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172 results on '"Zhang, Qiping"'

1. REACT: Two Datasets for Analyzing Both Human Reactions and Evaluative Feedback to Robots Over Time

Author

Candon, Kate, Georgiou, Nicholas C., Zhou, Helen, Richardson, Sidney, Zhang, Qiping, Scassellati, Brian, and Vázquez, Marynel

Subjects
Computer Science - Robotics, Computer Science - Human-Computer Interaction
Abstract

Recent work in Human-Robot Interaction (HRI) has shown that robots can leverage implicit communicative signals from users to understand how they are being perceived during interactions. For example, these signals can be gaze patterns, facial expressions, or body motions that reflect internal human states. To facilitate future research in this direction, we contribute the REACT database, a collection of two datasets of human-robot interactions that display users' natural reactions to robots during a collaborative game and a photography scenario. Further, we analyze the datasets to show that interaction history is an important factor that can influence human reactions to robots. As a result, we believe that future models for interpreting implicit feedback in HRI should explicitly account for this history. REACT opens up doors to this possibility in the future.

Published
2024

3. Predicting Human Impressions of Robot Performance During Navigation Tasks

Author

Zhang, Qiping, Tsoi, Nathan, Nagib, Mofeed, Choi, Booyeon, Tan, Jie, Chiang, Hao-Tien Lewis, and Vázquez, Marynel

Subjects
Computer Science - Robotics, Computer Science - Machine Learning
Abstract

Human impressions of robot performance are often measured through surveys. As a more scalable and cost-effective alternative, we investigate the possibility of predicting people's impressions of robot behavior using non-verbal behavioral cues and machine learning techniques. To this end, we first contribute the SEAN TOGETHER Dataset consisting of observations of an interaction between a person and a mobile robot in a VR simulation, together with impressions of robot performance provided by users on a 5-point scale. Second, we contribute analyses of how well humans and supervised learning techniques can predict perceived robot performance based on different observation types (like facial expression features, and features that describe the navigation behavior of the robot and pedestrians). Our results suggest that facial expressions alone provide useful information about human impressions of robot performance; but in the navigation scenarios that we considered, reasoning about spatial features in context is critical for the prediction task. Also, supervised learning techniques showed promise because they outperformed humans' predictions of robot performance in most cases. Further, when predicting robot performance as a binary classification task on unseen users' data, the F1 Score of machine learning models more than doubled in comparison to predicting performance on a 5-point scale. This suggested that the models can have good generalization capabilities, although they are better at telling the directionality of robot performance than predicting exact performance ratings. Based on our findings in simulation, we conducted a real-world demonstration in which a mobile robot uses a machine learning model to predict how a human that follows it perceives it. Finally, we discuss the implications of our results for implementing such supervised learning models in real-world navigation scenarios.

Published
2023

4. Games that Move You: A Cinematic User Experience Evaluation of VR Games

Author

Bedard, Andrew, Bedard, Kevin, Zhang, Qiping, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Chen, Jessie Y. C., editor, and Fragomeni, Gino, editor

Published
2024
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5. A Survey Study on U.S. College Students' Learning Experience in COVID-19

Author

Zhou, Jiahua and Zhang, Qiping

Abstract

COVID-19 affected various aspects of our life. Many college students were forced to take courses remotely. It was not clear how they adapted to this new environment and how their mental health was affected. The objective of this study is to understand college students' learning experience one year after the outbreak of COVID-19. An online survey was developed to investigate students' overall learning process, mental health, perception of the learning community and student support. Sixty-two college students in the U.S. were recruited through an online survey platform. Findings of this study revealed: (1) improved mental health of college students compared to the beginning of the pandemic; (2) an overall positive learning experience and perceived belongingness to their learning community, as well as high satisfaction with the student support; (3) the major hindrance in the online learning environment was the lack of interactions with teachers and classmates; (4) a relationship between family income and perception of the learning community was discovered, and the students from low-income families were found to feel more belonging to the learning community; (5) hybrid was the optimum learning mode during COVID-19; and (6) on-campus students perceived more student support than off-campus students. These findings provided a guideline for future research to further explore, and improve, the online learning environment.

Published
2021

6. The EMPATHIC Framework for Task Learning from Implicit Human Feedback

Author

Cui, Yuchen, Zhang, Qiping, Allievi, Alessandro, Stone, Peter, Niekum, Scott, and Knox, W. Bradley

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Robotics
Abstract

Reactions such as gestures, facial expressions, and vocalizations are an abundant, naturally occurring channel of information that humans provide during interactions. A robot or other agent could leverage an understanding of such implicit human feedback to improve its task performance at no cost to the human. This approach contrasts with common agent teaching methods based on demonstrations, critiques, or other guidance that need to be attentively and intentionally provided. In this paper, we first define the general problem of learning from implicit human feedback and then propose to address this problem through a novel data-driven framework, EMPATHIC. This two-stage method consists of (1) mapping implicit human feedback to relevant task statistics such as reward, optimality, and advantage; and (2) using such a mapping to learn a task. We instantiate the first stage and three second-stage evaluations of the learned mapping. To do so, we collect a dataset of human facial reactions while participants observe an agent execute a sub-optimal policy for a prescribed training task. We train a deep neural network on this data and demonstrate its ability to (1) infer relative reward ranking of events in the training task from prerecorded human facial reactions; (2) improve the policy of an agent in the training task using live human facial reactions; and (3) transfer to a novel domain in which it evaluates robot manipulation trajectories., Comment: Conference on Robot Learning 2020

Published
2020

7. Building an Inclusive Metaverse: Opening Doors to a Brighter Digital Future

Author

Bedard, Andrew, Zhang, Qiping, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gao, Qin, editor, Zhou, Jia, editor, Duffy, Vincent G., editor, Antona, Margherita, editor, and Stephanidis, Constantine, editor

Published
2023
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8. A Prototype Design and Usability Evaluation of a Chinese Educational System

Author

Li, Michael, Zhang, Qiping, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Soares, Marcelo M., editor, Rosenzweig, Elizabeth, editor, and Marcus, Aaron, editor

Published
2022
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9. An Altmetric Study on Dental Informatics

Author

Chen, Jessica, Zhang, Qiping, Arabnia, Hamid, Series Editor, Arabnia, Hamid R., editor, Deligiannidis, Leonidas, editor, Shouno, Hayaru, editor, Tinetti, Fernando G., editor, and Tran, Quoc-Nam, editor

Published
2021
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10. A Survey Study on Successful Marketing Factors for Douyin (Tik-Tok)

Author

Chen, Zining, Zhang, Qiping, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nah, Fiona Fui-Hoon, editor, and Siau, Keng, editor

Published
2021
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11. Emotional Pathways of Successful College Essays

Author

Xu, Eric, Zhang, Qiping, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Stephanidis, Constantine, editor, and Antona, Margherita, editor

Published
2020
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19. 'Core' Components in HCI Syllabi: Based on the Practice of CS and LIS Schools in North America

Author

Pei, Lei, Zhang, Qiping, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Nah, Fiona Fui-Hoon, editor, and Tan, Chuan-Hoo, editor

Published
2016
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20. Towards Inferring Users' Impressions of Robot Performance in Navigation Scenarios

Author

Zhang, Qiping, Tsoi, Nathan, Choi, Booyeon, Tan, Jie, Chiang, Hao-Tien Lewis, Vázquez, Marynel, Zhang, Qiping, Tsoi, Nathan, Choi, Booyeon, Tan, Jie, Chiang, Hao-Tien Lewis, and Vázquez, Marynel

Abstract

Human impressions of robot performance are often measured through surveys. As a more scalable and cost-effective alternative, we study the possibility of predicting people's impressions of robot behavior using non-verbal behavioral cues and machine learning techniques. To this end, we first contribute the SEAN TOGETHER Dataset consisting of observations of an interaction between a person and a mobile robot in a Virtual Reality simulation, together with impressions of robot performance provided by users on a 5-point scale. Second, we contribute analyses of how well humans and supervised learning techniques can predict perceived robot performance based on different combinations of observation types (e.g., facial, spatial, and map features). Our results show that facial expressions alone provide useful information about human impressions of robot performance; but in the navigation scenarios we tested, spatial features are the most critical piece of information for this inference task. Also, when evaluating results as binary classification (rather than multiclass classification), the F1-Score of human predictions and machine learning models more than doubles, showing that both are better at telling the directionality of robot performance than predicting exact performance ratings. Based on our findings, we provide guidelines for implementing these predictions models in real-world navigation scenarios.

Published
2023

22. SEAN-VR

Author

Zhang, Qiping, primary, Tsoi, Nathan, additional, and Vázquez, Marynel, additional

Published
2023
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23. What’s Your Point? : How Chinese and Americans Achieve Their Conversational Aims in Cross-Cultural and Gender Interactions in CMC

Author

Marksbury, Nancy, Zhang, Qiping, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Rau, P. L. Patrick, editor

Published
2013
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24. Cross-Cultural Comparison of Blog Use for Parent-Teacher Communication in Elementary Schools

Author

Zhang, Qiping, Hatcher, April, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, and Harris, Don, editor

Published
2011
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25. How Font Size and Tag Location Influence Chinese Perception of Tag Cloud?

Author

Zhang, Qiping, Qu, Weina, Wang, Li, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, and Harris, Don, editor

Published
2011
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26. Once Broken, Never Fixed? The Impact of Culture and Medium on Repairing Trust in CMC

Author

Zhang, Qiping, Marksbury, Nancy, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Rau, P. L. Patrick, editor

Published
2011
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28. A Study on Content and Management Style of Corporate Blogs

Author

Ma, Shanshan, Zhang, Qiping, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Schuler, Douglas, editor

Published
2007
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29. Gender Talk: Differences in Interaction Style in CMC

Author

Sun, Xiaoning, Wiedenbeck, Susan, Chintakovid, Thippaya, Zhang, Qiping, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Baranauskas, Cécilia, editor, Palanque, Philippe, editor, Abascal, Julio, editor, and Barbosa, Simone Diniz Junqueira, editor

Published
2007
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32. Demonstration of the EMPATHIC Framework for Task Learning from Implicit Human Feedback

Author

Yuchen Cui, Zhang, Qiping, Jain, Sahil, Allievi, Alessandro, Stone, Peter, Niekum, Scott, and Knox, W. Bradley

Subjects
General Medicine
Abstract

Reactions such as gestures, facial expressions, and vocalizations are an abundant, naturally occurring channel of information that humans provide during interactions. An agent could leverage an understanding of such implicit human feedback to improve its task performance at no cost to the human. This approach contrasts with common agent teaching methods based on demonstrations, critiques, or other guidance that need to be attentively and intentionally provided. In this work, we demonstrate a novel data-driven framework for learning from implicit human feedback, EMPATHIC. This two-stage method consists of (1) mapping implicit human feedback to relevant task statistics such as reward, optimality, and advantage; and (2) using such a mapping to learn a task. We instantiate the first stage and three second-stage evaluations of the learned mapping. To do so, we collect a dataset of human facial reactions while participants observe an agent execute a sub-optimal policy for a prescribed training task. We train a deep neural network on this data and demonstrate its ability to (1) infer relative reward ranking of events in the training task from prerecorded human facial reactions; (2) improve the policy of an agent in the training task using live human facial reactions; and (3) transfer to a novel domain in which it evaluates robot manipulation trajectories. In the video, we focus on demonstrating the online learning capability of our instantiation of EMPATHIC.

Published
2021
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36. The application of new gastric cancer screening score system for gastric cancer screening and risk assessment of gastric precancerous lesions in China.

Author

Wang, Xiaoteng, Zhang, Qiping, Han, Feng, and Wu, Yiming

Subjects
*STOMACH cancer, *EARLY detection of cancer, *DISEASE risk factors, *RISK assessment, *PRECANCEROUS conditions, *ANTIBODY titer, *ATROPHIC gastritis
Abstract

Objective: To evaluate the value of new gastric cancer screening score system for risk assessment of gastric precancerous lesions. Methods: A total of 520 patients were enrolled after the examination of endoscopy at Endoscopy Center, Department of Gastroenterology, from June 2018 to December 2021. The patients were divided into three groups according to age, gender, serum helicobacter pylori antibody test, pepsinogen I (PGI), pepsinogen II (PGII), pepsinogen I/II ratio (PGR) and gastrin-17 test results before endoscopy: Group A defined as low-risk group (0-11 points), Group B defined as middle-risk group (12-16 points), Group C defined as high-risk group (17-23 points). The detection rates of gastric cancer and atrophic gastritis in three groups were analyzed. According to the range and degree of atrophy/intestinal metaplasia, patients were divided into five groups on the basis of OLGA/OLGIM staging system. The levels of PG I, PG II and PGR were compared between different groups, and the correlation between new gastric cancer screening score system and OLGA/OLGIM staging system were evaluated. Statistical analysis was accomplished by ANOVA, chi-square test and Gamma coefficient analysis. Results: A total of 520 patients were enrolled. 268 patients were classified into group A,222 patients into group B and 30 patients into group C, respectively. According to the pathological results, 281 cases were non-atrophic gastritis, 230 cases atrophic gastritis and 9 cases gastric cancer. For OLGA staging system, 281 patients were divided into stage-0 group, 121 patients into stage-I group, 72 patients into stage-II group, 33 patients into stage-III group and 13 patients into stage-IV groups. The PGI and PGR level correlated inversely with the rising OLGA stages (F = 3.028, p =.016, F = 6.036, p <.001). For OLGIM staging system, 252 patients were divided into stage-0 group, 137 patients into stage-I group, 80 patients into stage-II group, 36 patients into stage-III group and 15 patients into stage-IV group. The PGR level correlated inversely with the rising OLGIM stages (F = 3.466, p＝.007). The detection rates of gastric cancer and atrophic gastritis in Group C were much higher than other groups. (X2 = 14.727, p <.001; X2 = 51.280, p <.001). Gamma coefficient analysis showed significant correlations between OLGA/OLGIM and the new gastric cancer screening score system (p <.001). Conclusions: The new gastric cancer screening score system is closely linked with histological OLGA/OLGIM staging system in the risk assessment of gastric precancerous lesions. The role of new gastric cancer screening score system in future gastric precancerous lesions screening and high risk population identifying was promising. [ABSTRACT FROM AUTHOR]

Published
2023
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37. Investigation on Mechanical and Microstructure Properties of Silt Improved by Titanium Gypsum-Based Stabilizer.

Author

Lin, Qiqi, Zhen, Xidong, Rong, Yu, Li, Yunlong, Zhang, Haiyan, Zhang, Qiping, Yao, Zhanyong, and Yao, Kai

Subjects
SILT, LIME (Minerals), TITANIUM, ALLUVIAL plains, SOLID waste, INDUSTRIAL wastes, ALLUVIAL streams
Abstract

Silt in the Yellow River alluvial plain is widely spread, but its uniform particle size and high roundness make it unsuitable as a subgrade filling material, while titanium gypsum (TG) is an industrial solid waste in Shandong Province, not only occupying land resources but also causing water and air pollution. In order to improve the engineering performance of silt, reduce the pollution of solid waste titanium gypsum to the environment and reduce the engineering cost, considering the engineering characteristics of titanium gypsum, it was combined in specific amounts with cement and lime to create a titanium gypsum-based stabilizer (TS) in this study. The effect of curing conditions and TS content on silt improvement was studied through laboratory experiments. The mechanical properties of the stabilized silt were investigated by unconfined compression test (UCT), and the mineral composition and pore structure were analyzed by scanning electron microscopy (SEM) test, X-ray diffraction (XRD) test, and mercury injection pore (MIP) test. The test results show that TS could effectively improve the unconfined compressive strength of silt, and the strength of stabilized silt gradually increases with the curing period and TS content. In terms of the porosity, it decreases with the increase of the curing period and TS content. From the microstructure perspective, this is mainly due to the formation of ettringite and C-S-H during the stabilization of silt by TS. [ABSTRACT FROM AUTHOR]

Published
2023
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