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Conference on Digital Avionics Systems (DASC)

作者： Jiwon Kim Kexin Wang Michael Dorneich Eliot Winer Lori Brown Geoffrey Whitehurst Industrial and Manufacturing Systems Engineering Iowa State University Ames IA USA Human Computer Interaction Iowa State University Ames IA USA Mechanical Engineering Iowa State University Ames IA USA Aviation Flight Science Western Michigan University Kalamazoo MI USA

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

This paper presents the preliminary evaluation results of an extended reality (XR) authoring tool designed for aviation weather training. XR has the potential to improve educational outcomes in flight training programs. However, the complexity and time demands of existing XR authoring tools have slowed wider adoption. This work strives to address these gaps by developing and continuously refining an instructor-centered, user-friendly XR authoring tool. Seventeen flight instructors participated in developing two XR lessons and evaluated the tool's effectiveness. Results indicated that the introduction of the XR authoring tool significantly boosted the instructors' confidence in creating XR content and alleviated concerns related to time consumption. Additionally, the findings showed that the XR authoring tool is highly usable and demands a low workload, with the XR lessons produced being highly satisfactory. By employing an instructor-centered approach, the XR authoring tool enabled instructors who are not XR experts to improve students' learning experiences, focusing on aviation weather content without the burden of technical complexities.

关键词： Training Authoring systems Extended reality Refining Focusing Aerospace electronics Complexity theory Usability Faces Meteorology

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The Landscape and Challenges of HPC Research and LLMs

arXiv

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arXiv 2024年

作者： Chen, Le Ahmed, Nesreen K. Dutta, Akash Bhattacharjee, Arijit Yu, Sixing Mahmud, Quazi Ishtiaque Abebe, Waqwoya Phan, Hung Sarkar, Aishwarya Butler, Branden Hasabnis, Niranjan Oren, Gal Vo, Vy A. Munoz, Juan Pablo Willke, Theodore L. Mattson, Tim Jannesari, Ali Department of Computer Science Iowa State University Ames United States Intel Labs United States Technion - Israel Institute of Technology Israel Human Learning Group United States

Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning. Both encoder-decoder models and prompt-based techniques have shown immense potential for natural language processing and code-based tasks. Over the past several years, many research labs and institutions have invested heavily in high-performance computing, approaching or breaching exascale performance levels. In this paper, we posit that adapting and utilizing such language model-based techniques for tasks in high-performance computing (HPC) would be very beneficial. This study presents our reasoning behind the aforementioned position and highlights how existing ideas can be improved and adapted for HPC tasks. © 2024, CC BY.

关键词： Natural language processing systems

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Capillaric field effect transistors

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Microsystems & Nanoengineering 2022年第2期8卷 59-71页

作者： Claude Meffan Julian Menges Fabian Dolamore Daniel Mak Conan Fee Renwick C.J.Dobson Volker Nock Department of Electrical and Computer Engineering University of CanterburyChristchurch 8041New Zealand Department of Microengineering Kyoto University615-8540 KyotoJapan School of Biological Sciences University of CanterburyChristchurch 8041New Zealand Biomolecular Interaction Centre School of Biological SciencesUniversity of CanterburyChristchurch 8041New Zealand School of Product Design University of CanterburyChristchurch 8041New Zealand Department of Biochemistry and Molecular Biology Bio21 Molecular Science and Biotechnology InstituteUniversity of MelbourneParkvilleVIC 3010Australia The MacDiarmid Institute for Advanced Materials and Nanotechnology Wellington 6140New Zealand

Controlling fluid flow in capillaric circuits is a key requirement to increase their uptake for assay *** action off-valves provide such functionality by pushing an occluding bubble into the channel using a difference in capillary ***,we utilized the binary switching mode of this structure to develop a powerful set of fundamental fluidic valving *** this work,we study the transistor-like qualities of the off-valve and provide evidence that these structures are in fact functionally complementary to electronic junction field effect *** view of this,we propose the new term capillaric field effect transistor to describe these types of *** support this conclusion,we present a theoretical description,experimental characterization,and practical application of analog flow resistance *** addition,we demonstrate that the valves can also be *** show modulation of the flow resistance from fully open to pinch-off,determine the flow rate-trigger channel volume relationship and demonstrate that the latter can be modeled using Shockley's equation for electronic ***,we provide a first example of how the valves can be opened and closed repeatedly.

关键词： transistors effect field

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Passive User Identification and Authentication with Smartphone Sensor Data 3

Passive User Identification and Authentication with Smartpho...

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3rd IEEE International Conference on Transdisciplinary AI, TransAI 2021

作者： Raval, Aaditya Anwar, Mohd Human-Centered AI Lab Computer Science Department North Carolina Agricultural and Technical State University GreensboroNC United States

ISBN: (纸本)9781665434126

A unique digital identity, user ID, is essential for everyday online activities in the Internet era. These user IDs represent a user in a digital environment using stored credentials on a system called authentication system. It is possible to capture unique patterns of user movements from smartphone sensor data. This paper presents a framework for passive user identification and authentication using onboard sensors of an Android smartphone. Using this framework, we propose a data preprocessing scheme that uses the absolute difference of consecutive repeated measurements of 7 onboard sensors. We developed 5 models for user identification and authentication using various machine learning and deep learning methods. The experimental results and performance assessment conclude that the Sequential Neural Network (SNN) model performs best with 98.24% accuracy for authenticating users (binary classification) and 84.41% accuracy during multi-class classification for user identification. Furthermore, all the models developed for this research, namely MLP, SNN, CNN, SVM, and Bi-LSTM, provide 100% precision during binary classification for passive user authentication. Our models were trained on 556,746 sensor data samples, each having 20 features. These features include the proximity sensor data, light sensor data, triaxial measurements from accelerometers, gravity sensors, gyroscopes, magnetometers, and rotational vector sensors. We study the possible influence of absolute difference samples for user identification and authentication. © 2021 IEEE.

关键词： Smartphones

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CounterQuill: Investigating the Potential of human-AI Collaboration in Online Counterspeech Writing

arXiv

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arXiv 2024年

作者： Ding, Xiaohan Ping, Kaike Gunturi, Uma Sushmitha Carik, Buse Stil, Sophia Wilhelm, Lance T. Daryanto, Taufiq Hawdon, James Lee, Sang Won Rho, Eugenia H. Department of Computer Science Virginia Tech Blacksburg United States IBM Mountain View United States College of Liberal Arts and Human Sciences Virginia Tech Blacksburg United States

Online hate speech has become increasingly prevalent on social media platforms, causing harm to individuals and society. While efforts have been made to combat this issue through content moderation, the potential of user-driven counterspeech as an alternative solution remains underexplored. Existing counterspeech methods often face challenges such as fear of retaliation and skill-related barriers. To address these challenges, we introduce CounterQuill, an AI-mediated system that assists users in composing effective and empathetic counterspeech. CounterQuill provides a three-step process: (1) a learning session to help users understand hate speech and counterspeech;(2) a brainstorming session that guides users in identifying key elements of hate speech and exploring counterspeech strategies;and (3) a co-writing session that enables users to draft and refine their counterspeech with CounterQuill. We conducted a within-subjects user study with 20 participants to evaluate CounterQuill in comparison to ChatGPT. Results show that CounterQuill’s guidance and collaborative writing process provided users a stronger sense of ownership over their co-authored counterspeech. Users perceived CounterQuill as a writing partner and thus were more willing to post the co-written counterspeech online compared to the one written with ChatGPT. © 2024, CC BY.

关键词： Social networking (online)

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Confidentiality Guaranteed E-Health Treatment for Cloud Cover Enhanced Systems Based on IoT 1

Confidentiality Guaranteed E-Health Treatment for Cloud Cove...

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1st International Conference on Advances in Computational science and Engineering, ICACSE 2020

作者： Kiran Kumar Reddy, P. Narasima Venkatesh, A. Arumugam, S.S. Jegan Amarnath, J. Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Telangana Secunderabad India Department of MBA-Human Resource Management R.V. Institute of Management Karnataka Bangalore India Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Malayambakkam India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Chennai India

The advances made with Internet-of- Things (IoTs) on networking technologies and hardware has made for an at the moment more relaxed human life. In addition to the so -called "intelligent"settings, e-healthcare services, as well as real-time diagnoses and diagnostic consulting, have increasingly been integrated into IoT. Cloud cover layers have also been hired, and have demonstrated their utility by offering quick response time and low latency to increase the capability of IoT based health systems. Such a development, however, retains the privacy of users as a serious obstacle, to some degree solving security/privacy problems. Such technology still had less privacy controls when it was at a childhood stage. Therefore, our latest work is on an eHealth System for the security of privacy problems in electronic medical reports (EMR). In addition, we have interacted with and compared work on the proposed work in terms of reaction time and delay. The findings demonstrate the complete work in conjunction with normal network parameters is effective to provide privacy. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment

arXiv

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arXiv 2024年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

The burgeoning size of Large Language Models (LLMs) has led to enhanced capabilities in generating responses, albeit at the expense of increased inference times and elevated resource demands. Existing methods of acceleration, predominantly hinged on knowledge distillation, generally necessitate fine-tuning of considerably large models, posing a challenge for average users. Furthermore, present techniques for expediting inference and reducing costs operate independently. To address these issues, we introduce a novel and intuitive Guidance-based Knowledge Transfer (GKT) framework. This approach leverages a larger LLM as a "teacher" to create guidance prompts, paired with a smaller "student" model to finalize responses. Remarkably, GKT requires no fine-tuning and doesn’t necessitate the teacher and student models to have the same vocabulary, allowing for extensive batch generation to accelerate the process while ensuring user customization. GKT can be seamlessly integrated into cloud-edge collaboration architectures, and is versatile enough for plug-and-play application across various models. When utilizing ChatGPT as teacher model and Llama2-70B as the student model, we can achieve 95.00% of ChatGPT’s performance at 52% of the cost. The results highlight substantial enhancements in accuracy and processing speed on the GSM8K and CSQA datasets, surpassing the performance of using either the student or teacher models in isolation. Our code is publicly available at https://***/Zoeyyao27/GKT © 2024, CC BY.

关键词： Distillation

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High Throughput Training Label Generation from Whole Brain Images 17th

High Throughput Training Label Generation from Whole Brain ...

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17th International Conference on Brain Informatics, BI 2024

作者： Marrett, Karl Moradi, Keivan Park, Chris Sin Yan, Ming Choi, Chris Zhu, Muye Akram, Masood Nanda, Sumit Xue, Qing Mun, Hyun-Seung Gutierrez, Adriana E. Rudd, Mitchell Zingg, Brian Magat, Gabrielle Wijaya, Kathleen Dong, Hongwei William Yang, X. Cong, Jason Department of Computer Science University of California Los Angeles Los Angeles United States Center for Neurobehavioral Genetics Semel Institute for Neuroscience & Human Behavior University of California Los Angeles Los Angeles United States Department of Psychiatry and Biobehavioral Sciences David Geffen School of Medicine University of California Los Angeles Los Angeles United States Department of Neuroscience David Geffen School of Medicine University of California Los Angeles Los Angeles United States

ISBN: (纸本)9789819632961

Neuronal labeling–a process that transforms image volumes into 3D geometries and skeletons of cells–bottlenecks the study of brain function, connectomics and pathology. Domain scientists are limited by reconstruction methods which have insufficient accuracy to be used without proofreading. Further gains in fidelity of automatic techniques may largely come from supervised methods (e.g., deep learning), however these require unique training data which is rare especially for novel imaging or experimental settings. Current protocols for label creation are fraught with issues in automation, which are exacerbated by modern data size (30 TB in this study). Furthermore for training data, quantity but not necessarily quality is paramount, which is why augmentation and weak supervision is so effective. We introduce an application that can produce adequate training labels automatically from raw lightsheet microscopy images of whole brains. This pipeline is effective at producing both weak and strong labels of two critical categories of neurons: cell bodies (somas) and whole cells. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neurons

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Gossamer: Scaling Image Processing and Reconstruction to Whole Brains 17th

Gossamer: Scaling Image Processing and Reconstruction to Who...

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17th International Conference on Brain Informatics, BI 2024

作者： Marrett, Karl Moradi, Keivan Park, Chris Sin Yan, Ming Choi, Chris Zhu, Muye Akram, Masood Nanda, Sumit Xue, Qing Mun, Hyun-Seung Gutierrez, Adriana E. Rudd, Mitchell Zingg, Brian Magat, Gabrielle Wijaya, Kathleen Dong, Hongwei Yang, X. William Cong, Jason Department of Computer Science University of California Los Angeles Los Angeles United States Center for Neurobehavioral Genetics Semel Institute for Neuroscience and Human Behavior University of California Los Angeles Los Angeles United States Department of Psychiatry and Biobehavioral Sciences David Geffen School of Medicine University of California Los Angeles Los Angeles United States Department of Neuroscience David Geffen School of Medicine University of California Los Angeles Los Angeles United States

ISBN: (纸本)9789819632930

Neuronal reconstruction–a process that transforms image volumes into 3D geometries and skeletons of cells–bottlenecks the study of brain function, connectomics and pathology. Domain scientists need exact and complete segmentations to study subtle topological differences. Existing methods are disk-bound, dense-access, coupled, single-threaded, algorithmically unscalable and require manual cropping of small windows and proofreading of skeletons due to low topological accuracy. Designing a data-intensive parallel solution suited to a neurons’ shape, topology and far-ranging connectivity is particularly challenging due to I/O and load-balance, yet by abstracting these vision tasks into strategically ordered specializations of search, we progressively lower memory by 4 orders of magnitude. This enables 1 mouse brain to be fully processed in-memory on a single server, at 67× the scale with 870× less memory while having 78% higher automated yield than APP2, the previous state of the art in performant reconstruction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neurons

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Position: Topological Deep Learning is the New Frontier for Relational Learning 41

Position: Topological Deep Learning is the New Frontier for ...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Birdal, Tolga Bronstein, Michael Carlsson, Gunnar Curry, Justin Gao, Yue Hajij, Mustafa Kwitt, Roland Liò, Pietro Di Lorenzo, Paolo Maroulas, Vasileios Miolane, Nina Nasrin, Farzana Ramamurthy, Karthikeyan Natesan Rieck, Bastian Scardapane, Simone Schaub, Michael T. Veličković, Petar Wang, Bei Wang, Yusu Wei, Guo-Wei Zamzmi, Ghada Department of Mathematics The University of Manchester Manchester United Kingdom Department of Computing Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Department of Mathematics Stanford University Stanford United States BlueLightAI Inc. United States University at Albany New York United States School of Software Tsinghua University Beijing China University of San Francisco San Francisco United States Department of Artificial Intelligence and Human Interfaces University of Salzburg Austria Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy Department of Mathematics University of Tennessee Knoxville United States Department of Electrical and Computer Engineering UC Santa Barbara Santa Barbara United States Department of Mathematics University of Hawai'i at Mānoa HI United States IBM Corporation New York United States Helmholtz Munich Munich Germany Technical University of Munich Munich Germany RWTH Aachen University Aachen Germany Google Deep-Mind United Kingdom School of Computing University of Utah Utah United States Computer Science and Engineering Department University of California San Diego San Diego United States Department of Mathematics Michigan State University East LansingMI United States University of South Florida Florida United States

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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