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Musical Analysis of a computer Music Project, by computer-Assistance, for computer Music Applications 26

Musical Analysis of a Computer Music Project, by Computer-As...

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26th International computer Music Conference, ICMC 2000

作者： Liu, Jian-Li Hiraga, Rumi Igarashi, Shigeru Doctoral Program in Engineering University of Tsukuba Japan Bunkyo University Japan Department of Computer Science University of Tsukuba Japan

Daphne is a computer-assisted musical analysis system that analyzes musical structure and other information necessary for computer music applications, especially for performance synthesis. The obtained analysis results are stored in a style which extends the information of a note event so that application systems can share analysis information. Providing sheets of real musical scores, Daphne’s design includes a unique function of analysis database which stores analysis result of multiple editions of a piece. Users can make use of the database to consider how performance is generated based on the interpretation of each edition. In this paper, we describe how analysis information is stored and shared among computer music applications — performance synthesis with rules, performance visualization, and automated accompaniment. © 2000 ICMC. All Rights Reserved.

关键词： computer music

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Support vector machine and neural network algorithm approach to classifying facial expression recognition 5

Support vector machine and neural network algorithm approach...

引用

5th International Conference on Informatics and Computing, ICIC 2020

作者： Fatchan, Muhamad Hery Purnomo, Mauridhi Affandy Zainul Fanani, Ahmad Marlinda, Linda Doctoral Program of Computer Science Universitas Dian Nuswantoro Semarang Indonesia Institut Teknologi Sepuluh Nopember Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9781728180472

One of the obstacles to detecting facial emotions is the lack of analysis in the process of emotional expression on human faces based on a photo camera. Identifying features and implementing different feature combinations can improve accuracy, in most cases. Trial detection through the use of feature vectors with higher dimensions that contain facial emotions can provide a lot of information on the accuracy of the data. The purpose of this study is to determine the highest accuracy results from the comparison of Support Vector Machine and Neural Network Algorithms. It can be seen that the Support Vector Machine has the highest accuracy value, which is 87%, while the Neural Network algorithm only has an accuracy of 85%. The results of the ROC Curve show that the Support Vector Machine achieves the best AUC value, namely 0.97. Comparison between Neural Network algorithm and Support Vector Machine for prediction of emotions using data types with many variations of emotions including anger, sadness, happy and surprise. The Support Vector Machine method is an accurate algorithm and this method is also very dominant over other methods. Based on the Accuracy, AUC, and T-test method, this method falls into the best classification. © 2020 IEEE.

关键词： Face recognition

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Survey of data mining techniques for intrusion detection systems 5

Survey of data mining techniques for intrusion detection sys...

引用

5th International Conference on Informatics and Computing, ICIC 2020

作者： Cahyo, Aditya Nur Winarko, Edi Musdholifah, Aina Doctoral Program of Computer Science Universitas Gadjah Mada Yogyakarta Indonesia Universitas Gadjah Mada Department of Computer Sciences and Electronic Yogyakarta Indonesia

ISBN: (纸本)9781728180472

Nowadays, the number of cyber-attacks is increasing;therefore, it is important for companies or organizations to secure their networks. Intrusion Detection System (IDS) is one of the core components used to secure networks. IDS's role is to detect an attack or attempted attack. The security tools are growing rapidly, along with the increasing number and variety of attacks carried out. The same with the development of IDS, a lot of research has been done to improve the ability of IDS to detect an attack. Many studies have used data mining techniques on IDS. This paper aims to conduct a comprehensive survey on the use of data mining techniques on IDS in the last five years. In this survey, we category the techniques used into several categories, and discuss each category in detail. © 2020 IEEE.

关键词： Surveys

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File Classification Containing Secret Key in Git Repository Using Character Level CNN and Shannon Entropy 4th

File Classification Containing Secret Key in Git Repository ...

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4th International Conference on Advances in Computational science and Engineering, ICACSE 2023

作者： Muhammad, Rafie Musdholifah, Aina Master Program in Computer Science FMIPA UGM Yogyakarta Indonesia Department of Computer Science and Electronics FMIPA UGM Yogyakarta Indonesia

ISBN: (纸本)9789819729760

Current technological developments are directly proportional to the number of information technology projects made. This project is usually in the form of a program formed from a code. To facilitate version control and contributions between developers, git tools are usually used which are placed on an online platform such as Github. If a repository on an online platform contains sensitive information such as secret key variables and the repository can be accessed by the public, then this can be used to harm the company or organization that owns the repository. Several ways have been done before to detect this secret key variable by using the regex method and using machine learning to minimize false positive results. In this research, we will try to build a text or file classification model that contains secret key variables using the character level CNN and Shannon entropy. The Shannon entropy algorithm is used during pre-processing to retrieve the group of words with the largest entropy value and the CNN character level will be used because it is considered suitable for this case since the value of the secret key is abstract and does not require a sequence context for the said secret key in a file. The results of this study were able to obtain an accuracy value of 96.1% for the accuracy value for the classification of files containing secret key. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Character level CNN Git repository Secret key Shannon entropy Text classification

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U-Net Based Vegetation Segmentation for Urban Green Space (UGS) Regulation in Yogyakarta City 8

U-Net Based Vegetation Segmentation for Urban Green Space (U...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Putri, Delfia Nur Anrianti Wijaya, Raden Bagus Muhammad AdryanPutra Adhy Nailfaaz Wahyono Computer Science Undergraduate Program Faculty of Mathematics and Natural Science Sleman Indonesia Faculty of Mathematics and Natural Science Department of Computer Science and Electronics Sleman Indonesia

ISBN: (纸本)9798350342604

Urban Green Space (UGS) plays a vital role in maintaining the balance of urban ecosystems. In Yogyakarta City, urban development and an increase in population accompanied by a decrease and insufficiency in the proportion of UGS correlate with rising urban temperatures. Monitoring the availability of UGS can serve as a foundation for decision-making in managing the city's UGS. With advancements in technology and computing in the field of computer Vision (CV), it's possible to recognize UGS from the geometric patterns of satellite images. This study proposes a vegetation segmentation method using satellite images and a Deep Learning (DL) method with the U-Net architecture to estimate the availability of UGS. Evaluation results show that the image dataset without preprocessing with the Red Green Blue (RGB) color channel gives an average Intersection over Union (IoU) of 80.74%. These results are better than data that underwent grayscaling and contrast stretching preprocessing. Based on a case study conducted in three sub-districts in the Yogyakarta region, the constructed U-Net model gave an average IoU evaluation of 60.88%. These results demonstrate the model's capability to provide a reference in calculating the UGS area at the sub-district level, which can then be utilized by the Yogyakarta City government to analyze the availability of UGS more efficiently. With this analysis, the government can make strategic decisions to manage UGS and create a sustainable urban environment. © 2023 IEEE.

关键词： Remote sensing

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Weighted majority voting by statistical performance analysis on ensemble multiclassifier 5

Weighted majority voting by statistical performance analysis...

引用

5th International Conference on Informatics and Computing, ICIC 2020

作者： Wardoyo, Retantyo Musdholifah, Aina Angga Pradipta, Gede Hariyasa Sanjaya, I. Nyoman Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Doctoral Program Department of Computer Science and Electronics Yogyakarta Indonesia Faculty of Medicine Udayana University/Sanglah General Hospital Department of Obstettics and Gynecology Bali Indonesia

ISBN: (纸本)9781728180472

Ensemble classifier method uses several base classifiers to predict a new test instance, while weighted majority voting has a scheme providing different weight values using several measurement parameters. However, the determination of the appropriate weight value to obtain an adequate ensemble model is a critical issue. This study, therefore, proposed a novel weighted majority voting scheme involving five base classifiers based on ensemble learning, including Random Forest, Decision Tree (C.45), Gradient Boosting Machine, XGBosst, and Bagging. The weighting scheme was formulated by analyzing the base classifier performance measured from the parameters of accuracy, recall, precision, and F Measure. The experiments were conducted using public datasets and umbilical cord data owned and the results showed the proposed method has the ability to improve performance in comparison with the base classifier and methods from previous studies with the best recorded in umbilical cord dataset with an average accuracy of 86.1%, a precision of 86%, a recall of 86%, and an F measure of 86%. © 2020 IEEE.

关键词： Decision trees

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Multiclassifier for Aspect-Based Sentiment Analysis on Indonesian Reviews of Kredit Pintar Online Lending App 12

Multiclassifier for Aspect-Based Sentiment Analysis on Indon...

引用

12th International Conference on Information and Communication Technology, ICoICT 2024

作者： Yuniar, Nanda Musdholifah, Aina Universitas Gadjah Mada Undergraduate Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350363432

Aspect-based sentiment analysis is carried out to determine user sentiment's polarity while still considering the aspects of the discussion so that all information in the reviews can be represented. The multiclassifier in this research means that two classifiers are used, the first is an aspect classifier to perform aspect classification, and the second is a sentiment classifier to perform sentiment classification based on its aspect. Two approaches were studied according to their classification work, namely, individual model in the form of an aspect classification and sentiment classification based on the aspects individually and sequentially model by testing the aspect and sentiment models sequentially. This study evaluates the performance of Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) algorithms and their combination in sequential testing. In addition, to overcome imbalance dataset, the use of oversampling types is also compared. In the individual approach, the SVM model with random oversampling showed the best performance with average accuracy, precision, recall, and f1 score of 92%, 86%, 80%, and 83% for the aspect model and 86%, 81%, 80%, and 80% for the sentiment model, respectively. While in the sequential approach, the combination of SVM-SVM with oversampling shows the best performance with an average accuracy, precision, recall, and f1-score of 79%, 56%, 50%, and 53%, while the performance of other model combinations is only in the maximum range of 77% accuracy, 55% precision, 51% recall, and 52% f1-score. © 2024 IEEE.

关键词： Support vector machines

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Spatio-Temporal Action Verification of Basketball Travelling Dribble Violation Using Mediapipe-YOLO-LSTM Framework 3

Spatio-Temporal Action Verification of Basketball Travelling...

引用

3rd International Workshop on Intelligent Systems, IWIS 2023

作者： Wicaksono, Haryo Bimo Wahyono Universitas Gadjah Mada Undergraduate Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350305043

Basketball is a very dynamic game where the movements the players make can be fast and subtle, yet fine-grained. As such the difference between an event occurring and not occurring can be small and occasionally unnoticeable, and the sequence of frames of which the dribble is done matters tremendously in order to determine whether a violation takes place. As such, a spatio-temporal action verification model is needed to assist referees in making decisions, as it is almost instantaneous in nature. The challenge is to achieve balance between speed and accuracy, since the violation detection has to be fast enough for real-time use. Mediapipe Pose Estimation is chosen for its lightweight nature and capability for human joint feature extraction, and YOLO is trained to detect and extract basketball coordinates and person in order to pinpoint a region of interest for Mediapipe Pose Estimation in the case of failure to detect a player. The raw dataset consists of videos of basketball players dribbling properly and committing a travelling violation. Long Short- Term Memory (LSTM) is used to model the temporal dynamics of the movements the players make, in the form of a binary classification as to whether the violation occurs. Fine-tuning is done on various hyperparameters of the LSTM model, such as the batch size, layers, number of units, optimizer, dropout rate, loss function, etc. Stacked LSTM architecture is found to be the most suitable model, at an accuracy of 0.8690. The resulting pipeline manages to run at least 10 FPS, with enough overhead to theoretically achieve a higher framerate, after various methods of optimizations. © 2023 IEEE.

关键词： Long short-term memory

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Gesture Recognition in Indonesian Sign Language Using Hybrid Deep Learning Models 3

Gesture Recognition in Indonesian Sign Language Using Hybrid...

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3rd International Workshop on Intelligent Systems, IWIS 2023

作者： Daffa Izzalhaqqi, Muhammad Yusuf Wahyono Universitas Gadjah Mada Undergraduate Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350305043

Sign language comprises unique hand gestures used to communicate between the hearing and hearing-impaired communities, with variations based on regions or countries. Due to the increasing global prevalence of hearing loss, there is a growing need for efficient sign language recognition systems. This paper presents the development of a hybrid CNN-LSTM model specifically designed to recognize static alphabetical gestures in Indonesian Sign Language (BISINDO). Primary data was collected to address the issue of publicly available dataset. These self-collected images underwent augmentation and preprocessing stages prior to model training. The architecture and hyperparameter configuration of the hybrid model were fine-tuned using the Randomized Search CV method. Performance analysis was conducted on both the hybrid model, its constituent models, and state-of-the-art model. The experimental results revealed remarkable performance by the hybrid model with excellent accuracies of 99.60%, 84.87%, and 98.00% on the training, validation, and testing sets respectively, along with a macro average score of 0.98, indicating high precision, recall, and F1-score across all the classes. However, it was observed that the VGG-16 and CNN exhibited slightly superior performance compared to the hybrid model, while the LSTM demonstrated the least favorable performance among the models. © 2023 IEEE.

关键词： Audition

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Optimal grid quantization

Optimal grid quantization

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作者： Mingzhou, Song Haralick, Robert M. Department of Computer Science Queens College of CUNY Flushing NY 11367 Computer Science Doctoral Program Graduate Center of CUNY New York NY 10016

Optimal quantization, a non-parametric technique for pattern recognition, determines a compact and efficient density representation of data by optimizing a global quantizer performance measure, which is a weighted combination of average log likelihood, entropy and correct classification probability. In multi-dimensions, we obtain the quantization grid using genetic algorithms. Smoothing is an important aspect as it affects the generalization ability of the quantizer. We propose a fast k neighborhood smoothing algorithm. Optimal quantization is much more efficient than other non-parametric methods. For not very well separated Gaussian mixture models, it produces much better results than the EM algorithm, which fails to converge to the true parameters of the underlying density. © 2002 IEEE.

关键词： Distributed computer systems

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