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43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024

作者： Hogue, Debra Kastl, Zak Karch, Joseph Nguyen, Don Schley, Lacey Lewis, Justin Connelly, Shane Weaver, Chris School of Computer Science The University of Oklahoma United States Us Air Force's 76th Software Engineering Group United States Oklahoma Aerospace and Defense Innovation Institute The University of Oklahoma United States The University of Oklahoma Department of Psychology Center for Applied Social Research United States

ISBN: (纸本)9798350349610

The MURDOC project introduces an application to enhance trustworthiness and explainability in computer vision models, focusing on camouflage detection. It aims to address the need for transparent and interpretable AI systems in sensitive domains such as security and defense. MURDOC integrates advanced eXplainable AI techniques including off-ramps for collecting interpretable insights, attention mechanisms for high-lighting relevant features, and a user-centric image pre-processing tool within the visualization interface. This paper offers an assessment of MURDOC's potential impact on trustworthiness and explainability in camouflaged object detection tasks. It discusses and assesses the potential effectiveness of off-ramps in gathering XAI output at various model stages, such as feature maps, attention mechanisms, and activation maps. Additionally, the paper investigates how user-driven image pre-processing mechanisms may enhance the trustworthiness of the model's predictions and decisions, allowing users to modify the input and observe prediction changes. As a work in progress, the development of MURDOC shows promise in bridging the gap for transparency and interpretability in camouflage detection. The paper also discusses the challenges, future directions, and potential applications of MURDOC's capabilities in diverse domains requiring transparent and trustworthy AI systems. The source code for the version of MURDOC in this paper is available on GitHub at: https://***/Department-70/DASC-2024. © 2024 IEEE.

关键词： Camouflage

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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Decision Support System for Spontaneous Volunteers During Emergency Response

Decision Support System for Spontaneous Volunteers During Em...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sruthy Anand Sethuraman N Rao A.D. Dev Nandan B.Madan Gopal Madhav A Das Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Disasters affect a large number of people yearly and recurrently at many locations. Emergencies compel the local community to participate in disaster response; they are mostly the first responders during any disaster. Hence, it is necessary to devise solutions to support the local community and enhance their resilience. This work proposes a comprehensive platform for the victims, administrators, and volunteers during a catastrophe. It encourages the participation of the local community in managing local resources, requesting help, and getting acknowledged for the service they lend. The platform helps recognize the local community’s strengths and provides a method to receive information from disaster managers. The platform will also bring more situational awareness to disaster managers and volunteers. It also helps disaster managers assess the disaster’s potential impact and vulnerability levels of the community at an early stage. The platform envisions providing a decision support system for the spontaneous volunteers to co-work with disaster managers in a regulated fashion by eliminating fake volunteers and messages. The research also focuses on the characteristics of technology adoption for the widespread acceptance of the solution.

关键词： Decision support systems Social networking (online) Disasters Urban areas Prototypes Disaster management Emergency services Real-time systems Mobile applications Resilience

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Enhancing Urban Home Security: Deep Learning Enabled Vehicle Identification

Enhancing Urban Home Security: Deep Learning Enabled Vehicle...

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Emerging Technology (INCET), International Conference for

作者： V S N Lokesh Yarramallu Sanjit Teja Tummalapalli Yatham Nitish Reddy Sruthy Anand Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The growing rate of crimes in cities is a genuine concern these days. Preventing crimes from happening is a need for the city residents and the administrator community. Various technology-enabled solution approaches can be integrated to prevent crimes in cities. In this work, we explore how the amalgamation of Deep Learning and IoT can prevent crimes and provide enhanced security to citizens. Crimes happen for several reasons, and the right technology-enabled solutions can help prevent crimes by alerting the intended stakeholders on time. This paper focuses on the importance of identifying vehicles and the importance of detecting non-resident vehicles to alert the residents and related security personnel about them. We detailed the steps used to identify the vehicle using YOLOv8. The work also explored various OCR tools to find the best one for number plate identification.

关键词： Deep learning Image color analysis Urban areas Smart homes Safety Planning Security

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Unified regularity measures for sample-wise learning and generalization

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Visual Intelligence 2024年第1期2卷 488-507页

作者： Chi Zhang Meng Yuan Xiaoning Ma Yu Liu Haoang Lu Le Wang Yuanqi Su Yuehu Liu Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’an710049China National KeyLaboratory of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong UniversityXi’an710049China National Engineering Research Center for Visual Information and Applications Xi’an Jiaotong UniversityXi’an710049China School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an710049China

Fundamental machine learning theory shows that different samples contribute unequally to both the learning and testing *** studies on deep neural networks(DNNs)suggest that such sample differences are rooted in the distribution of intrinsic pattern information,namely sample *** by recent discoveries in network memorization and generalization,we propose a pair of sample regularity measures with a formulation-consistent representation for both ***,the cumulative binary training/generalizing loss(CBTL/CBGL),the cumulative number of correct classifications of the training/test sample within the training phase,is proposed to quantify the stability in the memorization-generalization process,while forgetting/mal-generalizing events(ForEvents/MgEvents),i.e.,the misclassification of previously learned or generalized samples,are utilized to represent the uncertainty of sample regularity with respect to optimization *** effectiveness and robustness of the proposed approaches for mini-batch stochastic gradient descent(SGD)optimization are validated through sample-wise *** training/test sample selection applications show that the proposed measures,which share the unified computing procedure,could benefit both tasks.

关键词： Network generalization Memorization-generalization mechanism Sample regularity Forgetting events Sample reweighting

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EMVGAN: Emotion-Aware Music-Video Common Representation Learning via Generative Adversarial Networks 5

EMVGAN: Emotion-Aware Music-Video Common Representation Lear...

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5th International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia, MMArt-ACM 2022

作者： Tsai, Yu-Chih Pan, Tse-Yu Kao, Ting-Yang Yang, Yi-Hsuan Hu, Min-Chun Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science National Tsing Hua University Taiwan Institue of Information Systems and Applications National Tsing Hua University Taipei Taiwan Research Center for IT Innovation Academia Sinica Taiwan

ISBN: (纸本)9781450392402

Music can enhance our emotional reactions to videos and images, while videos and images can enrich our emotional response to music. Cross-modality retrieval technology can be used to recommend appropriate music for a given video and vice versa. However, the heterogeneity gap caused by the inconsistent distribution between different data modalities complicates learning the common representation space from different modalities. Accordingly, we propose an emotion-Aware music-video cross-modal generative adversarial network (EMVGAN) model to build an affective common embedding space to bridge the heterogeneity gap among different data modalities. The evaluation results revealed that the proposed EMVGAN model can learn affective common representations with convincing performance while outperforming other existing models. Furthermore, the satisfactory performance of the proposed network encouraged us to undertake the music-video bidirectional retrieval task. © 2022 ACM.

关键词： Generative adversarial networks

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Initial Work on Ultrafast Photoluminescent Surface Heating Optical Thermometry (UP-SHOT)

Initial Work on Ultrafast Photoluminescent Surface Heating O...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Collins, Noelle M. Klesko, Joseph P. Brener, Igal Norden, Tenzin McClintock, Luke M. Padmanabhan, Prashant Feezell, Daniel Winters, Caroline A. Materials Science Non-destructive Evaluation and Development Sandia National Laboratories AlbuquerqueNM United States Materials Science Thin Films and Packaging Sandia National Laboratories AlbuquerqueNM United States Optical Engineering Microsystems Engineering Science and Applications Sandia National Laboratories AlbuquerqueNM United States Center for Integrated Nanotechnologies Los Alamos National Laboratory Los AlamosNM United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States Sandia National Laboratories AlbuquerqueNM United States

ISBN: (纸本)9781624107238

Ultrafast (GHz) thermometry requires considerations of signal-to-noise, heat conduction, and temperature sensitivity. Ultrafast Photoluminescent Surface Heating Optical Thermometry (UP-SHOT) exploits the subnanosecond excitonic response of zinc oxide. Time-resolved photoluminescence is presented and discussed. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Heat conduction

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Conformal marked bisection for local refinement of n-dimensional unstructured simplicial meshes

arXiv

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arXiv 2022年

作者： Belda-Ferrín, Guillem Ruiz-Gironés, Eloi Gargallo-Peiró, Abel Roca, Xevi Computer Applications in Science and Engineering Barcelona Supercomputing Center - BSC Barcelona08034 Spain

We present an n-dimensional marked bisection method for unstructured conformal meshes. We devise the method for local refinement in adaptive ndimensional applications. To this end, we propose a mesh marking pre-process and three marked bisection stages. The pre-process marks the initial mesh conformingly. Then, in the first n − 1 bisections, the method accumulates in reverse order a list of new vertices. In the second stage, the n-th bisection, the method uses the reversed list to cast the bisected simplices as reflected simplices, a simplex type suitable for newest vertex bisection. In the final stage, beyond the n-th bisection, the method switches to newest vertex bisection. To allow this switch, after the second stage, we check that under uniform bisection the mesh simplices are conformal and reflected. These conditions are sufficient to use newest vertex bisection, a bisection scheme guaranteeing key advantages for local refinement. Finally, the results show that the proposed bisection is well-suited for local refinement of unstructured conformal meshes. © 2022, CC BY-NC-ND.

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Automatic penalty and degree continuation for parallel pre-conditioned mesh curving on virtual geometry

arXiv

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arXiv 2022年

作者： Ruiz-Gironés, Eloi Roca, Xevi Computer Applications in Science and Engineering Barcelona Supercomputing Center - BSC Barcelona08034 Spain

We present a distributed parallel mesh curving method for virtual geometry. The main application is to generate large-scale curved meshes on complex geometry suitable for analysis with unstructured high-order methods. Accordingly, we devise the technique to generate geometrically accurate meshes composed of high-quality elements. To this end, we advocate for degree continuation on a penalty-based second-order optimizer that uses global tight tolerances to converge the distortion residuals. To reduce the method memory footprint, waiting time, and energy consumption, we combine three main ingredients. First, we propose a matrix-free GMRES solver pre-conditioned with successive over-relaxation by blocks to reduce the memory footprint three times. We also propose an adaptive penalty technique, to reduce the number of non-linear iterations. Third, we propose an indicator of the required linear solver tolerance to reduce the number of linear iterations. On thousands of cores, the method curves meshes composed of millions of quartic elements featuring highly stretched elements while matching a virtual topology. © 2022, CC BY-NC-ND.

关键词： Energy utilization

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Effects of Prandtl number in two-dimensional turbulent convection

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Chinese Physics B 2021年第9期30卷 290-296页

作者： Jian-Chao He Ming-Wei Fang Zhen-Yuan Gao Shi-Di Huang Yun Bao School of Aeronautics and Astronautics Sun Yat-sen UniversityGuangzhou 510275China Center for Complex Flows and Soft Matter Research and Department of Mechanics and Aerospace Engineering Southern University of Science and TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Turbulence Research and Applications Southern University of Science and TechnologyShenzhen 518055China

We report a numerical study of the Prandtl-number(Pr)effects in two-dimensional turbulent Rayleigh-Bénard *** simulations were conducted in a square box over the Pr range from 0.25 to 100 and over the Rayleigh number(Ra)range from 10^(7) to 10^(10).We find that both the strength and the stability of the large-scale flow decrease with the increasing of Pr,and the flow pattern becomes plume-dominated at high *** evolution in flow pattern is quantified by the Reynolds number(Re),with the Ra and the Pr scaling exponents varying from 0.54 to 0.67 and-0.87 to-0.93,*** is further found that the non-dimensional heat flux at small Ra diverges strongly for different Pr,but their difference becomes marginal as Ra *** the thermal boundary layer,the spatially averaged thicknesses for all the Pr numbers can be described byδθ~Ra^(-0.30) approximately,but the local values vary a lot for different Pr,which become more uniform with Pr increasing.

关键词： turbulent convection Prandtl number direct numerical simulations(DNS)

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