fear, anger. These might be revealed through speech—words are often accompanied by our emotional states when we talk. Different acoustic emotional databases are freely available for solving the Emotional Speech Recognition (ESR) task. Unfortunately, many of them were generated under non-real-world conditions, i.e., actors played emotions, and recorded emotions were under fictitious circumstances where noise is non-existent. Another weakness in the design of emotion recognition systems is the scarcity of enough patterns in the available databases, causing generalization problems and leading to overfitting. This paper examines how different recording environmental elements impact system performance using a simple logistic regression algorithm. Specifically, we conducted experiments simulating different scenarios, using different levels of Gaussian white noise, real-world noise, and reverberation. The results from this research show a performance deterioration in all scenarios, increasing the error probability from 25.57% to 79.13% in the worst case. Additionally, a virtual enlargement method and a robust multi-scenario speech-based emotion recognition system are proposed. Our system’s average error probability of 34.57% is comparable to the best-case scenario with 31.55%. The findings support the prediction that simulated emotional speech databases do not offer sufficient closeness to real scenarios.
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Liang, Lidong, Liangheng Zhu, Wenyou Jia, and Xiaoliang Cheng. "Multirobot Task Planning Method Based on the Energy Penalty Strategy." Applied Sciences 13, no. 8 (2023): 4887. http://dx.doi.org/10.3390/app13084887.
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In multirobot task planning, the goal is to meet the multi-objective requirements of the optimal and balanced energy consumption of robots. Thus, this paper introduces the energy penalty strategy into the GA (genetic algorithm) to achieve the optimization of the task planning of multiple robots in different operation scenarios. First, the algorithm model is established, after which the objective function is constructed by taking the energy excess of the relative average energy consumption of each robot as the penalty energy, along with the total energy consumption of multiple robots. In the genetic operation, two-segment chromosome coding is used to realize the iterative optimization of the number and task sequences of robots through diversified cross and mutation operators. Then, in the task scenario with obstacles, the A* (A-Star) algorithm and GA are used to plan the optimal obstacle avoidance path and to realize the secondary optimization of the robot task sequence without changing the number of tasks. During optimization, the energy penalty strategy imposes punishment on the objective function through the size of the penalty energy, enabling the robot energy consumption to reach an equilibrium state by maintaining the total energy consumption at the minimum. Finally, the MATLAB platform is used to conduct the simulation experiments to compare with basic genetic algorithms and penalty function algorithms, after which the optimal allocation scheme and energy consumption iteration of the algorithm are analyzed under different robot numbers, task numbers, and task scenarios, and the simulation results include robot task sequences, total energy consumption, average energy consumption, and standard deviation of energy consumption.
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Neale, Dennis C., and Jonathan K. Kies. "Scenario-Based Design for Human-Computer Interface Development." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 6 (1996): 338–42. http://dx.doi.org/10.1177/154193129604000604.
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Scenario-based techniques have been receiving increased attention in the design of human-computer interaction. A cohesive methodology or framework, however, has yet to materialize, and scenario methods have not been well defined. Claims are being made about the ability of scenarios to play a role throughout the development life cycle. The objective of this paper is to examine the ability of scenarios to serve as the primary design representations early in the system design life cycle for envisioning the system, requirements specification, user-designer communication, and design rationale. These findings represent a case study in the design of a world-wide web site for the Human Factors Engineering Center at Virginia Tech. Example-based narratives were elicited using a “micro-scenario” generating task that involved prospective end-users brainstorming user-system interactions. Conclusions are drawn about the effectiveness of the technique for system development, and guidelines are provided for using scenarios to specify behavioral requirements.
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Li, Yu, Yi Zhang, Lu Gan, Gengwei Hong, Zimu Zhou, and Qiang Li. "RevMan: Revenue-aware Multi-task Online Insurance Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 303–10. http://dx.doi.org/10.1609/aaai.v35i1.16105.
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Online insurance is a new type of e-commerce with exponential growth. An effective recommendation model that maximizes the total revenue of insurance products listed in multiple customized sales scenarios is crucial for the success of online insurance business. Prior recommendation models are ineffective because they fail to characterize the complex relatedness of insurance products in multiple sales scenarios and maximize the overall conversion rate rather than the total revenue. Even worse, it is impractical to collect training data online for total revenue maximization due to the business logic of online insurance. We propose RevMan, a Revenue-aware Multi-task Network for online insurance recommendation. RevMan adopts an adaptive attention mechanism to allow effective feature sharing among complex insurance products and sales scenarios. It also designs an efficient offline learning mechanism to learn the rank that maximizes the expected total revenue, by reusing training data and model for conversion rate maximization. Extensive offline and online evaluations show that RevMan outperforms the state-of-the-art recommendation systems for e-commerce.