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International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

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An Authenticated Method for a Secure Changing Password

An Authenticated Method for a Secure Changing Password

引用

Electronic engineering, Information Technology & Education (EEITE), International Conference in

作者： Mostefa Kara Konstantinos Karampidis Laid Kenioua Manolis Karampidis Muath AlShaikh Giorgos Papadourakis Abdelkader Laouid Scientific and Technology Hub of Sidi Abdellah National Higher School of Mathematics Algiers Algeria Department of Electrical and Computer Engineering Hellenic Mediterranean University Crete Greece LIAP Laboratory El Oued University El Oued Algeria Computer Science Department College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia

ISBN: (数字)9798350372878

ISBN: (纸本)9798350372885

Despite their age, passwords are still widely used more than ever. In many cases, it is the solution that best fits the usage scenario. Researchers are proposing innovative designs and methods to support these scenarios. Many systems, especially sensitive ones, need to change the password. Classic systems rely on changing passwords in two stages, the first i s authentication, which usually consists of two or three rounds, and the second is creating a new password (PW), which also consists of two or three rounds. In this paper, a hidden transmission of a new random PW using the previous one is presented, we talk about an authenticated changing of PW. The verifier g enerates half of the new password and sends it to the prover, the prover authenticates the sent message using the old PW and then returns the authentication proof. After checking it, the verifier creates the second half of the PW. The proposed method combines the authentication and change PW processes into one process consisting of 3 rounds. Therefore, the proposal facilitates the process of automatic periodic change of PW between two entities (user, app, system, etc). Analysis and experiments show that our technique is robust, lightweight, and reduces many potential risks in the field.

关键词： Design methodology Education Authentication Passwords Proposals

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Value of Information in the Mean-Square Case and its Application to the Analysis of Financial Time-Series Forecast

arXiv

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arXiv 2024年

作者： Belavkin, Roman V. Pardalos, Panos Principe, Jose Department of Computer Science Middlesex University LondonNW4 4BT United Kingdom Department of Industrial and Systems Engineering University of Florida P.O. Box 116595 GainesvilleFL32611-6595 United States Department of Electrical & Computer Engineering University of Florida P.O. Box 116130 GainesvilleFL32611-6130 United States

The advances and development of various machine learning techniques has lead to practical solutions in various areas of science, engineering, medicine and finance. The great choice of algorithms, their implementations and libraries has resulted in another challenge of selecting the right algorithm and tuning their parameters in order to achieve optimal or satisfactory performance in specific applications. Here we show how the value of information (V (I)) can be used in this task to guide the algorithm choice and parameter tuning process. After estimating the amount of Shannon’s mutual information between the predictor and response variables, V (I) can define theoretical upper bound of performance of any algorithm. The inverse function I(V ) defines the lower frontier of the minimum amount of information required to achieve the desired performance. In this paper, we illustrate the value of information for the mean-square error minimization and apply it to forecasts of cryptocurrency *** Codes 94A15, 94A17, 94A34, 62J12, 62M45, 60G25 Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments

arXiv

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arXiv 2024年

作者： Jiao, Jianhao Geng, Ruoyu Li, Yuanhang Xin, Ren Yang, Bowen Wu, Jin Wang, Lujia Liu, Ming Fan, Rui Kanoulas, Dimitrios Department of Computer Science University College London LondonWC1E 6BT United Kingdom Nansha Guangdong Guangzhou511400 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Nansha Guangdong Guangzhou511400 China College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems The Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

The creation of a metric-semantic map, which encodes human-prior knowledge, represents a high-level abstraction of environments. However, constructing such a map poses challenges related to the fusion of multi-modal sensor data, the attainment of real-time mapping performance, and the preservation of structural and semantic information consistency. In this paper, we introduce an online metric-semantic mapping system that utilizes LiDAR-Visual-Inertial sensing to generate a global metric-semantic mesh map of large-scale outdoor environments. Leveraging GPU acceleration, our mapping process achieves exceptional speed, with frame processing taking less than 7ms, regardless of scenario scale. Furthermore, we seamlessly integrate the resultant map into a real-world navigation system, enabling metric-semantic-based terrain assessment and autonomous point-to-point navigation within a campus environment. Through extensive experiments conducted on both publicly available and self-collected datasets comprising 24 sequences, we demonstrate the effectiveness of our mapping and navigation methodologies. Code has been publicly released: https://***/gogojjh/cobra. Note to Practitioners—This paper tackles the challenge of autonomous navigation for mobile robots in complex, unstructured environments with rich semantic elements. Traditional navigation relies on geometric analysis and manual annotations, struggling to differentiate similar structures like roads and sidewalks. We propose an online mapping system that creates a global metric-semantic mesh map for large-scale outdoor environments, utilizing GPU acceleration for speed and overcoming the limitations of existing real-time semantic mapping methods, which are generally confined to indoor settings. Our map integrates into a real-world navigation system, proven effective in localization and terrain assessment through experiments with both public and proprietary datasets. Future work will focus on integrating kernel-based

关键词： Mesh generation

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Voronoi-diagram-enabled arbitrary-path topological transport

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Physical Review Applied 2025年第5期23卷 054041-054041页

作者： You Yang Zhixia Xu Shiqiang Fu Yun Zhou Jie Chang Shuo Bao Xianghong Kong Haotian Wu School of Information Science and Technology Dalian Maritime University Dalian 116026 China State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Department of Mechanical and Aerospace Engineering University of California San Diego San Diego California 92093 USA Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore

Topological physics in electromagnetics has enabled topological edge-state waveguides, yet traditional propagation path designs of topological photonics are restricted by the geometry of the underlying periodic lattice. Here, we propose a topological waveguide scheme based on Voronoi diagrams, allowing for arbitrary propagation paths and offering greater flexibility beyond the constraints of conventional periodic structures. Focusing on gyromagnetic photonic topological insulators and valley topological insulators, we detail the construction and implementation process. Our method ensures smooth connections between unit cells and efficient wave transmission, which are validated through both simulations and experiments. The proposed scheme greatly expands the flexibility for topological transport and enables more applications for complex topological devices and systems.

关键词： Geometry

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Optimal Sizing of Isolated Renewable Power systems with Ammonia Synthesis: Model and Solution Approach

arXiv

引用

arXiv 2023年

作者： Yu, Zhipeng Lin, Jin Liu, Feng Li, Jiarong Zhao, Yuxuan Song, Yonghua The State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100087 China Sichuan Energy Internet Reasearch Institute Tsinghua University Chengdu610213 China The Department of Electrical and Computer Engineering University of Macau 999078 China The Department of Electrical Engineering Tsinghua University Beijing100087 China

Isolated renewable power to ammonia (IRePtA) has been recognized as a promising way to decarbonize the chemical industry. Optimal sizing of the renewable power system is significant to improve the techno-economic of IRePtA since the investment of power sources exceeds 80% of the total investment. However, multi-timescale electricity, hydrogen, and ammonia storages, minimum power supply for system safety, and the multiyear uncertainty of renewable generation lead to difficulties in planning. To address the issues above, an IGDT-MILFP model is proposed. First, the levelized cost of ammonia (LCOA) is directly formulated as the objective, rendering a mixed integer linear fractional programming (MILFP) problem. Information gap decision theory (IGDT) is utilized to handle the multiyear uncertainty of renewable generation. Second, a combined Charnes-Cooper (C&C) transformation and Branch-and-Bound (B&B) method is proposed to efficiently solve the large-scale IGDT-MILFP model, giving robust and opportunistic planning results. Then, Markov Chain Monte Carlo (MCMC) sampling-based posteriori analysis is leveraged to quantify the long-run performance. Finally, a real-life system in Inner Mongolia, China, is studied. The results indicate that the proposed methods could reduce the computational burden by orders of magnitude for solving a large-scale MILFP problem. Moreover, the proposed IGDT-MILFP model is necessary and accurate to obtain an optimal capacity allocation with the lowest expected LCOA (3610 RMB/t) in long-run simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Ammonia

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Shannon capacity, Lovász theta number and the Mycielski construction1

arXiv

引用

arXiv 2023年

作者： Csonka, Bence Simonyi, Gábor Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics MTA-BME Lendület Hungary Arithmetic Combinatorics Research Group MKH Budapest Hungary Artificial Intelligence National Laboratory of Hungary Hungary Budapest Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Hungary

We investigate the effect of the well-known Mycielski construction on the Shannon capacity of graphs and on one of its most prominent upper bounds, the (complementary) Lovász theta number. We prove that if the Shannon capacity of a graph, the distinguishability graph of a noisy channel, is attained by some finite power, then its Mycielskian has strictly larger Shannon capacity than the graph itself. For the complementary Lovász theta function we show that its value on the Mycielskian of a graph is completely determined by its value on the original graph, a phenomenon similar to the one discovered for the fractional chromatic number by Larsen, Propp and Ullman. We also consider the possibility of generalizing our results on the Sperner capacity of directed graphs and on the generalized Mycielsky construction. Possible connections with what Zuiddam calls the asymptotic spectrum of graphs are discussed as *** Codes 05C76, 94A24, 05C15, 05C50 Copyright © 2023, The Authors. All rights reserved.

关键词： Directed graphs

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Neuromorphic Computing for Nuclear Safeguards

Neuromorphic Computing for Nuclear Safeguards

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： A. D. Nicholson J. Ghawaly C. Schuman B. Witherspoon A. Young M. Swinney K. Patel L. Crawford E. Brown Oak Ridge National Laboratory Physics Division Oak Ridge United States of America Division of Computer Science and Engineering Louisiana State University Baton Rouge United States of America Department of Electrical Engineering and Computer Science University Of Tennessee Knoxville United States of America Oak Ridge National Laboratory Electrification and Energy Infrastructures Division Oak Ridge United States of America Oak Ridge National Laboratory Computer Science and Mathematics Division Oak Ridge United States of America Oak Ridge National Laboratory Nuclear Energy and Fuel Cycle Division Oak Ridge United States of America

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

Recently, ORNL, LSU and UTK have demonstrated the first use of neuromorphic computing for radiation anomaly detection. Neuromorphic technology is a cutting-edge field which promises to significantly revolutionize the ways in which advanced machine learning algorithms are designed and implemented across broad application spaces. In particular, neuromorphic computing enables traditionally power-hungry algorithms to be implemented in a way that significantly reduces their power consumption while simultaneously reducing the form factor of the computing hardware. Spiking neural networks were trained on data derived an extensive library of Monte Carlo radiation transport simulations and dynamic backgrounds collected in the field using evolutionally optimization methods developed by collaborators at UTK. This prototype system is composed of a commercial-off-the-shelf NaI(Tl) gamma-ray radiation detector, micro-channel analyzer and custom electronics which house a neuromorphic algorithm implemented on a fieldable programable gate array. This system uses three orders of magnitude less power than current edge computing systems while exceeding detection sensitivity of traditional anomaly detection methods (such as k-sigma) and approaching that over current high-performance machine learning algorithms. This system is now being extended to nuclear safeguards applications, including UF6 enrichment verification and cylinder *** effort includes developing new spiking neural networks to perform classification tasks, and integrating with higher resolution gamma-ray detectors as well as event driven cameras.

关键词： Temperature sensors Temperature measurement Machine learning algorithms Sensitivity Neuromorphic engineering Semiconductor detectors Heuristic algorithms

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MIntRec: A New Dataset for Multimodal Intent Recognition

arXiv

引用

arXiv 2022年

作者： Zhang, Hanlei Xu, Hua Wang, Xin Zhou, Qianrui Zhao, Shaojie Teng, Jiayan State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University School of Information Science and Engineering Hebei University of Science and Technology China

Multimodal intent recognition is a significant task for understanding human language in real-world multimodal scenes. Most existing intent recognition methods have limitations in leveraging the multimodal information due to the restrictions of the benchmark datasets with only text information. This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue. It formulates coarse-grained and fine-grained intent taxonomies based on the data collected from the TV series Superstore. The dataset consists of 2,224 high-quality samples with text, video, and audio modalities and has multimodal annotations among twenty intent categories. Furthermore, we provide annotated bounding boxes of speakers in each video segment and achieve an automatic process for speaker annotation. MIntRec is helpful for researchers to mine relationships between different modalities to enhance the capability of intent recognition. We extract features from each modality and model cross-modal interactions by adapting three powerful multimodal fusion methods to build baselines. Extensive experiments show that employing the non-verbal modalities achieves substantial improvements compared with the text-only modality, demonstrating the effectiveness of using multimodal information for intent recognition. The gap between the best-performing methods and humans indicates the challenge and importance of this task for the community. Copyright © 2022, The Authors. All rights reserved.

关键词： Character recognition

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UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography

arXiv

引用

arXiv 2022年

作者： Vasconcelos, Francisca He, Bobby Singh, Nalini Teh, Yee Whye Department of Electrical Engineering and Computer Science University of California Berkeley United States Department of Statistics University of Oxford United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

Implicit neural representations (INRs) have achieved impressive results for scene reconstruction and computer graphics, where their performance has primarily been assessed on reconstruction accuracy. As INRs make their way into other domains, where model predictions inform high-stakes decision-making, uncertainty quantification of INR inference is becoming critical. To that end, we study a Bayesian reformulation of INRs, UncertaINR, in the context of computed tomography, and evaluate several Bayesian deep learning implementations in terms of accuracy and calibration. We find that they achieve well-calibrated uncertainty, while retaining accuracy competitive with other classical, INR-based, and CNN-based reconstruction techniques. Contrary to common intuition in the Bayesian deep learning literature, we find that INRs obtain the best calibration with computationally efficient Monte Carlo dropout, outperforming Hamiltonian Monte Carlo and deep ensembles. Moreover, in contrast to the best-performing prior approaches, UncertaINR does not require a large training dataset, but only a handful of validation images. Copyright © 2022, The Authors. All rights reserved.

关键词： Calibration

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