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SSRN 2022年

作者： Ye, Zuzhao Yu, Nanpeng Wei, Ran Liu, Xiaoyue Cathy Department of Electrical and Computer Engineering University of California Riverside RiversideCA United States School of Public Policy University of California Riverside RiversideCA United States Department of Civil and Environmental Engineering University of Utah Salt Lake CityUT United States

In light of the growing concerns of global climate change, the pace of transportation electrification has greatly accelerated in recent years as an effort towards net-zero greenhouse gas (GHG) emissions. However, it remains unclear how to effectively deploy and operate public charging infrastructure to best serve an electrified transportation system within a multi-modal context while maximizing the benefits of decarbonization. To address this question, we propose a mechanism of shared charging hubs that can provide holistic energy management for both electric buses (EBs) and passenger electric vehicles (EVs).The deployment and operation of shared charging hubs is determined by a new spatio-temporal optimization model which aims to minimize GHG emission given a budget limit while avoiding the occurrence of massive spikes in peak power demand. This is achieved by coherently accommodating the charging demand of EBs and EVs, and explicitly integrating the time-varying electricity carbon intensity and vehicle-to-grid (V2G) technology. To demonstrate its effectiveness, the model is applied to the bus fleets operated by seven transit agencies and the park-and-ride facilities (for EVs) near twelve rail transit stations in Contra Costa County, California, USA. The results show that the shared charging hubs can lead to significant GHG emission reduction while mitigating the peak electricity demand. This research will help policymakers and transportation agencies make more informed decisions regarding the planning and design of charging infrastructure. © 2022, The Authors. All rights reserved.

关键词： Climate change

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Incentivizing or Disincentivizing Participation in Demand Response Programs? Estimation of Customer Willingness to Accept

SSRN

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SSRN 2025年

作者： Wang, Yu Howard, Gregory Zhang, Wendong Brown, Marilyn Morton, Abigail Cetin, Kristen McCalley, James Department of Political Science Iowa State University United States Department of Economics East Carolina University United States The Charles H. Dyson School of Applied Economics and Management Cornell University United States School of Public Policy Georgia Institute of Technology United States Federal Energy Regulatory Commission United States Department of Civil and Environmental Engineering Michigan State University United States Department of Electrical and Computer Engineering Iowa State University United States

Demand response has great potential to provide value flexibility services to the grid, which can be harnessed through direct load control (DLC) programs. Many utilities have provided direct financial incentives to encourage residential customers to enroll in DLC programs. However, financial incentives do not always result in high participation rate. This study aims to assess residential customers’ willingness to enroll in utility-run DLC programs and analyze how incentives affect participation intention. A survey using the contingent valuation method (CVM) collected a random sample of 591 residents in the U.S. Midwest from 2020-2022, which finds, overall, 56.7% of residents are willing to enroll in DLC. It is estimated that 61.5% of residents are willing to participate due to intrinsic motivations (i.e., without monetary incentive), while a small token ($15-24 sign-on bonus) only leads to about 50% of the customers agreeing to enroll. To reach the same level of participation from intrinsic motivations, utilities must offer a much higher incentive - $83-$90. Additionally, we find several factors affect acceptance of DLC programs, including knowledge learning, concerns of comfort loss, and personal beliefs on energy savings, reliability, and environmental benefit. Findings from this study have significant policy implications and help improve the design of utility DLC programs. When developing strategies to recruit participants, no mention of monetary incentives could increase the participation of resident customers. We also recommend that utilities consider educational and demonstration programs to engage stakeholders, and information campaigns to emphasize the potential benefits without compromising thermal comfort. © 2025, The Authors. All rights reserved.

关键词： Sales

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Toward Ethical Robotic Behavior in Human-Robot Interaction Scenarios

arXiv

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arXiv 2022年

作者： Chen, Shengkang Surendran, Vidullan Wagner, Alan R. Borenstein, Jason Arkin, Ronald C. Electrical and Computer Engineering Georgia Tech AtlantaGA United States Aerospace Engineering Penn State University University ParkPA United States School of Public Policy Georgia Tech AtlantaGA United States Interactive Computing Georgia Tech AtlantaGA United States

This paper describes current progress on developing an ethical architecture for robots that are designed to follow human ethical decision-making processes. We surveyed both regular adults (folks) and ethics experts (experts) on what they consider to be ethical behavior in two specific scenarios: pill-sorting with an older adult and game playing with a child. A key goal of the surveys is to better understand human ethical decision-making. In the first survey, folk responses were based on the subject’s ethical choices ("folk morality");in the second survey, expert responses were based on the expert’s application of different formal ethical frameworks to each scenario. We observed that most of the formal ethical frameworks we included in the survey (Utilitarianism, Kantian Ethics, Ethics of Care and Virtue Ethics) and "folk morality" were conservative toward deception in the high-risk task with an older adult when both the adult and the child had significant performance deficiencies. Copyright © 2022, The Authors. All rights reserved.

关键词： Human robot interaction

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GraphSAGE-Based Generative Adversarial Network for Short-Term Traffic Speed Prediction Problem

GraphSAGE-Based Generative Adversarial Network for Short-Ter...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Han Zhao Ruikang Luo Bowen Yao Yiyi Wang Shaoqing Hu Rong Su School of Electrical and Electronic Engineering Nanyang Technological University Singapore Lee Luan Yew School of Public Policy National University of Singapore School of Media and Communication Coventry University School of Computer Science Beijing Universitiy of Posts and Telecommunications China

ISBN: (纸本)9781665476881

Traffic speed prediction is a significant branch of the intelligent transportation system (ITS). A good prediction could alleviate the non-recurring congestion on the road and provide a strong decision-making basis for traffic management and control. However, it is always a challenging research problem due to the complexity of the road network and the dynamics of traffic conditions. Many deep learning-based methods have been applied to the traffic prediction problem, which could extract both spatial and temporal information efficiently. However, for some dataset that suffers from data paucity problem, the generalization ability of the model is not good and the performance degrades. To tackle this problem, we proposed a novel graph-based generative adversarial network for the traffic speed prediction problem. We design a generative network to generate some fake traffic data and use a discriminative network to distinguish between real and fake targets. The generator consists of a GraphSAGE and LSTM model to learn the representation of spatial-temporal traffic data. Several experiments have been conducted on several real-world traffic datasets, demonstrating that our proposed model outperforms other baseline models. The experiment results illustrate the importance of utilizing GAN in the training process, which improves the generalization ability of the prediction model.

关键词： Training Roads Predictive models Generative adversarial networks Feature extraction Data models Generators

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Unlocking Real-Time Data Access in Diabetes Management: Toward an Interoperability Model

SSRN

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SSRN 2024年

作者： Randine, Pietro Wolff, Miriam Kopperstad Pocs, Matthias Connell, Ian R.O. Cafazzo, Joseph A. Årsand, Eirik Faculty of Science and Technology Department of Computer Science UiT The Arctic University of Norway Tromsø Norway Faculty of Information Technology and Electrical Engineering Department of ICT and Natural Sciences Norwegian University of Science and Technology Trondheim Norway STELAR Security Technology Law Research Hamburg Germany Centre for Digital Therapeutics Toronto General Hospital University Health Network Toronto Canada Institute of Health Policy Management and Evaluation Institute of Biomedical Engineering Department of Computer Science the Rotman School of Management University of Toronto Toronto Canada Institute of Health Policy Management and Evaluation Dalla Lana School of Public Health University of Toronto Toronto Canada UiT The Arctic University of Norway Tromsø Norway

Background: In today's data-driven era, 'Openness' promotes transparency and accessibility. This trend is particularly relevant in new initiatives like the European Health Data Space or when processing health data. This article focuses on Diabetes, which relies on real-time medical device data, such as continuous glucose monitors (CGMs), insulin pumps or hybrid closed-loop systems. These devices provide vital information for monitoring and adjusting treatment plans, making real-time access to their data critical for effective diabetes ***: We explore real-time data access for third-party applications, emphasising primary (treatment) and secondary (research) use. Our exploration includes analysing how Application Programming Interfaces (APIs) contribute to secure data retrieval. Additionally, we investigate the impact of terms of service and copyright law on patient-driven innovation within open-source ***: We evaluated current diabetes medical devices and software solutions in Norway, emphasising their real-time data provision capabilities and API functionality. The study also addresses the legal frameworks relevant to these devices and software, particularly the needs of open-source DIY communities, and proposes an interoperability ***: Our research identified seven diabetes medical devices and nine regulated software solutions, with only one offering a publicly accessible API. This finding underlines a significant lack of real-time data access options. The analysis compares vendor-specific and open-source software, highlighting substantial limitations in interoperability and data accessibility. The DIY solutions, while innovative, presented technical and legal challenges, limiting their practical ***: Diabetes management needing real-time data involves navigating challenges related to security, transparency, and accessibility. We propose a model for interoperability that necessitates regulatory decisio

关键词： Interoperability

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The Impact of ChatGPT Exposure on User Interactions With a Motivational Interviewing Chatbot: Quasi-Experimental Study

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JMIR Formative Research 2025年 9卷 e56973页

作者： Zhu, Jiading Dong, Alec Wang, Cindy Veldhuizen, Scott Abdelwahab, Mohamed Brown, Andrew Selby, Peter Rose, Jonathan The Edward S. Rogers Sr. Department of Electrical & Computer Engineering University of Toronto Toronto ON Canada Department of Mechanical & Industrial Engineering University of Toronto Toronto ON Canada INTREPID Lab Centre for Addiction and Mental Health Toronto ON Canada Institute for Mental Health Policy Research Centre for Addiction and Mental Health Toronto ON Canada Department of Family and Community Medicine University of Toronto Toronto ON Canada Dalla Lana School of Public Health University of Toronto Toronto ON Canada

Background: The worldwide introduction of ChatGPT in November 2022 may have changed how its users perceive and interact with other chatbots. This possibility may confound the comparison of responses to pre-ChatGPT and post-ChatGPT iterations of pre-existing chatbots, in turn affecting the direction of their evolution. Before the release of ChatGPT, we created a therapeutic chatbot, MIBot, whose goal is to use motivational interviewing to guide smokers toward making the decision to quit smoking. We were concerned that measurements going forward would not be comparable to those in the past, impacting the evaluation of future changes to the chatbot. Objective: The aim of the study is to explore changes in how users interact with MIBot after the release of ChatGPT and examine the relationship between these changes and users’ familiarity with ChatGPT. Methods: We compared user interactions with MIBot prior to ChatGPT’s release and 6 months after the release. Participants (N=143) were recruited through a web-based platform in November of 2022, prior to the release of ChatGPT, to converse with MIBot, in an experiment we refer to as MIBot (version 5.2). In May 2023, a set of (n=129) different participants were recruited to interact with the same version of MIBot and asked additional questions about their familiarity with ChatGPT, in the experiment called MIBot (version 5.2A). We used the Mann-Whitney U test to compare metrics between cohorts and Spearman rank correlation to assess relationships between familiarity with ChatGPT and other metrics within the MIBot (version 5.2A) cohort. Results: In total, 83(64.3%) participants in the MIBot (version 5.2A) cohort had used ChatGPT, with 66 (51.2%) using it on a regular basis. Satisfaction with MIBot was significantly lower in the post-ChatGPT cohort (U=11,331.0;P=.001), driven by a decrease in perceived empathy as measured by the Average Consultation and Relational Empathy Measure (U=10,838.0;P=.01). Familiarity with ChatGPT was

关键词： artificial intelligence chatbot ChatGPT conversational agent digital health experimental large language models motivational interviewing natural language processing smoking cessation

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

引用

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Exploring the Structure of IoT Data: A Symbolic Analysis Perspective

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Shen, Yinghua Pedrycz, Witold Li, Wentao Xiao, Zhi Chen, Tianhua Hu, Xuan Chen, Yuan School of Economics and Business Administration Chongqing University Chongqing400044 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2G7 Canada College of Artificial Intelligence Southwest University Chongqing400715 China Department of Computer Science University of Huddersfield HuddersfieldHD1 3DH United Kingdom School of Public Policy and Administration Chongqing University Chongqing400044 China College of Management and Economics Tianjin University Tianjin300072 China

With the development of different kinds of techniques, especially the Internet of Things (IoT), a large amount of quantitative (either numeric or categorical) data have been generated, transmitted, and stored in the modern society. People hope to understand the interested phenomenon from the collected quantitative data by utilizing different data analysis methods. Exploring the structure of data (e.g., the cluster centers or prototypes) has always been a hot spot in the domain of data mining and knowledge discovery, yet it seems that the modeling and analyzing process still focus on a low-level abstraction of the data because normally, the structure found is only represented by some numeric data points. In this study, we highlight that a low-level abstraction may not be a user-friendly way for people to grasp the knowledge contained in the data. Instead, we explore the structure of the data from a perspective of symbolic analysis. Specifically, two modes of abstraction are proposed. In the vertical mode (i.e., values of each feature are abstracted), the numeric prototypes are characterized by the symbolic prototypes such that people could get rid of being stuck in minor details of each feature. In the horizontal mode (i.e., values of each prototype are abstracted), the linguistic summarization is used to describe all the features of each symbolic prototype such that people could immediately grasp the essential information conveyed in the symbolic prototype. We conduct comprehensive experimental studies on the publicly available data to illustrate the feasibility and validity of the proposed symbolic analysis process. © 2023 Yinghua Shen et al.

关键词： Internet of things

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Understanding policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address public Safety

arXiv

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arXiv 2023年

作者： Ardabili, Babak Rahimi Pazho, Armin Danesh Noghre, Ghazal Alinezhad Neff, Christopher Bhaskararayuni, Sai Datta Ravindran, Arun Reid, Shannon Tabkhi, Hamed Public Policy Program University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States Electrical Engineering and Computer Systems University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States Criminal Justice University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States

Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of personally identifiable information (PII) commonly pose an increased risk to personal privacy. To address these issues, this paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. Further, we propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications. Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems. The system shows the 17.8 frame-per-second (FPS) processing in extreme video scenes. However, considering privacy in designing such a system results in preferring the pose-based algorithm to the pixel-based one. This choice resulted in dropping accuracy in both action and anomaly detection tasks. The results drop from 97.48 to 73.72 in anomaly detection and 96 to 83.07 in the action detection task. On average, the latency of the end-to-end system is 36.1 seconds. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Semi-automatic segmentation of knee cartilage in longitudinal MR images by seed transfer 2

Semi-automatic segmentation of knee cartilage in longitudina...

引用

2nd International Conference on electrical, Communication and computer engineering, ICECCE 2020

作者： Kim, Myoung Jin Park, Taewoo Lee, Soochahn Jung, Ho Yup Yun, Il Dong Hankuk University of Foreign Studies Dept. Computer and Electronic System Yongin Korea Republic of Geumgang University Division of Public Policy Nonsan Korea Republic of School of Electrical Engineering Kookmin University Seoul Korea Republic of Chosun Niveristy Dept. Compter Engineering Gwangju Korea Republic of

ISBN: (数字)9781728171166

ISBN: (纸本)9781728171166

In this paper, we propose an efficient and effective semi-automatic segmentation framework for knee cartilage in longitudinal MR images. The proposed method provides a seamless and efficient combination of initial automatic segmentation and interactive segmentation. The main idea is to generate and transfer the seeds, rather than the labels, of the manual segmentation results of the initial image, to segment sequential images. Here, segmentation seeds are defined as sample voxels that are highly likely to be either the organ of interest or background. Similar to user annotations by scribbles, seeds can be used to determine labels for remaining voxels. Compared to methods that transfer segmentation labels, the proposed method is able to reduce the amount of required user annotations, since it does not require the user to identify and correct erroneous regions in the transferred segmentation labels, while still providing a robust initial segmentation based on transferred initial seeds. We evaluate the effectiveness of the proposed framework by measuring the interaction time of the user to segment the cartilage within the images. We compare the results with previous methods and show that the proposed method reduces the required interaction time. © 2020 IEEE.

关键词： Cartilage

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