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International Conference on information Management and Technology (ICIMTech)

作者： Gede Indra Raditya Martha Harco Leslie Hendric Spits Warnars Harjanto Prabowo Meyliana Achmad Nizar Hidayanto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia

Blockchain, Metaverse & NFT are technologies that were booming during the Pandemic. As a derivative product of blockchain, the Non-Fungible Token (NFT) is one of the technologies that has attracted the most interest from investors and the public because of its potential benefits and has always been closely associated with the Metaverse as a main purposes research of the NFTs. This paper is to explore the possibilities and provide an overview of the current use of NFT technology so that it can provide insight for further research & gap with the big picture that it is trying to present in this paper. However, after exploring further related to NFT in this research, it was found that the problem that most people are trying to solve through NFT is related to the topic of Decentralization & Web3 not as a Metaverse main backbone.

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Person Name Extraction from TV program Using Pre-trained Language Model and News Headline 10

Person Name Extraction from TV program Using Pre-trained Lan...

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10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021

作者： Oda, Kazuki Sasaki, Minoru Major in Computer and Information Sciences Graduate School of Science and Engineering Ibaraki University Ibaraki Hitachi Japan Ibaraki University Faculty of Engineering Dept. of Computer and Information Sciences Ibaraki Hitachi Japan

ISBN: (纸本)9781665424202

In this study, we focus on extracting person names from text contained in from TV programs and TV-CMs broadcast in Japan. Person name extraction from TV programs is a challenging task because person names are often recognized with other entity names such as locations and organizations. To tackle this problem, we experiment with an existing named entity extraction method using a list of names extracted from Wikipedia. However, this method results in low precision in extracting the person names, which leads to the problem that the list of person names is not effective for TV program texts. In this paper, to solve this problem, we propose a person name extraction method using Conditional Random Fields (CRF) with ELMo pre-training language model. Also, we propose to use news headlines to construct the person name extraction model for effective extraction of person names performing on the TV programs. As a result of our experiments, the proposed model trained with training data and news headlines provides the highest precision. These results show that adding the news headlines as external information to TV program data is effective for person name extraction. © 2021 IEEE.

关键词： Extraction

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Exploring Tourist Feedback on Riau Attractions Through Indonesian Language YouTube Opinion Using Naïve Bayes Algorithm

Exploring Tourist Feedback on Riau Attractions Through Indon...

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2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023

作者： Kurniawan, Rahmad Iskandar, Iwan Lestari, Fitra Harpizon, Habibi Al Rasyid Husti, Ilyas Faculty of Mathematics and Natural Sciences Universitas Riau Department of Computer Science Pekanbaru Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Informatics Engineering Pekanbaru Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798350313635

YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia's top Halal travel destination. Tourism is a vital contributor to the economic growth of regions, and each province in Indonesia competes to promote its tourist attractions to attract more visitors every year. However, the large volume of data can challenge the manual analysis of feedback from YouTube's features, such as likes, dislikes, and comments. A literature review suggests that the Naive Bayes algorithm, which uses machine learning, is helpful for sentiment analysis. Therefore, this study aims to analyze public sentiment toward tourist destinations in Riau province by analyzing YouTube comments using the Naïve Bayes algorithm. The study used 1680 opinions collected from 10 YouTube videos showcasing tourist destinations in Riau. The Naive Bayes algorithm classified 60% of the comments as positive, 32% as neutral, and 8% as negative. The experimental results indicated an accuracy and precision of 73%, a recall of 94%, and an F-1 Score of 82%. The study used the word frequency technique to reveal that Riau could become a popular halal tourist destination based on several frequently occurring words in the comments. © 2023 IEEE.

关键词： Sentiment analysis

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Analysis of delay factors on drilling projects on PT China Oilfields Services Limited 2

Analysis of delay factors on drilling projects on PT China O...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Sardjono, W. Perdana, W.G. Ansori, R. Utama, D.N. Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management BINA NUSANTARA University Jakarta11480 Indonesia Post Graduate Program School of Environmental Science University of Indonesia Jakarta Indonesia Graduate Program Master of Business Administration Faculty of Economic and Business University of Gadjahmada Yogyakarta Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

In carrying out drilling projects at PT China Oilfields Services Limited ( COSL) Indo, especially Project 1 to Project 4, there were a mismatch between the initial plan of the project and the actualisation in the field because there were inhibiting factors in implementing the project. The purpose of the research was to look the factors that caused for the additional time in well drilling project at PT. COSL Indo, to build a relationship model of these factors, and formulate a strategy for the company to be able to overcome the occurrence of additional time in the future project. Data analysis was conducted by using a factor analysis method and the location of the research was carried out in just one company with 102 valid respondents. The results of the research indicate that there are four factors that influence the time gap, namely Project Management Activities, Risk Analysis and Procurement, Manage Stairs, and Project Planning Development. By knowing the causes of the delay, companies can find the best solutions for future learning and the impact on environmental, social and economic problems can also be anticipated so that sustainable development occurs in the context of petroleum management. © Published under licence by IOP Publishing Ltd.

关键词： Risk analysis

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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

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IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

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The Second Order Asymptotics of Covert Communication over AWGN Channels

arXiv

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arXiv 2023年

作者： Yu, Xinchun Wei, Shuangqing Huang, Shao-Lun Zhang, Xiao-Ping The Institute of information and Data Science Tsinghua Shenzhen International Graduate School Shenzhen China The Division of Electrical and Computer Engineering School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States

This paper investigates the asymptotics of the maximal throughput of communication over AWGN channels by n channel uses under a covert constraint in terms of an upper bound δ of Kullback-Leibler divergence (KL divergence). It is shown that the first and second order asymptotics of the maximal throughput are √nδ log e and (2)1/2(nδ)1/4(log e)3/4 · Q−1(ϵ), respectively. The technique we use in the achievability is quasiΕ-neighborhood notion from information geometry. For finite blocklength n, the generating distributions are chosen to be a family of truncated Gaussian distributions with decreasing variances. The law of decreasing is carefully designed so that it maximizes the throughput at the main channel in the asymptotic sense under the condition that the output distributions satisfy the covert constraint. For the converse, the optimality of Gaussian distribution for minimizing KL divergence under the second order moment constraint is extended from dimension 1 to dimension n. Based on that, we establish an upper bound on the average power of the code to satisfy the covert constraint, which further leads to the direct converse bound in terms of covert metric. © 2023, CC BY-NC-ND.

关键词： Gaussian distribution

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Domain Adaptation in Color-Shifted Datasets Through Adversarial Learning Balancing

Domain Adaptation in Color-Shifted Datasets Through Adversar...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Hidayaturrahman Agung Trisetyarso Iman Herwidiana Kartowisastro Widodo Budiharto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusatara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusatara University Jakarta Indonesia Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation.

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CYPHONIC Adapter: Enabling Secure P2P Connectivity for Diverse IoT Devices

CYPHONIC Adapter: Enabling Secure P2P Connectivity for Diver...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Ren Goto Kazushige Matama Ryouta Aihata Michiyo Suda Hidekazu Suzuki Katsuhiro Naito Graduate School of Business Administration and Computer Science Aichi Institute of Technology Toyota Aichi Japan Faculty of Information Science Aichi Institute of Technology Toyota Aichi Japan Faculty of Information Engineering Meijo University Nagoya Aichi Japan

Peer-to-Peer (P2P) communication emerges as a potent paradigm in distributed processing models. However, network accessibility challenges and inherent limitations in ex-isting solutions have impeded the full realization of P2P's poten-tial. To address these issues, we've developed CYber PHysical Overlay Network over Internet Communication (CYPHONIC), an innovative solution enabling P2P-based direct device-to-device communication while ensuring robust zero-trust security. Additionally, we introduce the concept of a gateway device equipped with a CYPHONIC adapter to cater to conventional devices, often referred to as general nodes. Our paper focuses on implementing a remote camera monitoring solution, providing secure access to surveillance cameras. We aim to demonstrate that our overlay network seamlessly connects all CYPHONIC clients, including general nodes. We also present a comprehensive case study centered on remote camera monitoring. This case study showcases the effortless connectivity enabled by our overlay network, highlighting how CYPHONIC clients, including general nodes, can efficiently interconnect.

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Thy-Sys: A Preliminary Thyroid Wellness Assessment Through Machine Learning Using Pathological Factors 1

Thy-Sys: A Preliminary Thyroid Wellness Assessment Through M...

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1st International Conference in information and Computing Research, iCORE 2021

作者： Francisco, Ismael R. Ferolin, Mary Bernadette J. Pena, Christine F. Ferolin, Rosana J. University of San Carlos Department of Computer Information Sciences and Mathematics Cebu City Philippines University of San Carlos Graduate Engineering Program School of Engineering Department of Computer Engineering Cebu City Philippines

ISBN: (纸本)9781665402101

The thyroid gland is a butterfly-shaped organ located lower front of the neck that plays a critical role in one's overall well-being. According to survey, thyroid dysfunction is observed in 8.53% of Filipino adults aged 42 to 62 years old, implying that 1 out of 12 Filipino adults has some form of thyroid function abnormality. To promote awareness of thyroid health, the researchers developed the Thyroid System for Wellness Assessment (Thy-Sys) which aims to determine the wellness of a person's thyroid through machine learning. The system assesses the user's thyroid wellness based on their responses to questions related to observable characteristics of the more common thyroid diseases like hypothyroidism and hyperthyroidism. Prior to the development, four machine learning models, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and SVM-KNN were evaluated in classifying the state of the thyroid through pathological factors. Training and testing of the machine were done over a dataset with 1,464 entries and a total of 18 pathological attributes. 10-fold cross-validation was performed to verify the models' accuracy while observing other metric scores. The SVM-KNN model garnered the highest accuracy of 99.55% with 98.75% precision, and 98.61 % recall and F1 scores, and was integrated into the system. © 2021 IEEE.

关键词： Decision trees

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Pain Classification Using Statistical Feature Extraction Using Machine Learning Approach: A Pilot Study

Pain Classification Using Statistical Feature Extraction Usi...

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IEEE International Conference on Control System, Computing and engineering (ICCSCE)

作者： Sudimanto Agung Trisetyarso Iman Herwidiana Kartowisastro Widodo Budiharto Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Computer Engineering Department Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta Indonesia

Electrodermal activity (EDA) is a general term for all electrical phenomena occurring on the skin, both passive and active. EDA measurements are used by researchers to measure levels of stress, emotion, mental strain, and so on. Measuring human stress levels, emotions, and mental strain are generally associated with the skin conductance response. The function GSR sensor is not only used to read people’s psychology but also can be used as a pain sensor used to read the degree of pain in the skin. This pilot study uses sample data from ***. The *** data is galvanic skin response sensor data. The output of this sensor is the conductivity value that occurs in the skin. The data obtained from *** will be extracted using the mean, standard deviation, maximum, minimum, RMS, skewness, and peak-to-peak characteristics. The extracted functions are selected using the forward selection method. The results of the feature selection are three features with an accuracy percentage greater than 50%, namely the mean feature, the RMS feature, and the skewness feature. The machine learning models used are bagged tree, SVM, and K-NN models. Of the three models used, the bagged tree model has the highest accuracy rate, at 98.05%, with an F1 score is 0.9807. The KNN model with k=10 has the lowest level of accuracy compared to other models, at 96.75%.

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