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SSRN 2022年

作者： Daher, Ali W. Ferrari, Enrico Verda, Damiano Chible, Hussein Musellic, Marco Caviglia, Daniele D. Department of Electrical Electronic and Telecommunications Engineering and Naval Architecture University of Genoa Via Opera Pia 11 Liguria Genoa16145 Italy Ph.D. School for Sciences and Technology Lebanese University Beirut6573/14 Lebanon Institute of Electronics Computer and Telecommunication Engineering Consiglio Nazionale delle Ricerche Via De Marini 6 Genoa Liguria16149 Italy Rulex Innovation Labs Rulex inc. Via Felice Romani 9 Liguria Genoa16122 Italy

Image classification and interpretability is a basic procedure in modern AI systems as well as being employed in the fields of security, instrumentation, and image classification. Explainable Artificial Intelligence also plays a crucial role in providing machine learning techniques with a white-box output that allows the practitioner to follow or interpret the rules. Therefore, we present CACAO-X which is a framework for ML forecasts that utilizes an algorithm which converts images into time-series and employs CNN, LSTM, and MLP on the original images and converted data in a mixed input approach achieving accurate forecasts on multiple applications. Furthermore, Shapley values are employed on the proposed ML models to extract the local explainable features of such images. These features are again used with the same conversion algorithm to generate time-series and application specific landmarks using the Logic Learning Machine (LLM) to determine the explainable rules. In addition to being generated automatically, explicative labelling is adopted in the dataset which give insight to the operator to which landmarks, positions and angles contribute to the classification of each record. The system is robust as it achieves benchmark results in the gender classification application while remaining competitive in all others where it is used as a reference model for global image explainability. Additionally, the ML inference for every application is performed according to the edge-computing paradigm, by deploying the models on a limited resources Raspberry Pi platform through SSH access. Moreover, a novel callback function has been developed which is able to stop the training of the CACAO-NET model at a stable state based on the level of oscillation around the most recent mean accuracy. © 2022, The Authors. All rights reserved.

关键词： Image classification

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Paddy rice methane emissions across Monsoon Asia

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Remote Sensing of Environment 2023年 284卷

作者： Ouyang, Zutao Jackson, Robert B. McNicol, Gavin Fluet-Chouinard, Etienne Runkle, Benjamin R.K. Papale, Dario Knox, Sara H. Cooley, Sarah Delwiche, Kyle B. Feron, Sarah Irvin, Jeremy Andrew Malhotra, Avni Muddasir, Muhammad Sabbatini, Simone Alberto, Ma. Carmelita R. Cescatti, Alessandro Chen, Chi-Ling Dong, Jinwei Fong, Bryant N. Guo, Haiqiang Hao, Lu Iwata, Hiroki Jia, Qingyu Ju, Weimin Kang, Minseok Li, Hong Kim, Joon Reba, Michele L. Nayak, Amaresh Kumar Roberti, Debora Regina Ryu, Youngryel Swain, Chinmaya Kumar Tsuang, Benjei Xiao, Xiangming Yuan, Wenping Zhang, Geli Zhang, Yongguang Department of Earth System Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Precourt Institute for Energy Stanford University StanfordCA United States Department of Earth and Environmental Sciences University of Illinois Chicago ChicagoIL United States Department of Biological and Agricultural Engineering University of Arkansas FayettevilleAR United States University of Tuscia Viterbo Italy IAFES division Viterbo Italy Department of Geography The University of British Columbia VancouverBC Canada Department of Geography University of Oregon EugeneOR United States Department of Environmental Science Policy And Management UC Berkeley BerkeleyCA United States Campus Fryslan University of Groningen Groningen Netherlands Department of Computer Science Stanford University StanfordCA United States Department of Geography University of Zurich Zurich Switzerland International Rice Research Institute Laguna Philippines Ispra Italy Taiwan Agricultural Research Institute Taichung Wufeng Taiwan Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China USDA ARS Delta Water Management Research Unit JonesboroAR United States Fudan University Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering and Coastal Ecosystems Research Station of the Yangtze River Estuary Shanghai China / Jiangsu Key Laboratory of Agricultural Meteorology Nanjing University of Information Science and Technology Nanjing China Department of Environmental Science Shinshu University Matsumoto Japan China Meteorological Administration Institute of Atmospheric Environment Shenyang China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu Nanjing210023 China National Center for AgroM

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes;and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit

关键词： Climate change

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Social and geographical disparities in Twitter use during Hurricane Harvey

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International Journal of Digital Earth 2019年第11期12卷 1300-1318页

作者： Lei Zou Nina S.N.Lam Shayan Shams Heng Cai Michelle A.Meyer Seungwon Yang Kisung Lee Seung-Jong Park Margaret A.Reams Department of Geography College StationTXUSA Department of Environmental Sciences Louisiana State UniversityBaton RougeLAUSA Division of Computer Science and Engineering Center for Computation and TechnologyLouisiana State UniversityBaton RougeLAUSA Department of Sociology Louisiana State UniversityBaton RougeLAUSA Department of Landscape Architecture and Urban Planning Texas A&M UniversityCollege StationTXUSA School of Library and Information Science Center for Computation and TechnologyLouisiana State UniversityBaton RougeLAUSA

Socialmedia such as Twitter is increasingly beingused as an effective platform to observe human behaviors in disastrous ***,uneven social media use among different groups of population in different regions could lead to biased consequences and affect disaster *** paper studies the Twitter use during 2017 Hurricane Harvey in 76 counties in Texas and *** seek to answer a fundamental question:did socialgeographical disparities of Twitter use exist during the three phases of emergency management(preparedness,response,recovery)?We employed a Twitter data mining framework to process the data and calculate two indexes:Ratio and *** analyses between the Ratio indexes and the social-geographical characteristics of the counties at the three phrases reveal significant social and geographical disparities in Twitter use during Hurricane *** with higher disasterrelated Twitter use in Harvey generally were communities having better social and geographical *** results of Twitter use patterns can be used to compare with future similar studies to see whether the Twitter use disparities have increased or *** research is also needed to examine the effects of Twitter use disparities on disaster resilience and to test whether Twitter use can predict community resilience.

关键词： Social media Twitter Hurricane Harvey emergency management digital divide social and geographical disparities

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Generalized Multiprocess Kbest-Based Expert System for Improved Multitemporal Evapotranspiration Forecasting in California, United States

SSRN

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SSRN 2023年

作者： Lee, Jinwook Bateni, Sayed M. Jun, Changhyun Heggy, Essam Jamei, Mehdi Kim, Dongkyun Ghafouri, Hamid Reza Deenik, Jonathan L. Department of Civil and Environmental Engineering Water Resources Research Center University of Hawaii at Manoa HonoluluHI United States Department of Civil and Environmental Engineering College of Engineering Chung-Ang University Seoul Korea Republic of Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Canadian Centre for Climate Change and Adaptation University of Prince Edward Island St Peters PE Canada Department of Civil Engineering Faculty of Civil Engineering and Architecture Shahid Chamran University of Ahvaz Ahvaz Iran New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar Nasiriyah Iraq Department of Civil and Environmental Engineering Hongik University Seoul Korea Republic of Department of Tropical Plant and Soil Sciences University of Hawaii at Manoa HonoluluHI United States Shahid Chamran University of Ahvaz Iran

Evapotranspiration is an essential component of the hydrological cycle. Forecasting the reference crop evapotranspiration (ETo) using a reliable and generalized framework is crucial for agricultural operations, especially irrigation. This study was aimed at evaluating the performance of a generalized multivariate-multitemporal intelligent system including the K-Best selection (KBest), multivariate variational mode decomposition (MVMD), and cascade forward neural network (CFNN) for 1-, 3-, 7-, and 10-day-ahead forecasting of the daily ETo in twelve stations of California, one of the significant regions in the U.S. The input variables included solar radiation, maximum temperature, minimum temperature, average temperature, dew point, vapor pressure, and average relative humidity. The analysis covered a span of 20 years, from 2003 to 2022. In additional to the CFNN, two other machine learning models, namely, extreme learning machine (ELM) and bagging regression tree (BRT), were integrated with various preprocessing techniques to construct three hybrid models, i.e., MVMD-KBest-CFNN, MVMD-KBest-ELM, and MVMD-KBest-BRT. Using the MVMD technique, the antecedent information of input features was factorized into the intrinsic mode functions and residuals. Subsequently, the most influential sub-components were filtered using KBest to reduce the computational cost and enhance the accuracy before inputting the machine learning models. Several statistical indices, such as the correlation coefficient (R) and root mean square error (RMSE), were used in addition to various diagnostic validation methods to assess the robustness of the hybrid frameworks and standalone models. According to the results obtained in the testing phase, averaged across all stations, the MVMD-KBest-CFNN and the MVMD-KBest-ELM models outperformed MVMD-KBest-BRT model, with R values of 0.983, 0.980, 0.977, and 0.968 for 1-, 3-, 7-, and 10-day-ahead forecasts, respectively. The corresponding RMSE values were 0.

关键词： Variational mode decomposition

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Effective fusion of deep multitasking representations for robust visual tracking

arXiv

引用

arXiv 2020年

作者： Marvasti-Zadeh, Seyed Mojtaba Ghanei-Yakhdan, Hossein Kasaei, Shohreh Nasrollahi, Kamal Moeslund, Thomas B. Department of Electrical Engineering Yazd University Yazd Iran Sharif University of Technology Tehran Iran Vision and Learning Lab. University of Alberta Edmonton Canada Department of Electrical Engineering Yazd University Yazd Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Department of Architecture Design and Media Technology Aalborg University Aalborg Denmark

Visual object tracking remains an active research field in computer vision due to persisting challenges with various problem-specific factors in real-world scenes. Many existing tracking methods based on discriminative correlation filters (DCFs) employ feature extraction networks (FENs) to model the target appearance during the learning process. However, using deep feature maps extracted from FENs based on different residual neural networks (ResNets) has not previously been investigated. This paper aims to evaluate the performance of twelve state-of-the-art ResNet-based FENs in a DCF-based framework to determine the best for visual tracking purposes. First, it ranks their best feature maps and explores the generalized adoption of the best ResNet-based FEN into another DCF-based method. Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters. Finally, it introduces a new and efficient semantic weighting method (using semantic segmentation feature maps on each video frame) to reduce the drift problem. Extensive experimental results on the well-known OTB-2013, OTB-2015, TC-128, and VOT-2018 visual tracking datasets demonstrate that the proposed method effectively outperforms state-of-the-art methods in terms of precision and robustness of visual tracking. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings

arXiv

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arXiv 2021年

作者： Huang, Yunyou Wang, Nana Tang, Suqin Ma, Li Hao, Tianshu Jiang, Zihan Zhang, Fan Kang, Guoxin Miao, Xiuxia Guan, Xianglong Zhang, Ruchang Zhang, Zhifei Zhan, Jianfeng Guangxi Key Lab of Multi-Source Information Mining & Security School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Guilin Medical University Guilin China Department of Physiology and Pathophysiology Capital Medical University Beijing China University of Chinese Academy of Sciences China International Open Benchmark Council

This paper quantitatively reveals the state-of-the-art and state-of-the-practice AI systems only achieve acceptable performance on the stringent conditions that all categories of subjects are known, which we call closed clinical settings, but fail to work in real-world clinical settings. Compared to the diagnosis task in the closed setting, real-world clinical settings pose severe challenges, and we must treat them differently. We build a clinical AI benchmark named Clinical AIBench to set up real-world clinical settings to facilitate researches. We propose an open, dynamic machine learning framework and develop an AI system named OpenClinicalAI to diagnose diseases in real-world clinical settings. The first versions of Clinical AIBench and OpenClinicalAI target Alzheimer’s disease. In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system. In addition, OpenClinicalAI develops personalized diagnosis strategies to avoid unnecessary testing and seamlessly collaborates with clinicians. It is promising to be embedded in the current medical systems to improve medical services. © 2021, CC BY.

关键词： Diagnosis

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Dynamic Resilient Network Games with Applications to Multi-Agent Consensus

arXiv

引用

arXiv 2020年

作者： Nugraha, Yurid Cetinkaya, Ahmet Hayakawa, Tomohisa Ishii, Hideaki Zhu, Quanyan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo101-8430 Japan Department of Computer Science Tokyo Insitute of Technology Yokohama226-8502 Japan Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

A cyber security problem in a networked system formulated as a resilient graph problem based on a game-theoretic approach is considered. The connectivity of the underlying graph of the network system is reduced by an attacker who removes some of the edges whereas the defender attempts to recover them. Both players are subject to energy constraints so that their actions are restricted and cannot be performed continuously. For this two-stage game, which is played repeatedly over time, we characterize the optimal strategies for the attacker and the defender in terms of edge connectivity and the number of connected components of the graph. The resilient graph game is then applied to a multi-agent consensus problem. We study how the attacks and the recovery on the edges affect the consensus process. Finally, we also provide numerical simulation to illustrate the results. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

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Towards advanced interfaces for citizen curation

Towards advanced interfaces for citizen curation

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2020 AVI2CH Workshop on Advanced Visual Interfaces and Interactions in Cultural Heritage, AVI2CH 2020

作者： Bruni, Luis Emilio Diaz, Lily Gangemi, Aldo Daga, Enrico Kuflik, Tsvi Mulholland, Paul Pescarin, Sofia Damiano, Rossana Lieto, Antonio Peroni, Silivio Wecker, Alan Department of Architecture Design and Media Technology Alborg University Denmark Department of Media Media lab Aalto University Aalto Finland DHARC - Department of Classical Philology and Italian Studies University of Bologna ISTC-CNR Rome Italy Knowledge Media Institute Open University Milton Keynes United Kingdom Information Systems University of Haifa Haifa Israel CNR ISPC Rome Italy Computer Science and CIRMA University of Turin Turin Italy DHARC - Department of Classical Philology and Italian Studies University of Bologna Rome Italy

The SPICE project builds on the growing trend for museums, rather than providing an authoritative view, to present multiple voices related to their collection and exhibitions. In SPICE, an approach we term citizen curation is proposed as a way of supporting visitors to share their own interpretations of museum objects and reflect on the variety of interpretations contributed by others. In order to capture a wide range of voices, interfaces will be designed specifically to engage minority groups that tend to be under-represented in cultural activities. To achieve this goal, the interface will need to be intuitive, aesthetic and accessible for different audiences. The paper presents the challenges we face and initial proposals for engaging visitors in citizen curation. © Copyright 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Museums

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Generation expansion planning in the presence of wind power plants using a genetic algorithm model

arXiv

引用

arXiv 2020年

作者： Sahragard, Ali Falaghi, Hamid Farhadi, Mahdi Mosavi, Amir Estebsari, Abouzar Khorasan Regional Electricity Company Birjand Iran Department of Electric and Computer Engineering University of Birjand Iran Department of Computer and Industries Birjand University of Technology Birjand Iran Department of Informatics J. Selye University Komarno94501 Slovakia School of the Built Environment and Architecture London South Bank University London United Kingdom

One of the essential aspects of power system planning is generation expansion planning (GEP). The purpose of GEP is to enhance construction planning and reduce the costs of installing different types of power plants. This paper proposes a method based on Genetic Algorithm (GA) for GEP in the presence of wind power plants. Since it is desired to integrate the maximum possible wind power production in GEP, the constraints for incorporating different levels of wind energy in power generation are investigated comprehensively. This will allow obtaining the maximum reasonable amount of wind penetration in the network. Besides, due to the existence of different wind regimes, the penetration of strong and weak wind on GEP is assessed. The results show that the maximum utilization of wind power generation capacity could increase the exploitation of more robust wind regimes. Considering the growth of the wind farm industry and the cost reduction for building wind power plants, the sensitivity of GEP to the variations of this cost is investigated. The results further indicate that for a 10% reduction in the initial investment cost of wind power plants, the proposed model estimates that the overall cost will be *** Codes 68T01 Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power generation

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NeuroPod: a real-time neuromorphic spiking CPG applied to robotics

arXiv

引用

arXiv 2019年

作者： Gutierrez-Galan, Daniel Dominguez-Morales, Juan P. Perez-Peña, Fernando Linares-Barranco, Alejandro Robotics and Computer Technology Lab Universidad de Sevilla Spain Department of Computer Architecture and Technology Universidad de Cádiz Spain

Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being designed to take a step further, by performing more complex tasks that have been carried out by smart animals or humans up to date. To this end, inspiration needs to be taken from biological examples. For instance, insects are able to optimally solve complex environment navigation problems, and many researchers have started to mimic how these insects behave. Recent interest in neuromorphic engineering has motivated us to present a real-time, neuromorphic, spike-based Central Pattern Generator of application in neurorobotics, using an arthropod-like robot. A Spiking Neural Network was designed and implemented on SpiNNaker. The network models a complex, online-change capable Central Pattern Generator which generates three gaits for a hexapod robot locomotion. Reconfigurable hardware was used to manage both the motors of the robot and the real-time communication interface with the Spiking Neural Networks. Real-time measurements confirm the simulation results, and locomotion tests show that NeuroPod can perform the gaits without any balance loss or added delay. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

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