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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Flower, Christopher J. Mehrabad, Mahmoud Jalali Xu, Lida Moille, Gregory Srinivasan, Kartik Mittal, Sunil Hafezi, Mohammad Joint Quantum Institute National Institute of Standards and Technology University of Maryland College ParkMD United States Electrical and Computer Engineering Northeastern University BostonMA United States

We demonstrate the generation of a novel type of frequency comb, the topological frequency comb, in a lattice of hundreds of ring resonators. Our results bring together the fields of topological photonics and frequenc... 详细信息

ISBN: (纸本)9781957171395

关键词： Photonics

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Geometric discord of tripartite quantum systems

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Chinese Physics B 2023年第11期32卷 184-190页

作者：熊春河齐文韬缪茂可吴明晖 Interdisciplinary Center for Quantum Information School of PhysicsZhejiang UniversityHangzhou 310027China School of Computer and Computing Science Hangzhou City UniversityHangzhou 310015China School of Mathematical Sciences Zhejiang UniversityHangzhou 310027China School of information and electrical engineering Hangzhou City UniversityHangzhou 310015China

We study the quantification of geometric discord for tripartite quantum ***,we obtain the analytic formula of geometric discord for tripartite pure *** is already known that the geometric discord of pure states reduces to the geometric entanglement in bipartite systems,the results presented here show that this property is no longer true in tripartite ***,we provide an operational meaning for tripartite geometric discord by linking it to quantum state discrimination,that is,we prove that the geometric discord of tripartite states is equal to the minimum error probability to discriminate a set of quantum states with von Neumann ***,we calculate the geometric discord of three-qubit Bell diagonal states and then investigate the dynamic behavior of tripartite geometric discord under local *** is interesting that the frozen phenomenon exists for geometric discord in this scenario.

关键词： geometric discord tripartite quantum systems quantum state discriminations frozen discord

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PancakeFS: A Write Efficiently and Read Optimized Filesystem 4

PancakeFS: A Write Efficiently and Read Optimized Filesystem

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4th International Conference on Consumer Electronics and computer engineering, ICCECE 2024

作者： Wang, Yongkang Liu, Yang Chen, Zhiguang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Institute for Quantum Information College of Computer National University of Defense Technology State Key Laboratory of High-Performance Computing Changsha China

ISBN: (纸本)9798350384062

With the development of emerging technologies such as cloud computing and large AI models (such as LLM), many applications have placed higher demands on the intensive read and write processing of massive data. Addressing the limitations of traditional file systems, we propose PancakeFS, an LSM-Tree-based file system. Leveraging the write-friendly characteristics of LSM-Tree, PancakeFS exhibits efficient write performance. We improve read performance through the design of an inode allocation algorithm based on subtree partitioning and optimization of the disk layout for hot data reconstruction. Our experimental results demonstrate that PancakeFS outperforms Ext4 and TableFS in terms of both read and write performance. © 2024 IEEE.

关键词： Data handling

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Implementation of a continuous-time quantum walk on a sparse graph

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Physical Review A 2024年第5期110卷 052215-052215页

作者： Zhaoyang Chen Guanzhong Li Lvzhou Li Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 China

Continuous-time quantum walks (CTQWs) play a crucial role in quantum computing, especially for designing quantum algorithms. However, how to efficiently implement CTQWs is a challenging issue. In this paper, we study the implementation of CTQWs on sparse graphs, i.e., constructing efficient quantum circuits for implementing the unitary operator e−iHt, where H=γA (γ is a constant and A corresponds to the adjacency matrix of a graph). Our result is, for a d-sparse graph with N vertices and evolution time t, we can approximate e−iHt by a quantum circuit with gate complexity (d3∥H∥tNlog2(N))1+o(1), compared to the general Pauli decomposition, which scales like (∥H∥tN4log2(N))1+o(1). For sparse graphs, for instance, d=O(1), we obtain a noticeable improvement. Interestingly, our technique is related to graph decomposition. More specifically, we decompose the graph into a union of star graphs, and correspondingly, the Hamiltonian H can be represented as the sum of some Hamiltonians Hj, where each e−iHjt is a CTQW on a star graph which can be implemented efficiently.

关键词： quantum algorithms & computation quantum computation quantum walks

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Advancements in quantum image filtering:Exponential speedup on a subset of images

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Science China(Physics,Mechanics & Astronomy) 2024年第9期67卷 13-13页

作者： Lvzhou Li Institute of Quantum Computing and Software School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhou 510006China

The pursuit of quantum supremacy in computational tasks has driven the exploration of quantum algorithms capable of surpassing classical *** the realm of image processing,a notable advancement towards this objective is highlighted in the study by Cui et al.[1].Their work proposes a quantum image filtering(QImF)algorithm that exhibits exponential acceleration for a specific subset of images,offering a glimpse into the potential of quantum computing in image processing.

关键词： quantum subset filtering

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SVM Machine Learning Model for Detection of Etherlock Vulnerability in Solidity Smart Contracts 6

SVM Machine Learning Model for Detection of Etherlock Vulner...

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6th International Conference on Blockchain computing and Applications, BCCA 2024

作者： Almobaideen, Wesam Alnuman, Rashed Sajid, Tayyab Hasan, Qusai Department of Electrical Engineering and Computing Sciences Rochester Institute of Technology Dubai Dubai United Arab Emirates Department of Computer Science The University of Jordan Amman Jordan

ISBN: (纸本)9798350351538

Blockchain is a disruptive technology that changed how financial operations and ownership exist over the internet. With the implementation of smart contracts in blockchains, facilitation of transactions without intermediaries is possible and fully automated. Smart contracts allow relatively safe transactions and transfer of ownership, but as with any piece of software, it proved to have vulnerabilities and challenges. Unfortunately, considering that smart contracts hold and facilitate valuable asset and funds transfer, the damage caused by exploitation of these contracts is devastating with billions of dollars’ worth of cryptocurrency and other assets lost or stolen up until now. One attack on Ethereum smart contracts is the Etherlock attack, which exploits bad logic in the code to stall the transaction indefinitely, effectively sealing user funds. In this paper, we propose a high-accuracy Support Vector Machine model for detection of Etherlock vulnerability in Solidity smart contracts. The model is developed into a standalone tool and achieves 98.44% accuracy accompanied with 87% recall using a generated dataset of Smart Contracts from the Ethereum blockchain. Our promising results still show the necessity of more sophisticated accurate detection models for protection of crypto assets on the blockchain. © 2024 IEEE.

关键词： Smart contract

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Hybrid Xception-ResNext BiLSTM Model with Attention and Visual Synchronization for Robust Deepfake Detection

Hybrid Xception-ResNext BiLSTM Model with Attention and Visu...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suresh, M. Raghavan, J.Vijaya Roy, Abhisek Das, Swet Snigdha Srm Institute of Science and Technology Department of Computing Technologies Chennai India Manakula Vinayagar Institute of Technology Department of Electrical and Electronics Engineering Pondicherry India Computer Science Engineering Srm Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510022

Deepfake technology has become a significant problem since it allows for the creationof compelling manipulated videos. This research presents a novel hybrid deepfake detection system that combines the Xception and ResNext networks with a Bidirectional Long Short-Term Memory (BiLSTM) model and Convolutional Block Attention Module (CBAM). Apart from focusing on the temporal and spatial anomalies of deepfakes, our model also includes visual synchronization analysis and physiological signal detection. Extensive experiments are conducted across several datasets, exhibiting cutting-edge performance across various manipulation scenarios. The proposed method achieves good generalization, as they are in real-world circumstances. © 2024 IEEE.

关键词： Gallium nitride

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Object Detection Using Deep Learning for Blind People with Voice Feedback

Object Detection Using Deep Learning for Blind People with V...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Pongiannan, R.K. Franklin, J. Richard Pravin, A. Maria, Marissa Mishra, Sarvottam Kumar Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur India Computer Science Engineering Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798331510022

BlindSpace is an innovative application aimed at improving the lives of visually challenged individuals. Leveraging cutting-edge image captioning and object detection technologies, the app allows users to capture images using their smartphone's camera, providing detailed scene descriptions and audibly identifying objects within those images. This not only enhances users' understanding of their environment but also fosters independence and confidence. With voice commands and text-to-speech technology, the application offers a user-friendly experience and can be customized to individual preferences. Operating efficiently offline, BlindSpace stands as a significant leap in accessibility technology, bridging the gap between the visual and non-visual worlds for visually challenged individuals and promoting inclusivity and autonomy. © 2024 IEEE.

关键词： Smartphones

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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