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8th EAI International Conference on Management of Manufacturing Systems, MMS 2023

作者： Knapčíková, Lucia Behúnová, Annamária Tauberová, Rebeka Kaščak, Jakub Husár, Jozef The Technical University of Košice Faculty of Manufacturing Technologies with a seat in Prešov Department of Industrial Engineering and Informatics Prešov Slovakia Technical University of Kosice Faculty of Mining Ecology Process Control and Geotechnologies Institute of Earth Resources Kosice Slovakia The Technical University of Košice Faculty of Manufacturing Technologies with a seat in Prešov Department of Computer Aided Manufacturing Technology Prešov Slovakia

ISBN: (纸本)9783031531606

Considering the need for mass customization in Industry 4.0, it is necessary to develop non-traditional production methods. Therefore, additive manufacturing can become a key technology for the production of customized products thanks to its ability to create irregular objects or shapes. In addition to enabling new design possibilities, the additive manufacturing method holds promise for increasing social, economic, and environmental sustainability. It also has the potential to reduce energy consumption and lower polymer material consumption. Additive manufacturing contrasts with conventional processes, as it consists in applying materials layer by layer. Conventional manufacturing operations produce polymer waste that is difficult to reintroduce into manufacturing processes. The aim of the contribution is the possibility of using additive manufacturing in the design and processing of new effective use of the product with regard to the environmental aspect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： 3D printing

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Resilient Routing Table Computation Based on Connectivity Preserving Graph Sequences

Resilient Routing Table Computation Based on Connectivity Pr...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： János Tapolcai Péter Babarczi Pin-Han Ho Lajos Rónyai Department of Telecommunications and Media Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Deptartment of Electrical and Computer Engineering University of Waterloo Canada Eötvös Loránd Research Network and Department of Algebra Institute for Computer Science and Control Institute of Mathematics Budapest University of Technology and Economics Budapest Hungary

Fast reroute (FRR) mechanisms that can instantly handle network failures in the data plane are gaining attention in packet-switched networks. In FRR no notification messages are required as the nodes adjacent to the failure are prepared with a routing table such that the packets are re-routed only based on local information. However, designing the routing algorithm for FRR is challenging because the number of possible sets of failed network links and nodes can be extremely high, while the algorithm should keep track of which nodes are aware of the failure. In this paper, we propose a generic algorithmic framework that combines the benefits of Integer Linear Programming (ILP) and an effective approach from graph theory related to constructive graph characterization of k-connected graphs, i.e., edge splitting-off. We illustrate these benefits through arborescence design for FRR and show that (i) due to the ILP we have great flexibility in defining the routing problem, while (ii) the problem can still be solved very fast. We demonstrate through simulations that our framework outperforms state-of-the-art FRR mechanisms and provides better resilience with shorter paths in the arborescences.

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A Cloud-based Distributed Computing Approach for Extracting Molecular Descriptors 6

A Cloud-based Distributed Computing Approach for Extracting ...

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6th International Conference on Algorithms, Computing and Systems, ICACS 2022

作者： Didachos, Christos Kintos, Dionisis Panagiotis Fousteris, Manolis Gerogiannis, Vassilis C. Son, Le Hoang Kanavos, Andreas Department of Computer Engineering & Informatics University of Patras Patras Greece Department of Pharmacy Laboratory of Medicinal Chemistry University of Patras Patras Greece Department of Digital Systems University of Thessaly Larissa Greece Vnu Information Technology Institute Vietnam National University Hanoi Viet Nam Department of Informatics Ionian University Corfu Greece

ISBN: (纸本)9781450397407

Feature extraction is an initial and essential part for the development of accurate predictive machine learning classifiers. In the research field of drug discovery and development, the usage of molecular descriptors, which can be defined as mathematical representations of molecules' chemical properties, is a challenging task not only for machine learning studies but even for "classical wet lab"approaches. However, a high diversity of these descriptors is required in order to exploit all the available knowledge and, consequently, to maximize the potentially predictive power of approaches that could be applied for the discovery of new bioactive compounds against one or more molecular targets. Furthermore, the representation and normalization of these information is considered a rather time-consuming process. Herein, we present an approach that employs the power of cloud and distributing computing for the extraction, processing and representation of big datasets, leading to the generation of molecular descriptors in a reasonable time frame. © 2022 ACM.

关键词： Big data

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Feasibility of direct quantum communications between the UK and Ireland via 224 km of underwater fibre

Feasibility of direct quantum communications between the UK ...

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British and Irish Conference on Optics and Photonics, BICOP 2023

作者： Amies-Kin, Ben Schatz, Karolina P. Duan, Haofan Biswas, Ayan Bailey, Jack Felvinti, Adrian Winward, Jaimes Dixon, Mike Minder, Mariella Kumar, Rupesh Albosh, Sophie Lucamarini, Marco School of Physics Engineering and Technology York Centre for Quantum Technologies Institute for Safe Autonomy University of York YorkYO10 5FT United Kingdom EuNetworks Fiber UK Limited 5 Churchill Place LondonE14 5HU United Kingdom Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3036 Cyprus

Quantum key distribution is required for unconditionally secure communications against an eavesdropper with a powerful quantum computer. We present the first QKD feasibility study over long-haul submarine fibre, betwe... 详细信息

关键词： Quantum cryptography

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USEFUSE: UTILE STRIDE FOR ENHANCED PERFORMANCE IN FUSED LAYER ARCHITECTURE OF DEEP NEURAL NETWORKS

arXiv

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arXiv 2024年

作者： Ibrahim, Muhammad Sohail Usman, Muhammad Lee, Jeong-A Department of Mechanical Systems Engineering Kumoh National Institute of Technology Gumi-Si Korea Republic of Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Department of Computer Engineering Chosun University Gwangju Korea Republic of

Convolutional Neural Networks (CNNs) are crucial in various applications, but their deployment on resource-constrained edge devices poses challenges. This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic to minimize response time and enhance overall performance. The study proposes a methodology for fusing multiple convolution layers to reduce off-chip memory communication and increase overall performance. An effective mechanism detects and skips inefficient convolutions after ReLU layers, minimizing power consumption without compromising accuracy. Furthermore, efficient tile movement guarantees uniform access to the fusion pyramid. An analysis demonstrates the utile stride strategy improves operational intensity. Two designs cater to varied demands: one focuses on minimal response time for mission-critical applications, and another focuses on resource-constrained devices with comparable latency. This approach notably reduced redundant computations, improving the efficiency of CNN deployment on edge devices. © 2024, CC BY.

关键词： Convolutional neural networks

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Smart vegetable cutter for Smart Home

Smart vegetable cutter for Smart Home

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Saurabh Singhal Deepak Mangal Rakesh Kumar Bhisham Sharma Imed Ben Dhaou Department of Computer Engineering & Applications GLA University Mathura India Department of Computer Science and IT Jain University Bangalore India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Modern society strives for simpler, more efficient, dependable, speedier, and more automated lifestyle. Existing vegetable cutters in the market have drawbacks such as high investment costs, additional manpower, and time consumption. Moreover, none of them provide the feature of cleaning, peeling, and cutting in a single machine. The veggies offered in the market have pesticides and chemicals, and many people, in their haste, do not wash them properly, endangering their health. To reduce human labor while also providing good health care, a solution to this problem is required. This paper offers a novel method for developing a vegetable cutter and chopper using an Android application and IoT devices. In addition, smart vegetable cutters clean, peel, and cut veggies all in one piece of equipment. This strategy will assist human society in reducing the amount of additional manpower needed and would save them time.

关键词： Technological innovation Costs Smart cities Smart homes Medical services Choppers (circuits) Cleaning

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Wide-range Angle-sensitive Plasmonic Color Printing with a Lossy Resonator

Wide-range Angle-sensitive Plasmonic Color Printing with a L...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chowdhury, Sarah N. Simon, Jeffrey Nowak, Michal P. Prokopeva, Ludmila J. Pagadala, Karthik Fruhling, Colton Nyga, Piotr Bravo, Esteban Garcia Kildishev, Alexander V. Shalaev, Vladimir M. School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University 1205 W State St West LafayetteIN47906 United States Institute of Optoelectronics Military University of Technology 2 Kaliskiego St Warsaw00-908 Poland Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń87-100 Poland Department of Computer Graphics Technology Purdue University 401 N. Grant St Knoy Hall West LafayetteIN47907 United States

We demonstrate a wide gamut of color generation by large-scale, lithography-free, and environment-friendly plasmonic structures with a resolution of 100 urn for macroscopic color printing by utilizing femtosecond lase... 详细信息

ISBN: (纸本)9781957171258

关键词： Color

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Quantum Meets the Minimum Circuit Size Problem 13

Quantum Meets the Minimum Circuit Size Problem

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13th Innovations in Theoretical computer Science Conference, ITCS 2022

作者： Chia, Nai-Hui Chou, Chi-Ning Zhang, Jiayu Zhang, Ruizhe Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Engineering and Applied Sciences Harvard University BostonMA United States Department of Computer Science Boston University MA United States California Institute and Technology PasadenaCA United States Department of Computer Science The University of Texas AustinTX United States

ISBN: (纸本)9783959772174

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory - its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reductions and self-reductions. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction. © Nai-Hui Chia, Chi-Ning Chou, Jiayu Zhang, and Ruizhe Zhang;licensed under Creative Commons License CC-BY 4.0

关键词： Boolean functions

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Wide-range Angle-sensitive Plasmonic Color Printing with a Lossy Resonator

Wide-range Angle-sensitive Plasmonic Color Printing with a L...

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CLEO: Applications and technology, CLEO:A and T 2023 - Part of Conference on Lasers and Electro-Optics 2023

作者： Chowdhury, Sarah N. Simon, Jeffrey Nowak, Michal P. Prokopeva, Ludmila J. Pagadala, Karthik Fruhling, Colton Nyga, Piotr Bravo, Esteban Garcia Kildishev, Alexander V. Shalaev, Vladimir M. Boltasseva, Alexandra School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University 1205 W State St West LafayetteIN47906 United States Institute of Optoelectronics Military University of Technology 2 Kaliskiego St Warsaw00-908 Poland Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń87-100 Poland Department of Computer Graphics Technology Purdue University 401 N. Grant St Knoy Hall West LafayetteIN47907 United States

ISBN: (纸本)9781957171258

We demonstrate a wide gamut of color generation by large-scale, lithography-free, and environment-friendly plasmonic structures with a resolution of 100 µm for macroscopic color printing by utilizing femtosecond laser photomodification of multi-functional optical elements. CLEO 2023 © Optica Publishing Group 2023, © 2023 The Author(s)

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Application of machine learning models for risk estimation and risk prediction of classical swine fever in Assam, India

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VirusDisease 2023年第4期34卷 514-525页

作者： Suresh, Kuralayanapalya Puttahonnappa Barman, Nagendra Nath Bari, Tarushree Jagadish, Dikshitha Sushma, Bylaiah Darshan, H.V. Patil, Sharanagouda S. Bora, Mousumi Deka, Abhijit Spatial Epidemiology Laboratory Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics Karnataka Yelahanka Bengaluru India Department of Veterinary Microbiology College of Veterinary Science Assam Agricultural University Guwahati India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Matthikere Karnataka Bengaluru India Virology Laboratory Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics Karnataka Yelahanka Bengaluru India Department of Veterinary Pathology College of Veterinary Science Assam Agricultural University Guwahati India

The present study is aimed to develop an early warning system of Classical swine fever (CSF) disease by applying machine learning models and to study the climate-disease relationship with respect to the spatial occurrence and outbreaks of the disease in the north-eastern state of Assam, India. The disease incidence data from the year 2005 to 2021 was used. The linear discriminant analysis (LDA) revealed that significant environmental and remote sensing risk factors like air temperature, enhanced vegetation index, land surface temperature, potential evaporation rate and wind speed were significantly contributing to CSF incidences in Assam. Furthermore, the climate-based disease modelling was applied to relevant ecological and environmental risk factors determined using LDA and risk maps were generated. The western and eastern regions of the state were predicted to be at high risk of CSF with presence of significant hotspots. For the districts that are significantly clustered, the Basic reproduction number (R₀) was calculated after the predicted results were superimposed onto the risk maps. The R₀ value ranged from 1.04 to 2.07, implying that the eastern and western regions of Assam are more susceptible to CSF. Machine learning models were implemented using R statistical software version 3.1.3. The random forest, classification tree analysis and gradient boosting machine were found to be the best-fitted models for the study group. The models’ performance was measured using the Receiving Operating Characteristic (ROC) curve, Cohen’s Kappa, True Skill Statistics, Area Under ROC Curve, ACCURACY, ERROR RATE, F1 SCORE, and Logistic Loss. As a part of the suggested study, these models will help us to understand the disease transmission dynamics, risk factors and spatio-temporal pattern of spread and evaluate the efficacy of control measures to battle the economic losses caused by CSF outbreaks. © 2023, The Author(s), under exclusive licence to Indian V

关键词： Assam Classical swine fever Incidence Machine learning Risk modelling Swine

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