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13th International Conference on Wireless Communications and Signal Processing, WCSP 2021

作者： Zhang, Yao Xia, Wenchao Zhu, Yongxu Zheng, Gan Gao, Feifei Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Nanjing University of Posts and Telecommunications Department of Wireless Communication Key Lab of Jiangsu Province Nanjing210003 China London South Bank University Division of Computer Science and Informatics London United Kingdom Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University LeicestershireLE11 3TU United Kingdom Institute for Artificial Intelligence Tsinghua University State Key Lab of Intelligent Technologies and Systems Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China

ISBN: (纸本)9781665407854

This paper focuses on a generalized downlink cell-free massive multiple-input multiple-output (CF-mMIMO) system with normalized conjugate beamforming (NCB) and channel non-reciprocities, assuming the access points (APs) are equipped with low-resolution digital-to-analog converters (DACs). Specifically, the channel non-reciprocities consist of the amplitude and phase non-reciprocities at the APs and user equipments. Closed-form expressions of the achievable sum-rates are derived for evaluating the impacts of channel non-reciprocities and DAC resolutions and comparing against the conjugate beamforming (CB) counterpart. Simulation results reveal that the downlink sum-rates are immune to the amplitude non-reciprocity of transceivers but strongly sensitive to the AP phase non-reciprocity. In particular, a small degree of AP phase non-reciprocity can result in substantial rate degradations. Besides, 5-bit DACs are enough in CF-mMIMO systems to approach the same sum-rates as CF-mMIMO systems with high-resolution DACs. Moreover, the NCB scheme outperforms the CB scheme in terms of the downlink sum-rates for both reciprocal and non-reciprocal channels. © 2021 IEEE.

关键词： Beamforming

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Big Data Governance for Building A Smart Cities 10

Big Data Governance for Building A Smart Cities

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10th International Conference on ICT for Smart Society, ICISS 2019

作者： Zulkarnain, Novan Kosala, Raymond Ranti, Benny Supangkat, Suhono Harso Bina Nusantara University Computer Science Department BINUS Graduate Program Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Computer Science Department Depok16424 Indonesia Institute of Technology Bandung School of Electrical and Informatics Engineering Bandung40132 Indonesia

ISBN: (纸本)9781728148809

There have been many studies conducted related to Smart City, IT Governance and Big Data. In this study aims to find out how the relationship between the three and how to form a framework to explain it. The methodology used is qualitative by looking for some literature on smart city framework, IT Governance framework, and a Big Data framework. From these results, an overall picture of the relationship between the three is concluded, where Big Data has a role in IT Governance. and also the relationship of IT Governance to the realization of Smart City. And the final results of this study produce a framework to explain the relationship between Smart City, IT Governance and Big Data. © 2019 IEEE.

关键词： Big data

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Deep Learning for COVID-19 Contamination Analysis and Prediction Using ECG Images on Raspberry Pi 4

SSRN

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SSRN 2022年

作者： Mhamdi, Lotfi Dammak, Oussama Cottin, Francois Bendhaou, Imed Biotechnology Institute of Monastir. Environment street. Monastir 5000 Tunisia College of first common year Department of Mathematics Um Al Qura University POBox 14035 Mecca21955 Saudi Arabia Science faculty of Gabes University of Gabes Omar Ibn AlKhattab-Zrig road Gabes6029 Tunisia Paris-Saclay University CIAMS EA 4532 Orsay91405 France Orleans University CIAMS EA 4532. Orleans45067 France Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hikma University Jeddah22246-4872 Saudi Arabia Department of Technology Higher Institute of Computer Sciences and Mathematics University of Monastir Monastir5000 Tunisia Department of Computing University of Turku TurkuFI20014 Finland

Background: The main goal of this paper is to automatically diagnose COVID-19 contamination based on the artificial intelligence approach. We used Convolutional Neural Network (CNN) deep learning algorithm for analyzing the ECG images to detect cardiac abnormalities, consequent of the contamination by the SARS-CoV-2 virus;responsible for the Covid-19 ***: We designed, trained and evaluated the performance of two deep learning models (MobileNet V2 and VGG16) in detecting and distinguishing between two different classes (healthy subjects and COVID-19 positive cases). Indeed, this virus attacks the human respiratory system, which could affect the heart system. Thus, the development of a deep learning model could be useful to help for a quick and efficient diagnosis and prediction, and for decision making among ***: The overall classification rate achieved by the models was 99.34% and 99.67% for MobileNet V2 and VGG16, ***: This approach can efficiently contribute to the diagnosis of COVID-19 contamination. Therefore, the performed deep learning model may be used for predicting abnormal cardiac activities, consequent of the contamination by the virus. © 2022, The Authors. All rights reserved.

关键词： Coronavirus

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3D mapping of fire hotspot in East Rinjani forest area using GIS and remote sensing

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AIP Conference Proceedings 2024年第1期3026卷

作者： Ari Hernawan Wahyu Rahmaniar Jia Ching Wang Wirarama Wedashwara Andy Hidayat Jatmika Muhammad Ari Rifqi Feisal Dirgantara Department of Informatics Engineering Faculty of Engineering University of Mataram Mataram Indonesia Institute of Innovative Research Tokyo Institute of Technology Kanagawa 226-8503 Japan Departement of Computer Science and Information Engineering National Central University Taoyuan Taiwan Institiute of Earth Science Academica Sinica Taipei Taiwan Research Center for Geological Resources National Research and Innovation Agency (BRIN) Bogor Indonesia

Wildfires in Indonesia reached their peak during the drought of 2015, with estimated burnt forest and land of up to 2.6 million hectares. During the dry season of 2020, high temperatures and high winds were responsible for losing 659.02 hectares from forest fires in Lombok Island. The fires on the Island caused economic hardship, disrupted commerce, and led to short and long-term health problems for most of the population. This study aimed to use Geographic Information Systems (GIS) and Remote Sensing (RS) to map forest fire hotspots, establish fire brigades, and suppress forest recovery programs. Wildfire annual data were acquired from Satu Data NTB website, DEM from ASTER Global Elevation Model, and NDVI from Sentinel2B. DEM and NDVI were calculated and projected into 3D objects. The Wildfire geolocations were then added to 3D objects and the scale according to wildfire level. The performance of forest recovery is measured using the development of NDVI (Normalized Difference Vegetation Index) in affected fire areas. Results show the overall vegetation index increases in the area where fire forest has occurred. Combined RS and GIS efforts show the prospective application to evaluate forest fire control and recovery programs’ performance.

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Detecting the geospatialness of prepositions from natural language text 14

Detecting the geospatialness of prepositions from natural la...

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14th International Conference on Spatial Information Theory, COSIT 2019

作者： Radke, Mansi Das, Prarthana Stock, Kristin Jones, Christopher B. Computer Science and Engineering Department Visvesvaraya National Institute of Technology Nagpur India Massey Geoinformatics Collaboratory Massey University Auckland New Zealand School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

ISBN: (纸本)9783959771153

There is increasing interest in detecting the presence of geospatial locative expressions that include spatial relation terms such as near or within . Being able to do so provides a foundation for interpreting relative descriptions of location and for building corpora that facilitate the development of methods for spatial relation extraction and interpretation. Here we evaluate the use of a spatial role labelling procedure to distinguish geospatial uses of prepositions from other spatial and non-spatial uses and experiment with the use of additional machine learning features to improve the quality of detection of geospatial prepositions. An annotated corpus of nearly 2000 instances of preposition usage was created for training and testing the classifiers. © 2019 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Learning algorithms

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Author Correction: AutoTransOP: translating omics signatures without orthologue requirements using deep learning

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NPJ systems biology and applications 2024年第1期10卷 148页

作者： Nikolaos Meimetis Krista M Pullen Daniel Y Zhu Avlant Nilsson Trong Nghia Hoang Sara Magliacane Douglas A Lauffenburger Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA. Department of Biology and Biological Engineering Chalmers University of Technology Gothenburg SE 41296 Sweden. School of Electrical Engineering and Computer Science Washington State University Pullman WA 99164-236 USA. Institute of Informatics University of Amsterdam Amsterdam The Netherlands. MIT-IBM Watson AI Lab Cambridge MA 02139 USA. Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA. lauffen@mit.edu.

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DroneAttention: Sparse Weighted Temporal Attention for Drone-Camera Based Activity Recognition

arXiv

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arXiv 2022年

作者： Yadav, Santosh Kumar Luthra, Achleshwar Pahwa, Esha Tiwari, Kamlesh Rathore, Heena Pandey, Hari Mohan Corcoran, Peter College of Science and Engineering National University of Ireland GalwayH91TK33 Ireland CogniX Quadrant-2 10th Floor Cyber Towers Madhapur Telangana Hyderabad500081 India Department of CSIS Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333031 India Department of Computer Science The University of Texas San Antonio United States Department of Computing & Informatics Bournemouth University Dorset PooleBH12 5BB United Kingdom

Human activity recognition (HAR) using drone-mounted cameras has attracted considerable interest from the computer vision research community in recent years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention. The proposed SWTA is comprised of two parts. First, temporal segment network that sparsely samples a given set of frames. Second, weighted temporal attention, which incorporates a fusion of attention maps derived from optical flow, with raw RGB images. This is followed by a basenet network, which comprises a convolutional neural network (CNN) module along with fully connected layers that provide us with activity recognition. The SWTA network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a margin of 25.26%, 18.56%, and 2.94%, respectively. © 2022, CC BY.

关键词： Drones

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Secure Human Action Recognition by Encrypted Neural Network Inference

arXiv

引用

arXiv 2021年

作者： Kim, Miran Jiang, Xiaoqian Lauter, Kristin Ismayilzada, Elkhan Shams, Shayan Department of Mathematics Hanyang University Seoul Korea Republic of Department of Computer Science Hanyang University Seoul Korea Republic of School of Biomedical Informatics University of Texas Health Science Center HoustonTX United States SeattleWA United States Department of Computer Science and Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of Department of Applied Data Science San Jose State University San JoseCA United States

Advanced computer vision technology can provide near real-time home monitoring to support "aging in place" by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average inference latency of 1.2 seconds and 2.4 seconds on real-world test datasets using small and large neural nets, respectively. We show that our method enables a 613x speedup over the latency-optimized LoLa and achieves an average of 3.1x throughput increase in secure inference compared to the throughput-optimized nGraph-HE2. Copyright © 2021, The Authors. All rights reserved.

关键词： Cryptography

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Space shift keying with reconfigurable intelligent surfaces: Phase configuration designs and performance analysis

arXiv

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arXiv 2021年

作者： Li, Qiang Wen, Miaowen Wang, Shuai Alexandropoulos, George C. Wu, Yik-Chung Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou510632 China School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China Department of Electrical and Electronic Engineering Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Sifakis Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens15784 Greece Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong

Reconfigurable intelligent surface (RIS)-assisted transmission and space shift keying (SSK) appear as promising candidates for future energy-efficient wireless systems. In this paper, two RIS-based SSK schemes are proposed to efficiently improve the error and throughput performance of conventional SSK systems, respectively. The first one, termed RIS-SSK with passive beamforming (RIS-SSK-PB), employs an RIS for beamforming and targets the maximization of the minimum squared Euclidean distance between any two decision points. The second one, termed RIS-SSK with Alamouti space-time block coding (RIS-SSK-ASTBC), employs an RIS for ASTBC and enables the RIS to transmit its own Alamouti-coded information while reflecting the incident SSK signals to the destination. A low-complexity beamformer and an efficient maximum-likelihood (ML) detector are designed for RIS-SSK-PB and RIS-SSK-ASTBC, respectively. Approximate expressions for the average bit error probabilities of the source and/or the RIS are derived in closed-form assuming ML detection. Extensive computer simulations are conducted to verify the performance analysis. Results show that RIS-SSK-PB significantly outperforms the existing RIS-free and RIS-based SSK schemes, and RIS-SSK-ASTBC enables highly reliable transmission with throughput improvement. © 2021, CC BY-NC-ND.

关键词： Energy efficiency

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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