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6th International Conference on Intelligent Sustainable systems, ICISS 2023

作者： Efendy, Yulius Syaputra, Muhammad Heru Honggiarto, Kevin Gaol, Ford Lumban Matsuo, Tokuro Bina Nusantara University Alam Sutra School of Information System Kota Tangerang13960 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819917259

This research aims to analyze and determine the impact of the Covid-19 pandemic on people’s behavior in Indonesia by using the Ojek online application during the Covid-19 pandemic. The research method that used in this research is descriptive method with qualitative approach method and questionnaire based on secondary data from literature references and the data collection from Google Form. This research was conducted in December 2020. The Covid-19 pandemic has had quite an unsettling impact on the community in their activities. This impact forces people to stay at home and if there is something to be done outside, they have to use a mask and still follow the health protocols that have been implemented. PSBB (Large-Scale Social Restrictions) regulations in several regions have been enforced and regulations related to online motorcycle taxi transportation have also been issued by the government. And it turns out that during this pandemic, there are peoples that keep using the Ojek online application during this pandemic, even if only stay at home. Although there has been a decline in the passenger shuttle sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Taxicabs

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Deep Learning-based Multiuser Physical Layer Communication Without Known Channel

Deep Learning-based Multiuser Physical Layer Communication W...

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IEEE Conference on Wireless Communications and Networking

作者： Jiequ Ji Zehui Xiong Kun Zhu Tony Quek Department of Information System and Design Singapore University of Technology Design Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

With the recent development of deep learning (DL), DL-based autoencoder techniques provide a novel paradigm for end-to-end physical layer optimization. In this paper, we address the dynamic interference in an end-to-end communication system with a multiuser Gaussian interference channel. In this context, the standard constellation is not optimal under high interference conditions. To address this issue, we propose an adaptive learning algorithm for learning and predicting dynamic interference. Note that existing DL-based autoencoders are unable to train end-to-end learning systems by deep learning without a known channel. Thus, we propose a generative adversarial network (GAN)-based training scheme to imitate the real channel. Simulation results show that compared with traditional PSK and QAM modulation schemes, our proposed adaptive learning-based auto encoder can achieve significantly lower block error rate (BLER) in presence of interference. Besides, the BLER performance of our proposed GAN-based training scheme is close to that of the optimal training scheme with known channel on different channel models.

关键词： Training Deep learning Adaptive learning Heuristic algorithms Simulation Modulation Prediction algorithms

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Invertible Mosaic Image Hiding Network for Very Large Capacity Image Steganography

Invertible Mosaic Image Hiding Network for Very Large Capaci...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zihan Chen Tianrui Liu Jun-Jie Huang Wentao Zhao Xing Bi Meng Wang College of Computer Science and Technology National University of Defense Technology China College of System Engineering National University of Defense Technology China School of Computer Science and Information Engineering Hefei University of Technology China

The existing image steganography methods either sequentially conceal secret images or conceal a concatenation of multiple images. In such ways, the interference of information among multiple images will become increasingly severe when the number of secret images becomes larger, thus restrict the development of very large capacity image steganography. In this paper, we propose an Invertible Mosaic Image Hiding Network (InvMIHNet) which realizes very large capacity image steganography with high quality by concealing a single mosaic secret image. InvMIHNet consists of an Invertible Image Rescaling (IIR) module and an Invertible Image Hiding (IIH) module. The IIR module works for downscaling the single mosaic secret image form by spatially splicing the multiple secret images, and the IIH module then conceal this mosaic image under the cover image. The proposed InvMIHNet successfully conceal and reveal up to 16 secret images with a small number of parameters and memory consumption. Extensive experiments on ImageNet-1K, COCO and DIV2K show InvMIHNet outperforms state-of-the-art methods in terms of both the imperceptibility of stego image, recover accuracy of secret image and security against steganlysis methods. The code is available at https://***/Brittany-Chen/InvMIHNet.

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Unveiling Happiness Disparities: A Machine Learning Approach to City-Village Comparison 11

Unveiling Happiness Disparities: A Machine Learning Approach...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Rawat, Bhupesh Bist, Ankur Singh Sunarya, Po Abas Hardini, Marviola Santoso, Nesti Anggraini Tarmizi, Rasyid Era Hill University Computer Science Graphic Bhimtal Campus India University of Raharja Master of Science Indonesia University of Raharja Master of Information Technology Indonesia University of Raharja Information System Tangerang Indonesia University of Raharja Dept. of Bussiness Managemen Tangerang Indonesia

ISBN: (纸本)9798350305968

Happiness is the core function behind all activities. There are various factors that influence happiness of a person. Generally, we constitute happiness with consumption of goods and items. In this paper, we will try to analyze the factors that influence happiness. Core of this paper is to analyze the comparison of happiness index between cities vs villages. Next objective of this paper is to suggest the approach to live happy and highlight core functions for the same. Extended Neural network is used to obtain the objectives. © 2023 IEEE.

关键词： Machine learning

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An improved multi-attribute decision-making based network selection algorithm for heterogeneous vehicular network

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Frontiers of computer Science 2022年第3期16卷 211-213页

作者： Lei NIE Bo LIU Peng LI Heng HE Libing WU School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430065China School of Cyber Science and Engineering and School of Computer Science Wuhan UniversityWuhan 430072China Shenzhen Research Institute of Wuhan University Shenzhen 518057China

1 Introduction With sharply rising quantity of urban vehicles over the past few decades,traffic jams and safety have gradually become outstanding *** recent years,Vehicular Ad hoc Network(VANET)is appeared and utilized to solve traffic related *** is a type of self-organized and open-structured network,and provides Vehicle-to-Everything(V2X)*** all kinds of applications in VANET rely on efficient data transmission and interaction[1-3].

关键词： network sharply utilized

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BAT: Mining Binary-API Topic for Multi-service Application Development 26

BAT: Mining Binary-API Topic for Multi-service Application D...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： He, Pengfei Liu, Linlin You, Dianlong Shen, Limin Chen, Zhen Yanshan University School of Information Science and Engineering Qinhuangdao China Chinese Academy of Sciences National Science Libraries Beijing China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

ISBN: (纸本)9798350331684

With the growing popularity of API-driven multiservice application (mashup) development, the burgeoning web APIs have left developers drowning in the sea of web API selections. Matching developers with the most appropriate APIs is the key to improving user satisfaction and promoting more popular web applications. As a result, more and more researchers pay attention to web API recommender systems based on collaborative filtering. However, employing collaborative filtering to recommend APIs is challenging due to the severe sparsity of mashup and API interactions. To address this problem, we propose a probabilistic generative model, called the Binary-API Topic model (BAT), to parameterize mashups and APIs. Technically, BAT is equipped with a mechanism to extract binary-APIs and predict unknown pairwise interactions. To improve generality and capture more relevance from a limited number of interactions, we learn binary-API topics by directly modeling the generation of API co-occurrence patterns across the repository (all mashup collections from ***). The main advantage of BAT is that it preserves API co-occurrence patterns in model learning and exploits the rich global relevance. Finally, through extensive experiments, we demonstrate that BAT can achieve the highest performance on the sparse real-world data set. © 2023 IEEE.

关键词： Collaborative filtering

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Generalized Normalized Euclidean Distance Based Fuzzy Soft Set Similarity for Data Classification

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computer systems Science & Engineering 2021年第7期38卷 119-130页

作者： Rahmat Hidayat Iwan Tri Riyadi Yanto Azizul Azhar Ramli Mohd Farhan Md.Fudzee Ansari Saleh Ahmar Faculty of Computer and Information Technology Universiti Tun Hussein Onn MalaysiaBatu PahatMalaysia Department of Information Technology Politeknik Negeri PadangPadangIndonesia Department of Information System Universitas Ahmad DahlanYogyakartaIndonesia Department of Statistics Universitas Negeri MakassarMakassarIndonesia

Classification is one of the data mining processes used to predict predetermined target classes with data learning *** study discusses data classification using a fuzzy soft set method to predict target classes *** study aims to form a data classification algorithm using the fuzzy soft set *** this study,the fuzzy soft set was calculated based on the normalized Hamming *** parameter in this method is mapped to a power set from a subset of the fuzzy set using a fuzzy approximation *** the classification step,a generalized normalized Euclidean distance is used to determine the similarity between two sets of fuzzy soft *** experiments used the University of California(UCI)Machine Learning dataset to assess the accuracy of the proposed data classification *** dataset samples were divided into training(75%of samples)and test(25%of samples)*** were performed in MATLAB R2010a *** experiments showed that:(1)The fastest sequence is matching function,distance measure,similarity,normalized Euclidean distance,(2)the proposed approach can improve accuracy and recall by up to 10.3436%and 6.9723%,respectively,compared with baseline ***,the fuzzy soft set method is appropriate for classifying data.

关键词： Soft set fuzzy soft set classification normalized euclidean distance similarity

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Linkedin's Influence on User Success in The Professional World 9

Linkedin's Influence on User Success in The Professional Wor...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Oktavia, Tanty Roekman, Faza Fakhriansyah Putera, Muhammad Ikhsan Hidayah, Muhammad Alif Rafi, Muhammad Hosoda, Takaaki Gaol, Ford Lumban BINUS Graduate Program Information System Management Bina Nusantara University Information System Management Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan BINUS Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

Social media has now become a place for others to exchange information in the form of writing, photos, videos, and audio. Social media has become a place for humans to interact from all over the world. In the use of social media, attractive professional information is conceptualized as access to relevant information and opening up better career opportunities. User content about work and choosing personal information compilation strategies consistently predict information usefulness. This study discusses the effect of using a linked-in application on the success of its users in the professional world. It is hoped that it can provide insight into the use of this recruitment application, making it easier for users to find work. The research model used in this research is quantitative. The survey was conducted on connected users in Indonesia using an online questionnaire, and the data collected was analyzed using PLS-SEM. Of the 68 respondents who took the survey, it can be said that all research models have a positive relationship with the successful use of the linked-in platform. © 2022 IEEE.

关键词： Surveys

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Digital Transformation and its Impact on Operational Efficiency and Competitive Advantage in Islamic Banks 2

Digital Transformation and its Impact on Operational Efficie...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Shehadeh, Maha Almajali, Dmaithan Abu-Alsondos, Ibrahim A. Alkhwaldi, Abeer F. Al-Gasaymeh, Anwar S. Applied Science Private University Finance and Banking Science Department Amman Jordan Applied Science Private University Management Information System Department Amman Jordan American University in the Emirates College of Computer Information Technology Dubai United Arab Emirates Mutah University Department of Management Information Systems Karak Jordan

ISBN: (纸本)9798350335644

This study aimed to identify the impact of digital transformation on Islamic banks operating in Jordan by using the descriptive analytical method. Primary data were obtained through a questionnaire and distributed to the study sample, which consisted of 68 employees of 4 Islamic banks operating in Jordan. The structural equation model was used in the process of testing the study hypotheses. We obtained a number of results, the most important of which is the presence of a statistically significant effect at the significance level (α ≤ 0.05) of digital transformation in Islamic banks operating in Jordan in relation to operational efficiency and competitive advantage. We recommend that Islamic banks need to develop a strategy for digital transformation aimed at innovation and competition, without prejudice to the controls of Islamic finance. There is a need to focus on managing the risks associated with digital transformation, as well as treating the risks associated with modern technologies, especially those that threaten Islamic banks and financial stability. © 2023 IEEE.

关键词： Competition

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Energy-Efficient Resource Management for Multi-UAV NOMA Networks Based on Deep Reinforcement Learning

Energy-Efficient Resource Management for Multi-UAV NOMA Netw...

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IEEE Conference on Vehicular technology (VTC)

作者： Xiangda Lin Helin Yang Kailong Lin Liang Xiao Zhaoyuan Shi Wanting Yang Zehui Xiong Department of Information & Communication Engineering Xiamen University Xiamen China School of Computer and Information Anqing Normal University Anhui China Pillar of Information System Technology & Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Cellular-connected unmanned aerial vehicles (UAVs) play an essential role in cellular networks. Combined with non-orthogonal multiple access (NOMA) technique, UAVs can provide better performance in various communication scenarios. In this paper, we investigate a NOMA-enhanced UAV-assisted cellular network where multiple UAVs are deployed as aerial base stations to provide communication services for mobile ground users in the presence of a malicious jammer. We propose a two-step learning-based resource scheduling approach. First, an algorithm based on K-means clustering is proposed to partition ground users (GUs) to reduce mutual interference. Moreover, a cooperative multi-agent twin delayed deep deterministic algorithm is proposed to jointly optimize UAVs' trajectories, power allocation and GU association to maximize the system energy efficiency (EE) while guaranteeing minimum quality-of-service (QoS) requirements. Extensive results demonstrate that the proposed solution can efficiently improve EE and QoS performances under jamming attacks compared with existing popular approaches.

关键词： Cellular networks NOMA Vehicular and wireless technologies Clustering algorithms Quality of service Energy efficiency Partitioning algorithms

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