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10th international Conference on ICT for Smart Society, ICISS 2019

作者： Zulkarnain, Novan Kosala, Raymond Ranti, Benny Supangkat, Suhono Harso Bina Nusantara University Computer Science Department BINUS Graduate Program Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Computer Science Department Depok16424 Indonesia Institute of Technology Bandung School of Electrical and Informatics Engineering Bandung40132 Indonesia

ISBN: (纸本)9781728148809

There have been many studies conducted related to Smart City, IT Governance and Big Data. In this study aims to find out how the relationship between the three and how to form a framework to explain it. The methodology used is qualitative by looking for some literature on smart city framework, IT Governance framework, and a Big Data framework. From these results, an overall picture of the relationship between the three is concluded, where Big Data has a role in IT Governance. and also the relationship of IT Governance to the realization of Smart City. And the final results of this study produce a framework to explain the relationship between Smart City, IT Governance and Big Data. © 2019 IEEE.

关键词： Big data

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Hierarchical Clustering Based on Dendrogram in Sustainable Transportation Systems

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 15724-15739页

作者： Sangaiah, Arun Kumar Javadpour, Amir Ja'fari, Forough Zhang, Weizhe Khaniabadi, Shadi Mahmoodi International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon Instituto Politécnico de Viana Do Castelo ADiT-Laboratory Electrotechnics and Telecommunications Department Porto4900-347 Portugal Harbin Institute of Technology Department of Computer Science and Technology Shenzhen150001 China Sharif University of Technology Department of Computer Engineering TehranP932 FM4 Iran Usm Engineering Campus Department of Computer Science and Engineering George Pulau Pinang14300 Malaysia

Each group in a data-driven automobile network has its cluster head. A group can communicate with each other and members of other groups once it has been founded. Vehicles belonging to each group near the other group allow intergroup communication. Because nodes in automotive networks move so quickly, routing in these networks is a complex problem to solve. Each cluster in hierarchical clustering can be partitioned into multiple sub-clusters. Put another way, and the data is stored in a cluster, which is then divided into more clusters. The data is stored directly in separate clusters in non-hierarchical approaches. A dendrogram is a type of hierarchical tree. We anticipate increasing information sharing in clusters by properly clustering vehicles on the road and establishing clusters of the desired size in the relevant dendrogram. We can select clusters of the necessary extent and compare the Quality of Service (QoS) network's outcomes by breaking the dendrogram at different levels. The findings reveal that the suggested method outperforms AIVISN in delay, PDR, overhead, and Drooped packets compared to AIVISN, 7.12%, 12.21%,8.32%, and 7.34%, respectively. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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Revolutionising engineering pedagogy: The role of 3D printing in modern engineering education

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PLET: programmed Learning & Educational Technology 2025年第2期62卷

作者： Muhammad Tajammal Munir Prashant K. Jamwal Bing Li Susan Carter Shahid Hussain a College of Engineering and Technology American University of the Middle East Egaila Kuwait b Department of Electrical and Computer Engineering School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan c Water Research Center Shenzhen International Graduate School Tsinghua University Shenzhen China d School of Critical Studies in Higher Education The University of Auckland Auckland New Zealand e Human-Centred Technology Research Center Faculty of Science and Technology University of Canberra Canberra Australia

ABSTRACTThis paper presents a comprehensive study on the integration of 3D printing technology in biomedical robotics education with undergraduate students learning about the design of robotic systems. The research investigated the effectiveness of this new teaching strategy in enhancing students’ understanding of complex engineering principles by comparing their learning experiences, and academic performance with results from traditional teaching methods. The students’ learning and performance were evaluated through a survey and grade analysis. The Mann – Whitney U test was used to analyse the data, revealing statistically significant improvements in both student satisfaction and grades. The Mann-Whitney U test results show a clear preference for the 2017 3D printing and project-based learning strategy over the 2016 traditional method. With a U-value of 0 and a critical U-value atp< 0.05 being 23, coupled with a z-score of −3.742, the data significantly indicates enhanced student outcomes with the newer teaching approach compared to conventional methods. The study also found that 3D printing technology fostered creativity, critical thinking, and enhanced spatial visualisation skills among students. However, we also identify potential challenges, including equipment maintenance, safety concerns, and the need for faculty expertise. The study concludes by providing potential strategies to overcome these challenges. This research offers valuable insights for educators and policy-makers in leveraging technology for improved learning outcomes in engineering education.

关键词： 3D printing engineering education pedagogical theories curriculum integration

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Spectral Learning of Shared Dynamics Between Generalized-Linear Processes 38

Spectral Learning of Shared Dynamics Between Generalized-Lin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Oganesian, Lucine L. Sani, Omid G. Shanechi, Maryam M. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los AngelesCA United States

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences

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Applied sciences (Switzerland) 2024年第15期14卷 6573页

作者： Doungtap, Surasachai Wang, Jenq-Haur Phanichraksaphong, Varinya International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei10608 Taiwan

This article examines the incorporation of the Shopping Assistance Automatic Suggestion (SAAS) model into Virtual Reality (VR) environments in order to improve the online shopping experience. The SAAS model employs sophisticated deep learning methods to offer customized product recommendations, which are conveyed by non-player characters (NPCs) via voice-based interactions. Our goal is to develop an interactive shopping experience that replicates real-life interactions by integrating AI-powered recommendations with immersive VR technology. We gather and standardize data from several open commerce databases, such as Amazon Product and Customer Reviews. The SAAS model, in conjunction with GPT-3, BERT, and T5, undergoes training and testing to evaluate its effectiveness across multiple criteria. The results demonstrate that the SAAS model surpasses other models in delivering contextually aware and pertinent recommendations. The integration process outlines the specific steps involved in capturing, processing, and transforming user interactions in virtual reality (VR) into vocal suggestions provided by non-player characters (NPCs). This strategy improves customization and utilizes the immersive features of virtual reality to effectively engage people. The results of our research establish a higher standard for e-commerce, with the goal of enhancing the user experience of online purchasing by making it more instinctive, engaging, and pleasurable. © 2024 by the authors.

关键词： Data mining

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On-chip western blotting: In-situ renaturation of SDS-protein complexes unifies sodium dodecyl sulfate (SDS) sizing & blotting in one microdevice

On-chip western blotting: In-situ renaturation of SDS-protei...

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15th international Conference on Miniaturized Systems for Chemistry and Life sciences 2011, MicroTAS 2011

作者： Hou, Chenlu Herr, Amy E. University of California Electrical Engineering and Computer Science Berkeley United States University of California Bioengineering Berkeley United States University of California UCSF Graduate Program in Bioengineering Berkeley United States

ISBN: (纸本)9781618395955

Western blotting readily identifies specific proteins amidst complex biological backgrounds [1, 2]. Nevertheless, immunob-lotting suffers from tremendous labor-intensive and time-intensive requirements [3]. The slab-gel assays require 1-2 days for completion with multiple hands-on "blotting" steps and yield semi-quantitative information. Recently, our group has introduced new approaches for completing Western blotting. The microfluidic integration strategies introduced and used allow rapid results reporting, full assay automation, and limited sample consumption (1-10 uL). Our integration strategies use spatial, temporal, and spatiotemporal modulation of separation mechanisms in fully electrophoretic systems. The present study reports on recapitulation of immunoaffinity in previously sized proteins, using novel in-transit electrophoretic removal of SDS from SDS-protein complexes. Early results show both the length- and timescales for protein 'renaturation' are compatible with on-chip operation. Further, substantial binding affinity is recapitulated using this streamlined and promising approach.

关键词： Sodium dodecyl sulfate

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Correlating the Ambient Conditions and Performance Indicators of the LoRaWAN via Surrogate Gaussian Process-Based Bidirectional LSTM Stacked Autoencoder

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IEEE Transactions on Network and Service Management 2023年第3期20卷 3413-3427页

作者： Bhat, Showkat Ahmad Huang, Nen-Fu Hussain, Imtiyaz Sajjad, Uzair National Tsing Hua University College of Electrical and Computer Science Hsinchu1300044 Taiwan National Tsing Hua University Department of Computer Science Hsinchu1300044 Taiwan National Taipei University of Technology Graduate Program in Energy and Opto-Electronic Materials Taipei10608 Taiwan National Taipei University of Technology Department of Energy and Refrigerating Airconditioning Engineering Taipei10608 Taiwan

LoRa's biggest advantage is its flexibility, which is the ability to increase or decrease data rate and range while decreasing or increasing sensitivity. Whenever propagation conditions change frequently, this function allows the spreading factor to be modified accordingly. Despite their efficiency and scalability, adaptive data rate algorithms ignore and fail to factor in the complex correlation between ambient weather parameters influencing the communication channel design. In this research, a Bayesian surrogate Gaussian process-based bidirectional LSTM stacked autoencoder model (BSGP-BLSTM-SAE) is proposed to estimate the channel performance indicators such as received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) and to determine the correlation between the ambient weather conditions and performance indicators for the LoRaWAN network. Bayesian optimization algorithm has been used to optimize the hyper-parameters of the developed model. A LoRaWAN experimental multivariate time series dataset has been used for the evaluation of the developed model, which upon testing and validation produces high accuracy in predicting the channel performance indicators and ambient conditions of the experimental LoRaWAN network. The mean absolute error of the developed model was around 0.45. Thus, the proposed model can predict the link performance indicators and thereby assist in real-time optimization of the transmission parameters to enhance the network performance in LoRaWAN-based systems at different ambient conditions. © 2004-2012 IEEE.

关键词： Internet of things

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Development of Educational Game for Indonesian History Lesson Focusing on Indonesia Pre-Literacy Relics 'Prehistoria' for Grade X High School Student

Development of Educational Game for Indonesian History Lesso...

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2020 international Conference on computer engineering, Network, and Intelligent Multimedia, CENIM 2020

作者： Nugroho, Supeno Mardi Susiki Rahman, Muhammad Daffa Abiyyu Hariadi, Mochamad Arief, Muhammad Faculty of Intelligent Electrical and Informatics Technology Sepuluh Nopember Institute of Technology Computer Engineering Department Surabaya Indonesia Tokyo University of Technology Media Science Program Graduate School of Bionics Computer and Media Sciences Tokyo Japan

ISBN: (纸本)9781728182834

The development of game technology has opened up new possibilities in learning methods through educational games. However, in Indonesia, the application of this concept is lacking. One lesson that can benefit from educational games is Indonesian History in class X, with a focus on history in the pre-literacy period. This study aims to create a puzzle-Adventure type of digital game that can help educate students about the pre-literacy period, developed with Unity, and uses WebGL as a target device while referring to Indonesia's revised 2013 national curriculum. The result of this research is the creation of a digital game that can be played on average of 254.36 seconds, with potential for educational use. A survey result shows that many of the ancient objects and some of the ages are well-recognized. However, the survey also shows many of the gameplay aspects, such as UI, gameplay features, and adventure elements, need improvements. © 2020 IEEE.

关键词： Surveys

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Enhanced resource allocation in distributed cloud using fuzzy meta-heuristics optimization

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computer Communications 2023年第1期209卷 14-25页

作者： Sangaiah, Arun Kumar Javadpour, Amir Pinto, Pedro Rezaei, Samira Zhang, Weizhe International Graduate Institute of AI National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Computer Science and Technology Cyberspace Security Harbin Institute of Technology Shenzhen China ADiT-Lab Electrical and Telecommunications Department Instituto Politécnico de Viana do Castelo Portugal Leiden University P.O. Box 9512 RA Leiden2300 Netherlands

Cloud computing is a modern technology that has become popular today. A large number of requests has made it essential to propose a resources allocation framework for arriving requests. The network can be made more efficient and less costly this way. The cloud–edge paradigm has been considered a growing research area in the computing industry in recent years. The increase in the number of customers and requests for cloud data centers (CDCs) has created the need for robust servers and low power consumption mechanisms. Ways to reduce energy in the CDC having appropriate algorithms for resource allocation. The purpose of this study was to develop an intelligent method for dynamic resource allocation using Takagi–Sugeno–Kang (TSK) neural-fuzzy systems and ant colony optimization (ACO) techniques to reduce energy consumption by optimizing resource allocation in cloud networks. It predicts future loads using a drop-down window to track CPU usage. By optimizing virtual machine migration, ACO can reduce energy consumption. Simulations are provided by examining the implementation and a variety of parameters such as the number of requests made wasted resources, and requests rejected. In this paper, we propose the use of virtual machine migration to accomplish two main goals: evacuating additional and non-optimal virtual machines (scaling and shutting down additional active physical machines) and solving the resource granulation problem. We evaluated and compared our results with literature for rejection rates of virtual and physical machine applications. The performances of our algorithms are compared to different criteria such as performance in request rejection, dynamic CPU resource allocation with reinforcement learning, multi-objective resource allocation, NSGAIII, Whale optimization and Forecast Particle Swarm allocation. A comparison of some evaluation criteria showed that the proposed method is more efficient than other methods. © 2023 Elsevier B.V.

关键词： Ant colony optimization

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Image color correction, enhancement, and editing

arXiv

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arXiv 2021年

作者： Afifi, Mahmoud Nasser Mohammed Graduate Program in Electrical Engineering and Computer Science York University TorontoON Canada

This thesis presents methods and approaches to image color correction, color enhancement, and color editing. To begin, we study the color correction problem from the standpoint of the camera's image signal processor (ISP). A camera's ISP is hardware that applies a series of in-camera image processing and color manipulation steps, many of which are nonlinear in nature, to render the initial sensor image to its final photo-finished representation saved in the 8-bit standard RGB (sRGB) color space. As white balance (WB) is one of the major procedures applied by the ISP for color correction, this thesis presents two different methods for ISP white balancing. Afterwards, we discuss another scenario of correcting and editing image colors, where we present a set of methods to correct and edit WB settings for images that have been improperly white-balanced by the ISP. Then, we explore another factor that has a significant impact on the quality of camera-rendered colors, in which we outline two different methods to correct exposure errors in camera-rendered images. Lastly, we discuss post-capture auto color editing and manipulation. In particular, we propose auto image recoloring methods to generate different realistic versions of the same camera-rendered image with new colors. Through extensive evaluations, we demonstrate that our methods provide superior solutions compared to existing alternatives targeting color correction, color enhancement, and color editing. © 2021, CC BY.

关键词： Color

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