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7th International Conference on computer Science and Computational Intelligence, CSCI 2022

作者： Ramadhan, Andhika Naafi Pane, Kharisma Naufal Wardhana, Kun Rifki Suharjito Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Industrial Engineering Department BINUS Graduate Program-Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

Technological developments have resulted in a trend of cryptocurrencies that use a technology called blockchain to create and record all transactions made into a digital ledger. Along with the emergence of the trend of cryptocurrencies, this development has also resulted in crimes that hit the digital world, such as data leakage and cyber espionage. This threat can be prevented by applying blockchain technology to the database that has been used. Therefore, we need a system that can facilitate the use of blockchain technology and can process data from an existing relational database into a blockchain-based database. The system developed in this study was built with the FastAPI framework that uses the Python programming language and the React framework that uses the Javascript programming language. This system was tested using Katalon and Wireshark software to perform throughput testing and man-in-The-middle attacks. Evaluation of this system is assessed based on the average throughput time and also the results of Wireshark packet capture. The system designed in this research is expected to help overcome interoperability problems when using blockchain and improve relational database integrity. The results of the test show that the system is safe from man-in-The-middle attacks while sending data through API and has a faster throughput time than BigchainDB system by 4.151 seconds. © 2022 Elsevier B.V.. All rights reserved.

关键词： Blockchain

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The Effect of Bitrate Control Methods of MPEG-DASH Streaming on Multi-View Video and Audio QoE

The Effect of Bitrate Control Methods of MPEG-DASH Streaming...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Toshiro Nunome Yamato Wakayama Department of Engineering Graduate School of Engineering Computer Science Program Nagoya Institute of Technology Nagoya Japan

This paper investigates the effect of bitrate control methods on QoE of multi-view video and audio streaming with MPEG-DASH. We adopt three bitrate control methods for conventional single-view video streaming to the MVV-A system with MPEG-DASH. We conduct a subjective experiment changing available network bandwidth and investigate the effect of the methods on QoE.

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Resume Recommendation using RNN Classification and Cosine Similarity 7th

Resume Recommendation using RNN Classification and Cosine Si...

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7th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2023

作者： Huseyinov, Ilham Diallo, Issa Raed, Mhd Wasim Software Engineering Department Istanbul Aydin University Istanbul Turkey Graduate Institute of Science Artificial Intelligence and Data Science Program Istanbul Aydin University Istanbul Turkey Computer Engineering Department Istanbul Aydin University Istanbul Turkey

ISBN: (纸本)9783031437885

One way to save time and resources in the human recruitment and hiring process is to post open job positions on the Internet, but the overload of applications creates challenges for hiring managers and companies to select an adequate candidate. One of the solutions is to apply intelligent tools such as Deep Learning and recommender system algorithms to speed up the hiring process and identify the right candidates. In this paper, we propose a two-fold algorithmic approach to 1) building an RNN classifier for resume classification;2) using cosine similarity for resume recommendation to find a candidate that fits job requirements best after selecting all the resumes that belong to the right category. The performance of the proposed RNN classifier is evaluated in terms of accuracy, precision, recall, F1-score, and confusion matrix criteria. The experiment results have shown that the RNN classifier performs better than the other classifiers such as GNB, Linear SVM, RF, and BERT on the same dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Recommender systems

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A Fuzzy Decision Model for Assessing Digital Marketing Practices of Indonesian Companies

A Fuzzy Decision Model for Assessing Digital Marketing Pract...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Ditdit Nugeraha Utama Irmawan Rahyadi Jorge Tuñón Navarro Astika Prima Nitular Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Communication Department BINUS Graduate Program Master of Strategic Marketing Communication Bina Nusantara University Jakarta Indonesia Department of Communication Studies Universidad Carlos III de Madrid Getafe Spain

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

A standard of evaluating digital marketing practices is a challenging study to do. Many studies to do to find such a standard. Here, the study aims to develop a decision model as a standard to measure the performance of digital marketing activity conducted by several Indonesian companies. The fuzzy logic is the main method to develop the model via five selected parameters. Based on the expert’s evaluation, the constructed model can simulate indicating that the practices of Indonesia’s media company in digital marketing reach a performance value of 58.55 with a similarity value of 52.35% if compared with the manual calculation.

关键词： Fuzzy logic Seminars Companies Manuals Media Intelligent systems Information technology Standards

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Threats, Risks, and Mitigations in Cloud Computing Environment

Threats, Risks, and Mitigations in Cloud Computing Environme...

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International Conference on Informatics and Computing (ICIC)

作者： Nur Chalik Azhar Muhammad Zarlis Yulyani Arifin Benfano Soewito Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Information System Management Department Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Cloud computing has many benefits in various sectors. However, there are threats such as malware, data loss, and misuse of access rights, as well as risks, that occur in the cloud computing environment. This study aims to identify and further explore the types of threats, risks, and mitigation strategies in the cloud computing environment, as well as what risk mitigation components are needed to mitigate risks in the cloud computing environment. This research method uses Kitchenham because it is commonly used to conduct systematic literature reviews (SLR). Sources only refer to journals and proceedings indexed in the IEEE Explorer, ScienceDirect, Springer, ACM Digital Library, Wiley Online Library, and Emerald Insight database. The results of this study identified 173 papers based on keywords, 68 papers based on titles, and abstracts, and produced 59 selected papers based on inclusion and exclusion criteria, where the type of threat produced 20 findings, the type of risk 11 findings, and risk mitigation strategies 13 findings, and produced 4 findings for risk mitigation components. Based on the findings, it can be used to mitigate risks in the cloud computing environment.

关键词： Cloud computing Databases Prevention and mitigation Permission Libraries Malware Informatics Risk mitigation Systematic literature review

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The Information Systems and Model for Succession Planning in Higher Education Institutions

The Information Systems and Model for Succession Planning in...

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computer Applications (ICCA), IEEE Conference on

作者： Devyano Luhukay Ford Lumban Gaol Meyliana Harjanto Prabowo Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331534592

ISBN: (纸本)9798331534608

Succession planning is important in organization management. It is a strategic level management that involves not only finding the best successor but also including the development program for the successor so that they will be ready for the new prospective position. The integration and collaboration between all related stakeholders are important. Specifically, in succession planning at higher education, key positions play an important role so that institutional operations can run well. In this research, a succession planning model in higher education was built using the Systematic Literature Review(SLR), and the Software Development Lifecycle(SDLC) method was used for the application development, also, to be able to evaluate the implementation of one of the steps of the model, the simulation technique used machine learning (ML) to find the best algorithm for predicting the readiness of potential successors at higher education. Six algorithms were used: Logistic Regression, Decision Tree, K-Nearest Neighbor, Linear Discriminant Analysis, Gaussian Naïve Bayes, and Support Vector Machine. The data prepared for the simulation is based on the factors for the best successor candidate. The results obtained in this research are the succession planning model and the application design, as well as the Support Vector Machine (SVM) algorithm as the best algorithm for predicting the readiness status of successor candidates based on SVM’s accuracy score that is compared to the other algorithm in some variance amounts of data. The Succession Planning Model that is actualized in the application design provides a reference for the higher education institution to implement the Succession Model in their institution.

关键词： Support vector machines Machine learning algorithms Systematics Education Software algorithms Machine learning Predictive models Prediction algorithms Planning Information systems

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Classification of Highly Similar Fish Species Using Machine Learning

Classification of Highly Similar Fish Species Using Machine ...

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Leandro Magno C. Silva Franklin Cesar Flores Rosilene Luciana Delariva Gustavo Z. Felipe Yandre M. G. Da Costa Graduate Program in Computer Science State University of Maringá Federal Institute of Paraná Maringá Brazil Department of Informatics State University of Maringá Maringá Brazil Center for Biological and Health Sciences State University of Western Paraná Cascavel Brazil Graduate Program in Computer Science State University of Maringá Maringá Brazil

ISBN: (数字)9798350391886

ISBN: (纸本)9798350391893

Fish species inventory and monitoring is a timely concern due to the increasing environmental imbalance. In this sense, the development of machine learning strategies aiming to support automatic fish species identification is an important contribution, both in environmental and scientific terms. This study explores handcrafted features aiming to perform fish species identification in a quite challenging scenario, in which the inter-classes differences are minimal, as the species belong to the same family considering the hierarchy of biological classification (i.e. Characidae). For this purpose, it is proposed a set of features designed properly, aiming to capture visual properties closely related to each species that are difficult to be noted by experts in this domain of investigation. An additional contribution of this work is a dataset of 4870 images taken from 416 individuals from nine fish species. The Region of Interest (RoI) corresponding to the fish body is segmented, and horizontal and vertical histograms are collected from each sample, aiming to capture information related to the body shape, and other characteristics particular to each species. Experiments are performed using the Support Vector Machine (SVM), K-Nearest Neighbours (K-NN), Random Forest and Naive-Bayes classifiers and, in the best scenario, it is obtained an accuracy rate of 99%. This result corroborates the hypothesis that the use of handcrafted features specifically developed for this type of application can be quite effective in addressing this classification problem.

关键词： Support vector machines Image segmentation Visualization Histograms Image recognition Shape Fish

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An Object Driven Fuzzy Decision Model for Sugarcane Yield Prediction in Indonesia’s Sugar Industry

An Object Driven Fuzzy Decision Model for Sugarcane Yield Pr...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Ditdit Nugeraha Utama Muhammad Asrol Suharjito Christian Harito Napthaleni Muhammad Syafrudin Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul South Korea

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

A sugarcane yield of one plantation area depends on several independent variables. Practically it is challenging to predict accurately by using conventional methods. This study aims to develop a decision model based on a combination of fuzzy logic and object-oriented methods to predict sugarcane yield. The research is conducted in four main stages, employing object-oriented methods for model design and fuzzy logic for model construction. Object and activity diagrams are used for the object-oriented model design. The fuzzy membership functions employed are a combination of trapezoidal and triangular shapes. The resulting decision model can simulate 2,225 data from plantation areas in Indonesia. Based on the 10 examples of plantation area data in Indonesia, plantation number one obtained the largest sugarcane yield, which was 4.79%, with a similarity value of 0.90 (when compared to manual calculations as its ground truth). This similarity value is a higher value when compared to the average similarity value, which is 0.89.

关键词： Fuzzy logic Seminars Shape Object oriented modeling Plantations Manuals Predictive models Sugar industry Intelligent systems Information technology

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Spectral DefocusCam: hyperspectral imaging using defocus and a spectral filter array

Spectral DefocusCam: hyperspectral imaging using defocus and...

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Computational Optical Sensing and Imaging, COSI 2022

作者： Foley, Christian Monakhova, Kristina Yanny, Kyrollos Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley United States UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley United States

We propose a compact hyperspectral camera based on a focus-tunable lens and spectral filter array. The camera captures then fuses multiple defocused measurements to obtain a high-resolution hyperspectral volume. ©... 详细信息

ISBN: (纸本)9781557528209

关键词： Hyperspectral imaging

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Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation

arXiv

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arXiv 2023年

作者： Yandan, Zheng Anran, Hao Tuan, Luu Anh School of Computer Science and Engineering Singapore Interdisciplinary Graduate Program HealthTech Nanyang Technological University Singapore

Semi-supervised learning has been an important approach to address challenges in extracting entities and relations from limited data. However, current semi-supervised works handle the two tasks (i.e., Named Entity Recognition and Relation Extraction) separately and ignore the cross-correlation of entity and relation instances as well as the existence of similar instances across unlabeled data. To alleviate the issues, we propose Jointprop, a Heterogeneous Graph-based Propagation framework for joint semi-supervised entity and relation extraction, which captures the global structure information between individual tasks and exploits interactions within unlabeled data. Specifically, we construct a unified span-based heterogeneous graph from entity and relation candidates and propagate class labels based on confidence scores. We then employ a propagation learning scheme to leverage the affinities between labelled and unlabeled samples. Experiments on benchmark datasets show that our framework outperforms the state-of-the-art semi-supervised approaches on NER and RE tasks. We show that the joint semi-supervised learning of the two tasks benefits from their codependency and validates the importance of utilizing the shared information between unlabeled data. © 2023, CC BY.

关键词： Extraction

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