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Quantum 2.0, QUANTUM 2024 in Quantum 2.0 Conference and Exhibition

作者： Kawasaki, Akito Ide, Ryuhoh Brunel, Hector Suzuki, Takumi Nehra, Rajveer Nakashima, Katsuki Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi China, Fumihiro Yabuno, Masahiro Miki, Shigehito Terai, Hirotaka Yamashima, Taichi Sakaguchi, Atsushi Takase, Kan Endo, Mamoru Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Physics Ecole Normale Supérieure 24 rue Lhomond Paris75005 France Department of Electrical and Computer Engineering University of Massachusetts-Amherst AmherstMA01003 United States Department of Physics University of Massachusetts-Amherst AmherstMA01003 United States NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Advanced ICR Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe651-2492 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan

We generate and observe optical non-Gaussian states defined in wavepackets of sub-nanosecond time width—O(103) faster than previous research—using waveguide optical parametric amplifier made of PPLN crystal, enablin... 详细信息

ISBN: (纸本)9781557525185

关键词： Parametric amplifiers

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Density, Kinematic Viscosity and Cetane Number Prediction of Biofuel Feedstocks from Fatty Acid Compositions 1

Density, Kinematic Viscosity and Cetane Number Prediction of...

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Beeravalli, Vijayalaxmi Ashwath, Nanjappa Rasul, Mohammad Khan, Masud Capareda, Sergio CQUniversity School of Health Medical and Applied Sciences Rockhampton Australia CQUniversity School of Engineering and Technology Rockhampton Australia Auckland University of Technology School of Engineering Computing and Mathematical Sciences Auckland New Zealand Texas A&M University Department of Agricultural Engineering College StationTX United States

ISBN: (纸本)9781665450461

Stringent emission laws and the depletion of fossil fuels have made biodiesel one of the promising renewable and environmentally friendly fuel alternatives of the future. To get dependable engine performance and safe emission characteristics, it is imperative to know the Cetane number, kinematics viscosity and density of any fuel. While it is experimentally expensive and requires a continued concerted effort to know the actual properties of a fuel feedstock, recent Machine Learning methods offer wide possibilities to predict with close proximity certain characteristics of biomaterials within well-defined confidence levels. The present paper uses six different machine learning algorithms to predict three basic properties of fifty different biodiesel feedstocks from their fatty acid compositions. The issue of sparsity and anomaly in the data is dealt with through tested methods, following which properties for each feedstock are predicted within good accuracy levels. It is observed that the percentage error is very closely related to the coefficient of variation of a property, which will allow scientists to set variability bounds of predicted values. © 2022 IEEE.

关键词： Feedstocks

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A Comparative Study of Recent 2D Human Pose Estimation Methods

A Comparative Study of Recent 2D Human Pose Estimation Metho...

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computing and Informatics (ICCI), International Conference on

作者： Nada E. Elshami Ahmad Salah Heba Mohsen Department of Computer Science Faculty of Computers and Information Technology Future University in Egypt New Cairo Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman College of Computers and Informatics Zagazig University Zagazig Egypt

Human Pose Estimation is a computer vision technique utilized in various fields such as healthcare, security, and sports to detect the pose of single or multi-person utilizing various machine learning and deep learning methodologies. These methodologies can be applied in a variety of ways, including top-down and bottom-up approaches. Furthermore, this technique can be used with images or videos, as well as in 2D or 3D domains. This paper delves into recent advancements in deep learning methods used for estimating the pose, covering commonly employed datasets, evaluation metrics, and the challenges encountered in this field. It aims to assist new researchers in getting familiar with this technique based on work published between 2019 and 2023, with a spotlight on 2D and omitting 3D.

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Local meshless methods for elliptic PDEs with multipoint boundary conditions: investigating efficiency and accuracy of various RBFs

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The European Physical Journal Special Topics 2024年 1-18页

作者： Ahmad, Masood Khan, Muhammad Nawaz Ahmad, Imtiaz Department of Basic Sciences University of Engineering and Technology Peshawar Peshawar Pakistan Mathematics in Applied Sciences and Engineering Research Group Scientific Research Center Al-Ayen University Nasiriyah Iraq Institute of Informatics and Computing in Energy (IICE) Universiti Tenaga Nasional Kajang Malaysia

The present study addresses the numerical solution of two-dimensional steady-state heat conduction problems with nonlocal multi-point boundary conditions across three distinct domains: a unit rectangle with a quarter-circle cutout of radius 0.5, an irregular domain, and a Cassini curve. Dirichlet boundary conditions are imposed on specific segments, while nonlocal boundary conditions are applied to the remaining portions. The Kansa method is employed to solve the steady-state heat conduction equation, utilizing three types of radial basis functions (RBFs) to explore the influence of the shape parameter on accuracy and matrix conditioning. These include the inverse multiquadric RBF, a modified inverse multiquadric RBF proposed here for the first time, and a hybrid RBF [1]. As a meshless method, the Kansa approach eliminates the need for mesh generation or node connectivity within local subdomains. To evaluate accuracy and performance, the $$L_{\infty }$$ error norm is employed. The results demonstrate the effectiveness of the proposed techniques in solving the 2D steady-state heat conduction problem. A comparative analysis is conducted to assess the accuracy and computational efficiency of the methods.

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Simulation vignette generation from answer set specifications 17

Simulation vignette generation from answer set specification...

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17th Annual International Conference on Information Systems for Crisis Response and Management, ISCRAM 2020

作者： Rouwendal van Schijndel, Dashley K. Hannay, Jo E. Stolpe, Audun University of Oslo Department of Technology Systems Norway Norwegian Computing Center Department of Applied Research in Information Technology Norway University of Oslo Department of Technology Systems Norwegian Computing Center Department of Applied Research in Information Technology Norway

ISBN: (纸本)9781949373271

We investigate an approach that allows exercise managers to design simulations with an explicit focus on building skills, rather than having to focus on all the objects and interactions that a simulation must have. Exercise managers may design exercises at various levels of abstraction and always independently of how those sessions are implemented in simulations, while simulation components that implement the design are assembled and to some extent, automatically, behind the scenes. We outline (1) how Answer Set Programming can assist exercise managers in exercise planning and (2) how automated stage and content generation may be used to invoke appropriate simulation components to realize the design. For deliberate and recurrent training of decision-making skills, stages and content must vary to avoid familiarity (testing effects). We conclude by distilling a main research hypothesis that stipulates how (1) and (2) represent two modes of automated reasoning (so-called deductive versus abductive) and how that distinction clarifies the planning task. © 2020 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： Mixed reality

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GeoDisasterAINet: An Explainable Deep Ensemble Framework for Real-Time Urban and Rural Disaster Classification and Resilience

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IEEE Access 2025年 13卷 97944-97974页

作者： Narasimha Raju, Akella S Sreekanth, Seelam Gatla, Ranjit Kumar Rajababu, M. Kumar, Devineni Gireesh Flah, Aymen El-Bayeh, Claude Ziad El-Nagdy, Khaled A Alzaed, Ali Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankalattur Tamilnadu Chennai603203 India Institute of Aeronautical Engineering Talangana Hyderabad500043 India Department of Electrical and Electronics Engineering B V Raju Institute of Technology Telangana Narsapur India ENET Centre CEET VSB-Technical University of Ostrava Ostrava708 00 Czech Republic Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering and Technology University of Doha for Science and Technology Doha Qatar Department of Civil Engineering College of Engineering Taif University Taif21944 Saudi Arabia Aditya University Andhra Pradesh Surampalem533437 India

These Sustainable Development Goal 11 (SDG 11), whose target is to make urban and rural settlements resilient and disaster-prepared, integrates with it the significant objective of having disaster-resilient communities and cities. GeoDisasterAINet is an AI deep learning and machine learning model for distinguishing between rural and urban-area occurrences of floods, cyclones, earthquakes, and wildfires effectively. In this article, we introduce GeoDisasterAINet. There are four successive phases in model performance improvement, with increasingly enhancing classification accuracy, namely an integrated CNN (baseline model), an integrated CNN with XGBoost (feature improvement model), an integrated CNN with XGBoost and Multiclass SVM (refinement model for decision boundary), and LIME segmentation for interpretability and model performance ***7, ResNet-50, InceptionV3, and DenseNet-201 are architectures in use in GeoDisasterAINet, and these architectures make three settings for an ensemble: ERI-2025 (EfficientNetB7, ResNet-50, InceptionV3), DRI-2025 (DenseNet-201, ResNet-50, InceptionV3), and DE-2025 (DenseNet-201, EfficientNetB7). In balancing out the dataset, we used the Synthetic Minority Over-sampling Technique (SMOTE). With 99.25% accuracy in training, 96.37% accuracy in testing, 98% in precision, 97% in recall, and 97% in F1-score, ERI-2025 + XGBoost + Multiclass SVM, our best model, effectively classified items. LIME-based interpretability uncovered disaster-specific features such as storm behaviour in cyclones, infrastructure loss in earthquakes, wildfire propagation in wildfires, and water build-up in floods, and in confirming that the model was reliable, it helped in an effective location of disasters. GeoDisasterAINet is a strong AI-powered system that addresses directly SDG 11 through its contribution towards safer and disaster-resilient communities and cities. It does this through AI-powered deep learning and machine learning technology

关键词： Deep learning

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Optimizing Phishing URL Detection: A Comparative Analysis of Feature Counts and Training Times

Optimizing Phishing URL Detection: A Comparative Analysis of...

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Intelligent computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Moayyad Alajlouni Yahya Tashtoush Saud Alhajaj Al. Dossari Omar Darwish Department of Computer Science Jordan University of Science and Technology Irbid Jordan Department of Electrical Engineering Prince Sattam bin Abdulaziz University Wadi Addwasir Saudi Arabia Information Security and Applied Computing Department Eastern Michigan University Ypsilanti MI USA

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

Phishing attacks are the biggest cybersecurity threats in the digital world. Attackers exploit users by impersonating real, authentic web sites to obtain sensitive information such as passwords and bank statements. One common technique used in these attacks is malicious URLs. These malicious URLs mimic legitimate URLs, misleading users into interacting with malicious websites. This practice, URL phishing, presents a big threat to internet security, emphasizing the need for advanced detection methods. This paper presents a method for detecting malicious URLs using statistical features extracted from n-grams. These n-grams are extracted from the hexadecimal representation of URLs. We used Explainable AI (XAI) to explore the extracted features and evaluate their importance and role in phishing detection. A key advantage of our method is its ability to reduce the required features and the training time by using fewer features after applying XAI techniques. Our technique only uses statistical features extracted from n-grams and the n-gram itself, without requiring any high-level features. We applied XAI techniques, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). Through the explanation provided by XAI methods, we were able to determine the most important features in our feature set, enabling a reduction in feature count. Using fewer features (4, 7, 10, 13, 15), we got good accuracy compared to the 41 features and 87 features used in the original experiment and reduced the models' training times and complexity. This research aimed to enhance phishing URL detection by optimizing feature selection and analyzing the impact on training times. Our findings show the importance of using minimal statistical features to identify malicious URLs effectively and efficiently.

关键词： Training Uniform resource locators Accuracy Phishing Neural networks Internet security Passwords Feature extraction Computational efficiency Complexity theory

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Estimating the transfer factor of natural radionuclides from soil to food chain pathways at a farmland in the southwestern Nigeria

Research Square

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Research Square 2024年

作者： Oladapo, O.O. Lawal, M.K. Ajiboye, O. Aremu, A.A. Oni, E.A. Ayanlola, P.S. Department of Science Laboratory Technology Ladoke Akintola University Oyo State Ogbomoso Nigeria Department of Mathematical and Computing Sciences KolaDaisi University Oyo State Ibadan Nigeria Department of Pure and Applied Physics Ladoke Akintola University Oyo State Ogbomoso Nigeria

The transfer factors of naturally existing radionuclides (NRs) from soil to food chain pathways on a farmland in southwestern Nigeria were estimated by measuring the activity concentrations of NRs in 20 soil samples taken from the study site using sodium iodide NaI(Ti) spectrometer detector. According to the results, the mean activity concentrations of 238U, 232Th, and 40K were 12.53±4.66 Bq/kg, 20.76±7.51 Bq/kg, and 310.79±85.91Bq/kg, respectively. These results fell below the recommended upper limit of NRs in soil. The calculated outdoor absorbed dose rate (DR), radium equivalent activity (Re) and gamma representative index (Iγ) were 32.02nGyh-1, 66.15 Bq/kg and 0.5 Bq/kg respectively. The DR, Re and Iγ were within the recommended range. The activity concentrations of 238U and 232Th in the vegetation and in the meat and milk produced by grazing animals on the farm were estimated. 238U and 232Th were estimated to be present in vegetation at concentrations of 0.2572 and 1.9049 (Bq/kg), respectively;in cow milk produced from cow grazing at the research site, the concentrations were 2.408 and 1.714 (Bq/L) and;in cow meat, they were 0.802 and 0.175 (Bq/kg). These values are considerably safe and may not pose significant health concerns when food processed at the study site is consumed. The calculated transfer factors from soil into vegetation for 238U and 232Th were 0.02 and 0.0903 respectively while those from soil into cow milk were 1.902e-4 and 8.256e-6, respectively;those from soil into cow meat were 6.4e-5 and 8.43e-7 respectively. © 2024, CC BY.

关键词： Radiation hazards

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Steady-state dynamics and nonlocal correlations in thermoelectric Cooper pair splitters

arXiv

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arXiv 2024年

作者： Arora, Arnav Midha, Siddhant Zyuzin, Alexander Hakonen, Pertti Muralidharan, Bhaskaran Department of Physics Indian Institute of Technology Roorkee India Department of Electrical Engineering Indian Institute of Technology Bombay Powai Mumbai400076 India Low Temperature Laboratory Department of Applied Physics Aalto University PO Box 15100 EspooFI-00076 Finland QTF Centre of Excellence Department of Applied Physics Aalto University PO Box 15100 EspooFI-00076 Finland Center of Excellence in Quantum Information Computing Science and Technology Indian Institute of Technology Bombay Powai Mumbai400076 India

Recent experiments on Cooper pair splitters using superconductor-quantum dot hybrids have embarked on creating entanglement in the solid-state, by engineering the sub-gap processes in the superconducting region. Using the thermoelectric Cooper pair splitter setup [Nat. Comm., 12, 21, (2021)] as a prototype, we present a comprehensive analysis of the fundamental components of the observed transport signal, aiming to critically clarify the operating regimes and confirm the nonlocal and nonclassical nature of correlations arising from crossed Andreev processes. By making a nexus with quantum discord, we identify operating points of nonlocal quantum correlations in the CPS device—information that cannot be extracted from the transport signal alone. A notable consequence of our analysis is the finding that contact-induced level broadening of the quantum dot’s discrete energy spectrum, along with its hybridization with the superconducting segment, can lead to shifted resonances in the crossed Andreev process as well as a parity reversal in the thermoelectric current. Our work thereby provides detailed insights into the gate voltage control of the quantum correlations in superconducting-hybrid Cooper pair splitters, revealing new avenues for harnessing quantum correlations in solid-state systems. © 2024, CC BY.

关键词： Semiconductor quantum dots

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Comparative Analysis of Differential Privacy Implementations on Synthetic Data

Comparative Analysis of Differential Privacy Implementations...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Huwida E. Said Qusay H. Mahmoud Mandeep Goyal Faiza Hashim Department of Computing and Applied Technolgy College of Technological Innovation Zayed University Dubai UAE Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Computer Science and Software Engineering College of Information Technology UAE University AI-Ain UAE

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments.

关键词： Differential privacy Costs Accuracy Conferences Noise Machine learning Software Synthetic data

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