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4th IEEE World Engineering Education Conference, EDUNINE 2020

作者： Rojas, Julio Ernesto Quispe Tuesta, Julio Enrique Quispe Universidad Nacional Pedro Ruiz Gallo Computing and Electronics Departament Lambayeque Peru Universidad Nacional Tecnologica de Lima sur Systems Engineering Program Lima Peru

ISBN: (纸本)9781728166384

The development of competencies in the professional training of an engineer includes: knowledge, skills and attitudes. For the development of skills, laboratory practices with physical equipment, virtual systems and remote systems are alternatives;the practices in the place of work performance through technical visits or pre-professional practices in the factories and maintenance workshops. Dual learning includes the participation of students in theoretical sessions in the center of studies and practical sessions of the installation and maintenance of machines and equipment in factories and workshops. The semidual learning proposal considers 50% of the practical sessions in the laboratories of the study center and 50% in the facilities of the industrial company. The experience of the application in the course of electrical machines is reported, where the application of the Semi-dual learning method in five academic semesters was planned, from 2019 - 1 to 2021 - 1, to date it was applied in the semester 2019 1, with satisfactory results. © 2020 IEEE.

关键词： Professional aspects

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Integration of Artificial Intelligence in Software Defined Networking Technology through Developing Machine Learning Algorithms 3

Integration of Artificial Intelligence in Software Defined N...

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3rd International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Naibaho, Lamhot Saxena, Pulkit Raj Sharma, Kuldeep Alfurhood, Badria Sulaiman Pallavi, L. Pant, Bhasker Faculty of Letters and Languages Christian University of Indonesia English Language Education Study Program Indonesia Teer Thanker Mahaveer University College of Computing Science and Information Technology Uttar Pradesh Moradabad India Lovely Professional University Department of Computer Science and Engineering Punjab Jalandhar Phagwara India College of Computer Information Sciences Princess Nourah Bint Abdulrahman University Department of Computer Sciences Saudi Arabia B v Raju Institute of Technology Cse Telangana Narsapur India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

A future networking design called "software-defined networking"combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computer science, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN. © 2023 IEEE.

关键词： Software defined networking

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Clarifying Assumptions about Artificial Intelligence before Revolutionising Patent Law

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GRUR International 2022年第4期71卷 295-321页

作者： Kim, Daria Alber, Maximilian Kwok, Man Wai Mitrovic, Jelena Ramirez-Atencia, Cristian Perez, Jesus Alberto Rodriguez Zille, Heiner Max Planck Institute for Innovation and Competition Munich Germany Charite Institute of Pathology Berlin Germany Chinese University of Hong Kong Department of Physics (Formerly) Hong Kong BMBF Program for Women in AI Faculty of Computer Science and Mathematics Chair of Data Science University of Passau Germany Department of Computer Systems Universidad Politecnica de Madrid Spain School of Computing Science College of Science and Engineering University of Glasgow United Kingdom Faculty of Computer Science Otto von Guericke University Magdeburg Germany

This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of 'artificial ingenuity'. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where 'intelligent' systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited. © 2022 Published by OUP and CH Beck on behalf of GRUR e.V.

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GraCo: Granularity-Controllable Interactive Segmentation

GraCo: Granularity-Controllable Interactive Segmentation

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yian Zhao Kehan Li Zesen Cheng Pengchong Qiao Xiawu Zheng Rongrong Ji Chang Liu Li Yuan Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Peng Cheng Laboratory Shenzhen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Department of Automation and BNRist Tsinghua University Beijing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Interactive Segmentation (IS) segments specific objects or parts in the image according to user input. Current IS pipelines fall into two categories: single-granularity out-put and multi-granularity output. The latter aims to allevi-ate the spatial ambiguity present in the former. However, the multi-granularity output pipeline suffers from limited interaction flexibility and produces redundant results. In this work, we introduce Granularity-Controllable Interactive Segmentation (GraCo), a novel approach that allows precise control of prediction granularity by introducing ad-ditional parameters to input. This enhances the customization of the interactive system and eliminates redundancy while resolving ambiguity. Nevertheless, the exorbitant cost of annotating multi-granularity masks and the lack of avail-able datasets with granularity annotations make it difficult for models to acquire the necessary guidance to control out-put granularity. To address this problem, we design an any-granularity mask generator that exploits the semantic property of the pre-trained IS model to automatically gen-erate abundant mask-granularity pairs without requiring additional manual annotation. Based on these pairs, we propose a granularity-controllable learning strategy that efficiently imparts the granularity controllability to the IS model. Extensive experiments on intricate scenarios at ob-ject and part levels demonstrate that our GraCo has signifi-cant advantages over previous methods. This highlights the potential of GraCo to be a flexible annotation tool, capable of adapting to diverse segmentation scenarios. The project page: https://***/GraCo.

关键词： Image segmentation Adaptation models Annotations Interactive systems Pipelines Semantics Redundancy

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Lessons learned from the hospital to home community care program in Singapore and the supporting AI multiple readmissions prediction model

Health Care Science

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Health Care Science 2023年第3期2卷 153-163页

作者： John Abisheganaden Kheng Hock Lee Lian Leng Low Eugene Shum Han Leong Goh Christine Gia Lee Ang Andy Wee An Ta Steven M.Miller Department of Respiratory and Critical Care Medicine Tan Tock Seng HospitalNational Healthcare GroupSingaporeSingapore National Working Group for the Hospital to Home Program SingaporeSingapore Department of Family Medicine and Continuing Care Singapore General HospitalSingHealth GroupSingaporeSingapore SingHealth Community Hospitals SingHealth GroupSingaporeSingapore Population Health and Integrated Care Office SingHealth GroupSingaporeSingapore Office of Community Development Changi General HospitalSingHealth GroupSingaporeSingapore Data Analytics and AI Department Integrated Health Information SystemsSingaporeSingapore School of Computing and Information Systems Singapore Management UniversitySingaporeSingapore

In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since *** this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 *** the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.

关键词： hospital to home community care hospital to home lessons learned transitional care integrated care multiple readmissions AI prediction model machine learning in healthcare healthcare technology

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Generalizing Reduction-Based Algebraic Multigrid

arXiv

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arXiv 2022年

作者： Zaman, Tareq Nytko, Nicolas Taghibakhshi, Ali MacLachlan, Scott Olson, Luke West, Matthew Interdisciplinary Program in Scientific Computing Memorial University of Newfoundland NL Canada Department of Computer Science University of Illinois at Urbana-Champaign IL United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign IL United States

Algebraic Multigrid (AMG) methods are often robust and effective solvers for solving the large and sparse linear systems that arise from discretized PDEs and other problems, relying on heuristic graph algorithms to achieve their performance. Reduction-based AMG (AMGr) algorithms attempt to formalize these heuristics by providing two-level convergence bounds that depend concretely on properties of the partitioning of the given matrix into its fine- and coarse-grid degrees of freedom. MacLachlan and Saad (SISC 2007) proved that the AMGr method yields provably robust two-level convergence for symmetric and positive-definite matrices that are diagonally dominant, with a convergence factor bounded as a function of a coarsening parameter. However, when applying AMGr algorithms to matrices that are not diagonally dominant, not only do the convergence factor bounds not hold, but measured performance is notably degraded. Here, we present modifications to the classical AMGr algorithm that improve its performance on matrices that are not diagonally dominant, making use of strength of connection, sparse approximate inverse (SPAI) techniques, and interpolation truncation and rescaling, to improve robustness while maintaining control of the algorithmic costs. We present numerical results demonstrating the robustness of this approach for both classical isotropic diffusion problems and for non-diagonally dominant systems coming from anisotropic diffusion. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Effect of Periodic Poling on Self-phase Modulation in Thin-film Lithium Niobate Waveguides

Effect of Periodic Poling on Self-phase Modulation in Thin-f...

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Frontiers in Optics, FiO 2022

作者： Gül, Gamze Abdelsalam, Kamal Fathpour, Sasan Lee, Kim F. Kanter, Gregory S. Kumar, Prem Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States CREOL The College of Optics and Photonics University of Central Florida OrlandoFL32816 United States Department of Electrical and Computer Engineering University of Central Florida OrlandoFL32816 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We study self-phase modulation of sub-picosecond telecom wavelength pulses in partially periodically poled thin-film lithium niobate waveguides. We experimentally and computationally investigate the effect of phase mi... 详细信息

ISBN: (纸本)9781557528209

关键词： Thin films

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Aedes Aegypti Egg Classification Model Using Support Vector Machine

Aedes Aegypti Egg Classification Model Using Support Vector ...

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International Conference on Computer Research and Development (ICCRD)

作者： Cherry R. Gumiran Arnel C. Fajardo Ruji P. Medina Minh S. Dao Joel M. Gumiran Graduate Program Technological Institue of the Philippines Quezon City Philippines Philippines Isabela State University Cauayan College of Computing Studies Information and Communications Technology Philippines Graduate Prgram Technological Institue of the Philippines Quezon City Philipines Philippines Big Data Integration Research Center National Institute of Information and Communications Technology Tokyo Japan

Aedes aegypti is the dengue fever vector, affecting 3.9 billion people worldwide. Found primarily in subtropical areas such as the Philippines. Numerous approaches and procedures had used to identify this disease carrier throughout its life cycle. However, recognizing it early, primarily during the egg stage, helps monitor the disease carrier's existence. On the other hand, classifying the egg remains an issue due to its resemblance to another mosquito species. As a result, extracted traits of the Aedes egg had added to the classification model. Furthermore, the Support Vector Machine algorithm's effectiveness as a classifier improves classification accuracy, mainly when the feature extracted has been added. Thus, this paper used the Support Vector Machine in designing a model to classify Aedes aegypti eggs. This model differentiates it from non-Aedes with a classification accuracy of 98.23%, which is greater than the classification accuracy compared to previous studies using the pixel and region-based method. In addition, the newly developed model had increased accuracy compared to the model in which the features had omitted from the classification process by 3.26 percent. In addition, the model was validated on a non-Aedes dataset in which it exhibited similar characteristics. As a result, the model reveals improved categorization and is determined to be non-Aedes. Thus, this model demonstrates a considerable boost in classification accuracy.

关键词： Shape Computational modeling Support vector machine classification Color Feature extraction Classification algorithms Viruses (medical)

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Evolutionary Trends of Intelligent IoT Networking for Commercial and Industrial Use Cases

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中国通信（英文版） 2023年第8期20卷前插1-前插4页

作者： Shuai Han Rong Bo Nadra Guizani Sai Xu Ruoyu Su Liang Xiao Cheng Li Department of Electronics and Communication Engineering Harbin Institute of Technology Communications Research Centre Canada Engineering program and the computing Research Association School of Cybersecurity Northwestern Polytechnical UniversityXi'anChina Nanjing University of Posts and Telecommunications Department of Informatics and Communication Engineering Xiamen Univer- sityFujianChina Department of Electrical and Computer Engineering of Memorial University St.John'sCanada

The intelligent Internet of Things(IIoT)is one of the key enablers for the digital transformation of traditional industries towards Industry *** services have diversified requirements due to the nature of their commercial and industrial *** various devices in IIoT environments have their unique properties in terms of resource limita-tions,network lifetimes,and application Quali-ty-of-Service(QoS)requirements which affect the performance under such applications ***,IoT devices typically have constrained power,storage,computing,high latency,massive multiple access and communications resources which limit the performance of IIoT for commercial and industrial Use *** the other hand,devices used in IIoT were not initially designed with security in mind and they had been considered *** response to the above challenges,domestic and foreign scholars have gradually carried out research on network architecture,collaborative scheduling strategy,energy supply technology,resource man-agement,and ***,the research on IIoT is still in its infancy,and has not yet formed comprehen-sive and systematic theoretical methods and technical standards.

关键词： Special issues and sections Internet of Things Intelligent systems Commercialization Requirements engineering Industrial engineering

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A novel strong duality-based reformulation for trilevel infrastructure models in energy systems development

arXiv

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arXiv 2023年

作者： Herrala, Olli Gabriel, Steven A. Oliveira, Fabricio Ekholm, Tommi Aalto University Department of Mathematics and Systems Analysis Espoo Finland University of Maryland Department of Mechanical Engineering/Applied Mathematics Statistics & Scientific Computing Program College ParkMD United States Norwegian University of Science and Technology Department of Industrial Economics and Technology Management Trondheim Norway Finnish Meteorological Institute Helsinki Finland

We explore the class of trilevel equilibrium problems with a focus on energy-environmental applications and present a novel single-level reformulation for such problems, based on strong duality. To the best of our knowledge, only one alternative single-level reformulation for trilevel problems exists. This reformulation uses a representation of the bottom-level solution set, whereas we propose a reformulation based on strong duality. Our novel reformulation is compared to this existing formulation, discussing both model sizes and computational performance. In particular, we apply this trilevel framework to a power market model, exploring the possibilities of an international policymaker in reducing emissions of the system. Using the proposed methods, we are able to obtain globally optimal solutions for a five-node case study representing the Nordic countries and assess the impact of a carbon tax on the electricity production portfolio. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy resources

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