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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Masoumeh Rostam Niakan Mojtaba Hajihosseini Seyed Saeed Madani Carlos Ziebert Department of Electricity and Energy Economics NRI Tehran Iran Laboratory for Renewable Energy Systems University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Institute of Applied Materials-Applied Materials Physics Karlsruhe Institute of Technology Karlsruhe Germany

The growing demand for efficient energy storage solutions has sparked increased interest in precise battery voltage prediction. In this study the Long Short-Term Memory networks, are applied in the time series forecasting, to improve battery voltage prediction compared to conventional models. The developed forecasting system includes three main parts: Pre-processing, modeling, and evaluation. In the pre-processing, the voltage and current time series are normalized, and divided to the test and train subsets. Then the windows of consecutive samples are generated for both train and test subsets. The forecasting LSTM models are learnt from training data set, in the modeling phase. Finally, the Mean Absolute Error (MAE) is used as the evaluation criterion. This model is compared to two other neural network models. The study concludes that LSTM outperforms the other models, highlighted by a significantly lower MAE. With a comprehensive methodology and successful experimental framework, this paper demonstrates the efficacy of LSTM in predicting battery voltage, indicating its superiority over simple neural networks for time series forecasting.

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Efficient Vehicle Detection and Classification using YOLO v8 for Real-Time Applications

Efficient Vehicle Detection and Classification using YOLO v8...

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Information Technologies and Communications (GCITC), Global Conference on

作者： M. Ravichandran Kumar Laxmikant Annamalai Muthu Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electrical and Electronics Engineering University of Technology & Applied Science Nizwa Oman

In the realm of real-time applications such as autonomous driving and surveillance, efficient vehicle detection stands as a paramount concern. The revolutionary You Only Look Once (YOLO) framework, renowned for its rapid object recognition, has reshaped this landscape. This study delves into the fusion of real-time vehicle detection and YOLO v8, optimizing its architecture for swift and precise identification. Empirical validation underscores the model's prowess, achieving a remarkable accuracy of 97.9% on mAP50 and 91.3% on mAP50-95. This achievement not only attests to the model's accuracy but also reaffirms its real-time processing capabilities. The implications resonate across transportation safety and urban planning, promising transformative potential. This research underscores the power of YOLO v8 in intelligent object detection, poised to reshape and enhance various domains.

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Continuous-variable quantum computation with optical quantum entanglement and quantum teleportation in time domain 21

Continuous-variable quantum computation with optical quantum...

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Quantum Communications and Quantum Imaging XXI 2023

作者： Asavanant, Warit Kawasaki, Akito Ide, Ryuhoh Suzuki, Takumi Brunel, Hector Charoensombutamon, Baramee Sakaguchi, Atsushi Fukui, Kosuke Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Takase, Kan Yoshikawa, Jun-Ichi Endo, Mamoru Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Physics Department Ecole Normale Supérieure 24 rue Lhomond Paris France NTT Device Technology Labs. NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan

ISBN: (纸本)9781510665989

Enabling large-scale and high-speed quantum computation is a key to practical quantum computation. Continuous-variable approach in optical systems offer advantages in scalability and speed by leveraging their temporal degree of freedom and inherent large carrier frequency. In this paper, we investigate the generation and manipulation of quantum entanglement through a time-domain multiplexing approach. By employing time-domain multiplexing, we generate a two-dimensional cluster state-a universal resource for large-scale quantum computation-and perform quantum operations in the time domain with cluster state. Additionally, our ongoing research focuses on the generation and measurement of broadband optical quantum entanglement through an optical parametric oscillator, which holds potential as a foundation for high-speed quantum computing surpassing limitations of existing systems. By further engineering the quantum entanglement, we have also theoretically formulated a practical teleportation-based architectures for quantum computation in time domain. These advancements form the groundwork for the development of practical optical quantum computation. © 2023 SPIE.

关键词： Quantum computers

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Coherent Optical Driving of a Superconducting Qubit with an Electro-Optic Transducer

Coherent Optical Driving of a Superconducting Qubit with an ...

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CLEO: Applications and technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Warner, Hana K. Holzgrafe, Jeffrey Yankelevich, Beatriz Barton, David Poletto, Stefano Xin, C.J. Sinclair, Neil Zhu, Di Sete, Eyob Langley, Brandon Batson, Emma Colangelo, Marco Shams-Ansari, Amirhassan Joe, Graham Berggren, Karl K. Jiang, Liang Reagor, Matthew Loncar, Marko John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Hyperlight Corporation CambridgeMA02138 United States Rigetti Computing BerkeleyCA94710 United States Research Laboratory for Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60639 United States

We describe coherent optical control of a superconducting qubit with an electro-optic transducer as a step towards enabling optical interconnects between superconducting processor nodes. © Optica Publishing Group... 详细信息

关键词： Qubits

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Bi-Directional Transformers vs. word2vec: Discovering Vulnerabilities in Lifted Compiled Code

Bi-Directional Transformers vs. word2vec: Discovering Vulner...

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Cyber Awareness and Research Symposium (CARS)

作者： Gary A. McCully John D. Hastings Shengjie Xu Adam Fortier The Beacom College of Computer and Cyber Sciences Dakota State University Madison SD USA Department of Cyber Intelligence and Information Operations College of Applied Science & Technology University of Arizona Tucson AZ USA College of Computing Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350386417

ISBN: (纸本)9798350386424

Detecting vulnerabilities within compiled binaries is challenging due to lost high-level code structures and other factors such as architectural dependencies, compilers, and optimization options. To address these obstacles, this research explores vulnerability detection using natural language processing (NLP) embedding techniques with word2vec, BERT, and RoBERTa to learn semantics from intermediate representation (LLVM IR) code. Long short-term memory (LSTM) neural networks were trained on embeddings from encoders created using approximately 48k LLVM functions from the Juliet dataset. This study is pioneering in its comparison of word2vec models with multiple bidirectional transformers (BERT, RoBERTa) embeddings built using LLVM code to train neural networks to detect vulnerabilities in compiled binaries. Word2vec Skip-Gram models achieved 92% validation accuracy in detecting vulnerabilities, outperforming word2vec Continuous Bag of Words (CBOW), BERT, and RoBERTa. This suggests that complex contextual embeddings may not provide advantages over simpler word2vec models for this task when a limited number (e.g. 48K) of data samples are used to train the bidirectional transformer-based models. The comparative results provide novel insights into selecting optimal embeddings for learning compiler-independent semantic code representations to advance machine learning detection of vulnerabilities in compiled binaries.

关键词： Codes Accuracy Semantics Neural networks Bidirectional control Transformers Encoding Natural language processing Long short term memory Context modeling

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Boosting Generation Rate of Squeezed Single Photon States by Generalized Photon Subtraction

Boosting Generation Rate of Squeezed Single Photon States by...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Hiroko Tomoda Akihiro Machinaga Kan Takase Jun Harada Takahiro Kashiwazaki Takeshi Umeki Shigehito Miki Hirotaka Terai Masahiro Yabuno Fumihiro China Daichi Okuno Shuntaro Takeda Department of Applied Physics School of Engineering The University of Tokyo Tokyo Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing Saitama Japan NTT Device Technology Labs NTT Corporation Kanagawa Japan Advanced ICT Research Institute National Institute of Information and Communications Technology Kobe Japan Graduate School of Engineering Kobe University Kobe Japan

We theoretically and experimentally proved that the generation rate of squeezed single photon states can be increased by generalized photon subtraction method. Our work will encourage universal and fault-tolerant cont... 详细信息

ISBN: (纸本)9798350369311

关键词： Fault tolerance Fault tolerant systems Information processing Boosting Electro-optical waveguides Photonics

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Next Generation Image Watermarking via Combined DWT-SVD Technique 2

Next Generation Image Watermarking via Combined DWT-SVD Tech...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Na'amneh, Qais Almomani, Ammar Nasayreh, Ahmad Nahar, Khalid M.O. Gharaibeh, Hasan Al Mamlook, Rabia Emhamed Alauthman, Mohammad Applied Science Private University Department Of Cyber Security Amman Jordan School Of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I. T. Department Irbid Jordan Yarmouk University Department Of Computer Sciences Irbid211633 Jordan Yarmouk University Faculty Of Information Technology Computer Sciences Irbid211633 Jordan Trine University Dep. Of Business Administration IN United States University Of Zawia Dep.of Mechanical And Industrial Engineering Tripoli Libya University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan

ISBN: (纸本)9798350394962

Personal computers and the Internet are used in different areas and are easier to use. Most data is easy to transmit and duplicate in digital format, and being tampered with and stolen easily leads to issues for content owners and service providers. The Paper describes a secure (tampering-resistant) method for watermarking images for protection and authentication and digital media like audio, video, and multimedia data, and a combination of singular value decomposition (SVD) and Discrete wavelet transform (DWT) for insertion watermark by MATLAB to increase accuracy, simplicity, and flexibility Readability, secure robustness, and imperceptibility the visibility of the image. The proposed algorithm consists of three main steps: Firstly, we decomposed the original image into four sub-bands and the original watermark. Secondly, embed the watermark in the LL sub-band by replacing the singular value for the watermark. Finally, embed a watermark in the other three sub-bands by adding LL values for a watermark in each sub-band in the original image. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Leveraging K-Means Clustering for Analysis of Arabic Hate Speech Tweets

Leveraging K-Means Clustering for Analysis of Arabic Hate Sp...

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2024 Global Congress on Emerging Technologies, GCET 2024

作者： Salloum, Said Tahat, Khalaf Mansoori, Ahmed Alfaisal, Raghad Tahat, Dina University of Salford School of Science Engineering and Environment Manchester United Kingdom United Arab Emirates University Yarmouk University Media & Creative Industries Jordan United Arab Emirates University Media & Creative Industries Department United Arab Emirates Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Tanjung Malim Malaysia Al Ain University Applied Sociology Department United Arab Emirates

ISBN: (纸本)9798331542603

As hate speech is becoming common on social media platforms, it is important to detect, and curb hate speech in order to provide a better and safe environment online. Given the heavy usage of manual methods of hate speech detection, researchers started putting efforts in the direction of machine-learning-based automated methods sooner or later. Many available datasets and models on hate speech detection are largely inadequate for Arab hate speech because of the complexity of language and cultural nuances. In this paper, the researchers eased these difficulties, as they, used the K-Means to apply compilation for Arab hate speech in the L-HSAB dataset on the tweets. Methodology consisted of Prospecting and Pre-processing, Term Frequency Inverse Document frequency (TF-IDF) dimension reductions, through Principal Component Analysis (PCA), and assembly via K-Means. They helped to identify different sets of hate speech tweets and understand the common topics and topics. It has led to new understandings of how Arab hate speech flows on the Internet and now offers the potential for tailored interventions. Automated hate speech analysis via machine learning would allow policymakers to formulate tailored modification strategies focused on making the Internet safer and the community more harmonious. © 2024 IEEE.

关键词： Tweets

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A Novel Feature Extraction Technique for ECG Arrhythmia Classification Using ML

A Novel Feature Extraction Technique for ECG Arrhythmia Clas...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Mohammad Mominur Rahman Ashhadul Islam Skander Charni Halima Bensmail Thomas Hilbel Samir Brahim Belhaouari Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar College of Engineering Qatar University Doha Qatar Computational Biology group (CS&E) Qatar Computing Research Institute (QCRI) Doha Qatar Department of Electrical Engineering and Applied Sciences Westphalian University of Applied Sciences Gelsenkirchen Germany

Feature extraction is the process of transforming raw data into features that are more relevant for machine learning algorithms. The goal of feature extraction is to find a set of features that can be used to accurately predict the target variable. The specific features that are extracted will depend on the specific application. For example, features that are extracted for the purpose of diagnosing arrhythmias will be different from the features that are extracted for the purpose of assessing myocardial infarction. A generalized new algorithm for feature extraction could be helpful for all complex feature extraction data sets. In this paper, we propose a random selection process to generate the required number of new features with the help of existing specific features of the electrocardiogram (ECG) signal. We have named this novel feature extraction method the Random Feature Explorer (RFE). The proposed method was tested and evaluated using Physio Net's MIT-BIH datasets. The results indicate that the suggested method achieved an accuracy of 99.79% in arrhythmia classification. We have made the source code for our proposed method available on GitHub for open access and reproducibility. The code can be accessed at https://***/3NnrH4A

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Universal Scaling Bounds on a Quantum Heat Current

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Physical Review Letters 2023年第9期131卷 090401-090401页

作者： Shunsuke Kamimura Kyo Yoshida Yasuhiro Tokura Yuichiro Matsuzaki Faculty of Pure and Applied Sciences University of Tsukuba Tsukuba 305-8571 Japan Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan

In this Letter, we derive new bounds on a heat current flowing into a quantum L-particle system coupled with a Markovian environment. By assuming that a system Hamiltonian and a system-environment interaction Hamiltonian are extensive in L, we prove that the absolute value of the heat current scales at most as Θ(L3) in a limit of large L. Furthermore, we present an example of noninteracting particles globally coupled with a thermal bath, for which this bound is saturated in terms of scaling. However, the construction of such a system requires many-body interactions induced by the environment, which may be difficult to realize with the existing technology. To consider more feasible cases, we consider a class of the system where any nondiagonal elements of the noise operator (derived from the system-environment interaction Hamiltonian) become zero in the system energy basis, if the energy difference exceeds a certain value ΔE. Then, for ΔE=Θ(L0), we derive another scaling bound Θ(L2) on the absolute value of the heat current, and the so-called superradiance belongs to a class for which this bound is saturated. Our results are useful for evaluating the best achievable performance of quantum-enhanced thermodynamic devices, including far-reaching applications such as quantum heat engines, quantum refrigerators, and quantum batteries.

关键词： Open quantum systems & decoherence Quantum control

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