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IEEE/ACM Symposium on Edge computing (SEC)

作者： Alex Xie Yang Peng Division of Computing and Software Systems University of Washington Bothell Bothell WA USA

ISBN: (数字)9781665486118

ISBN: (纸本)9781665486125

Recent advances in deep neural networks (DNNs) have greatly benefited mobile applications that perform real-time video analytics. However, mobile devices' computing power usually limits them from inferring complex DNN models timely. Edge intelligence has emerged to help mobile apps offload DNN inference tasks to powerful edge servers for accelerated inference services. One major challenge that edge intelligence faces is maintaining a satisfactory quality of service when users move across edge servers. To address this issue, we propose a novel solution to help improve the quality of inference services for real-time video analytics applications that use chained DNN models. This solution includes two schemes: one maximizes the use of mobile devices to improve inference quality during the handover between edge servers, and the other provides offloading decisions to minimize the end-to-end inference delay when edge servers are available. We evaluate the proposed scheme using a DNN-based realtime traffic monitoring application through testbed and simulation experiments. The results show that our solution can improve inference quality by 52% during handover compared to the greedy algorithm-based solution.

关键词： Performance evaluation Analytical models Visual analytics Handover Real-time systems Mobile handsets Mobile applications

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Smart De Novo Macromolecular Structure Modeling from Cryo-EM Maps

SSRN

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SSRN 2022年

作者： Si, Dong Chen, Jason Nakamura, Andrew Chang, Luca Guan, Haowen Division of Computing and Software Systems University of Washington Bothell BothellWA98011 United States

The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, x-ray crystallography has been the main method for structure determination, however, cryogenic electron microscopy (cryo-EM) has increasingly become more popular due to recent advancements in hardware and software. The number of cryo-EM maps deposited in the EMDataResource (formerly EMDatabase) since 2002 has been dramatically increasing and it continues to do so. De novo macromolecular complex modeling is a labor-intensive process, therefore, it is highly desirable to develop software that can automate this process. Here we discuss our automated, data-driven, and artificial intelligence approaches including map processing, feature extraction, modeling building, and target identification. Recently, we have enabled DNA/RNA modeling in our deep learning-based prediction tool, DeepTracer. We have also developed DeepTracer-ID, a tool that can identify proteins solely based on the cryo-EM map. In this paper, we will present our accumulated experiences in developing deep learning-based methods surrounding macromolecule modeling applications. © 2022, The Authors. All rights reserved.

关键词： Electron microscopy

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software and data provenance as a basis for eScience workflow 17

Software and data provenance as a basis for eScience workflo...

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17th IEEE International Conference on eScience, eScience 2021

作者： Conquest, Joseph Stiber, Michael University of Washington Bothell Computing and Software Systems Division School of Science Technology Engineering and Mathematics BothellWA98011 United States

ISBN: (纸本)9781665403610

While tracking data and software provenance is necessary in eScience, and often implemented in scientific workflow management tools, such tools generally don't provide graphical UIs that use provenance to guide workflow. Graphitti Workbench was built to center workflow around its PROV-DM based ProVis software and data provenance visualizer, making provenance more readily accessible to scientists. Using ProVis as the system desktop highlights the importance of provenance to inform future experiments and improves provenance understandability. Our usability study indicates that this solution is highly acceptable and that it provides easy deployment of experiments using provenance-based creation. © 2021 IEEE.

关键词： e-Science

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SymAware: A software Development Framework for Trustworthy Multi-Agent systems with Situational Awareness

arXiv

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arXiv 2024年

作者： Casablanca, Ernesto Zhang, Zengjie Marchesini, Gregorio Haesaert, Sofie Dimarogonas, Dimos V. Soudjani, Sadegh School of Computing Newcastle University Newcastle United Kingdom Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for software systems Germany

Developing trustworthy multi-agent systems for practical applications is challenging due to the complicated communication of situational awareness (SA) among agents. This paper showcases a novel efficient and easy-to-use software framework for multi-agent simulation, named SymAware which provides a rich set of predefined data structures to compute, store, and communicate SA for agents. It also provides an abstract interface for the agents to compute their control inputs taking into account the awareness of the situation, knowledge, and risk of surrounding agents. Besides, utilizing a cluster of specialized components, SymAware hides the heavy computation of physical rendering and communication interfacing of simulation engines behind the control threads, resulting in high implementation efficiency in bridging the gap between conceptual prototyping and practical applications. Three multiagent case studies are used to validate the efficacy and efficiency of this software framework. © 2024, CC BY.

关键词： software design

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Psychosis iREACH: Reach for Psychosis Treatment using Artificial Intelligence

Psychosis iREACH: Reach for Psychosis Treatment using Artifi...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lee, Jonathan Kopelovich, Sarah Cheng, Sunny Chieh Si, Dong University of Washington - Bothell Computing & Software Systems Division BothellWA United States University of Washington - Seattle Department of Psychiatry and Behavioral Sciences SeattleWA United States University of Washington - Tacoma School of Nursing & Healthcare Leadership TacomaWA United States

ISBN: (纸本)9781665468190

Psychosis iREACH aims to optimize the delivery of evidence-based cognitive behavioral therapy to family caregivers who have a loved one with psychosis. It is an accessible digital platform that can utilize the user's intent and entities to determine the appropriate response. The platform is implemented based on an artificial intelligence and natural language understanding (NLU) framework, RASA. We developed the web application of the platform, and the chatbot has been integrated into the platform to collect data and evaluate the performance. The results showed that the NLU model's accuracy, precision, recall, and F1- score of the intent prediction are 88.31%, 89.80%, 88.22%, and 88.65% respectively. The link to the website is https://***/. © 2022 IEEE.

关键词： Project management

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Analysis and Design of Mobile Model for DTN

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Chinese Journal of Electronics 2023年第4期18卷 635-638页

作者： Zhiting Lin Yugui Qu Yusheng Ji Baohua Zhao Department of Computer Science University of Science and Technology of China Hefei China Province Key Laboratory of Software in Computing and Communication Hefei China Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

Delay tolerant networks may become unexpectedly partitioned due to node mobility or variation in signal strength. However, most widely used models in some relative works are generally very simplistic. In order to exploit intelligent forwarding algorithms, a novel nodal mobile model of delay tolerant networks is presented to map reality with more accuracy. And several approaches are introduced to analyze the network structure, such as n-cliques, n-clans, degree, closeness and betweenness. Our research results showed that the centralizations became smaller when the wireless connections were concerned. It meant that quite a number of nodes became potential relays because of the new structure of networks.

关键词： Wireless communication Analytical models Wireless sensor networks Protocols Social networking (online) Sensitivity analysis Social network theory

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Chinese Landmark Recognition

Chinese Landmark Recognition

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2020 International Conference on computing, Networking and Communications, ICNC 2020

作者： Wu, Hao Chen, Min University of Washington Bothell Division of Computing and Software Systems School of STEM United States

ISBN: (纸本)9781728149059

Landmark recognition is a useful yet challenging task due to the lack of large annotated landmark datasets. Previous related work often neglects non-English speaking areas, such as Chinese landmarks. Tests using commercial landmark recognition APIs also indicate the incompleteness of such landmark data. Our project releases Chinese landmark and attraction dataset for recognition of human-made and natural landmarks in China. The dataset contains 42,548 images depicting 987 unique landmarks. Moreover, we build a landmark recognition model based on Convolution Neural Network and transfer learning techniques. Lastly, an iOS application is built based on our landmark recognition model. It allows user to do landmark prediction on images taken by camera and images from device gallery. © 2020 IEEE.

关键词： Convolutional neural networks

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Integration of Artificial Intelligence and Wearable Internet of Things for Mental Health Detection

引用

International Journal of Cognitive computing in Engineering 2024年第1期5卷 307-315页

作者： Wang, Wei Chen, Jian Hu, Yuzhu Liu, Han Chen, Junxin Gadekallu, Thippa Reddy Garg, Lalit Guizani, Mohsen Hu, Xiping Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518000 China School of Software Dalian University of Technology Dalian116621 China Division of Research and Development Lovely Professional University Phagwara India Center of Research Impact and Outcome Chitkara University Punjab India Faculty of Information and Communication Technology University of Malta Msida Malta United Arab Emirates

The integration of Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) for mental health detection is a promising area of research with the potential to revolutionize mental health monitoring and diagnosis. Since early detection of mental diseases, i.e., depression, is of great importance for diagnosis and treatment, a fast and convenient way is urgently needed. Traditional diagnostic methods are time-consuming, laborious, over-subjective, and easily lead to misdiagnosis. The advance in information techniques and wearable devices brings innovation to mental disease detection. Therefore, this article first compares intelligent depression detection methods and traditional methods to illustrate the significance and then analyzes the opportunities of the wearable device. Then we provide specific psychophysiological data measured by wearable devices and introduce relevant datasets for depression detection. An illustrative example of depression detection with sleep data is presented and discussed and our proposed ensemble method has improved nearly 10% to baselines. Analytical results demonstrate the great potential of using wearable device-measured psychophysiological data to detect depression intelligently. © 2024 The Authors

关键词： Internet of things

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DeepTracer-Denoising: Deep Learning for 3D Electron Density Map Denoising

DeepTracer-Denoising: Deep Learning for 3D Electron Density ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haowen Guan Dong Si Center of Data Science New York University New York City US Division of Computing and Software Systems University of Washington Bothell Bothell US

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Cryo-electron microscopy (Cryo-EM) is widely used in molecular structure determination and drug discovery. Experimental cryo-EM images suffer from the noises introduced by electron beam dose and sample preparation. Although many approaches have been proposed to improve the signal-to-noise ratio (SNR) for cryo-EM image denoising, the noises are still presented after 3D reconstruction and can obstruct the analysis and visualization of the 3D density map. Here we present DeepTracer-Denoising, a method for 3D electron density map denoising. We employ a 3D Neural Network to learn the pattern of noises and the biological structure from density maps. Our method is designed to work on medium to high-resolution maps ranging from 2.5 A to 10.0A. It is configurated with two modes to tackle both background noise and structural noise in a 3D density map. Our method can correctly identify 97.70% background noise while preserving 96.46% density of the native structure. For the maps that contain structural noise, DeepTracer-Denoising achieves an overall accuracy of 98.95%.

关键词： Training Visualization Three-dimensional displays Noise reduction Distance measurement Background noise Convolutional neural networks

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Co-Production of a Cognitive Behavioral Therapy Digital Platform for Families of Individuals Impacted by Psychosis

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Journal of Technology in Behavioral Science 2024年第1期9卷 140-148页

作者： Cheng, Sunny Chieh Kopelovich, Sarah Si, Dong Divina, Myra Gao, Ningjun Serene Wang, Mia Yunqi Kim, Jamie Jaesook Li, Ziyi Blank, Jennifer Brian, Rachel Turkington, Douglas School of Nursing and Healthcare Leadership University of Washington Tacoma WA United States Department of Psychiatry and Behavioral Sciences University of Washington School of Medicine Seattle WA United States Division of Computing and Software Systems University of Washington Bothell WA United States Department of Human Centered Design & Engineering University of Washington Seattle WA United States Department of Economics University of Washington Seattle WA United States Department of Statistics University of Washington Seattle WA United States Newcastle University Newcastle Upon Tyne United Kingdom

Education and skills training have consistently been recognized as an unmet need for caregivers of individuals with psychosis, as traditional models of clinician-delivered family interventions remain inaccessible to most caregivers across low-, middle-, and high-income countries. We endeavored to expand access to psychosis education and skills training by co-developing a digital training platform with experts and families. We engaged in collaborative co-design sessions with caregivers who had received training in Psychosis REACH—an evidence-based psychosocial family intervention for psychosis—to elicit design principles, develop a functional prototype, and engage in usability testing of an online interactive training program. Seven participants reported six design principles for a prototype. These included simplicity, accessibility, skill acquisition, usability, sense of community, and hope. Guided by these principles, participants’ preferred features were (1) online modules with didactic content, (2) a training “coach” conversational agent to provide instruction and feedback to users, and (3) a conversational agent simulation of an individual experiencing psychosis with whom users can practice skills. Participants completed the usability testing. The levels of ease of use ranged from 4.2 to 4.8 on a 5-point scale with 5 being easiest to use and the level of usefulness ranged from 4 to 4.75 on a 5-point scale, with 5 being most useful. This study is the first to develop a digital platform for family caregivers to support interactive skills training for psychosis caregiving at home. Iterative user-centered design resulted in a functional alpha prototype ripe for testing. Future directions include testing the design requirements on more diverse users and evaluating the effects of the online platform on the caregiving experience. © 2024, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Caregiver Co-production Cognitive behavior therapy Digital platform Mental health Psychosis Usability User-centered design

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