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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Shweta Bhattacharjee Porna Md Firoz Kabir Md Imran Chowdhury Rana Md Saiful Islam Sajol Tonmoy Roy Mohammad Aman Ullah Khan Md Al Adnan Girigula Durga Bhavani Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Master’s in Information Technology University of the Cumberlands USA Bachelor of Business Administration International American university Los Angeles California USA Department of Computer Science and Engineering Louisiana State University Louisiana USA Data Analytics & Information Systems Utah State University Logan USA Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Bsc in software engineering Noakhali Science And Technology University School of Computer Science & Engineering (SCOPE) VIT-AP University Amaravati Andhra Pradesh India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

The classification of mango leaf diseases is critical for effective disease management and ensuring high-quality yields in mango cultivation. This paper presents a comprehensive study on using deep learning techniques to classify various mango leaf diseases, leveraging convolutional neural networks (CNNs) and hybrid models. A total of 7,524 images were used in our study. These included 4,000 training samples and 3,524 testing samples. The images were split into eight groups, which were powdery mildew, cutting weevil, anthracnose, bacterial canker, sooty mold, gall midge, healthy, and die back. The suggested method starts with feature extraction using VGG19 and MobileNetB1, then classification using both standalone models (ResNet50V2 + EfficientNetB1 and VGG16 + MobileNetB1). We employed data augmentation techniques like random brightness adjustment, rotation, and flipping to enhance the robustness of the model. We conducted hyperparameter tuning using hyperband and Bayesian optimization to optimize the model’s performance. Experimental results demonstrate that the hybrid models achieved superior performance, with ResNet50V2 and EfficientNetB1 attaining a perfect accuracy of $100 \%$ on the test set. These findings highlight the potential of deep learning techniques to improve the accuracy and reliability of mango leaf disease diagnosis, contributing significantly to the advancement of precision agriculture.

关键词： Deep learning Training Precision agriculture Accuracy Robustness Convolutional neural networks Tuning Optimization Diseases Testing

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An Empathetic AI Coach for Self-Attachment Therapy

arXiv

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arXiv 2022年

作者： Alazraki, Lisa Ghachem, Ali Polydorou, Neophytos Khosmood, Foaad Edalat, Abbas Department of Computing Imperial College London United Kingdom Computer Science and Software Engineering California Polytechnic State University San Luis Obispo United States

In this work, we present a new dataset and a computational strategy for a digital coach that aims to guide users in practicing the protocols of self-attachment therapy. Our framework augments a rule-based conversational agent with a deep-learning classifier for identifying the underlying emotion in a user’s text response, as well as a deep-learning assisted retrieval method for producing novel, fluent and empathetic utterances. We also craft a set of human-like personas that users can choose to interact with. Our goal is to achieve a high level of engagement during virtual therapy sessions. We evaluate the effectiveness of our framework in a non-clinical trial with N=16 participants, all of whom have had at least four interactions with the agent over the course of five days. We find that our platform is consistently rated higher for empathy, user engagement and usefulness than the simple rule-based framework. Finally, we provide guidelines to further improve the design and performance of the application, in accordance with the feedback received. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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VRVideos: A flexible pipeline for Virtual Reality Video Creation

VRVideos: A flexible pipeline for Virtual Reality Video Crea...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Anthony Dickson Jeremy Shanks Jonathan Ventura Alistair Knott Stefanie Zollmann Depart. of Computer Science University of Otago Dunedin New Zealand Depart. of Computer Science and Software Engineering California Polytechnic State University San Luis Obispo USA School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9781665457262

Recent advances in Neural Radiance Field (NeRF)-based methods have enabled high-fidelity novel view synthesis for video with dynamic elements. However, these methods often require expensive hardware, take days to process a second-long video and do not scale well to longer videos. We create an end-to-end pipeline for creating dynamic 3D video from a monocular video that can be run on consumer hardware in minutes per second of footage, not days. Our pipeline handles the estimation of the camera parameters, depth maps, 3D reconstruction of dynamic foreground and static background elements, and the rendering of the 3D video on a computer or VR headset. We use a state-of-the-art visual transformer model to estimate depth maps which we use to scale COLMAP poses and enable RGB-D fusion with estimated depth data. In our preliminary experiments, we rendered the output in a VR headset and visually compared the method against ground-truth datasets and state-of-the-art NeRF-based methods.

关键词： Headphones Visualization Solid modeling Three-dimensional displays Pipelines Estimation Virtual reality

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The Role of AI in Healthcare Policy Development and Management 1

The Role of AI in Healthcare Policy Development and Manageme...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Yadav, Sapna Mane, Pravin Swarnkar, Virendra Kumar Rawandale, Shital Kumar Patil, Alaknanda S. Katke, Kiran S. Bhat, Niyati Entrepreneurship Mindset Curriculum State Council of Educational Research and Training Delhi India Institute of Management and Entrepreneurship Development Pune India Bharti Vishwavidyalaya Department of Computer Science & Engineering Chhattisgarh Durg India Dean Industry Institute Interaction PCET's Pimpri Chinchwad College of Engineering Maharashtra Pune India Jspm Ntc E&tc Department Pune India Sanjivani College of Engineering Kopergaon Ahmednagar Department of Computer Engineering India Software Developer Master of Science California State University Long Beach CA90840 United States

ISBN: (纸本)9798350330915

This study looks at how artificial intelligence (AI) is incorporated into healthcare policy, with an emphasis on how it affects administrative procedures, treatment optimization, especially clinical choice support. Using a deductive methodology and interpretive thinking as a philosophy, an exploratory design with secondary data collecting was used. In the present-day environment, AI is being used more frequently in healthcare legislation, especially within medical settings. Clinical decision-making systems driven by AI have shown to offer a great promise for lowering diagnostic mistakes and increasing treatment precision. Improving patient outcomes through individualized AI-driven treatments for treatment improvement appears promising. But there are issues with algorithm prejudices as well as information privacy, among other ethical and legal issues. Establishing thorough guidelines, ongoing education, and auditing procedures are among the suggestions that are made. In order to guarantee fair and efficient integration of health care legislation, future research should prioritize longitudinal research and improving artificial intelligence algorithms © 2023 IEEE.

关键词： Artificial Intelligence Clinical Decision Support Ethical Considerations Healthcare Policy Treatment Optimization

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Analyzing and Comparing Results for Multiple Machine Learning Models 4

Analyzing and Comparing Results for Multiple Machine Learnin...

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2023 4th International Conference on computer engineering and Intelligent Control, ICCEIC 2023

作者： Ng, Jefferson Li, Yijian Ji, Xuxiao Yang, Can Wan, Jiaqiao Lu, Xingyu University of California Department of Mechanical Engineering Irvine92697 United States Courant Institute of Mathematical Sciences New York University NY10012 United States University of Waterloo School of Computer Science WaterlooCAN2L 3G1 United States Oregon State University Department of Electrical Engineering and Computer Science Corvallis97331 United States Zhengzhou University Software College Zhengzhou450000 China United World College Changshu China Jiangsu215500 China

ISBN: (纸本)9798350308877

In the contemporary landscape of atmospheric sciences, the ability to recognize specific weather phenomena accurately and efficiently has become paramount, largely due to the exponential growth in sensor-generated data. This paper undertakes an in-depth analysis of advanced algorithms, namely YOLOV8, ResNet50, and Convolutional Neural Networks (CNN), for the purpose of weather pattern identification and classification. Building on the foundation of deep learning, the YOLOV8's real-time object detection capabilities are leveraged to discern intricate weather patterns in diverse datasets, from meteorological stations to satellite imagery. On the other hand, Resnet 50 which is another CNN based model was also tested along with CNN for comparison. An exhaustive evaluation of these algorithms covers various metrics, including accuracy, precision, computational efficiency, and real-world applicability. Special emphasis is placed on the crucial stages of data preprocessing, feature extraction, and model tuning, highlighting their impact on the algorithms' overall performance. Our findings suggest that, when appropriately optimized, both YOLOV8 and CNN exhibit exceptional capabilities in discerning and classifying intricate weather patterns, whereas ResNet50 exhibits comparatively less performance. © 2023 IEEE.

关键词： Convolutional neural networks

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Technology Roadmap for Flexible Sensors

引用

ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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A Stylometric Dataset for Bengali Poems 22

A Stylometric Dataset for Bengali Poems

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Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval

作者： Mirza Kamrul Bashar Shuhan Rupasree Dey Sourav Saha Md Shafa Ul Anjum Tarannum Shaila Zaman Software Research and Engineering bKash Limited Bangladesh Computer Science Oregon State University United States of America Business Administration Shahjalal University of Science and Technology Bangladesh Engineering Progoti Systems Limited Bangladesh Computer & Information Science SUNY Polytechnic Institute United States of America

ISBN: (纸本)9781450397629

Poetry is a form of literature that conveys feelings using different styles, aesthetics, and rhythms. The Bengali language has an enriched collection of poems. Every poet has an individual style of expressing their thoughts and emotions. However, stylometric research in this branch of the Bengali language is still in its early stage of development. In this paper, we have presented a stylometric dataset, which has 6,070 poems of 137 poets stored in the textual format. To the best of our knowledge, this is the first stylometric dataset for Bengali poems which will add an extra dimension to the expanding research arena of the Bengali language. To explore the usability of this dataset, we developed poem genre classifiers using deep learning that can classify these poems. Performance analysis of some deep learning classifiers has been presented in addition to classification. The classifiers include GRU and CNN. Among these two, GRU showed better performance by 91.48 in terms of the F1-score. The dataset will be publicly available at https://***/shuhanmirza/Bengali-Poem-Dataset after publishing this article.

关键词： Bengali Poems Corpora Datasets Language Resources Natural Language Processing Stylometric Datasets

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Llm-Based Automated Modeling in Symbolic Execution for Securing Medical software

SSRN

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SSRN 2024年

作者： Chen, Jingxue Deng, Liangjun Qiu, Yao Zhao, Pengbiao Lei, Hang Song, Jingcheng Wang, Xiaopei School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Institute of Building Information Modeling Ningbo Polytechnic College Zhejiang Ningbo315800 China College of Information Science and Engineering Linyi University Shandong Linyi276000 China Department of Computer Science and Engineering University of California RiversideCA92521 United States

There are lots of sensitive information in medical consumer devices. Since Consumer electronics products in the healthcare mainly focus on health monitoring and personal health management, such as wearable ECG monitoring devices, smart weight scales and so on. It is paramount to ensure the security and reliability of software in healthcare delivery, especially for sensitive medical data of integration with artificial intelligence (AI). Therefore, it is necessitating strict security verification and continuous vigilance against potential software vulnerabilities. WebAssembly, known for its security, portability, and speed, has emerged as a crucial technology for medical device software, significantly impacting the healthcare electronics sector, particularly in AI-generated content. There is a novel machine learning-based approach to address the challenges of symbolic execution in WebAssembly by automatically modeling unknown functions using a fine-tuned large language model (LLM) with 20 billion parameters, enhancing the execution's integrity and efficiency. The method, applicable across multiple programming languages and extendable to WebAssembly-compiled programs, shows promising experimental results in model performance and efficiency. This ensures enhanced security and reliability of software for CE, setting the stage for more flexible WebAssembly standards in medical CE and AI-driven healthcare devices. © 2024, The Authors. All rights reserved.

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An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks

SSRN

引用

SSRN 2024年

作者： Yang, Chen Liang, Peng Ma, Zinan School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen518055 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science Wuhan University Wuhan430072 China Department of Computer Science and Engineering Chalmers University of Technology GothenburgS-412 96 Sweden

Context: Stakeholders constantly make assumptions in the development of deep learning (DL) frameworks. These assumptions are related to various types of software artifacts (e.g., requirements, design decisions, and technical debt) and can turn out to be invalid, leading to system failures. Existing approaches and tools for assumption management usually depend on manual identification of assumptions. However, assumptions are scattered in various sources (e.g., code comments, commits, pull requests, and issues) of DL framework development, and manually identifying assumptions has high costs (e.g., time and resources).Objective: The objective of the study is to evaluate different classification models for the purpose of identification with respect to assumptions from the point of view of developers and users in the context of DL framework projects (i.e., issues, pull requests, and commits) on ***: We constructed a new and largest dataset (i.e., AssuEval) of assumptions collected from the TensorFlow and Keras repositories on GitHub;explored the performance of seven traditional machine learning models (e.g., Support Vector Machine, Classification and Regression Trees), a popular DL model (i.e., ALBERT), and a large language model (i.e., ChatGPT) of identifying assumptions on the ***: The experiment results show that ALBERT achieves the best performance (f1-score: 0.9584) of identifying assumptions on the AssuEval dataset, which is much better than the other models (the 2nd best f1-score is 0.6211, achieved by ChatGPT). Though ChatGPT is the most popular large language model, we do not recommend using it to identify assumptions in DL framework development because of its low performance on the task. Fine-tuning ChatGPT specifically for assumption identification could improve the ***: This study provides researchers with the largest dataset of assumptions for further research (e.g., assumption classification, evaluation, and

关键词： Large datasets

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Educating Project Stakeholders: A Preliminary Report 42

Educating Project Stakeholders: A Preliminary Report

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42nd IEEE/ACM International Conference on software engineering Workshops, ICSEW 2020

作者： Dekhtyar, Alex Da Silva, Bruno Slocum, Karson Department of Computer Science and Software Engineering California Polytechnic State University San Luis Obispo United States

ISBN: (纸本)9781450379632

In college coursework, we take care to educate future professional software engineers on how software development process works. computer science and software engineering students across the globe study software process models, gather requirements, design, implement and test their software, work on software maintenance, learn to submit bug reports, build project roadmaps, construct UML diagrams, and deploy software. Yet, ever since the emergence of consumer-facing software, software development often is a collaboration between professional software engineers and multiple stakeholders whose education, professional expertise, and general experience lie outside of computing. We teach future software engineers how to develop software. Why don't we do the same with other future stakeholders? This paper is a description of a pilot software engineering Without Programming course developed and taught at our university for the first time in 2020. In this early stage report (the course is ongoing as of the submisison deadline, but will have been completed by the time of the workshop) we outline the need for the course, its learning objectives, its organization, and the expected results. © 2020 Owner/Author.

关键词： Education computing

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