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1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Condrobimo, A. Raharto Abbas, Bahtiar Saleh Trisetyarso, Agung Suparta, Wayan Kang, Chul-Ho Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Civil Engineering Department University of Technology Yogyakarta Yogyakarta Indonesia Department of Electronic and Communication Engineering Kwangwoon University Korea Republic of

ISBN: (纸本)9781538609545

This study aims to apply data mining techniques with cluster analysis on stock data registered in LQ45 in Indonesia Stock Exchange. The cluster analysis used in this method is k-means algorithm, the data in this research is taken from Indonesia Stock Exchange. The cluster analysis in this study analyzed the characteristics of data volumes and stock values, while the results in this study were presented in the form of cluster members visually. Therefore, this cluster analysis in this research can be used for quick and efficient identifier for each member of LQ45 index cluster based on share value for each cluster and its volume. The identification results can be used by beginner-level investors that begun to be interested in stock investments to help make informed decisions about stock trading on desired cluster groups. © 2018 IEEE.

关键词： Cluster analysis

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Blockchain Technology Based Credibility Verification in E-Journal Entities

Blockchain Technology Based Credibility Verification in E-Jo...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Watini, Sri Kedah, Zulkarnain Lukita, Chandra Sokibi, Petrus Mustika, Gadis Rahardja, Reymund Yulianjani, Arsi Sukmana, Husni Teja University of Panca Sakti Bekasi Early Childhood Teacher Education Bekasi Indonesia University of Malaysian Computer Science Vice Chancellor Selangor Malaysia University of Catur Insan Cendekia Information System Cirebon Indonesia University of Raharja Information System Tangerang Indonesia University of Raharja Digital Business Tangerang Indonesia University of Raharja Information Management Study Program Tangerang Indonesia Syarif Hidayatullah Islamic University Lecture of Information Engineering Jakarta Indonesia

ISBN: (纸本)9781665468763

In the industrial era 4.0, it has surpassed increasingly complex technological advances in the information system that required a very high infrastructure and facilities and prevented fraud. Counterfeiting is a procedure following an entity, statistics, or documents (observe forged documents) with plans to deceive. Falsification of extensive information certificates is exacerbated by the current economic situation in Indonesia, which is currently occurring due to the widespread epidemic, namely COVID-19. The spread of the COVID-19 outbreak has had a significant impact and changes in all sectors, especially certificate forgery in Indonesia. The imitation of busy certificates is the main focus of this journal, focusing on journal publications combined with Blockchain technology. The sophistication of blockchain technology as authentication is comparing two or more items or additional tests to ensure the accuracy, correctness, or correctness of the information. This method uses qualitative methods with data sources based on case studies in controlled supervision with the basic concept of cryptography as the basis of analysis. From the decisions made, it is hoped that this will minimize the level of forgery of certificates widely measured during the COVID-19 era. © 2022 IEEE.

关键词： Blockchain

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Development and Validation of Customer Intention Factors for Live-Stream Shopping 7

Development and Validation of Customer Intention Factors for...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Fernando, Erick Hidayat, Desman Pengaribuan, Christian Haposan Prabowo, Yulius Denny Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Information System Tangerang Indonesia Bina Nusantara University Binus Entrepreneurship Center Management Department Jakarta11480 Indonesia Sampoerna University Management Study Program Faculty of Business Jakarta12780 Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307504

Along with technological improvements, online shopping is currently developing quickly. Online shops started by selling electronics, clothing, food, and home appliances and continue to evolve, selling various things. Currently, digital marketing has a novel technique, namely live shopping, where sellers can present, promote and offer their products directly through live streaming on social media and e-commerce platforms. This research will focus on gaining insight into the factors influencing customers to watch and purchase through live streaming. This research is an early-stage study of exploring and searching for research models. The study used a literature review approach by reviewing similar previous research articles. Several variable components were used in this study. The findings of the factors include visibility capacity, meta-vocation capacity, purchase orientation capacity, social presence, price, perceived product quality, perceived enjoyment, purchase orientation, perceived usefulness, and intention to buy. These factors were tested and proved to be valid with Cronbach's Alpha value being more than 0.5 and reliable with the value of the loading factor a greater than 0.6. Criteria The factors of the findings for further analysis of the relationship between factors and other variables. © 2023 IEEE.

关键词： Domestic appliances

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Disclosure Privacy Information Social Media on TikTok

Disclosure Privacy Information Social Media on TikTok

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International Conference on Information Management and Technology (ICIMTech)

作者： Dedy Syamsuar Fazri Fahrezi Ahnaf Favian Edy Irwansyah Achmad Fakhri Irawan Professional Engineer Program Dept. Information System Department Faculty of Engineering School of Information System Bina Nusantara University Jakarta Indonesia Information System Department School of Information System Bina Nusantara University Jakarta Indonesia Computer Science Department School of Information System Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information; nevertheless, perceived risks can greatly discourage users.

关键词： Privacy Video on demand Social networking (online) Writing Control systems Calculus Information management Web sites

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An improved parameterized approach for real time optimal motion planning of AUV moving in dynamic environment

An improved parameterized approach for real time optimal mot...

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IEEE Conference on Robotics, Automation and Mechatronics

作者： Wenhao Luo Jun Peng Jing Wang Department of Automation Key Laboratory of System Control And Information Processing Shanghai China School of Information Science and Engineering Hunan Engineering Laboratory Changsha China Computer Engineering Program Bethune-Cookman University Daytona Beach FL USA

In this paper, we present an improved analytic method to the optimal trajectory generation of an autonomous underwater vehicle (AUV) in a dynamic environment. The proposed approach explicitly incorporates both the AUV kinematic and the geometric constraints due to dynamic obstacles and the terrain while rendering the near-shortest path by a performance index related to the path length. In particular, the proposed design is based on a family of parameterized trajectories determined by three adjustable parameters, which provides a unified way to reformulate the geometric constraints and performance index into a set of parameterized constraint equations. To that end, such a constrained optimization problem boils down to optimize those adjustable parameters, which can be analytically solved in the parameter space. The proposed solution enhances the methodologies of real-time path planning for robots in 3D environment. Simulation results verify the effectiveness of the proposed method.

关键词： Trajectory Planning Vehicle dynamics Dynamics Collision avoidance Kinematics Optimization

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Automatic LPI Radar Waveform Recognition Using Vision Transformer

Automatic LPI Radar Waveform Recognition Using Vision Transf...

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IEEE International Conference on Radar

作者： Junseob Kim Sunghwan Cho Sunil Hwang Yeongyoon Choi Department of Electrical Engineering Korea Military Academy Republic of Korea Maneuver System Coordination and Contract Team Defense Acquisition Program Administration Republic of Korea Department of Electrical Engineering and Computer Science GIST Republic of Korea

This paper investigates the classification of low probability of intercept (LPI) radar signals by exploiting the intrinsic advantages of the Vision Transformer (ViT). Due to the characteristics of LPI radar signals, such as intrapulse modulation, wide frequency bands, and low transmission power, these signals are challenging to be detected and classified using traditional analytic methods. This has led to the adoption of various deep learning techniques to overcome these limitations. On the one hand, the ViT, originally developed for natural language processing, has demonstrated outstanding performance in computer vision by replacing the structure of the convolutional neural network (CNN) with the transformer, specifically leveraging self-attention. Therefore, this paper explores a method based on the ViT technique for classifying LPI signal images. The simulation results show that the proposed ViT method outperforms the traditional CNN method by 12.8% at −10dB SNR.

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Interplay of Purcell Effect, Stimulated Emission, and Leaky Modes in the Photoluminescence Spectra of Microsphere Cavities

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Physical Review Applied 2019年第5期11卷 051001-051001页

作者： Ching-Hang Chien Shang-Hsuan Wu Trong Huynh-Buu Ngo Yia-Chung Chang Research Center for Applied Sciences Academia Sinica Taipei 115 Taiwan Nano Science and Technology Program TIGP Academia Sinica Taipei 115 Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu 300 Taiwan Department of Electrical and Computer Engineering University of Texas at Austin USA Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta T6G 2V4 Canada

A theoretical model for describing the emission spectra of microsphere cavities is presented and its predictions of detailed line shapes of emission spectra associated with whispering gallery modes (WGMs) of various orders in ZnO microspheres (MSs) are verified experimentally by photoluminescence (PL) spectroscopy. The interplay of the Purcell effect, quality factor, and leaky modes in spontaneous and stimulated emission spectra related to WGMs of all orders is revealed. The key success of the theory is based on the expansion of the full Green’s function of the MS in terms of all possible resonance modes in complex frequency space, which allows incorporation of contributions from leaky modes, stimulated emission processes, and the Purcell effect. We show that the spontaneous emission spectrum calculated according to Mie theory (without the Purcell effect) is dominated by the contribution of leaky modes, while the spontaneous and stimulated emission enhanced by the Purcell effect is responsible for the main WGM resonance peaks observed experimentally. It is found that the stimulated emission peaks are doubly enhanced by their respective mode quality factor Q: one factor from the Purcell effect and the other factor from the photon number derived from the rate equation. After combining all these effects, the theory can provide a quantitative description of fine features of both TE and TM modes (including higher-order modes) observed in the PL spectra of ZnO MSs. Surprisingly, it is found that for ZnO MS with a diameter larger than 5 μm, the PL emission spectrum is dominated by higher-order modes. The quantitative understanding of the interplay of these emission mechanisms should prove useful for optimizing the performance of light-emitting devices based on microresonators.

关键词： Cavity quantum electrodynamics Lasers Light-matter interaction Optical pumping Photonics Semiconductor quantum optics Spontaneous emission Whispering gallery mode resonators

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Critical Factors of Immersive Learning in Higher Education: A systematic Literature Review

Critical Factors of Immersive Learning in Higher Education: ...

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International Conference on ICT For Smart Society (ICISS)

作者： Sunardi Meyliana Arief Ramadhan Yusep Rosmansyah Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Departement School of information system Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of electrical engineering and informatics Bandung Institute of Technology Bandung Indonesia

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education.

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Optimizing Wireless Sensor Network Topology with Deep Reinforcement Learning for Multi-Source/Destination Scenarios

Optimizing Wireless Sensor Network Topology with Deep Reinfo...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Kosei Kobayashi Masayoshi Aritsugi Pedro Henrique González Israel Mendonça Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

Wireless sensor networks are widely valued for their effectiveness in real-time data collection. As the amount of data exchanged within such networks grows, designing a robust network topology that maximizes area coverage with minimal sensors has become a critical challenge. The choice of topology impacts key network metrics, including sensor coverage, communication range, connectivity, inference, and installation and management costsIn this paper, we address the Wireless Sensor Network Planning Problem with Multiple Sources/Destinations, presenting an optimization approach based on deep reinforcement learning. This problem is noteworthy, as sensors in various applications are often required to share data within distinct destinationsWe leverage deep reinforcement learning to effectively address the complex task of selecting optimal sensor locations. Our reinforcement learning agent dynamically learns network structure by iteratively adding and removing sensors, optimizing both sensor coverage and the total number of sensors used. Experiment across diverse scenarios demonstrate the effectiveness of our method for network planning problems of varying scales, achieving full coverage with fewer sensors than traditional approaches. Additionally, our approach also produce solutions for large instances where Mixed Integer programming solvers were not able to. Overall, our method was able to reduce the number of sensors used by up to 22.3% compared to other methods.

关键词： Measurement Integer programming Wireless sensor networks Network topology Deep reinforcement learning Real-time systems Sensors Planning Topology Optimization

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Optimization of Node Reduction Using BRKGA for GNN-Based Traffic Speed Forecasting

Optimization of Node Reduction Using BRKGA for GNN-Based Tra...

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Congress on Evolutionary Computation

作者： Yuto Inokuchi Pedro Henrique González Israel Mendonça Masayoshi Aritsugi Graduate School of Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

While Spatio-Temporal Graph Convolutional Networks (STGCNs) are an effective method for traffic speed fore-casting, their training and inference tend to be time-consuming. In this paper, we aim to refine these networks by strategically reducing their number of nodes, thereby boosting computational efficiency. The nodes in these graphs represent data observed for road segments, and by analyzing the interconnections and layout of the graph, we can identify nodes with minimal contribution to overall performance. Removing these nodes can potentially decrease computation time while maintaining the prediction accuracy. We employ the Biased Random-Key Genetic Algorithm (BRKGA) to identify a good set of nodes for removal, based on a predefined percentage reduction of the original graph size (e.g., retaining 95 % of the original graph). We evaluate different graph size configurations, ranging from 95 % to 70 % node retention, to determine the least impactful node set performance. Our experiments on three real-world datasets reveal that reducing nodes can decrease computation time by up to 29%, and as a byproduct of removing noise, even improve the prediction accuracy.

关键词： Training Accuracy Graph convolutional networks Roads Noise Layout Evolutionary computation

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