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MobVGG: Ensemble technique for birds and drones prediction

Heliyon 2024年第21期10卷 e39537页

作者： Muhammad Saqib, Sheikh Mazhar, Tehseen Iqbal, Muhammad Almogren, Ahmad Shahazad, Tariq Rehman, Ateeq Ur Hamam, Habib Department of Computing and Information Technology Gomal University Dera Ismail Khan 29220 Pakistan Department of Computer Science School Education Department Government of Punjab Layyah 31200 Pakistan Chair of Cyber Security Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh 11633 Saudi Arabia Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan School of Computing Gachon University Seongnam 13120 South Korea Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Detection of aerial activities, including drones and birds, has practical implications for automating bird surveys and developing radar systems for aerial object collision detection. Convolutional neural networks (CNNs) have been extensively utilized for image recognition and classification tasks, albeit prior research predominantly focuses on single-class 'drone' classification. However, a gap persists in achieving high accuracy for multi-class classification. To address the limitations of traditional CNNs, such as vanishing gradients and the necessity for numerous layers, this study introduces a novel model termed "MobVGG.” This model combines the architectures of MobileNetV2 and VGG16 to accurately classify images as either 'bird' or 'drone'. The dataset comprises 4212 images for each category of 'bird' and 'drone'. The stringent methodology was applied for dataset preparation and model training to ensure the reliability of the findings. Comparative analysis with previous research demonstrates that the proposed MobVGG model, trained on both 'bird' and 'drone' images, achieves superior accuracy (96 %) compared to benchmark studies. Our paper targets researchers and graduate students as its primary audience. © 2024 The Authors

关键词： Convolutional neural networks (CNNs) Deep learning Ensemble technique MobileNetV2 VGG16

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The content and structure of dreams are coupled to affect

arXiv

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arXiv 2024年

作者： Leckie, Luke Bershad, Anya K. Heppler, Jes McClay, Mason Rappe, Sofiia Foster, Jacob G. School of Biological Sciences University of Bristol United Kingdom Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior UCLA Los AngelesCA United States Department of Philosophy University of California Berkeley BerkeleyCA United States Department of Psychology UCLA Los Angeles United States Department of Philosophy II Chair of Philosophy of Language and Cognition Ruhr University Bochum Germany Department of Informatics Luddy School of Informatics Computing and Engineering Indiana University United States Cognitive Science Program Indiana University Bloomington United States Santa Fe Institute United States

Dreams offer a unique window into the cognitive and affective dynamics of the sleeping and the waking mind. Recent quantitative linguistic approaches have shown promise in obtaining corpus-level measures of dream sentiment and topic occurrence. However, it is currently unclear how the affective content of individual dreams relates to their semantic content and structure. Here, we combine word embedding, topic modeling, and network analysis to investigate this relationship. By applying Discourse Atom Topic Modeling (DATM) to the DreamBank corpus of >18K dream reports, we represent the latent themes arising within dreams as a sparse dictionary of topics and identify the affective associations of those topics. We show that variation in dream affect (valence and arousal) is associated with changes in topical content. By representing each dream report as a network of topics, we demonstrate that the affective content of dreams is also coupled to semantic structure. Specifically, positively valenced dreams exhibit more coherent, structured, and linear narratives, whilst negatively valenced dreams have more narrative loops and dominant topics. Additionally, topic networks of high arousal dreams are structurally dominated by few high arousal topics and incoherent topical connections, whereas low arousal dreams contain more loops. These findings suggest that affective processes are associated with both the content and structure of dreams. Our approach showcases the potential of integrating natural language processing and network analysis with psychology to elucidate the interplay of affect, cognition and narrative in dreams. This methodology has broad applications for the study of narrated experience and psychiatric symptomatology. © 2024, CC BY-NC-ND.

关键词： Semantics

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A Role-Based Encryption Scheme for Securing Outsourced Cloud Data in a Multi-Organization Context

arXiv

引用

arXiv 2020年

作者： Sultan, Nazatul H. Varadharajan, Vijay Zhou, Lan Barbhuiya, Ferdous A. Assam India Department of Computing Faculty of Engineering and Built Environment Global Innovation Chair University of Newcastle Callaghan Australia University of Newcastle Callaghan Australia

—Role-Based Access Control (RBAC) is a popular model which maps roles to access permissions for resources and then roles to the users to provide access control. Role-Based Encryption (RBE) is a cryptographic form of RBAC model that integrates traditional RBAC with the cryptographic encryption method, where RBAC access policies are embedded in encrypted data itself so that any user holding a qualified role can access the data by decrypting it. However, the existing RBE schemes have been focusing on the single-organization cloud storage system, where the stored data can be accessed by users of the same organization. This paper presents a novel RBE scheme with efficient user revocation for the multi-organization cloud storage system, where the data from multiple independent organizations are stored and can be accessed by the authorized users from any other organization. Additionally, an outsourced decryption mechanism is introduced which enables the users to delegate expensive cryptographic operations to the cloud, thereby reducing the overhead on the end-users. Security and performance analyses of the proposed scheme demonstrate that it is provably secure against Chosen Plaintext Attack and can be useful for practical applications due to its low computation overhead. Copyright © 2020, The Authors. All rights reserved.

关键词： Cryptography

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Differentially Private Graph Classification with GNNs

arXiv

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arXiv 2022年

作者： Mueller, Tamara T. Paetzold, Johannes C. Prabhakar, Chinmay Usynin, Dmitrii Rueckert, Daniel Kaissis, Georgios Chair for AI in Medicine and Healthcare Department of Informatics Technical University of Munich Germany Department of Diagnostic and Interventional Radiology Faculty of Medicine Technical University of Munich Germany Department of Informatics Technical University of Munich Germany Department of Quantitative Bio Medicine University of Zurich Switzerland Department of Computing Imperial College London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Germany

Graph Neural Networks (GNNs) have established themselves as the state-of-the-art models for many machine learning applications such as the analysis of social networks, protein interactions and molecules. Several among these datasets contain privacy-sensitive data. Machine learning with differential privacy is a promising technique to allow deriving insight from sensitive data while offering formal guarantees of privacy protection. However, the differentially private training of GNNs has so far remained under-explored due to the challenges presented by the intrinsic structural connectivity of graphs. In this work, we introduce differential privacy for graph-level classification, one of the key applications of machine learning on graphs. Our method is applicable to deep learning on multi-graph datasets and relies on differentially private stochastic gradient descent (DP-SGD). We show results on a variety of synthetic and public datasets and evaluate the impact of different GNN architectures and training hyperparameters on model performance for differentially private graph classification. Finally, we apply explainability techniques to assess whether similar representations are learned in the private and non-private settings and establish robust baselines for future work in this area. © 2022, CC BY.

关键词： Graph neural networks

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From High-Entropy Alloys to High-Entropy Ceramics: The Radiation-Resistant Highly Concentrated Refractory Carbide (CrNbTaTiW)C

SSRN

引用

SSRN 2023年

作者： Tunes, Matheus Araujo Fritze, Stefan Osinger, Barbara Willenshofer, Patrick Alvarado, Andrew M. Martinez, Enrique Menon, Ashok S. Ström, Petter Greaves, Graeme Lewin, Erik Jansson, Ulf Pogatscher, Stefan Saleh, Tarik A. Vishnyakov, Vladimir M. El-Atwani, Osman Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Department of Chemistry Angstrom Laboratory Uppsala University UppsalaSE-751 21 Sweden Department of Physics and Astronomy Applied Nuclear Physics Uppsala University UppsalaSE-751 21 Sweden Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States Department of Materials Science and Engineering Clemson University ClemsonSC29623 United States Department of Mechanical Engineering Clemson University ClemsonSC29623 United States Chair of Non-Ferrous Metallurgy Montanuniversität Leoben8700 Austria School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom

High-entropy materials represent the state-of-the-art on the alloy design strategy for future applications in extreme environments. Recent data indicates that high-entropy alloys (HEAs) exhibit outstanding radiation resistance in face of existing diluted alloy counterparts due to suppressed damage formation and evolution. An extension of the HEA concept is presented in this paper towards the synthesis and characterization of novel high-entropy ceramics as emergent materials for application in environments where energetic particle irradiation is a major concern. A novel carbide within the quinary refractory system CrNbTaTiW has been synthesized using magnetron-sputtering. The material exhibited nanocrystalline grains, singlephase crystal structure and C content around 50 at.%. Heavy-ion irradiation with in-situ Transmission Electron Microscopy was used to assess the irradiation response of the new highentropy carbide (HEC) at 573 K and a comparison with the HEA within the system is made. No displacement damage effects appear within the microstructures of both HEA and HEC up to a dose of 17 displacements-per-atom. Surprisingly, the HEC has not amorphized under the investigated conditions. Xe was implanted in both materials and bubbles nucleated, but smaller sizes compared with conventional nuclear materials shedding light they are potential candidates for use in nuclear energy. © 2023, The Authors. All rights reserved.

关键词： Entropy

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Enabling BIM for property management of existing buildings based on automated As-IS capturing 36

Enabling BIM for property management of existing buildings b...

引用

36th International Symposium on Automation and Robotics in Construction, ISARC 2019

作者： Becker, R. Lublasser, E. Martens, J. Wollenberg, R. Zhang, H. Brell-Cokcan, S. Blankenbach, J. Geodetic Institute Chair for Computing in Civil Engineering and Geo Information Systems RWTH Aachen University Germany Individualized Production in Architecture RWTH Aachen University Germany

Digitization and automation in construction are increasing particularly due to the establishment of Building Information Modelling (BIM). The models of BIM contain geometric as well as semantic information. The level of abstraction ranges from coarse models up to detailed modeled technical components of the buildings. So far, BIM has been developed and used for the planning and construction phase of the building’s lifecycle. In order to fully use the benefits of BIM also for the operation and refurbishment phase, BIM models need to provide a reliable data basis of the as-built and respectively the as-is situation. However, up to now many properties have neither been planned nor constructed using BIM, at times not even digital planning information is available. Therefore, the digital model must be created from the real world. The author’s research proposes the development of an automated as-is capturing process of existing buildings as well as the data integration into BIM as a basis for property management. Suitable capturing techniques have been analyzed. Up to now, these techniques and the subsequent data transfer are still characterized by lots of manual work. Accelerating this process requires methods for the automation of data segmentation, classification and the modeling process. Conventional data capturing techniques such as laser scanning measure only visible surfaces. However, knowledge about inbuilt materials, constructional layers or thickness of e.g. walls is also important for optimized planning and utilization. This paper summarizes the results of a joint research project in cooperation with a property management company. © 2019 International Association for Automation and Robotics in Construction I.A.A.R.C. All rights reserved.

关键词： Robotics

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Towards prevention of sportsmen burnout: Formal analysis of sub-optimal tournament scheduling

arXiv

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arXiv 2021年

作者： Naqvi, Syed Rameez Ahmad, Adnan Islam, S.M. Riazul Akram, Tallha Abdullah-Al-Wadud, M. Alamri, Atif Department of Electrical and Computer Engineering COMSATS University Islamabad Wah Campus Wah47040 Pakistan Departmnet of Computer Science and Engineering Sejong University Seoul05006 Korea Republic of Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Research Chair of Pervasive and Mobile Computing King Saud University Riyadh11543 Saudi Arabia

Scheduling a sports tournament is a complex optimization problem, which requires a large number of hard constraints to satisfy. Despite the availability of several such constraints in the literature, there remains a gap since most of the new sports events pose their own unique set of requirements, and demand novel constraints. Specifically talking of the strictly time bound events, ensuring fairness between the different teams in terms of their rest days, traveling, and the number of successive games they play, becomes a difficult task to resolve, and demands attention. In this work, we present a similar situation with a recently played sports event, where a suboptimal schedule favored some of the sides more than the others. We introduce various competitive parameters to draw a fairness comparison between the sides and propose a weighting criterion to point out the sides that enjoyed this schedule more than the others. Furthermore, we use root mean squared error between an ideal schedule and the actual ones for each side to determine unfairness in the distribution of rest days across their entire schedules. The latter is crucial, since successively playing a large number of games may lead to sportsmen burnout, which must be prevented. © 2021, CC BY.

关键词： Scheduling

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Enhancing the OGC WPS interface with GeoPipes support for real-time geoprocessing

引用

International Journal of Digital Earth 2018年第1期11卷 48-63页

作者： Stefan Herle Jörg Blankenbach Geodetic Institute and Chair for Computing in Civil Engineering and Geo Information Systems RWTH Aachen UniversityAachenGermany

Real-time geospatial information is used in various applications such as risk management or alerting ***,the rise of new sensing technologies also increases the demand for processing the data in real ***’s spatial data infrastructures,however,do not meet the requirements for real-time *** OpenGIS®Web Processing Service(WPS)is not designed to process real-time *** has some major drawbacks in asynchronous processing and cannot handle(geo)data streams out of the *** previous papers,we introduced the GeoPipes approach to share spatiotemporal data in real *** implemented the concept extending the Message Queue and Telemetry Transport(MQTT)protocol by a spatial and temporal dimension,which we call *** this paper,we demonstrate the integration of the GeoPipes idea in the WPS interface to expose standardized real-time geoprocessing *** proof of the concept is illustrated in some exemplary real-time geo processes.

关键词： Real-time geoprocessing web processing service sensor web Internet-ofthings GeoPipes

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Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

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Computer Systems Science & engineering 2023年第10期47卷 855-871页

作者： Z.Faizal Khan Saeed M.Alshahrani Abdulrahman Alghamdi Someah Alangari Nouf Ibrahim Altamami Khalid A.Alissa Sana Alazwari Mesfer Al Duhayyim Fahd N.Al-Wesabi Department of Computer Engineering College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Science and Humanities DawadmiShaqra UniversityShaqraSaudi Arabia Department of Mathematics College of EducationShaqra UniversityShaqraSaudi Arabia Saudi Aramco Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAlaflaj16828Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia

The Internet of Things(IoT)is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare,in health service to energy,and in developed to *** develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that *** progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence(AI)andMachine Learning(ML)devices are crucial fact to realize security in IoT *** can be required for minimizing the issues of security based on IoT devices ***,this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection fromthe cloud and IoT *** proposed MFORELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT *** accomplish this,the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful ***,the proposed models will receive the pre-processing data and carry out the classifier *** boosting the efficiency of the proposed models,theMFOtechnique was utilized to *** experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset,and the outcomes are examined under distinct aspects.

关键词： Mayfly optimization machine learning artificial intelligence cybersecurity threat detection

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Comparing ensemble methods combined with different aggregating models using micrograph cell segmentation as an initial application example

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Journal of Pathology Informatics 2023年 14卷 100304-100304页

作者： Göb, St. Sawant, S. Erick, F.X. Schmidkonz, C. Ramming, A. Lang, E.W. Wittenberg, T. Götz, Th.I. Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany Chair of Visual Computing Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Nuclear Medicine Friedrich-Alexander-Universität Erlangen-Nürnberg and University Hospital Erlangen Erlangen Germany Department of Internal Medicine University Hospital Erlangen Erlangen Germany CIML Group Biophysics University of Regensburg Regensburg Germany Department of Industrial Engineering and Health Technical University of Applied Sciences Amberg-Weiden Weiden Germany

Strategies such as ensemble learning and averaging techniques try to reduce the variance of single deep neural networks. The focus of this study is on ensemble averaging techniques, fusing the results of differently initialized and trained networks. Thereby, using micrograph cell segmentation as an application example, various ensembles have been initialized and formed during network training, whereby the following methods have been applied: (a) random seeds, (b) L₁-norm pruning, (c) variable numbers of training examples, and (d) a combination of the latter 2 items. Furthermore, different averaging methods are in common use and were evaluated in this study. As averaging methods, the mean, the median, and the location parameter of an alpha-stable distribution, fit to the histograms of class membership probabilities (CMPs), as well as a majority vote of the members of an ensemble were considered. The performance of these methods is demonstrated and evaluated on a micrograph cell segmentation use case, employing a common state-of-the art deep convolutional neural network (DCNN) architecture exploiting the principle of the common VGG-architecture. The study demonstrates that for this data set, the choice of the ensemble averaging method only has a marginal influence on the evaluation metrics (accuracy and Dice coefficient) used to measure the segmentation performance. Nevertheless, for practical applications, a simple and fast estimate of the mean of the distribution is highly competitive with respect to the most sophisticated representation of the CMP distributions by an alpha-stable distribution, and hence seems the most proper ensemble averaging method to be used for this application. © 2023 The Authors

关键词： Alpha-stable function Cell segmentation Combine ensembles Deep neural networks

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