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Chip-scale spontaneous quasi-phase matched second harmonic generation in a micro-racetrack resonator

Science China(Physics,Mechanics & Astronomy) 2023年第8期66卷 66-74页

作者： Tingge Yuan Jiangwei Wu Yi'an Liu Xiongshuo Yan Haowei Jiang Hao Li Zhaokang Liang Qiang Lin Yuping Chen Xianfeng Chen State Key Laboratory of Advanced Optical Communication Systems and Networks School of Physics and Astronomy.Shanghai Jiao Tong UniversityShanghai 200240China Collaborative Innovation Center of Light Manipulations and Applications Shandong Normal UniversityJinan 250358China Department of Electrical and Computer Engineering University of RochesterNew York 14627USA

In this paper,we demonstrate efficient spontaneous quasi-phase matched(SQPM)second harmonic generation(SHG)in a microracetrack resonator on X-cut thin film lithium *** approach does not involve poling,but exploits the anisotropy of the crystals to allow the phase-matching condition to be fulfilled spontaneously as the TE-polarized light circulates in a specifically designed racetrack *** experiment,normalized on-chip conversion efficiencies of 1.01×10-4/W and 0.43×10-4/W are achieved by 37th-order and 111th-order SQPM,*** configurable SQPM will benefit the application of nonlinear frequency conversion and quantum source generation in chip-scale integrated photonics compatible with standard CMOS fabrication processes.

关键词： lithium niobate on insulator quasi-phase matching second harmonic generation

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Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 601-615页

作者： Ding, Jiepin Xia, Jun Yang, Yaning Zhou, Junlong Chen, Mingsong Li, Keqin East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai200062 China Ningxia Normal University School of Physics and Electronic Information Engineering Guyuan756500 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China State University of New York Department of Computer Science New PaltzNY12561 United States

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521s to 475s. © 2016 IEEE.

关键词： Job shop scheduling

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User Behaviour based Insider Threat Detection using a Hybrid Learning Approach

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4573-4593页

作者： Singh, Malvika Mehtre, B.M. Sangeetha, S. Govindaraju, Venu Centre of Excellence in Cyber Security Institute for Development and Research in Banking Technology Established by Reserve bank of India Telangana Hyderabad500057 India Department of Computer Applications National Institute of Technology Trichy Tamil Nadu Tiruchirappalli620015 India Center for Unified Biometrics and Sensors Department of Computer Science and Engineering State University of New York at Buffalo New York BuffaloNY14260 United States

Insider threats constitute a major cause of security breaches in organizations. They are the employees/users of an organization, causing harm by performing any malicious activity. Most of the existing methods to detect insider threats are based on machine and deep learning and have the following limitations: they use predefined rules or stored signatures and fail to detect new or unknown threats;they require explicit feature engineering, which results in more false positives;they require a large amount of training data, and are computationally expensive. In this paper, an improved user behavior-based insider threat detection method is proposed using a hybrid learning approach that overcomes the above limitations. It uses bi-directional long-short-term memory for feature extraction, a feed-forward artificial neural network (using distance measurements) for feature selection, and a support vector machine for classification-normal user or malicious user. The genetic algorithm’s fast global search strategy is used for the support vector machine’s initial kernel selection. Finally, alerts are generated for each user based on their combined anomaly score. The proposed method is tested using the CMU-CERT r4.2 insider threat dataset, and its performance is evaluated using the following parameters: accuracy, precision, recall, f-measure, and area under curve-receiver operating characteristic curve. The results show a significant improvement over the existing methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Support vector machines

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A Systematic Literature Review of Decentralized applications in Web3: Identifying Challenges and Opportunities for Blockchain Developers

A Systematic Literature Review of Decentralized Applications...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Hossain Faruk, Md Jobair Raya, Pratusha Siam, Md Kamrul Cheng, Jerry Q. Shahriar, Hossain Cuzzocrea, Alfredo Bringas, Pablo Garcia New York Institute of Technology Department of Computer Science United States Kennesaw State University Department of Software Engineering United States University of West Florida Center for Cybersecurity United States University of Deusto D4K Group Bilbao Spain University of Calabria IDEA Lab Rende Italy University of Paris City Department of Computer Science Paris France

ISBN: (纸本)9798350362480

The Internet has opened the floor to stakeholders by redefining the way of organizing, communicating, and collaborating that was initiated by the Web's development. The advancement of the World Wide Web is an outright phenomenon and significant and we witnessed the evolution of the Web. As decentralized technologies continue to gain traction, Web3, or the decentralized internet, has emerged as a promising approach to enable a more secure, transparent, and privacy-preserved digital landscape. In this paper, we thoroughly conduct a systematic study to explore the challenges and opportunities encountered by blockchain developers in the context of decentralized applications (dApps) in Web3. We analyze a set of peer-reviewed research articles, whitepapers, and technical reports and present an in-depth understanding of the current state of Web3 development and its implications. Our finding indicates the opportunities that Web3 can facilitate, such as expanded use cases, enhanced security and privacy, decentralized infrastructure, and the potential for enabling inclusive development resources for blockchain developers. Additionally, we highlight various challenges that blockchain developers deal with including scalability, security, privacy, interoperability, and the need for standardized tools and frameworks along with various challenges in the software development lifecycle (SDLC). While there are significant challenges to overcome, the potential benefits of Web3 are substantial and could lead to a more inclusive, secure, and transparent digital ecosystem. Furthermore, we emphasize the importance of continued research, collaboration, and innovation among stakeholders to address the identified challenges and capitalize on Web3's opportunities. © 2024 IEEE.

关键词： Decentralized finance

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Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

Optimizing CDN Architectures: Multi-Metric Algorithmic Break...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Md Nurul Absur Sourya Saha Sifat Nawrin Nova Kazi Fahim Ahmad Nasif Md Rahat Ul Nasib Department of Computer Science City University of New York New York USA Department of Computer Science Chalmers University of Technology Gothenburg Sweden College of Computing and Software Engineering Kennesaw State University Georgia USA Samsung Austin Research Center Austin Texas USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume growth and scalability across different geographic locations are required. This paper investigates data-driven evaluations of CDN algorithms in dynamic server selection for latency reduction, bandwidth throttling for efficient resource management, real-time Round Trip Time analysis for adaptive routing, and programmatic network delay simulation to emulate various conditions. Key performance metrics, such as round-trip time (RTT) and CPU usage, are carefully analyzed to evaluate scalability and algorithmic efficiency through two experimental setups: a constrained edge-like local system and a scalable FABRIC testbed. The statistical validation of RTT trends, alongside CPU utilization, is presented in the results. The optimization process reveals significant trade-offs between scalability and resource consumption, providing actionable insights for effectively deploying and enhancing CDN algorithms in edge and distributed computing environments.

关键词： Content distribution networks Heuristic algorithms Scalability computer architecture Dynamic scheduling Fabrics Performance analysis Servers Resource management Optimization

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Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

Optimizing CDN Architectures: Multi-Metric Algorithmic Break...

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2025 International Conference on Computing, Networking and Communications, ICNC 2025

作者： Absur, Md Nurul Saha, Sourya Nova, Sifat Nawrin Fahim Ahmad Nasif, Kazi Ul Nasib, Md Rahat City University of New York Department of Computer Science New York United States Chalmers University of Technology Department of Computer Science Gothenburg Sweden Kennesaw State University College of Computing and Software Engineering Georgia United States Samsung Austin Research Center AustinTX United States

ISBN: (纸本)9798331520960

关键词： Cluster computing

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Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

arXiv

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arXiv 2024年

作者： Absur, Md. Nurul Saha, Sourya Nova, Sifat Nawrin Nasif, Kazi Fahim Ahmad Nasib, Md. Rahat Ul Department of Computer Science City University of New York New York United States Department of Computer Science Chalmers University of Technology Gothenburg Sweden College of Computing and Software Engineering Kennesaw State University Georgia United States Samsung Austin Research Center AustinTX United States

关键词： Scalability

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科学研究的第五范式——以智能驱动的材料设计为例

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engineering 2023年第5期24卷 126-137,I0003,I0004页

作者： Can Leng Zhuo Tang Yi-Ge Zhou Zean Tian Wei-Qing Huang Jie Liu Keqin Li Kenli Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China National Supercomputing Center in Changsha Changsha 410082China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Institute of Chemical Biology and Nanomedicine State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringHunan UniversityChangsha 410082China Department of Applied Physics School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Computer Science State University of New YorkNew PaltzNY 12561USA

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地... 详细信息

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地阐明了第五范式的本质,旨在促进第五范式在其他领域的培养。第五范式平台主要包括模型自动构建(原始数据提取)、指纹自动构建(神经网络特征选择)以及跨学科知识串联的重复迭代(“火山图”)。与分解一起进行的是对迭代中实现的体系结构的性能评估。通过讨论,第五范式的智能驱动平台可以极大地简化和改进研究中极其繁琐和具有挑战性的工作,并通过补偿机器学习中缺少样本和替代一些由于计算资源不足而导致的数值计算来实现数值计算与机器学习之间的相互反馈,从而加速探索过程。在数据驱动的学科中,跨学科专家的协同作用和对动态数据需求的急剧增长仍然是一个挑战。我们相信,对第五范式平台的一瞥可以为其在其他领域的应用铺平道路。

关键词：机器学习自动构建天河一号数据驱动神经网络动态数据知识整合科学研究

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FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Gochoo, Munkhjargal Otgonbold, Munkh-Erdene Ganbold, Erkhembayar Hsieh, Jun-Wei Chang, Ming-Ching Chen, Ping-Yang Dorj, Byambaa Al Jassmi, Hamad Batnasan, Ganzorig Alnajjar, Fady Abduljabbar, Mohammed Lin, Fang-Pang United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates United Arab Emirates University Emirates Center for Mobility Research United Arab Emirates National Yang Ming Chiao Tung University College of AI and Green Energy Taiwan University at Albany - State University of New York NY United States National Yang Ming Chiao Tung University Department of Computer Science Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

ISBN: (纸本)9798350302493

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of state-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications. © 2023 IEEE.

关键词： Object detection

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The interdependent networked community resilience modeling environment(IN-CORE)

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Resilient Cities and Structures 2023年第2期2卷 57-66页

作者： John W.van de Lind Jamie Kruse Daniel T.Cox Paolo Gardoni Jong Sung Lee Jamie Padgett Therese P.McAllister Andre Barbosa Harvey Cutler Shannon Van Zandt Nathanael Rosenheim Christopher M.Navarro Elaina Sutley Sara Hamideh Harold H.Short Endowed Chair Professor and Co-Director Center of Excellence for Risk-Based Community Resilience PlanningDepartment of Civil and Environmental EngineeringColorado State UniversityFort CollinsCO80523USA Co-Director Center of Excellence for Risk-Based Community Resilience Planning and THCAS Distinguished ProfessorDepartment of EconomicsEast Carolina UniversityGreenvilleNC27858USA CH2M Hill Professor in Civil Engineering School of Civil and Construction EngineeringOregon State UniversityCorvallisOR97331USA Alfredo H.Ang Family Professor and Excellence Faculty Scholar Department of Civil and Environmental Engineeringand DirectorMid-America Earthquake(MAE)CenterUniversity of Illinois at Urbana-ChampaignChampaignIL61820USA Deputy Associate Director National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-ChampaignChampaignIL61820USA Professor and Chair Department of Civil and Environmental EngineeringRice UniversityHoustonTX77005USA Acting Chief Materials and Structural Systems DivisionNational Institute of Standards and TechnologyGaithersburgMD20899USA Professor Department of EconomicsColorado State UniversityFort CollinsCO80523USA Professor School of ArchitectureLandscape Architecture&Urban PlanningTexas A&M UniversityCollege StationTX77843USA Research Associate Professor Landscape Architecture&Urban PlanningTexas A&M UniversityCollege StationTX77843USA Lead Research Software Engineer National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-ChampaignChampaignIL61820USA Associate Professor and Associate Dean for Diversity EquityInclusionand BelongingDepartment of Civil and Environmental EngineeringUniversity of KansasLawrenceKansasUSA Associate Professor State University of New York-Stony BrookSchool of Marine and Atmospheric SciencesStony BrookNew YorkUSA

In 2015,the U.S National Institute of Standards and technology(NIST)funded the center of Excellence for Risk-Based Community Resilience Planning(CoE),a fourteen university-based consortium of almost 100 col-laborators,including faculty,students,post-doctoral scholars,and NIST *** paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE-the Interdisciplinary Networked Community Resilience Modeling Environment(IN-CORE).IN-CORE enables communities,consul-tants,and researchers to set up complex interdependent models of an entire community consisting of people,businesses,social institutions,buildings,transportation networks,water networks,and electric power networks and to predict their performance and recovery to hazard scenario events,including uncertainty propagation through the chained *** modeling environment includes a detailed building inventory,hazard scenario models,building and infrastructure damage(fragility)and recovery functions,social science data-driven house-hold and business models,and computable general equilibrium(CGE)models of local *** important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the *** illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population,economics,physical services,and social *** overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas(CSA)that encompass an array of community resilience metrics(CRM)and support community resilience informed *** testbed within IN-CORE has been developed by a team of engineers,social scientists,urban planners,and *** models,begin with a community description,i.e.,people,businesses,buildings,infras-tructure,and progresses to the damage and loss of functions caused by a hazard scenario

关键词： IN-CORE Community Resilience Natural hazards Disasters Risk Uncertainty propagation Decision-support Mitigation Adaptation Tornado Tsunami Earthquake Hurricane

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