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16th International Conference on Information Security and Cryptology, Inscrypt 2020

作者： Liang, Zhichuang Shen, Shiyu Shi, Yuantao Sun, Dongni Zhang, Chongxuan Zhang, Guoyun Zhao, Yunlei Zhao, Zhixiang School of Computer Science Fudan University Shanghai China School of Mathematical Sciences Fudan University Shanghai China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Department of Applied Mathematics Reading Academy Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9783030718510

Number theoretic transform (NTT) is a basic mathematic operation, and is particularly fundamental to the practical implementations of cryptographic algorithms based on lattices with algebraic structures. In this work, we make a systematic and comprehensive study of NTT and its variants. We first review the NTT technique and the recent advances raised in the implementations of practical lattice-based cryptography. We clarify the relationship of some existing NTT variants, and prove their computational equivalence. We then make the generalizations of NTT, analyze their exact computational complexity, and derive the optimal bounds. Finally, we show the applications of our results to some prominent practical lattice-based algorithms. © 2021, Springer Nature Switzerland AG.

关键词： Cryptography

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Quantum versus semi-classical signatures of correlated triple ionization in Dalitz plots

arXiv

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arXiv 2024年

作者： Efimov, Dmitry K. Katsoulis, Georgios P. Rozpętkowski, Tymoteusz Chwalowski, Sergiusz Emmanouilidou, Agapi Prauzner-Bechcicki, Jakub S. Institute of Theoretical Physics Faculty of Fundamental Problems of Technology Wroclaw University of Science and Technology Wroclaw50-370 Poland Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Jagiellonian University in Kraków Faculty of Physics Astronomy and Applied Computer Science Marian Smoluchowski Institute of Physics Lojasiewicza 11 Krakow30-348 Poland

We investigate correlated three-electron escape in Ne when driven by an intense, infrared laser field. We do so by employing a reduced-dimensionality quantum-mechanical model and two three-dimensional semi-classical models. One semi-classical model is a recently developed one that accounts with effective coulomb potentials for the interaction between two bound electrons (ECBB) while it fully accounts for all other interactions. The other semi-classical model is the Heisenberg one, which effectively accounts for the interaction of each electron with the core via a soft-core potential. We identify and compare the signatures of correlated three-electron escape for both quantum and semi-classical models on Dalitz plots and find a better agreement between the quantum and the ECBB model. We also show that a central "spot" on the Dalitz plots is reproduced by all models. Using the ECBB model we associate this "spot" with the direct triple ionization pathway and argue this to be the case also for the quantum model. Devising a simple classical model that accounts for the direct pathway of triple ionization, we show that the width of this spot in the Dalitz plots solely depends on the time of tunnel-ionization. © 2024, CC BY-NC-ND.

关键词： Coulomb interactions

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Design and Implement Driveway Door Access Control by Using VLC and RFID technology

Design and Implement Driveway Door Access Control by Using V...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Huang Hung Yong-Yi Fanjiang Cheng-Yen Li Sheng-Wei Chiu Tzu-Chieh Hung Meng-Huan Chiang Dept. of Information Technology Graduate Institute of Applied Science and Engineering Fu Jen Catholic University Lee-Ming Institute of Technology New Taipei City Taiwan Dept. of Computer Science and Information Engineering Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City Taiwan Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City Taiwan Dept. of Vehicle Engineering Lee-Ming Institute of Technology New Taipei City Taiwan Dept. of International High School Kang Chiao International School New Taipei City Taiwan

In recent years, the application of radiofrequency identification technology in smart access control systems has become a trend. Users only need to take out the RFID electronic key and approach the sensor of the gate to open the gate through signal induction. For housewives who always have a lot of bags and bags in their hands, there is no need to free up one hand to find the key to open the door and avoid being in a hurry. However, it is relatively inconvenient for drivers. Instead, it is much more convenient to use wireless controllers or license plate recognition. This study proposes a system that combines RFID with optical communication technology and vehicle headlights to open the garage door with a light code.

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A System Approach to Detect Medical Errors in Operational Data in Hospitals

A System Approach to Detect Medical Errors in Operational Da...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Saad Aldoihi Khalid Alblalaihid Fozah Alzemaia Alia Almoajel Omar Hammami Shatha Alwablely Institute of Earth and Space Science King Abdulaziz City for Science and Technology Riyadh Saudi Arabia College of Education King Saud University Riyadh Saudi Arabia College of Applied Medical Sciences King Saud University Riyadh Saudi Arabia Computer Science and Systems Engineering Department Institut Polytechnique de Paris Palaiseau Cedex France

Medical errors represent a significant challenge in healthcare systems worldwide, leading to increased patient morbidity, mortality, and healthcare costs. Early detection and prevention of such errors in hospital operational data can significantly improve patient safety and overall healthcare quality. This paper proposes a novel, data-driven approach to model a healthcare system for detecting medical errors using advanced machine learning techniques. We leverage electronic health records (EHR) and other hospital operational data sources to develop a comprehensive framework that can automatically identify potential errors in real-time. The model aims to identify patterns and anomalies in the data to detect potential errors and provide insights for process improvement. The proposed model can help healthcare providers to proactively monitor and address medical errors, thereby reducing the risk of harm to patients.

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A Hybrid Machine Learning Model for Classifying Gene Mutations in Cancer using LSTM, BiLSTM, CNN, GRU, and GloVe

arXiv

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arXiv 2023年

作者： Aburass, Sanad Dorgham, Osama Al Shaqsi, Jamil Department of Computer Science Maharishi International University FairfieldIA United States Department of Computer Science Luther College DecorahIA United States Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Al-Salt19117 Jordan School of Information Technology Skyline University College University City of Sharjah P.O. Box 1797 Sharjah United Arab Emirates Information Systems Department Sultan Qaboos University Oman

In our study, we introduce a novel hybrid ensemble model that synergistically combines LSTM, BiLSTM, CNN, GRU, and GloVe embeddings for the classification of gene mutations in cancer. This model was rigorously tested using Kaggle's Personalized Medicine: Redefining Cancer Treatment dataset, demonstrating exceptional performance across all evaluation metrics. Notably, our approach achieved a training accuracy of 80.6%, precision of 81.6%, recall of 80.6%, and an F1 score of 83.1%, alongside a significantly reduced Mean Squared Error (MSE) of 2.596. These results surpass those of advanced transformer models and their ensembles, showcasing our model's superior capability in handling the complexities of gene mutation classification. The accuracy and efficiency of gene mutation classification are paramount in the era of precision medicine, where tailored treatment plans based on individual genetic profiles can dramatically improve patient outcomes and save lives. Our model's remarkable performance highlights its potential in enhancing the precision of cancer diagnoses and treatments, thereby contributing significantly to the advancement of personalized healthcare. © 2023, CC BY.

关键词： Classification (of information)

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ML-Based Feature Selection Technique for Imbalanced Data Streams

ML-Based Feature Selection Technique for Imbalanced Data Str...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Ayushi Pillay Monika Arya Sumit Kumar Sar Bhupesh Kumar Dewangan Tanupriya Choudhury Ketan Kotecha Sanjana Dewangan Bhilai Institute of Technology Durg Chhatisgarh India Department of Computer Science and Engineering School of Engineering OP Jindal University Raigarh India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Pune India School of Science OP Jindal University Raigarh India

In the data stream classification domain, efficient feature selection (FS) plays an ornamental role in improving the overall performance of machine learning (ML) frameworks. Also, the imbalanced data significantly affects the classifier's performance. This study proposes an efficient ML framework with a hybrid FS based on the filter and wrapper approach. Another primary goal of this rigorous study is to show the efficacy of ML using the proposed FS in the classification of imbalanced data. First, feature subsets are rapidly selected using statistical (filter) FS methods. Modified correlation-based feature subset selection with greedy stepwise search and chi-square method has been implemented as filter-based methods. Then the selected feature subset is fine-tuned using the wrapper approach recursive feature elimination (RFE) and an ensemble of classifiers. The framework is built and tested on imbalanced datasets from the healthcare domain. The proposed framework has been compared to three datasets used in disease prediction. The framework effectively achieves better diagnostic and prediction performance with the optimal number of features without computational overheads.

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A novel data driven approach for combating energy theft in urbanized smart grids using artificial intelligence

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Expert Systems with Applications 2024年 253卷

作者： Shahzadi, Nazia Javaid, Nadeem Akbar, Mariam Aldegheishem, Abdulaziz Alrajeh, Nabil Bouk, Safdar Hussain Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Urban Planning College of Architecture and Planning King Saud University Riyadh11574 Saudi Arabia Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Old Dominion University 5115 Hampton Blvd NorfolkVA23529 United States

Electricity Theft (ET) causes monetary losses for power utilities in the energy sector. It occurs when electricity is consumed without being billed. Several methods are available for automatically detecting ET. Most of these methods evaluate Electricity Consumption (EC) records. However, these methods either have a low Detection Rate (DR) or a high deployment cost with a high False Positive Rate (FPR). Moreover, it is difficult to identify fraudulent consumers based solely on EC records. In addition, owing to data imbalances, such methods prove to be inefficient for classification. To solve the aforementioned problems, we have proposed a combination of various techniques. The first one is the Fastfood Transform, which is used for dimensionality reduction, along with the Time Series Lag Embedded Network (TLENET) neural network, used for classification between honest and dishonest consumers. The second one is the Wavelet Transform used for dimensionality reduction with TLENET, and the third one is the Nyström method used for dimensionality reduction with TLENET. To tackle the risk of high variance that results in overfitting in Deep Learning (DL) models, a Localized Random Affine Shadowsampling (LoRAS) data balancing technique is used. We have employed various data balancing techniques to analyze the performance of our system model. A game theory based approach, SHapley Additive exPlanations (SHAP), is implemented for explaining the output of our deep model. We have used a real-world dataset, referred as the State Grid Corporation of China (SGCC), to perform the simulations. Our model has achieved 94% accuracy, 92% F1-score, 93% Area Under Curve-Receiver Operating Characteristics (AUC-ROC), and 87% Matthews Correlation Coefficient (MCC) with LoRAS, Wavelet Transform, and TLENET. With dimensionality reduction using Fastfood Transform, our model has achieved 93% accuracy, 92% F1-score, 92% AUC-ROC, and 85% MCC. When the Nyström method is employed for dimensionality redu

关键词： Wavelet transforms

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Deep learning method for optical fiber curvature measurements based on time series data

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Journal of the Optical Society of America B 2024年第5期41卷 1207-1216页

作者： Brian Pamukti Muhammad Fajar Faliasthiunus Pradipta Shien-Kuei Liaw Fu-Liang Yang Ya-Mei Yang Graduate Institute of Electro-Optical Engineering National Taiwan University of Science and Technologyhttps://***/00q09pe49 Taipei 10607 Taiwan Department of Electronics and Computer Engineering National Taiwan University of Science and Technologyhttps://***/00q09pe49 Taipei 10607 Taiwan Research Center for Applied Sciences Academia Sinica Taipei 115006 Taiwan The Green Energy and Environment Research Laboratories Industrial Technology Research Institute Tainan 71150 Taiwan

Curvature detection is an essential technique for monitoring landslides, which are frequent and destructive disasters. Existing methods for curvature detection using fiber-optic sensors have limitations such as complex fabrication or large data size. We propose a data processing method for high-accuracy curvature detection that employs deep learning. We experimented using different levels of curvature and compared our method with other methods. Our method achieves 99.82% accuracy for classification and root mean square error of ${0.042}\;{{\rm m}^{- 1}}$ for regression with a simpler structure and smaller data size. Our approach demonstrates its potential for landslide detection and integration with communication systems.

关键词： Brillouin optical time domain analysis Deep learning Effective refractive index Fiber Bragg gratings Fiber optic sensors Optical frequency domain reflectometry

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Enhancing Space Exploration with Real-Time Streaming Data Analysis

Enhancing Space Exploration with Real-Time Streaming Data An...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Tarunika Sharma Anitha Gatla Sorabh Lakhanpal Gulshan Dhasmana Yaragudipati Sri Lalitha Sajjad Ziara Department of Applied Sciences New Horizon College of Engineering Bangalore India Department of Information Technology Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of Information Technology GRIET Bachupally Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Space travel has inspired creativity and science. In this ever-changing and unpredictable environment, real-time streaming data analysis helps make missions safer and more efficient. This research discusses and assesses three strategies to improve space exploration using sophisticated data analysis. RNNs detect weird things in real time in the first technique. This detects system issues and environmental threats early. Second, Convolutional Neural Networks (CNNs) automate guidance and scientific research. This helps the robot decide without human input. Bayesian networks accurately forecast solar flares and geomagnetic storms in the third technique. We simulate experiments to demonstrate how these strategies may enhance human safety, mission success, and science. Real-time streaming data processing may revolutionize how we solve global riddles as space research advances.

关键词： Wireless communication Data analysis Space missions Weather forecasting Transforms Streaming media Real-time systems Geomagnetic storms Bayes methods Convolutional neural networks

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BMEAUT at SemEval-2020 Task 2: Lexical entailment with semantic graphs 14

BMEAUT at SemEval-2020 Task 2: Lexical entailment with seman...

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14th International Workshops on Semantic Evaluation, SemEval 2020

作者： Kovács, Ádám Gémes, Kinga Kornai, Andras Recski, Gábor Dept. of Automation and Applied Informatics Budapest University of Technology and Economics Hungary SZTAKI Institute of Computer Science TU Wien Austria

ISBN: (纸本)9781952148316

In this paper we present a novel rule-based, language independent method for determining lexical entailment relations using semantic representations built from Wiktionary definitions. Combined with a simple WordNet-based method our system achieves top scores on the English and Italian datasets of the Semeval-2020 task "Predicting Multilingual and Cross-lingual (graded) Lexical Entailment" (Glavaš et al., 2020). A detailed error analysis of our output uncovers future directions for improving both the semantic parsing method and the inference process on semantic graphs. reported in this document. © 2020 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings. All rights reserved.

关键词： Semantics

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