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Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability:A comprehensive systematic review

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Environmental Science and Ecotechnology 2024年第3期19卷 29-59页

作者： Simon Elias Bibri John Krogstie Amin Kaboli Alexandre Alahi School of Architecture Civil and Environmental Engineering(ENAC)Civil Engineering Institute(IIC)Visual Intelligence for Transportation(VITA)Swiss Federal Institute of Technology Lausanne(EPFL)LausanneSwitzerland Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway School of Engineering Institute of Mechanical EngineeringSwiss Federal Institute of Technology Lausanne(EPFL)LausanneSwitzerland

The recent advancements made in the realms of Artificial Intelligence(AI)and Artificial Intelligence of Things(AIoT)have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart *** strides have,in turn,impacted smart eco-cities,catalyzing ongoing improvements and driving solutions to address complex environmental *** aligns with the visionary concept of smarter eco-cities,an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental ***,there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying *** bridge this gap,this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leadingedge AI and AIoT solutions for environmental *** ensure thoroughness,the study employs a unified evidence synthesis framework integrating aggregative,configurative,and narrative synthesis *** the core of this study lie these subsequent research inquiries:What are the foundational underpinnings of emerging smarter eco-cities,and how do they intricately interrelate,particularly urbanism paradigms,environmental solutions,and data-driven technologies?What are the key drivers and enablers propelling the materialization of smarter eco-cities?What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities?In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices,and what potential benefits and opportunities do they offer for smarter eco-cities?What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities?The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologi

关键词： Smarter eco-cities Smart eco-cities Smart cities Artificial intelligence Artificial intelligence of things Machine learning Environmental sustainability Climate change

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Detecting Scam Tokens and Backdoor Functions in EVM Based Networks 6th

Detecting Scam Tokens and Backdoor Functions in EVM Based ...

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6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024

作者： Lorenzo, Sergio Anguita Miguel-Alonso, Jose Gomez-Goiri, Aitor Department of Computer Architecture and Technology University of the Basque Country UPV/EHU Paseo M. Lardizabal 1 San Sebastian20018 Spain Astondo Bidea 700 Derio48160 Spain

ISBN: (纸本)9783031819278

The blockchain ecosystem is currently becoming an intricate landscape given the large number of attacks, scams, and hacks that occur. With such risk surrounding Blockchain DApps and users, we propose a method to help in the earlier detection of scam tokens. While blockchain technology continues to grow in multiple industries, the proliferation of fraudulent activities in the decentralized ecosystem has become a significant threat to the well-being and trustworthiness of the whole ecosystem. This scam tokens are deployed in plain sight in decentralized networks and listed in platforms such as CoinMarketCap and other token issuance platforms. In this paper, we identify over 1450 unreported scam tokens, 167 different backdoor functions, and 1428 malicious contract creators. By increasing the collaboration between industry stakeholders and web3 companies, our scam tokens dataset and detection tools can help in web3 threat detection while creating a more secure and resilient blockchain environment, fostering trust and innovation in the decentralized space. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Phishing

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Unsupervised Fouling Reconstruction in the Pipe Bend

Unsupervised Fouling Reconstruction in the Pipe Bend

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Iablonskyi, Denys Rasgado-Moreno, Carlos-Omar Ratassepp, Madis Klami, Arto Haggstrom, Edward Salmi, Ari University of Helsinki Department of Computer Science Department of Physics Helsinki Finland Tallinn University of Technology Department of Civil Engineering and Architecture Tallinn Estonia University of Helsinki Department of Computer Science Helsinki Finland University of Helsinki Department of Physics Helsinki Finland

ISBN: (纸本)9798350346459

Guided wave tomography allows the investigation of extended areas with a limited number of measurements, typically using transducer arrays. Most imaging methods rely heavily on the material properties such as dispersion curves, that in the case of fouling deposition are not always known. In the case of complex shaped structures, geometrical anisotropy can bring additional complexity. Here we present an unsupervised machine learning approach based on the Gaussian process to detect and characterize the fouling in a pipe bend that relies only on the difference between clean/healthy and fouled/damaged measured signals. © 2023 IEEE.

关键词： Nondestructive examination

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Forecasting El Niño Six Months in Advance Utilizing Augmented Convolutional Neural Network 14

Forecasting El Niño Six Months in Advance Utilizing Augment...

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14th International Conference on computer and Knowledge Engineering, ICCKE 2024

作者： Naisipour, Mohammad Saeedpanah, Iraj Adib, Arash Pour, Mohammad Hossein Neisi Department of Civil Engineering Faculty of Engineering University of Zanjan Iran Department of Civil Engineering Civil Engineering and Architecture Faculty Shahid Chamran University of Ahvaz Iran Department of Computer Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9798331511272

—One of the most significant phenomena related to large-scale climatic changes is the El Niño Southern Oscillation (ENSO), which can be instrumental in predicting dissasters across various regions of the globe. This study introduces a novel method aimed at predicting ENSO over a longer duration. This approach utilizes Convolutional Neural Networks (CNN) to anticipate ENSO occurrences up to six months ahead. The model utilizes anomaly maps as input data. Augmented Heat Content (HC) in conjunction with SST which is the surface temperature of the oceans are predictors, while the Niño 3.4 Index serves as the target for predictions. By applying convolutional tensors, the method extracts features from anomaly maps and processes them through a fully connected neural network to uncover relationships between the Niño Index and these features. A strategic data augmentation technique is employed to boost the quantity of merely HC input data, addressing the issue of limited observations related to recharged thermocline. The model has shown to provide consistent predictions when validated against observations over an extended period, confirming its reliability. Furthermore, the method's relatively low computational demand makes it a valuable tool for predicting ENSO and its related impacts. ©2024 IEEE.

关键词： Convolutional neural networks

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Detection of COVID-19 Using SIS-CODE Algorithm

Detection of COVID-19 Using SIS-CODE Algorithm

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International Conference on Business Intelligence and Information technology, BIIT 2022

作者： Kim, Daegeon Govathoti, Sudeepthi Department of Architecture Engineering Dongseo University Busan Korea Republic of Department of Computer Science and Engineering GITAM School of Technology GITAM Deemed-to-be-University Telangana Hyderabad India

ISBN: (纸本)9789819934157

The COVID-19 pathological test can be carried out with a sample taken from the nose, the back of the throat, or both, using a special swab. Currently, laboratories are checking through a process called Polymerase Chain Reaction (PCR) on a collected sample from the suspected person. PCR is used to find genetic material on the virus. PCR amplifies the genetic material such that the virus can be detected easily. In general, the PCR process is used to match the genetic material with the virus that causes COVID-19 is unique. Blood tests are also can be done to detect if a person has previously been exposed to the virus or not. This blood test is useful for knowing if a person may have developed immunity. In our paper, we propose a unique technique which will work on the microscopic image of Coronavirus affected tissue and detects the tissue is affected or damaged by the Coronavirus or not. In this research, we use the microscopic image(s) of a smear of epithelial throat/bronchial tract tissue. Firstly, image smoothing is conducted, and then Region of Interest (ROI) is detected. Then that 24-bit colour image (ROI) is converted to grey to bi-colour images using edge detection by setting a threshold. At last, the contours is detected and counted. In our technique, the resultant contours are the affected areas, and the count is the number of possible affected areas, an approximation. Thus, the infection depth can be guessed. Our technique is not only detecting the infection but also identifies the approximate number of attacked spots in a specific tissue area. Thus, the proposed technique identifies the person has COVID-19 or Coronavirus disease. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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NfvInsight:A Framework for Automatically Deploying and Benchmarking VNF Chains

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Journal of computer Science & technology 2022年第3期37卷 680-698页

作者： Tian-Ni Xu Hai-Feng Sun Di Zhang Xiao-Ming Zhou Xiu-Feng Sui Sa Wang Qun Huang Yun-Gang Bao State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China Peng Cheng Laboratory Shenzhen 518055China Department of Computer Science and Technology Peking UniversityBeijing 100871China

With the advent of virtualization techniques and software-defined networking(SDN),network function virtualization(NFV)shifts network functions(NFs)from hardware implementations to software appliances,between which exists a performance *** to narrow the gap is an essential issue of current NFV ***,the cumbersomeness of deployment,the water pipe effect of virtual network function(VNF)chains,and the complexity of the system software stack together make it tough to figure out the cause of low performance in the NFV *** pinpoint the NFV system performance,we propose NfvInsight,a framework for automatic deployment and benchmarking VNF *** framework tackles the challenges in NFV performance *** framework components include chain graph generation,automatic deployment,and fine granularity *** design and implementation of each component have their *** the best of our knowledge,we make the first attempt to collect rules forming a knowledge base for generating reasonable chain *** deploys the generated chain graphs automatically,which frees the network operators from executing at least 391 lines of bash commands for a single *** diagnose the performance bottleneck,NfvInsight collects metrics from multiple layers of the software ***,we collect the network stack latency distribution ingeniously,introducing only less than 2.2%*** showcase the convenience and usability of NfvInsight in finding bottlenecks for both VNF chains and the underlying *** our framework,we find several design flaws of the network stack,which are unsuitable for packet forwarding inside one single server under the NFV *** optimization for these flaws gains at most 3x performance improvement.

关键词： network function virtualization(NFV) service chain performance bottleneck network stack latency

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Genetic Programming and Coevolution to Play the Bomberman™ Video Game 26th

Genetic Programming and Coevolution to Play the Bomberman...

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26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Gold, Robert Branquinho, Henrique Hemberg, Erik O’Reilly, Una-May García-Sánchez, Pablo ALFA MIT CSAIL Cambridge United States Centre for Informatics and Systems of the University of Coimbra Department of Informatics Engineering University of Coimbra Coimbra Portugal Department of Computer Architecture and Computer Technology University of Granada Granada Spain

ISBN: (纸本)9783031302282

The field of video games is of great interest to researchers in computational intelligence due to the complex, rich and dynamic nature they provide. We propose using Genetic Programming with coevolution and lexicographic fitness to generate an agent that plays the Bomberman™game. We investigate two sets of Genetic Programming building blocks: one contains conditions relative to movement, and the other does not. We aim to see whether the benefits of these movement-related conditions outweigh the negatives caused by increased search space size. We show that the benefits gained do not outweigh the increase in search space size. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic programming

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Supporting Disaster Resilience Building through Cross-Community Knowledge Sharing 13

Supporting Disaster Resilience Building through Cross-Commun...

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13th Annual IEEE Global Humanitarian technology Conference, GHTC 2023

作者： Koga, Motoya Kaneko, Tetsuya Leleito, Emanuel Karashima, Kazuki Aharari, Ari Sojo University Department of Architecture Kumamoto Japan Nagoya University Graduate School of Engineering Nagoya Japan Maebashi Institute of Technology Dept.of Architecture Maebashi Japan Sojo University Department of Computer and Information Sciences Kumamoto Japan

ISBN: (纸本)9798350321326

Collaborative action in disaster mitigation efforts contributes to knowledge and experience sharing, resource sharing, improvement of response and coordination, and standardization of good practice. In this study, we propose a workshop model and develop a virtual collaboration tool to support and promote cross-community knowledge sharing and collaboration focusing on disaster mitigation efforts. The workshop model involves pre-fieldwork training, actual fieldwork, and post-fieldwork information exchange. The collaboration tool enables information collection, sharing and record keeping. The effectiveness of the workshop model and the collaboration tool is demonstrated through use in a citizen participatory disaster preparedness workshops linking two elementary schools located in the central and southern regions of Japan. © 2023 IEEE.

关键词： Disaster prevention

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ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for Rapid Intraoperative Histology 7

ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Shi, Lulin Hou, Xingzhong Wong, Ivy H.M. Chan, Simon C.K. Chen, Zhenghui Lo, Claudia T.K. Wong, Terence T.W. Translational and Advanced Bioimaging Laboratory Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

Virtual staining has shown great promise in realizing a rapid and low-cost clinical alternative for pathological examinations, eliminating the need for chemical reagents and laborious staining procedures. However, most of the previous studies mainly focus on thin slice samples, which still require tissue sectioning and are unsuitable for intraoperative use. In this paper, we propose a multi-scale model to virtually stain label-free and slide-free biological tissues, allowing hematoxylin- and eosin- (H&E) staining generation in less than a minute for an image with 100 million pixels. We name this ThickV-Stain model, specifically developed to virtually stain intricated and unprocessed thick tissues. We harness the ability of a multi-scale network to encourage the model to capture multiple-level micromorphological characteristics from low-resolution images. Experimental results highlight the advantages of our method for virtual staining on unprocessed thick samples. We also show the effectiveness of ThickV-Stain on thin sections, showing generalizability to other clinical workflows. The proposed method enables us to obtain virtually stained images from unstained samples within minutes and can be seamlessly integrated with downstream pathological analysis tasks, providing an efficient alternative scheme for intraoperative assessment as well as general pathological examination. © 2024 CC-BY 4.0, L. Shi, X. Hou, I.H.M. Wong, S.C.K. Chan, Z. Chen, C.T.K. Lo & T.T.W. Wong.

关键词： Tissue

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Performance Evaluation of Infrastructure as a Service across Cloud Service Providers

Performance Evaluation of Infrastructure as a Service across...

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2023 International Electrical Engineering Congress, iEECON 2023

作者： Sithiyopasakul, Saran Archevapanich, Tuanjai Purahong, Boonchana Sithiyopasakul, Paisan Lasakul, Attasit Benjangkaprasert, Chawalit School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Computer Engineering Bangkok Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Communication Engineering Nonthaburi Thailand

ISBN: (纸本)9798350332360

The purpose of this research aims to monitor, analyze, and compare the performance of infrastructure as a service (IaaS) between the selective cloud providers. To assure which cloud provider has more stability, reliability, and scalability. This paper focuses on performance testing based on a deployed web server in the cloud environment. The main feature of cloud computing is scalability thus most common IaaS cloud service providers (CSPs) have Auto Scaling features for instances or virtual machines. Not only does this paper gives the experimental results of the scaling scalability testing, but it also provides the results of recovery testing to inspect how long a web server is able to recover from failures and load testing which simulated traffic requests. Testing was conducted in the major public clouds of Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). Azure performs the most efficiently of almost all testing but hardest to configure. © 2023 IEEE.

关键词： Virtual machine

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