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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Hesham Mamdouh Mohamed Tarek Abdelrahman Radwan Adel Saeed Ahmed Abdeen Monier Ashraf Khaled M. Fouad Ibrahim Abdelbaky School of Engineering and Applied Science (EAS) Nile University Giza Egypt CS program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The increasing sophistication of cyber attacks necessitates effective intrusion detection systems. We propose a novel intrusion detection method integrating deep learning with big data management using Apache Spark. Leveraging the comprehensive CSE-CIC-IDS2018 dataset, we apply extensive data preprocessing, including handling missing and unreliable values, duplicates, and redundant columns. In addition, implementation of a Random Forest based feature importance approach is derived to prioritize the most impactful Features. Furthermore, stratified k-fold cross-validation is used for a model selection process on a class-imbalanced dataset. Our weighted Random Forest classifier achieves a remarkable weighted average F1-score of 0.999 and a test inference time of 0.673 seconds using only the top 34 features, outperforming previous studies without sampling techniques. The proposed architecture offers a scalable and accurate solution for intrusion detection in cloud architectures, demonstrating the effectiveness of combining deep learning and big data technologies for cybersecurity.

关键词： Deep learning Cloud computing Data preprocessing Network intrusion detection Cluster computing Forestry Big Data Random forests Cyberattack Testing

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DFDFTr + RAC-DAC: Dense Fusion with Deep Fusion Transformer and Realistic Actor-Critic combined with Decoupled Actor-Critic for end-to-end continuous multi-task robotic arm control

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Expert Systems with Applications 2025年 276卷

作者： Dang, Thi Phuc Nguyen, Truong Chinh Dau, Sy Hieu Department of Computer Science Faculty of Information Technology Industrial University of Ho Chi Minh City 12 Nguyen Van Bao Ho Chi Minh7000 Viet Nam Department of Applied Physics Faculty of Applied Science University of Technology - Viet Nam National University HCMC Ho Chi Minh City To Hien Thanh Ho Chi Minh7000 Viet Nam

In this paper, we propose novel methods to address the challenges of intelligent robotic arm operations in complex environments. Our approach includes a deep learning method for 6D object pose estimation and a deep reinforcement learning method for controlling complex tasks. For 6D object pose estimation, we introduce the Dense Fusion combined with Deep Fusion Transformer model (DFDFTr). This method integrates Dense Fusion and Deep Fusion Transformer, applies re-parameterization techniques such as Reparameterized Convolution and Batch Normalization Fusion, and incorporates specialized neural networks for each stage of the model. Experimental results on the LineMOD and Occlusion LineMOD datasets demonstrate that our method outperforms one-stage approaches like DenseFusion and MPF6D in accuracy and achieves results comparable to advanced two-stage methods such as PVN3D and DTr. Leveraging re-parameterization, our method maintains competitive inference speed with significantly fewer parameters than PVN3D and MPF6D, PoseCNN+ICP without sacrificing accuracy. Additionally, it efficiently processes multiple objects, handling 8 objects in 0.1 s compared to 0.07 s for a single object. For continuous task control, we propose a deep reinforcement learning approach using Realistic Actor-Critic combined with Decoupled Actor-Critic (RAC-DAC), integrated with techniques such as Relay Hindsight Experience Replay (RHER), Multi-step Hindsight Experience Replay (MHER), Curiosity-Driven Prioritization (CDP), and Projected Conflicting Gradients (PCGrad). Experimental results show that RAC-DAC-RHER achieves significantly higher success rates than DDPG-RHER and TD3-RHER, particularly in challenging tasks such as object grasping. It also ensures efficient learning and high accuracy. Furthermore, by refining hyperparameters and integrating MHER, CDP, PCGrad, MLP, and the Clipping Mechanism, we enhanced learning performance, mitigated instabilities, and enabled higher learning rates in spars

关键词： Robotic arms

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Long-range photodetection of organic exciton-polaritons

Long-range photodetection of organic exciton-polaritons

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CLEO: science and Innovations, S and I 2022

作者： Liu, Bin Huang, Xinjing Forrest, Stephen R. Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Physics University of Michigan Ann ArborMI48109 United States Department of Material Science and Engineering University of Michigan Ann ArborMI48109 United States

In light-matter strong coupling regime, we observe long-range photodetection response at room temperature mediated by organic exciton-polaritons, which results from strong interactions between organic excitons and low... 详细信息

ISBN: (纸本)9781557528209

关键词： Excitons

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Controllable and switchable chiral near-fields in symmetric graphene metasurfaces

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Chinese physics B 2021年第12期30卷 563-570页

作者： Li Hu Hongxia Dai Fayin Cheng Yuxia Tang Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology Chongqing Technology and Business UniversityChongqing 400067China Department of Applied Physics School of Computer Science and Information EngineeringChongqing Technology and Business UniversityChongqing 400067China Soft Matter and Interdisciplinary Research Center College of PhysicsChongqing UniversityChongqing 400044China

A strong chiral near-field plays significant roles in the detection,separation and sensing of chiral *** this paper,a simple and symmetric metasurface is proposed to generate strong chiral near-fields with both circularly polarized light and linearly polarized light illuminations in the mid-infrared *** to the near-field interaction between plasmonic resonant modes of two nanosheets excited by circularly polarized light,there is a strong single-handed chiral near-field in the gap between the two graphene nanosheets and the maximum enhancement of the optical chirality could reach two orders of *** expected,the intensity and the response wavelength of the chiral near-fields could be controlled by the Fermi level and geometrical parameters of the graphene nanosheets,as well as the permittivity of the ***,based on the interaction between the incident field and scattered field,the one-handed chiral nearfield in the gap also could be generated by the linearly polarized light *** the two cases,the handedness of the chiral near-field could be switched by the polarized direction of the incident *** results have potential opportunities for applications in molecular detection and sensing.

关键词： plasmonic resonance chiral near-field graphene metasurface

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Non-Classical Quantum State Generation using Superconducting-Nanostrip Photon-Number Resolving Detector with Optical Sampling

Non-Classical Quantum State Generation using Superconducting...

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2024 Frontiers in Optics, FiO 2024

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Miki, Shigehito Yabuno, Masahiro Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Superconducting-nanostrip photon detectors with optical sampling method now function as true photon-number resolving detectors in real-time without multiplexing. We applied this technique for quantum state generation ... 详细信息

关键词： Quantum computers

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Learner Action Patterns in the Problem-Solving Process Related to program Code Composition Based on Tracking System Activities 7

Learner Action Patterns in the Problem-Solving Process Relat...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： Data mining

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Comparative Analysis of Tree Classifiers to Mitigate Fraud in Electronic Loan Application

Comparative Analysis of Tree Classifiers to Mitigate Fraud i...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Foster Yeboah Jones Yeboah George Yeboah Kofi Bampoh Owusu Nyarko-Boateng College of Engineering and Applied Science University Of Cincinnati Cincinnati USA Maths Physics and Computer Science Department University Of Cincinnati Cincinnati USA Information Technology Department EProcess International S.A Accra Ghana School of IT IPMC Accra Ghana School of Information Tevhnology University of Cincinnati Cincinnati USA

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

Loan disbursement is crucial and remains the core to every bank. With the advancement of technology and the proliferation of mobile devices financial institutions are working to bring the banking hall to the doorsteps of their clients. There are systems that allow applicants to apply for loans online. This inadvertently has enormous benefits and risks, but applicants may not be honest providing such data. To minimize risks and maximize benefits, a system for predicting fraud is necessary. This research, through experiments and comparative analysis, confirmed that data mining can effectively assess applicants’ creditworthiness. The findings revealed that the Random Forest classifier produced the most robust model, offering the highest classification accuracy among the selected classifiers, with an accuracy rate of 0.7773.

关键词： Accuracy Banking Mobile handsets Fraud Blockchains Internet of Things Data mining Artificial intelligence Random forests

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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon Coupling in Hybrid Organic-Inorganic Perovskites 16

Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Kim, Dasom Hou, Jin Lee, Geon Agrawal, Ayush Kim, Sunghwan Zhang, Hao Bao, Di Baydin, Andrey Wu, Wenjing Tay, Fuyang Huang, Shengxi Hagenmüller, Daivd Chia, Elbert E. M. Kim, Dai-Sik Seo, Minah Mohite, Aditya D. Kono, Junichiro Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Sensor System Research Center Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Chemical and Biomolecular Engineering Rice University HoustonTX77005 United States Ulsan44919 Korea Republic of Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Smalley-Curl Institute Rice University HoustonTX77005 United States Université de Strasbourg Cnrs Strasbourg67000 France KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Department of Physics and Astronomy Rice University HoustonTX77005 United States

Methods for harnessing vibrational states are desired for phonon-based technologies. We realized ultrastrong coupling of two phonon modes in perovskite materials induced by ultrastrong coupling with a common photonic ... 详细信息

ISBN: (纸本)9798350372076

关键词： Organic-inorganic materials

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Entirety of Quantum Uncertainty and Its Experimental Verification

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Chinese physics Letters 2021年第7期38卷 11-16页

作者： Jie Xie Li Zhou Aonan Zhang Huichao Xu Man-Hong Yung Ping Xu Nengkun Yu Lijian Zhang National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences and School of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Centre for Quantum Software and Information School of SoftwareFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNSWAustralia

As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.

关键词： observable union operators

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