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arXiv 2023年

作者： Tukan, Murad Fares, Fares Grufinkle, Yotam Talmor, Ido Mualem, Loay Braverman, Vladimir Feldman, Dan DataHeroes Israel The Robotics and Big Data lab University of Haifa Haifa Israel The Computer Science Department University of Haifa Haifa Israel The Computer Science Department Rice University HoustonTX United States

Navigating toy drones through uncharted GPS-denied indoor spaces poses significant difficulties due to their reliance on GPS for location determination. In such circumstances, the necessity for achieving proper navigation is a primary concern. In response to this formidable challenge, we introduce a real-time autonomous indoor exploration system tailored for drones equipped with a monocular RGB camera. Our system utilizes ORB-SLAM3, a state-of-the-art vision feature-based SLAM, to handle both the localization of toy drones and the mapping of unmapped indoor terrains. Aside from the practicability of ORB-SLAM3, the generated maps are represented as sparse point clouds, making them prone to the presence of outlier data. To address this challenge, we propose an outlier removal algorithm with provable guarantees. Furthermore, our system incorporates a novel exit detection algorithm, ensuring continuous exploration by the toy drone throughout the unfamiliar indoor environment. We also transform the sparse point to ensure proper path planning using existing path planners. To validate the efficacy and efficiency of our proposed system, we conducted offline and real-time experiments on the autonomous exploration of indoor spaces. The results from these endeavors demonstrate the effectiveness of our methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Drones

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Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models

arXiv

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arXiv 2024年

作者： Song, Ruixin Spadon, Gabriel Pelot, Ronald Matwin, Stan Soares, Amilcar Department of Computer Science Memorial University of Newfoundland St. John’sNL Canada Institute for Big Data Analytics Dalhousie University HalifaxNS Canada Industrial Engineering Department Dalhousie University HalifaxNS Canada Institute of Computer Science Polish Academy of Sciences Warsaw Poland Department of Computer Science and Media Technology Linnaeus University Växjö Sweden

Aquatic non-indigenous species (NIS) pose significant threats to biodiversity, disrupting ecosystems and inflicting substantial economic damages across agriculture, forestry, and fisheries. Due to the fast growth of global trade and transportation networks, NIS has been introduced and spread unintentionally in new environments. This study develops a new physics-informed model to forecast maritime shipping traffic between port regions worldwide. The predicted information provided by these models, in turn, is used as input for risk assessment of NIS spread through transportation networks to evaluate the capability of our solution. Inspired by the gravity model for international trades, our model considers various factors that influence the likelihood and impact of vessel activities, such as shipping flux density, distance between ports, trade flow, and centrality measures of transportation hubs. Accordingly, this paper introduces transformers to gravity models to rebuild the short- and long-term dependencies that make the risk analysis feasible. Thus, we introduce a physics-inspired framework that achieves an 89% binary accuracy for existing and non-existing trajectories and an 84.8% accuracy for the number of vessels flowing between key port areas, representing more than 10% improvement over the traditional deep-gravity model. Along these lines, this research contributes to a better understanding of NIS risk assessment. It allows policymakers, conservationists, and stakeholders to prioritize management actions by identifying high-risk invasion pathways. Besides, our model is versatile and can include new data sources, making it suitable for assessing international vessel traffic flow in a changing global landscape. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Exploring Advanced Algorithms in Multi-Dimensional Spatial data Modeling for Enhanced Geospatial Analysis

Exploring Advanced Algorithms in Multi-Dimensional Spatial D...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Asha V S Sreekanth Ginni Nijhawan Vipashi Kansal R.P Ram Kumar Tamam Ali Abd Ulabbas Abedi Department of Computer Applications N.H.C.E Bangalore India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India LPU Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of CSBS GRIET Bachupally Hyderabad Telangana India College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

This study examines map analysis with the goal of improving current methods by looking at cutting-edge algorithms for modeling multidimensional spatial data. Tensor Decomposition (TD), Convolutional Neural Networks (CNNs), and Spatiotemporal Regression (STR) are all parts of the suggested method that work together to solve the complex problems that come up when representing spatial data. The abstract provides a concise summary of the primary objectives and methodologies of the study, while also elucidating the rationale behind its execution. In our work, we use new developments in deep learning and machine learning to go beyond common ways of looking at space, such as graph-based spatial analysis, geostatistical methods, and space partitioning strategies. When you combine convolutional neural networks with tensor decomposition, you can get hierarchical features out of the data and figure out what the complex spatial patterns mean. Using spatial-temporal regression also helps us understand spatial events better by adding temporal changes to the analysis framework. Our study's goal is to push the boundaries of geographic research by combining the newest techniques. This will make it possible to create more precise and all-encompassing methods that can correctly understand the complex nature of location data in many dimensions.

关键词： Analytical models Adaptation models Tensors Computational modeling Urban planning Spatial databases data models Spatiotemporal phenomena Geospatial analysis Convolutional neural networks

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FedComm: Federated Learning as a Medium for Covert Communication

arXiv

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arXiv 2022年

作者： Hitaj, Dorjan Pagnotta, Giulio Hitaj, Briland Perez-Cruz, Fernando Mancini, Luigi V. Department of Computer Science Sapienza University of Rome Italy Computer Science Laboratory SRI International United States Swiss Data Science Center Computer Science Department ETH Zürich Switzerland

Proposed as a solution to mitigate the privacy implications related to the adoption of deep learning, Federated Learning (FL) enables large numbers of participants to successfully train deep neural networks without revealing the actual private training data. To date, a substantial amount of research has investigated the security and privacy properties of FL, resulting in a plethora of innovative attack and defense strategies. This paper thoroughly investigates the communication capabilities of an FL scheme. In particular, we show that a party involved in the FL learning process can use FL as a covert communication medium to send an arbitrary message. We introduce FedComm, a novel covert-communication technique that enables robust sharing and transfer of targeted payloads within the FL framework. Our extensive theoretical and empirical evaluations show that FedComm provides a stealthy communication channel, with minimal disruptions to the training process. Our experiments show that FedComm successfully delivers 100% of a payload in the order of kilobits before the FL procedure converges. Our evaluation also shows that FedComm is independent of the application domain and the neural network architecture used by the underlying FL scheme. © 2022, CC BY-NC-SA.

关键词： Deep neural networks

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A Unified Framework for Convergence Analysis in Social Networks

A Unified Framework for Convergence Analysis in Social Netwo...

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IEEE Conference on Decision and Control

作者： Ti-Chung Lee Jun-Kai Huang Youfeng Su Department of Electrical Engineering National Sun Yat-sen University Kaohsiung Taiwan College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper proposes a unified framework for the stability analysis of discrete-time nonlinear systems from social networks, including the Friedkin-Johnsen opinion model, two opinion dynamics models in the study of social power, and a general nonlinear polar opinion model. Three novel convergence results are proposed to treat various conditions based on LaSalle invariance principle. Several applications are provided to illustrate the power of the proposed framework.

关键词： Analytical models Social networking (online) Stability analysis Nonlinear systems Convergence

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Expedited Block Transmission in Blockchain Network by using Clusters

Expedited Block Transmission in Blockchain Network by using ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaoqi Hua Jian Chen Peiyun Zhang Zhangjie Fu Haibin Zhu Kezhong Lu Jigang Ren School of Software Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics of Ministry of Education and the School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Mathematics Nipissing University North Bay ON Canada School of Big Data and Artificial Intelligence Chizhou University Chizhou China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Blockchain technology has garnered increasing attention from researchers. Because blockchain systems may contain malicious or spatially limited nodes that may delay block verification and reduce block transmission rate, this work proposes a block transmission model by designing and using special clusters. This work proposes the cluster formation and selection mechanisms. Nodes are grouped into clusters in a blockchain, and clusters with high fitness values are chosen to transmit blocks by calculating their trust values and block transmission rates. The proposed method is compared with the peers: Layer-Chain, BlockP2P-EP and RNS. According to experimental findings, the proposed method is superior to its peers regarding the time needed for block synchronization and transmission, block occupation storage ratio, transaction throughput, and block transmission success ratio.

关键词： Throughput Blockchains Peer-to-peer computing Delays Synchronization Cybernetics

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Utilizing Machine Learning Techniques for Forecasting Agricultural Commodity Prices

Utilizing Machine Learning Techniques for Forecasting Agricu...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Minu Inba Shanthini Watson Benjamin D. Gowtham S.Anand Kumar Reddy D. Durga Radha Krishna Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

Crop prices in India are vulnerable to environmental factors such as rainfall and temperature which can cause supply variations and price shifts. Government policies are intended to keep prices stable during severe weather occurrences. An extensive crop price prediction approach utilizing diverse machine learning techniques and encompassing features such as temperature extremes, humidity, precipitation, and probabilities is introduced in this research work. Employing algorithms including linear regression, decision trees, random forests, K-Nearest Neighbors (KNN), XGBoost, and Bayesian regression, the research conducts comprehensive evaluation and comparative analysis to identify optimal techniques for crop price prediction, thereby yielding valuable insights for agricultural applications. Amid the intricacies of contemporary agricultural markets, where numerous factors influence prices, machine learning emerges as a potent tool capable of capturing intricate influences. By exploring a range of machine learning algorithms, complex relationships within data are unveiled, establishing a basis for evidence-based methodology selection. The research results heighten forecasting precision and empower stakeholders with actionable insights, facilitating informed decisions, risk mitigation, and capitalization on opportunities. This signifies a substantial stride towards the convergence of machine learning and agriculture, fostering enhanced predictive accuracy in crop price forecasting.

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Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

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Cancer Innovation 2023年第4期2卷 283-289页

作者： Xuantong Gong Xuefeng Liu Xiaozheng Xie Yong Wang Department of Ultrasound National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBejjing Advanced Innovation Center for Big Data and Brain Computing(BDBC)Beihang UniversityBeijingChina School of Computer and Communication Engi neering.University of Science and Technology Beijing BeijingChina

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women *** means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients'*** extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's ***,radiomics provides a new approach to noninvasive assessment of breast cancer *** is one of the commonest clinical means of examining breast *** recent years,some results of research into ultrasound radiomics for diagnosing breast cancer,predicting lymph node status,treatment response,recurrence and survival times,and other aspects,have been *** this article,we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer *** aim to provide a reference for radiomics researchers,promote the development of ultrasound radiomics,and advance its clinical application.

关键词： breast cancer deeplearning prognosisprediction radiomics ultrasound

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An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gaussian Mixture and RCNN

An Advanced and Ideal Method for Tumor Detection and Classif...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Samrat Ray K. Ramkumar Ch. V Anupama P. Misba Marybai K Anguraju P. Preethi Dean and Head of International Relations IIMS Pune India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamilnadu Department: Information Technology Malla Reddy Engineering College Hyderabad Telengana Artificial Intelligence and Data Science department Panimalar Engineering College Chennai Department of Computer Science and Engineering Kongunadu College of Engineering and Technology Trichy Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Extensive research and methodological improvements over time attest to the necessity of brain tumour detection and analysis for any indication system. Accurate tumour detection is essential for this study; hence an efficient automated method must be implemented Numerous segmentation algorithms have been developed to classify brain tumors with greater precision. Medical image processing experts agree that brain picture segmentation presents unique difficulties. A novel automatic detection and classification system is proposed in this work. The suggested method is broken down into several stages, including preprocessing of MRI images, segmentation of those images, extraction of features, and classification of those features. The noise in the MRI picture is reduced during the preprocessing stage by applying an adaptive filter. The grey level co-occurrence matrix (GLCM) is used for feature extraction, while the improved Means clustering (IMC) technique is used for image segmentation. To categorize MRI scans, we used a deep learning algorithm scan after features were extracted from the scans them as benign lesions, gliomas, meningiomas, or pituitary tumours. In order to accomplish the classification task, recurrent convolutional neural networks (RCNN) were utilised. For a given input dataset containing photographs of the brain, the proposed technique yields superior classification results. The investigations made use of MRI pictures from Kaggle's data sets, which included 464 testing sets and 3264 training sets. The outcomes show that the proposed strategy outperforms its predecessors. The suggested RCNN method is then compared to BP, VG-Net, and RCNN, three of the most popular classification techniques currently in use. The suggested classifier successfully identified 96.21 percent of brain tumour tissues in magnetic resonance imaging scans.

关键词： Deep learning Training Image segmentation Magnetic resonance imaging Noise Feature extraction Classification algorithms Convolutional neural networks Tumors Testing

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Energy-efficient and Privacy-preserving Edge Intelligence for 6G-Empowered Intelligent Transportation Systems

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IEEE Network 2025年

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Xiao, Ziheng Guo, Tingting Liu, Shenghao Sun, Jiaqi Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wuhan Kedao Geographic Information Engineering Co. Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of

6G-empowered Intelligent Transportation Systems (ITS) generate large amounts of data through millions of devices and sensors at the terminal and network edge. Edge intelligence advances the frontier of data-driven artificial intelligence (AI) to the network edge, which enables AI applications to better utilize edge data. Broader connectivity, higher device density, and stronger data exchange present challenges for reliable device communication and privacy data protection in 6G-empowered ITS. To address these challenges, this paper proposes an energy-efficient and privacy-preserving edge intelligence framework named EP-EI. EP-EI deploys a deep reinforcement learning-based cluster head selection model at the edge to ensure energy-efficient communication for terminal devices. Meanwhile, EP-EI designs a privacy-aware hierarchical computing framework for terminal-edge-cloud to prevent privacy leakage. Furthermore, the application scenarios, challenges, and future directions of EP-EI are discussed. Finally, simulation experiments in a specific intelligent transportation case study demonstrate that EP-EI can provide energy-efficient and privacy-protecting services. © 2025 IEEE.

关键词： Differential privacy

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