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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Chandra Prabowo, Harjanto Fransditya M., M. Feisal Meyliana Gunawan, Michael Andrew Gaol, Ford Lumban Winanti Universitas Bina Nusantara Mobile Application and Technology Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Universitas Bina Nusantara Cyber Security Jakarta Indonesia

ISBN: (纸本)9781665404228

The attendance management system is a system that needed for learning activity in a University. In some University, the attendance management system has been used tapping system which using NFC technology. Actually, that attendance management system is effective for managing each of attendance information. However, until now we can still see many cheats for this attendance system. For example, leave their cards to their classmates and tell them to tap for them. For that, one of the many effective solutions to resolve that problem is to add face recognition technology in the current attendance system. In our experiment, we know that to add face recognition to the current attendance management system surely need a camera and also face dataset for the system. At the beginning, we need at least 9 images with different emotions and face positions to let the system recognizes one's face. To make this system more accurate at recognizing one's face, we would update the face dataset in every face recognizing process. © 2020 IEEE.

关键词： Face recognition

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Contemporary studies of task-technology fit: A review of the literature 6

Contemporary studies of task-technology fit: A review of the...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Hidayat, Desman Pangaribuan, Christian Haposan Putra, Okta Prihatma Bayu Irawan, Iwan Bina Nusantara University Binus Entrepreneurship Center Management Department Jakarta11480 Indonesia Sampoerna University Management Study Program Faculty of Business Jakarta12780 Indonesia Bina Nusantara University Binus Business School Undergraduate Program Management Departement Jakarta11480 Indonesia Character Building Development Center Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Information technology can influence task characteristics for the improvement of task performance. This review of the literature aims to explore the application of the TTF theory by exploring the scope and range of the available literature that applies the specific elements of the theory and summarizing and disseminating the research findings, specifically the environments where TTF has been applied. It was found that the education field is the most researched topic in TTF for the last three years, followed by social media, business, and management fields. Overall, the results of this study provide an understanding of applications of the theory and assists in providing avenues for future studies to investigate TTF studies further. © 2021 IEEE.

关键词： Social networking (online) Bibliographies Education Asia Developing countries Information management Task analysis

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Designing Wireless Sensor Network Routing on Agriculture Area Using the LEACH Protocol 3

Designing Wireless Sensor Network Routing on Agriculture Are...

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3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

作者： Gitakarma, Made Santo Priyambodo, Tri Kuntoro Suyanto, Yohanes Sumiharto, Raden Wijayanto, Danur Universitas Gadjah Mada Post Graduate Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia Universitas Gadjah Mada Master Program of Computer Science Yogyakarta Indonesia

ISBN: (纸本)9781728184067

Microelectronic technology that supports the establishment of wireless sensor networks (WSN) has brought hope to the ease of Internet of Things (IoT) technology that can generate smart environments. A WSN consists of a collection of sensor nodes that are small, intelligent, and inadequate in storage, energy, and processing power. One of the well-known routing techniques in WSN is LEACH. There have been many LEACH studies and their development with random node distribution. Here we designed several scenarios in WSN routing with the position of the sensor nodes specified in the mapping area. MATLAB simulation results show network lifetime can be extended by reducing data packet size. In five experiments, the number of 40 nodes was the optimal number to extend network lifetime. Each scenario has advantages and disadvantages that can be referenced in developing the LEACH protocol. © 2020 IEEE.

关键词： Internet of things

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Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems

arXiv

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

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InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning

arXiv

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arXiv 2023年

作者： Shin, Juyeb Jeong, Hyeonjun Rameau, Francois Kum, Dongsuk Robotics Program Korea Advanced Institute of Science and Technology Daejeon34051 Korea Republic of Graduate School of Mobility Korea Advanced Institute of Science and Technology Daejeon34051 Korea Republic of Computer Science department State University of New York - Korea Songdo21985 Korea Republic of

For scalable autonomous driving, a robust map-based localization system, independent of GPS, is fundamental. To achieve such map-based localization, online high-definition (HD) map construction plays a significant role in accurate estimation of the pose. Although recent advancements in online HD map construction have predominantly investigated on vectorized representation due to its effectiveness, they suffer from computational cost and fixed parametric model, which limit scalability. To alleviate these limitations, we propose a novel HD map learning framework that leverages graph modeling. This framework is designed to learn the construction of diverse geometric shapes, thereby enhancing the scalability of HD map construction. Our approach involves representing the map elements as an instance-level graph by decomposing them into vertices and edges to facilitate accurate and efficient end-to-end vectorized HD map learning. Furthermore, we introduce an association strategy using a Graph Neural Network to efficiently handle the complex geometry of various map elements, while maintaining scalability. Comprehensive experiments on public open dataset show that our proposed network outperforms state-of-the-art model by 1.6 mAP. We further showcase the superior scalability of our approach compared to state-of-the-art methods, achieving a 4.8 mAP improvement in long range configuration. Our code is available at https://***/juyebshin/InstaGraM. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization

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Procedia computer science 2021年 179卷 17-24页

作者： I Nyoman Setiawan Robert Kurniawan Budi Yuniarto Rezzy Eko Caraka Bens Pardamean Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Department of Information Management College of Informatics Chaoyang University of Technology Taiwan (ROC) Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia 11530 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia. 11530.

Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results.

关键词： SVR harris hawks optimization parameter optimization kernel forecasting

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Solving Combinatorial Optimization Problems on Fujitsu Digital Annealer

arXiv

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arXiv 2023年

作者： Kao, Yu-Ting Liao, Jia-Le Hsu, Hsiu-Chuan Graduate Institute of Applied Physics National Chengchi University B.S. Program in Electrophysics National Chengchi University Department of Money and Banking National Chengchi University Taipei City Taiwan B.S. Program in Electrophysics National Chengchi University Department of Computer Science National Chengchi University Taipei City Taiwan Graduate Institute of Applied Physics National Chengchi University Department of Computer Science National Chengchi University Taipei City Taiwan

Combinatorial optimization problems are ubiquitous in various disciplines and applications. Many heuristic algorithms have been devoted to solve these types of problems. In order to increase the efficiency for finding the optimal solutions, an application–specific hardware, called digital annealer (DA) has been developed for solving combinatorial optimization problems using quadratic unconstrained binary optimization (QUBO) formulations. In this study, we formulated the number partitioning problem and the graph partitioning problem into QUBO forms and solved such problems with the DA developed by Fujitsu Ltd. The QUBO formulation of the number partitioning problem was fully connected. The DA found the overall runtime for the optimal solution to be less than 30 seconds for 6500 binary variables. For the graph partitioning problem, we adopted modularity as the metric for determining the quality of the partitions. For Zachary’s Karate Club graph, the modularity obtained was 0.445, a 6% increase against D–wave Quantum Annealer and Simulated Annealing. Moreover, to explore the DA’s potential applications to real–world problems, we used the search for communities or virtual microgrids in a power distribution network as an example. The problem was formulated into graph partitioning. It is shown that the DA effectively identified community structures in the IEEE 33–bus and IEEE 118–bus network. © 2023, CC BY.

关键词： Simulated annealing

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D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

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SN computer science 2021年第1期2卷 48页

作者： Demiray, Bekir Z. Sit, Muhammed Demir, Ibrahim Department of Computer Science University of Iowa Iowa City United States Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City United States Department of Civil and Environmental Engineering University of Iowa Iowa City United States Department of Electrical and Computer Engineering University of Iowa Iowa City United States

Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network *** study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Deep learning DEM DEM reconstruction Generative adversarial networks (GANs) Super-resolution

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Journalist system using named entity recognition based on Twitter location extraction

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AIP Conference Proceedings 2023年第1期2691卷

作者： Abba Suganda Girsang Sani Muhamad Isa Raditya Fajar BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Interactions that occur on Twitter social media are easier to do and can easily reach all levels of society, as well as conversations tweeted by users. They can easily spread information or issues that are developing. Based on this, the use of conversational data on the Twitter platform can get an overview of issues that are developing and even those that are just being formed from community conversations on this Twitter platform. Still related to previous research, which researched to implement the Twitter social media monitoring system to provide access to additional information for journalists. This study aligns with its predecessors but comes with a richer data collection mechanism, so it can gather more conversations. The difficulty to be answered in this study is the difficulty of journalist determining a bunch of location of an incident on a Twitter conversation, which describes the name of a place or contains the address of a location in it. Also in this study, there are additional modifications to extract location names using the NER model, which has been customized according to the structure of naming location names in Indonesia. So from the text of the Twitter conversation, whether it's an address name or a description of a location name, it can get a location name that refers to the name of a place where it could be the location where an event occurred. The results of an Indonesian language location NER model, with an accuracy score of 97.67%, with a precision value of 90.57%, recall 59.30% and f1-score 71.67%.

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Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations

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Plant Communications 2024年第8期5卷 1-17页

作者： Xiaotong Liu Daisuke Igarashi Rachel A.Hillmer Thomas Stoddard You Lu Kenichi Tsuda Chad L.Myers Fumiaki Katagiri Department of Plant and Microbial Biology University of Minnesota-Twin CitiesSt PaulMN 55108USA Department of Computer Science and Engineering University of Minnesota-Twin CitiesMinneapolisMN 55455USA Bioinformatics and Computational Biology Graduate Program University of Minnesota-Twin CitiesMinneapolisMN 55455USA Institute for Innovation Ajinomoto Co.Inc.KawasakiJapan State Key Laboratory of Agricultural Microbiology Hubei Hongshan LaboratoryHubei Key Lab of Plant PathologyCollege of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China Present address:225 S 6th St MinneapolisMN 55402USA Present address:Biotalys Inc.2520 Meridian Parkway Suite 480DurhamNC 27713USA Present address:Life Sciences Institute and Department of Cell and Developmental Biology University of Michigan Medical SchoolAnn ArborMI 48109USA

Rapid plant immune responses in the appropriate cells are needed for effective defense against *** transcriptome analysis is often used to describe overall immune responses,collection of transcriptome data with sufficient resolution in both space and time is *** reanalyzed public Arabidopsis time-course transcriptome data obtained after low-dose inoculation with a Pseudomonas syringae strain expressing the effector AvrRpt2,which induces effector-triggered immunity in ***-peak time-course patterns are prevalent among thousands of upregulated *** implemented a multicompartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each *** decomposed peaks reveal an“echoing”pattern:the peak times of the first and second peaks correlate well across most upregulated *** demonstrated that the two peaks likely represent responses of two distinct cell populations that respond either cell autonomously or indirectly to ***,the peak decomposition has extracted spatial information from the time-course *** echoing pattern also indicates a conserved transcriptome response with different initiation times between the two cell populations despite different elicitor types.A gene set highly overlapping with the conserved gene set is also upregulated with similar kinetics during pattern-triggered *** of a WRKY network via different entry-point WRKYs can explain the similar but not identical transcriptome responses elicited by different elicitor *** discuss potential benefits of the properties of the WRKY activation network as an immune signaling network in light of pressure from rapidly evolving pathogens.

关键词： Arabidopsis Pseudomonas syringae plant immune response multi-compartment model WRKY transcription factors resilient immune signaling network

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