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Improving social media use for disaster resilience: challenges and strategies

International Journal of Digital Earth 2023年第1期16卷 3023-3044页

作者： Nina S.N.Lam Michelle Meyer Margaret Reams Seungwon Yang Kisung Lee Lei Zou Volodymyr Mihunov Kejin Wang Ryan Kirby Heng Cai Department of Environmental Sciences Louisiana State UniversityBaton RougeUSA Landscape Architecture and Urban Planning Texas A&M UniversityCollege StationUSA School of Information Studies Center for Computation and TechnologyLouisiana State UniversityBaton RougeUSA Division of Computer Science and Engineering Louisiana State UniversityBaton RougeUSA Department of Geography Texas A&M UniversityCollege StationUSA Escambia County Natural Resources Management PensacolaFloridaUSA

This paper develops a social media-disaster resilience analysis framework by categorizing types of social media use and their challenges to better understand and assess its role in disaster resilience research and *** framework is derived primarily from several case studies of Twitter use in three hurricane events in the United States-Hurricanes Isaac,Sandy,and *** paper first outlines four major contributions of social media data for disaster resilience research and management,which include serving as an effective communication platform,providing ground truth information for emergency response and rescue operations,providing information on people's sentiments,and allowing predictive ***,there are four_key challenges to its uses,which include,easy spreading of false information,social and geographical disparities of Twitter use,technical issues on processing and analyzing big and noisy data,especially on improving the locational accuracy of the tweets,and algorithm bias in Al and other types of ***,the paper proposes twenty strategies that the four sectors of the social media community-organizations,individuals,social media companies,and researchers-could take to improve social media use to increase disaster resilience.

关键词： Twitter use challenges and strategies disaster resilience social and geographical disparities emergency rescue Hurricane Isaac Hurricane Sandy Hurricane Harvey

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Totipotent Neural Controllers for Modular Soft Robots: Achieving Specialization in Body-Brain Co-Evolution Through Hebbian Learning

SSRN

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SSRN 2024年

作者： Ferigo, Andrea Iacca, Giovanni Medvet, Eric Nadizar, Giorgia Department of Information Engineering and Computer Science University of Trento Italy Department of Engineering and Architecture University of Trieste Italy Department of Mathematics and Geosciences University of Trieste Italy Department of Information and Communication Technology

Multi-cellular organisms typically originate from a single cell, the zygote, that then develops into a multitude of structurally and functionally specialized cells. The potential of generating all the specialized cells that make up an organism is referred to as cellular "totipotency", a concept introduced by the German plant physiologist Haberlandt in the early 1900s. In an attempt to reproduce this mechanism in synthetic organisms, we present a model based on a kind of modular robot called Voxel-based Soft Robots (VSRs), where both the body, i.e., the arrangement of voxels, and the brain, i.e., the Artificial Neural Network (ANN) controlling each module, are subject to an evolutionary process aimed at optimizing the locomotion capabilities of the robot. In an analogy between totipotent cells and totipotent ANN-controlled modules, we then include in our model an additional level of adaptation provided by Hebbian learning, which allows the ANNs to adapt their weights during the execution of the locomotion task. Our in silico experiments reveal two main findings. Firstly, we confirm the common intuition that Hebbian plasticity effectively allows better performance and adaptation. Secondly and more importantly, we verify for the first time that the performance improvements yielded by plasticity are in essence due to a form of specialization at the level of single modules (and their associated ANNs): thanks to plasticity, modules specialize to react in different ways to the same set of stimuli, i.e., they become functionally and behaviorally different even though their ANNs are initialized in the same way. This mechanism, which can be seen as a form of totipotency at the level of ANNs, can have, in our view, profound implications in various areas of AI and applications thereof, such as modular robotics and multi-agent systems. © 2024, The Authors. All rights reserved.

关键词： Neural networks

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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FMC-CEE: A Collaborative Execution Environment with Flash Memory Controllers in IoT

FMC-CEE: A Collaborative Execution Environment with Flash Me...

引用

IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Zheng Zhang Jingfeng Xue Thar Baker Tian Chen Yuhang Zhao Weizhi Meng School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Architecture Technology and Engineering University of Brighton Brighton UK School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark

Applications running on an Internet of Things (IoT) device are usually deployed in an untrusted environment. This introduces risks of vulnerability to malware, and loss of intellectual property associated with security sensitive code. Trusted execution environments (TEEs) and TEE-based applications have been widely adopted to run security sensitive workloads and protect the security of applications. However, existing approaches require specialized CPU support or hardware peripherals equipped with co-processors, precluding widely deployment on low-cost IoT devices. In this paper, we propose a flash memory controller-based collaborative execution environment (FMC-CEE), a lightweight security solution constructed on the target flash device to provide code confidentiality and basic security primitives for low-cost IoT devices and embedded devices. FMC-CEE leverages the microprocessor of the target flash device as a co-processor that executes security-sensitive workloads collaboratively with the target system. We implemented a prototype of FMC-CEE on a Trans-Flash (TF) card and executed security-sensitive tasks of the target host. The experimental results show that FMC-CEE takes $590.748 \mu \mathrm{s}$ to execute the remote code (512 bytes), thus incurring very little overhead on the host system.

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Cell Phone Image-Based Persian Rice Detection and Classification Using Deep Learning Techniques

arXiv

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arXiv 2024年

作者： Kelishami, Mahmood Saeedi Kelishami, Amin Saeidi Kelishami, Sajjad Saeedi Department of Mathematics Rasht Branch Islamic Azad University Iran Department of Computer Engineering Sharif University of Technology Iran Department of Architecture Rasht Branch Islamic Azad University Iran

This study introduces an innovative approach to classifying various types of Persian rice using image-based deep learning techniques, highlighting the practical application of everyday technology in food categorization. Recognizing the diversity of Persian rice and its culinary significance, we leveraged the capabilities of convolutional neural networks (CNNs), specifically by fine-tuning a ResNet model for accurate identification of different rice varieties and employing a U-Net architecture for precise segmentation of rice grains in bulk images. This dual-methodology framework allows for both individual grain classification and comprehensive analysis of bulk rice samples, addressing two crucial aspects of rice quality assessment. Utilizing images captured with consumer-grade cell phones reflects a realistic scenario in which individuals can leverage this technology for assistance with grocery shopping and meal preparation. The dataset, comprising various rice types photographed under natural conditions without professional lighting or equipment, presents a challenging yet practical classification problem. Our findings demonstrate the feasibility of using non-professional images for food classification and the potential of deep learning models, like ResNet and U-Net, to adapt to the nuances of everyday objects and textures. This study contributes to the field by providing insights into the applicability of image-based deep learning in daily life, specifically for enhancing consumer experiences and knowledge in food selection. Furthermore, it opens avenues for extending this approach to other food categories and practical applications, emphasizing the role of accessible technology in bridging the gap between sophisticated computational methods and everyday tasks. © 2024, CC BY.

关键词： Convolutional neural networks

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Detection of anomalies in bee colony using transitioning state and contrastive autoencoders

引用

computers and Electronics in Agriculture 2022年 200卷

作者： Cejrowski, Tymoteusz Szymański, Julian Faculty of Electronics Telecommunications and Informatics Department of Computer Architecture Gdańsk University of Technology Poland

Honeybees plays vital role for the environmental sustainability and overall agricultural economy. Assisting bee colonies within their proper functioning brings the attention of researchers around the world. Electronics systems and machine learning algorithms are being developed for classifying specific undesirable bee behaviors in order to alert about upcoming substantial losses. However, classifiers could be impaired when used for general honeybee colony state inference. Application of the classifier models for the hazardous situations detection without focusing on the model's genericity could result with systems that are not applicable in the real environment. Furthermore, the detection of a specific phenomenon does not provide researchers with any new conclusions about the honeybee colony life but only with the binary information about hazardous situation presence. In our research we propose a method for inferring the bee colony state using a sensitive contrastive autoencoder and an anomaly detection model. With presented approach, hive's internal state is modeled with the use of an autoencoder's latent vector extended with in-hive temperature dynamics. We test our methodology with a bee feeding experiment where the glucose syrup application was detected and the length of food intake was estimated. As our methodology has been applied successfully, we argue that contrastive autoencoders can be used for precise inference about the behavior of honeybees. © 2022 The Author(s)

关键词： Anomaly detection

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A Clustering-BIST Design for Inter-Layer Vias in 3D ICs Based on Walking Pattern Approach

TechRxiv

引用

TechRxiv 2023年

作者： Menbari, Ahmad Rahimi, Hemin Jahanirad, Hadi Ziener, Daniel The Department of Electrical Engineering University of Kurdistan Sanandaj Iran The Department of Computer Architecture and Embedded Systems Ilmenau University of Technology Ilmenau Germany

In the realm of 3D monolithic integrated circuits, inter-layer vias are prone to defects during fabrication, assembly, and operation that necessitates robust Built-In Self-Test solutions. This paper introduces an innovative BIST design aimed at the detection and localization of different types of faults in irregularly placed ILVs using a walking pattern methodology. In the proposed BIST methodology, the ILVs are clustered based on the fault occurrence probability distribution. Then, an effective detection approach is developed for all stuck-at faults, bridging faults, and almost all multiple faults in a single cluster. This approach allows designers to adjust different parameters such as fault coverage percentage, fault localization, and test time in alignment with specific requisites. The proposed BIST method reduces test configurations to two and test time to just 20 cycles by dividing ILVs into more clusters, overcoming a key limitation of recent approach. This methodology also demonstrates improved efficiency in terms of power consumption, area, and hardware utilization, particularly for large-scale benchmarks. For instance, when considering LU32PEENG and the scenario where ILVs are divided into 64 clusters, the power, area, and hardware overhead are 0.82% and 1.03%, and 1.14%, respectively. © 2023, CC BY.

关键词： Built-in self test

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Single Beacon Cruise Positioning Algorithm in Wireless Sensor Networks

引用

Chinese Journal of Electronics 2023年第3期22卷 558-562页

作者： ZHU Darong HAN Jianghong OU Jian MIN Jie School of Computer and Information Hefei University of Technology Hefei China School of Electronics and Information Engineering Anhui University of Architecture Hefei China Department of Mathematics and Physics Anhui University of Architecture Hefei China

Node localization is important in WSNs (Wireless sensor networks) applications. We present a new cruise positioning algorithm, where a single beacon node cruises an entire network, broadcasting its position periodically with fixed signal strength. Meawhile, an unknown node obtains the location and the corresponding distance of the beacon node. Two possible position coordinate sets for the unknown node can then be calculated, and the coordinate mean of the point set with a small variance is an unbiased estimator of the unknown node's coordinate. The positioning algorithm's computation is simple, positioning accuracy is not limited by a physical distance measuring tool, and there are no restrictions on the path of motion, which makes it more realistic. An algorithm simulation is presented to compare the original and cruise location algorithm. Numerical results show that the cruise localization algorithm can achieve good positioning accuracy.

关键词： Wireless communication Location awareness Wireless sensor networks Position measurement Broadcasting Distance measurement Hardware

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Gamefication of Youth's Career Guidance Self-Identification

Gamefication of Youth's Career Guidance Self-Identification

引用

2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Poliakov, Mykyta Mezzane, Daoud Terenchuk, Svitlana Riabchun, Yuliia Rusnak, Patrik Biloshchytska, Svitlana Kyiv National University Of Construction And Architecture Department Of Information Technology Design And Applied Mathematics Kyiv Ukraine Inovative Materials Energy And Sustainable Devellopment Laboratpry Cadi Ayyad University Marrakech Morocco Kyiv National University Of Construction And Architecture Department Of Information Technology Kyiv Ukraine University Of Zilina Faculty Of Management Science And Informatics Zilina Slovakia Astana It University Department Of Computer Technology And Data Nur-Sultan Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The work is devoted to solving the youth's professional orientation problem in modern conditions of limited offline communication. The urgency of development of new methods and tools of adolescents' professional orientation, which are based on modern information technologies, is shown. The research's focus is aimed at the introduction of computer game technologies in the work of career guidance centers, pre-university departments of colleges and higher education institutions. The expediency and effectiveness of the existing professional orientation computer game tasks usage in career guidance work with youth who need decision support at the stage of choosing a future profession are substantiated. It is shown that in the conditions of virtual reality the estimate reliability of personal qualities for compliance with a certain professional activity depends on the selection of game tasks. A clear criterion for automatic measurement of activity interest in professional activity, which depends on the task selection time, is proposed. There are fragments of computer game tasks that allow a person to self-estimate their ability to perform activities in different situations and professional relationships without communicating with career counselors and psychologists. © 2022 IEEE.

关键词： computer games

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Investigation of Circular Patch Antenna for Wi-Fi and Bluetooth Applications 3

Investigation of Circular Patch Antenna for Wi-Fi and Blueto...

引用

3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Ghassan Majeed, Maryam Qasim, Haitham Bashar Kosimova, Shakhlokhon Diame, Hussein Alaa Lafta, Alaa M. Alchilibi, Haider Al-Kunooze University College Computer Engineer Department Basra Iraq Al-Esraa University College Forensic Sciences Department Baghdad Iraq Fergana Polytechnic Institute Department of Architecture Fergana Uzbekistan National University of Science and Technology Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad10022 Iraq

ISBN: (纸本)9798350330724

The purpose of this paper is to examine and contrast the Novel Circular Patch Antenna employing FR4 and RT Droid (CPA). Resonant frequency of 2.5 GHz in a static medium. To create the antenna, we use the HFSS and two different sample sets. Ten samples are taken from each antenna at 80% G-Power. Assume that the members of Group 1 are all CPAs that use the FR4 substrate. The Novel circular patch antenna (CPA) on RT roger's substrate is in the second group under consideration. This antenna operates at 2.5 GHz to measure reflectance at both ends. A VSWR of 2.1786 dB was measured at 2.4 GHz for a circular CPA designed for RT rogers, while the measured value for a CPA with FR4 was 0.7088 dB. The significance level in SPSS was 0.038 (p 0.05). The findings of this study show that RT rogers is approximately as effective as the FR4 substrate. © 2023 IEEE.

关键词： Natural frequencies

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