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arXiv 2023年

作者： Belardinelli, Francesco Jamroga, Wojciech Mittelmann, Munyque Murano, Aniello Imperial College London United Kingdom Interdisc. Centre on Security Reliability and Trust SnT University of Luxembourg Luxembourg Institute of Computer Science Polish Academy of Sciences Warsaw Poland University of Naples "Federico II" Italy

In this paper, we investigate the probabilistic variants of the strategy logics ATL and ATL∗ under imperfect information. Specifically, we present novel decidability and complexity results when the model transitions are stochastic and agents play uniform strategies. That is, the semantics of the logics are based on multi-agent, stochastic transition systems with imperfect information, which combine two sources of uncertainty, namely, the partial observability agents have on the environment, and the likelihood of transitions to occur from a system state. Since the model checking problem is undecidable in general in this setting, we restrict our attention to agents with memoryless (positional) strategies. The resulting setting captures the situation in which agents have qualitative uncertainty of the local state and quantitative uncertainty about the occurrence of future events. We illustrate the usefulness of this setting with meaningful examples. © 2023, CC BY.

关键词： Stochastic systems

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Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest 1

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International Conference on Advances in Communication Technology and computer Engineering, ICACTCE 2023

作者： Wang, Dan Hai, Tao Ayandiran, Doyinsola Uzochukwu, Chijioke Victor Ding, Xiaofeng Iwendi, Celestine Boulouard, Zakaria School of Mathematics and Statistics Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Creative Technologies University of Bolton BoltonBL3 5AB United Kingdom LIM Hassan II University of Casablanca Casablanca Morocco School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

Climate change has made accurate rainfall forecasting more difficult than ever. In this paper, the decision tree algorithm and Random Forest is used to predict the rainfall accuracy based on historical climate data. The classification and regression tree (CART) approach is employed to this result, producing a better accuracy rate. The algorithm can determine the probabilities of rain on any given day, making it an ideal choice for various applications involving large datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Algorithms for Large-Scale Quasi-Real Time Monitoring of Architectural and Cultural Heritage Based on MT-DInSAR Data

Algorithms for Large-Scale Quasi-Real Time Monitoring of Arc...

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Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2

作者： Talledo, D.A. Miano, A. Di Carlo, F. Bonano, M. Mele, A. Stella, A. Lanari, R. Meda, A. Prota, A. Saetta, A. Department of Architecture and Arts University IUAV of Venice Dorsoduro 2206 Venice30123 Italy Department of Structures for Engineering and Architecture University of Naples Federico II Via Claudio 21 Naples80125 Italy Department of Civil Engineering and Computer Science Engineering University of Rome "Tor Vergata" Via del Politecnico 1 Rome00133 Italy - Institute for Electromagnetic Sensing of the Environment Via Diocleziano 328 Naples80124 Italy

ISBN: (纸本)9783031391088

Structural Health Monitoring (SHM) is a field of increasing interest and worthy of new approaches and innovative applications. Italian territory is rich of architectural and cultural heritage that needs to be managed, monitored and conserved. This work presents a possible framework for the monitoring of such heritage that is suitable for large scale analyses and permits a fast and quick assessment, useful to the stakeholders responsible for its management. The proposed algorithms are based on an appealing technique, that gained popularity during the last years in the field of SHM, represented by the advanced multi-temporal differential synthetic aperture radar interferometry. Although the exploitation of these technique for the SHM of architectural and cultural heritage is still an open issue, different applications available in literature show promising potentialities. The algorithms and procedures proposed in this work are applied to a case study area located in the historic centre of Rome (Italy) to show the potentialities of the method. To this aim, COSMO-SkyMed ascending and descending datasets are processed applying the small baseline method obtaining deformation time series and mean velocity maps of the permanent scatterers located in the investigated area, for both geometry acquisitions. The information from these datasets are then elaborated and integrated with information from other data sources in order to provide a first estimation of potentially critical elements within the analyzed stock at urban level. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Structural health monitoring

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Fusion of hyperspectral, multispectral, color and 3D point cloud information for the semantic interpretation of urban environments 4

Fusion of hyperspectral, multispectral, color and 3D point c...

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4th ISPRS Geospatial Week 2019

作者： Weinmann, M. Weinmann, M. Institute of Photogrammetry and Remote Sensing Karlsruhe Institute of Technology Germany Institute of Computer Science II-Computer Graphics University of Bonn Germany

In this paper, we address the semantic interpretation of urban environments on the basis of multi-modal data in the form of RGB color imagery, hyperspectral data and LiDAR data acquired from aerial sensor platforms. We extract radiometric features based on the given RGB color imagery and the given hyperspectral data, and we also consider different transformations to potentially better data representations. For the RGB color imagery, these are achieved via color invariants, normalization procedures or specific assumptions about the scene. For the hyperspectral data, we involve techniques for dimensionality reduction and feature selection as well as a transformation to multispectral Sentinel-2-like data of the same spatial resolution. Furthermore, we extract geometric features describing the local 3D structure from the given LiDAR data. The defined feature sets are provided separately and in different combinations as input to a Random Forest classifier. To assess the potential of the different feature sets and their combination, we present results achieved for the MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set. © Authors 2019.

关键词： Classification (of information)

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Multidimensional Impacts of Generative AI and an In-Depth Analysis of LLMs with Their Expanding Horizons in Technology and Society

Multidimensional Impacts of Generative AI and an In-Depth An...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： K Ramu Ginni Nijhawan G Lalitha Praveen V Asha Ahmed J Fakher Dept. of CSE Velammal Institute of Technology Chennai TN India Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh Department of Computer Applications New Horizon College of Engineering Bangalore Department of Forensic Sciences College of Science National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Generative AI and large language models (LLMs) have transformed technology and society, as this paper details. Generative AI advanced AI greatly. Because they can read and write, LLMs, a form of Generative AI, have altered how humans and robots communicate. This study focuses on how these technologies influence schooling, healthcare, finance, and morals. Recent studies and case studies will help us understand how LLM is applied in many sectors. Our key concerns with these activities are efficiency, efficacy, and morality. The technique also polls and interviews professionals to learn about LLM usage and issues. LLM is quicker and more precise in translating languages, creating content, and analyzing data. Sadly, prejudice, privacy, and abuse issues persisted. LLMs are versatile enough to be utilized in odd fields like individualized learning and mental health treatment, according to the research. LLMs and creative AI can advance technology and society. Despite their benefits, they pose moral and practical issues that must be addressed. This study seeks a compromise between the two LLM methods. A more moral, research-focused use might make them more beneficial and less hazardous.

关键词： Surveys Performance evaluation Ethics Privacy Generative AI Reviews Large language models

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Developing Robust Cybersecurity Policies and Governance Frameworks in Response to Evolving Legal and Regulatory Landscapes

Developing Robust Cybersecurity Policies and Governance Fram...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Radhika Pandey Shaik Anjimoon V Asha Atul Singla Irfan Khan Zain Alabdeen Hasan Abed DoCSE GyanGanga Institute of Technology and Science Jabalpur M.P India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This research introduces a comprehensive cybersecurity framework consisting of five interlinked algorithms: Threat Intelligence Integration, Risk Assessment and Management, Compliance Mapping, Incident Response Planning, and Employee Training and Awareness. Through comparative performance evaluation and ablation study, the framework demonstrates consistent superiority across key parameters. Visual representations, including bar charts, line charts, and pie charts, provide intuitive insights into effectiveness, adaptability, compliance, and overall performance. The ablation study underscores the synergistic importance of each algorithm, emphasizing the framework’s holistic nature. This research contributes to advancing cybersecurity methodologies, offering a dynamic and adaptive approach to safeguarding against evolving cyber threats.

关键词： Training Performance evaluation Visualization Technological innovation Law Heuristic algorithms Planning

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A Survey of DDoS Attacks Detection Schemes in SDN Environment

A Survey of DDoS Attacks Detection Schemes in SDN Environmen...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Surabhi Gusain Rawat Mohammad S. Obaidat Sumit Pundir Mohammad Wazid Ashok Kumar Das Devesh Pratap Singh Kuei-Fang Hsiao Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Computer Science Department University of Texas-Permian Basin University Blvd. Odessa TX USA

Denial-of-service (DoS) attacks are a major concern in the field of Internet technology, and they pose a difficult security challenge. Among the different types of DoS attacks, distributed denial-of-service (DDoS) attacks are especially concerning because they can quickly drain the resources of a target, disrupting their computational and communication capabilities without warning. In response to the severity of this problem, various defense strategies have been developed to defend against DDoS attacks. In this paper, a survey on the various DDoS attacks detection schemes is presented. An overview of the DDoS attack methodology is given. The information on different types of DDoS detection methods in SDN environment is also provided. Then a comparative study of different DDoS attack detection schemes is given, which contains the comparisons of different schemes in terms of their advantages, limitations, accuracy values, and time complexity.

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Multi-frame Correlated Representation Network for Video Super-Resolution

Multi-frame Correlated Representation Network for Video Supe...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Shuo Jin Meiqin Liu Yu Guo Chao Yao Mohammad S. Obaidat Institute of Information Science Beijing Jiaotong University Beijing China School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China King Abdullah II School of Information Technology The University of Jordan Amman Jordan The Amity University Noida UP India

Video super-resolution aims to reconstruct the high-resolution video from its degraded observation. Existing methods primarily utilize explicit alignment strategies, including optical flow and motion compensation, and implicit alignment strategies, including deformable convolution and non-local attention, to model the temporal information. However, these alignment strategies with motion estimation and motion compensation (MEMC) introduce inaccurate inter-frame information to some extent, which largely affects the reconstruction performance. To avoid the error compensation information, we propose to model the temporal information from the perspective of the self-similarity between frames and design a multi-frame correlated representation network (MCRNet) for video super-resolution. To address the issue of temporal information distortion caused by large-scale pixel displacement and object occlusion, MCRNet extracts temporal information through similar regions on multiple frames, which are aggregated with allocated weights for information compensation. Moreover, we design a multi-scale non-local information fusion module for non-local correlation matching of spatio-temporal features in the multi-scale space, which maintains the scale consistency of spatio-temporal features. Experimental results indicate that MCRNet achieves promising gains over other competing methods which employ explicit and implicit alignment strategies on different datasets.

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Decentralized AI for Edge Devices with Federated Learning in the Internet of Things

Decentralized AI for Edge Devices with Federated Learning in...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Gouri M.S M. Nagarajan Mohammed Waheeduddin Hussain T. Jayachitra Jothikumar R S. Sathya Department of Computer Science and Engineering Baselios Mathews II College of Engineering Lake View Muthupilakadu Sasthamcotta Kollam Kerala India Department of Electronics and Communication Engineering Bharath Institute of Higher Education and Research Chennai Department of Information Technology Shadan College of Engineering and Technology Peerancheru Hyderabad India Department of Electrical Electronics and Communication Engineering School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh Department of Computer Science and Engineering Shadan College of Engineering and Technology Peerancheru Hyderabad India Department of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani

As a result of the development of the Internet of Things (IoT), connectivity and applications that are driven by data have entered a new age. Decentralised artificial intelligence with federated learning has evolved as a game-changing concept in this environment, giving answers to urgent challenges like as data privacy, network latency, and resource optimisation. This ecosystem is characterised by constant change. In this research project, we investigate the fundamentals of decentralised artificial intelligence and its consequences for edge devices in IoT networks. The research presented in this paper demonstrates, via a series of tests, that it is possible to strike an appropriate balance between data privacy and model performance by combining decentralised AI with federated learning. The research results provide system designers with helpful information by drawing attention to the trade-offs that exist between the number of devices, the number of communication cycles, and the level of privacy that is afforded. With the help of decentralised artificial intelligence, businesses are able to take advantage of the opportunities presented by the Internet of Things (IoT) without sacrificing the confidentiality of their data or the responsiveness of their real-time applications. This paper provides a cornerstone reference, setting the way for further research that may improve privacy approaches, expand to larger IoT networks, and identify fresh uses for decentralised AI in the dynamic environment of the Internet of Things (IoT).

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Deformable 3D/3D CT-to-digital-tomosynthesis image registration in image-guided bronchoscopy interventions

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computers in Biology and Medicine 2024年 171卷 108199-108199页

作者： Saad, Fatima Frysch, Robert Saalfeld, Sylvia Kellnberger, Stephan Schulz, Jessica Fahrig, Rebecca Bhadra, Krish Nürnberger, Andreas Rose, Georg Institute for Medical Engineering Otto-von-Guericke University Magdeburg Germany Forschungscampus STIMULATE Otto-von-Guericke University Magdeburg Germany Department of Simulation and Graphics Otto-von-Guericke University Magdeburg Germany Siemens Healthcare GmbH Forchheim Germany CHI Memorial Rees Skillern Cancer Institute Chattanooga United States Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany

Traditional navigational bronchoscopy procedures rely on preprocedural computed tomography (CT) and intraoperative chest radiography and cone-beam CT (CBCT) to biopsy peripheral lung lesions. This navigational approach is challenging due to the projective nature of radiography, and the high radiation dose, long imaging time, and large footprints of CBCT. Digital tomosynthesis (DTS) is considered an attractive alternative combining the advantages of radiography and CBCT. Only the depth resolution cannot match a full CBCT image due to the limited angle acquisition. To address this issue, preoperative CT is a good auxiliary in guiding bronchoscopy interventions. Nevertheless, CT-to-body divergence caused by anatomic changes and respiratory motion, hinders the effective use of CT imaging. To mitigate CT-to-body divergence, we propose a novel deformable 3D/3D CT-to-DTS registration algorithm employing a multistage, multiresolution approach and using affine and elastic B-spline transformation models with bone and lung mask images. A multiresolution strategy with a Gaussian image pyramid and a multigrid strategy within the B-spline model are applied. The normalized correlation coefficient is included in the cost function for the affine model and a multimetric weighted cost function is used for the B-spline model, with weights determined heuristically. Tested on simulated and real patient bronchoscopy data, the algorithm yields promising results. Assessed qualitatively by visual inspection and quantitatively by computing the Dice coefficient (DC) and the average symmetric surface distance (ASSD), the algorithm achieves mean DC of 0.82±0.05 and 0.74±0.05, and mean ASSD of 0.65±0.29mm and 0.93±0.43mm for simulated and real data, respectively. This algorithm lays the groundwork for CT-aided intraoperative DTS imaging in image-guided bronchoscopy interventions with future studies focusing on automated metric weight setting. © 2024

关键词： Image registration

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