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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yao Yu Siyu Wang Shumei Liu Lei Guo Zhaolong Ning Shimin Gong Mohammad S. Obaidat School of Computer Science and Engineering Northeastern University Shenyang China Chongqing University of Posts and Telecommunications China State Key Laboratory for Novel Software Technology Nanjing University China School of Intelligent Systems Engineering Sun Yat-sen University China ECE Department Nazarbayev University Astana Kazakhstan College of Computing and Informatics University of Sharjah UAE The King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

By considering the Simultaneous Wireless Information and Power Transfer (SWIPT) schemes, this paper focuses on secure transmission model design in multiple-input-single-output (MISO) channels. In these channels, the channel state information is assumed to be perfect. Our objective is to maximize the worst-case secrecy rate with respect to both potential eavesdroppers and obvious eavesdroppers under the constraints of energy-harvesting and total transmission power. We present an optimization model to indirectly obtain maximum security rate in a single receiver system. Due to the high computational complexity of the solution process caused by the formulated non-convex optimization problem, we propose a novel indirect method to handle this issue. Then, a Semi-Definite Programming (SDP) relaxation method is used to approach the optimal solution. Moreover, we reveal the conditions for ensuring that the above semi-definite relaxation is compact. Simulation results demonstrate that the gained performance in our system is much better than those of the existing competing schemes.

关键词： Receivers Interference Signal to noise ratio Transmitters Optimization Security Communication system security

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OER: Six Perspectives on Global Misconceptions and Challenges

OER: Six Perspectives on Global Misconceptions and Challenge...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Dave Towey Sorel Reisman Henry Chan Claudio Demartini Edmundo Tovar Tiziana Margaria School of Computer Science University of Nottingham Ningbo China Ningbo People’s Republic of China Department of Information Systems and Decision Sciences California State University Fullerton Fullerton U.S.A. Department of Computing The Hong Kong Polytechnic University Hong Kong China Control & Computer Engineering Department Politecnico di Torino Turin Italy School of Computer Engineers Universidad Politécnica de Madrid Madrid Spain Department of Computer Science and Information Systems University of Limerick Limerick Ireland

ISBN: (数字)9781728126654

ISBN: (纸本)9781728126661

Open Educational Resources (OERs) are freely accessible materials for teaching, learning, and research that have been made available such that they can be freely used, modified, and shared. Prompted by the potential positive impact that OERs can have, a growing community of OER enthusiasts has been advocating for their adoption in higher education, and elsewhere. The growing demand for computer science education and training (especially in areas related to artificial intelligence, machine learning, and big data) has led to speculation as to how OERs may be best leveraged to support such teaching and learning. The recent occasion of an international OER workshop at the 2019 IEEE Conference on Computers, Software and Applications (COMPSAC) allowed for six OER enthusiasts, all with extensive OER experience, to reflect on the status of OER adoption and understanding. These reflections, addressing misconceptions and challenges, form the basis for this paper.

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The Development of an IoT Instrumented Bike: for Assessment of Road and Bike Trail Conditions

The Development of an IoT Instrumented Bike: for Assessment ...

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International Symposium in Sensing and Instrumentation in IoT Era (ISSI)

作者： Peijie Qiu Xilun Liu Siwei Wen Yuchang Zhang Kyle N. Winfree Chun-Hsing Ho Electrical Engineering in the School of Informatics Computing Cyber Systems at Northern Arizona University The United States School of Informatics Computing Cyber Systems at Northern Arizona University 1295 S. Knoles Dr The United States Department of Civil Environmental Engineering at Northern Arizona University 2112 S Huffer Ln. The United States

ISBN: (纸本)9781538656396

The purpose of this paper is to present a method to detect bike trail conditions while displaying real-time data on a mobile application. Using a Global Positioning System (GPS) unit, microprocessor and multiple accelerometers built on the concept of Internet of Things (IoT), the entire device employs real-time data to collect, analyze, and identify bumps and cracks in public infrastructure. The main purpose of this research is aimed at helping the government evaluate the severity of road conditions to better assist in strategic decision making about maintaining or repairing damaged infrastructural areas as well as to notify pedestrians of potential bumps or cracks along their route. The authors propose an algorithm that sets a window range to vertically process the raw accelerometer data to accurately distinguish bumps. By integrating this algorithm onto a mobile application, the data collected by this program can be shared between users to help avoid potential risks or dangerous road conditions and to assist in the evaluation of infrastructural deterioration.

关键词： Accelerometers Photonics Global Positioning System Instruments Roads Databases Microprocessors

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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DEVS specification for modeling and simulation of the UML activities

DEVS specification for modeling and simulation of the UML ac...

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2017 International Symposium on Model-Driven Approaches for Simulation engineering, Mod4Sim 2017, Part of the 2017 Spring Simulation Multi-Conference, SpringSim 2017

作者： Alshareef, Abdurrahman Sarjoughian, Hessam S. Arizona Center for Integrative Modeling and Simulation School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S. Mill Avenue TempeAZ85281 United States

ISBN: (纸本)9781510838260

Recently, the existence of the foundational UML subset (fUML) has made a significant impact on driving the mainstream in behavioral modeling. Recent advances on concepts like UML actions and behavioral metamodeling promise better mechanisms in handling behavior. We argue that a greater potentiality can be achieved through establishing a rigorous mathematical grounding for the subset. We also argue that Parallel DEVS can be used as a suitable candidate. Thus, we propose formalizing the creation of UML activities as a set of atomic and coupled DEVS models. These abstractions have distinct structures and behaviors. That is to say, the foundational elements of UML activities and actions are modeled and mapped into a set of atomic as well as coupled models. Then, they can collectively serve as a basis for grounding different diagrams via coupling. We demonstrate the use of the formalism for the modeling and simulation of activities. ©2017 Society for Modeling & Simulation International (SCS).

关键词： Behavioral research

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Fluorescence image histology pattern transformation using image style transfer

arXiv

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arXiv 2019年

作者： Izadyyazdanabadi, Mohammadhassan Belykh, Evgenii Zhao, Xiaochun Moreira, Leandro Borba Gandhi, Sirin Cavallo, Claudio Eschbacher, Jennifer Nakaji, Peter Preul, Mark C. Yang, Yezhou School of Computing Informatics and Decision System Engineering Arizona State University TempeAZ United States Loyal and Edith Davis Neurosurgery Research Laboratory Department of Neurosurgery Barrow Neurological Institute St. Joseph's Hospital and Medical Center PhoenixAZ United States Department of Neuropathology Barrow Neurological Institute St. Joseph's Hospital and Medical Center PhoenixAZ United States

Confocal laser endomicroscopy (CLE) allow on-the-fly in vivo intraoperative imaging in a discreet field of view, especially for brain tumors, rather than extracting tissue for examination ex vivo with conventional light microscopy. Fluorescein sodium-driven CLE imaging is more interactive, rapid, and portable than conventional hematoxylin and eosin (H&E)-staining. However, it has several limitations: CLE images may be contaminated with artifacts (motion, red blood cells, noise), and neuropathologists are mainly trained on colorful stained histology slides like H&E while the CLE images are gray. To improve the diagnostic quality of CLE, we used a micrograph of an H&E slide from a glioma tumor biopsy and image style transfer, a neural network method for integrating the content and style of two images. This was done through minimizing the deviation of the target image from both the content (CLE) and style (H&E) images. The style transferred images were assessed and compared to conventional H&E histology by neurosurgeons and a neuropathologist who then validated the quality enhancement in 100 pairs of original and transformed images. Average reviewers' score on test images showed 84 out of 100 transformed images had fewer artifacts and more noticeable critical structures compared to their original CLE form. By providing images that are more interpretable than the original CLE images and more rapidly acquired than H&E slides, the style transfer method allows a real-time, cellular-level tissue examination using CLE technology that closely resembles the conventional appearance of H&E staining and may yield better diagnostic recognition than original CLE grayscale images. Copyright © 2019, The Authors. All rights reserved.

关键词： Fluorescence

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A data-driven compressive sensing framework tailored for energy-efficient wearable sensing

A data-driven compressive sensing framework tailored for ene...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kai Xu Yixing Li Fengbo Ren School of Computing Informatics and Decision Systems Engineering Arizona State University USA

ISBN: (纸本)9781509041183

Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring. However, conventional model-driven CS frameworks suffer from limited compression ratio and reconstruction quality when dealing with physiological signals due to inaccurate models and the overlook of individual variability. In this paper, we propose a data-driven CS framework that can learn signal characteristics and personalized features from any individual recording of physiologic signals to enhance CS performance with a minimized number of measurements. Such improvements are accomplished by a co-training approach that optimizes the sensing matrix and the dictionary towards improved restricted isometry property and signal sparsity, respectively. Experimental results upon ECG signals show that the proposed method, at a compression ratio of 10×, successfully reduces the isometry constant of the trained sensing matrices by 86% against random matrices and improves the overall reconstructed signal-to-noise ratio by 15dB over conventional model-driven approaches.

关键词： Data-driven compressive sensing mobile healthcare wearable sensing Internet of things (IoT) Compressed sensing compression ratio CONTAINMENT SPRAY carbon monosulfide Cesium data driven Energy efficiency signals conditioned stimulus Frameworks Isometric projections Isometry Wearable Dictionaries as Topic matrices

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Recent progress and trends in predictive visual analytics

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Frontiers of Computer Science 2017年第2期11卷 192-207页

作者： Junhua LU Wei CHEN Yuxin MA Junming KE Zongzhuang LI Fan ZHANG Ross MACIEJEWSKI State Key Lab of Computer Aided Design and Computer Graphics Zhejiang University Hangzhou 310058 China College of Science Zhejiang University of Technology Hangzhou 310023 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou 310023 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287-8809 USA

A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem snurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the pre- diction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual ana- lytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summariza- tion of the predictive analytics workfiow.

关键词： predictive visual analytics visualization visual analytics data mining predictive analysis

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A system for learning continuous human-robot interactions from human-human demonstrations

A system for learning continuous human-robot interactions fr...

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2017 IEEE International Conference on Robotics and Automation, ICRA 2017

作者： Vogt, David Stepputtis, Simon Grehl, Steve Jung, Bernhard Ben Amor, Heni Faculty of Mathematics and Informatics Technical University Bergakademie Freiberg Freiberg09599 Germany School of Computing Informatics Decision Systems Engineering Arizona State University 660 S. Mill Ave TempeAZ85281 United States

ISBN: (纸本)9781509046331

We present a data-driven imitation learning system for learning human-robot interactions from human-human demonstrations. During training, the movements of two interaction partners are recorded through motion capture and an interaction model is learned. At runtime, the interaction model is used to continuously adapt the robot's motion, both spatially and temporally, to the movements of the human interaction partner. We show the effectiveness of the approach on complex, sequential tasks by presenting two applications involving collaborative human-robot assembly. Experiments with varied object hand-over positions and task execution speeds confirm the capabilities for spatio-temporal adaption of the demonstrated behavior to the current situation. © 2017 IEEE.

关键词： Human robot interaction

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Brain morphometry analysis with surface foliation theory

arXiv

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arXiv 2018年

作者： Wen, Chengfeng Lei, Na Ma, Ming Qi, Xin Zhang, Wen Wang, Yalin Gu, David Xianfeng Department of Computer Science Stony Brook University Stony BrookNY11794 United States School of Software and Technology Dalian University of Technology Dalian116620 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

Brain morphometry study plays a fundamental role in neuroimaging research. In this work, we propose a novel method for brain surface morphometry analysis based on surface foliation theory. Given brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for morphometry analysis purpose. In this work, we propose a set of novel surface features rooted in surface foliation theory. To the best of our knowledge, this is the first work to make use of surface foliation theory for brain morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying brain cortical surfaces between patients with Alzheimer's disease and healthy control subjects demonstrate the efficiency and efficacy of our method. Copyright © 2018, The Authors. All rights reserved.

关键词： Brain

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