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An Investigation of Different Positive and Negative Training Policies for the Task of Hierarchical Music Genre Classification

An Investigation of Different Positive and Negative Training...

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Cinthya Oestreich Silva Renato Tateiwa Suguiy Carlos N. Silla Graduate Program in Computer Science (PPGIA) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Brazil

Within the growth of the music stream companies, the need to classify correctly the musics in their catalogs by genres becomes a really important task. With that, they can suggest songs suitable to the music taste of the user, offering a better user experience. Also, because of the size of their catalogs, this task ought to be done automatically. One way to handle the music genre classification problem is to employ a hierarchical classification approach. In this approach, there is a parent-child relation between the genres, characterizing a hierarchical classification problem. This kind of problem can be tackled by using different approaches, in this paper we investigated the use of the Local Classifier per node approach and different positive and negative training policies. The main contribution of this work is to investigate the impact of different positive and negative training samples policies to train the different classifiers with different versions of the Free Music Archive Database. The computational results show that extracting features from just a part of the music produce similar results from analysing the entire music. In addition, the Random Forest was the best classifier and the less inclusive policy produced the highest scores.

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Identification of Diseases in Greenhouse Tomato Cultivation: A New Dataset and Baseline Results

Identification of Diseases in Greenhouse Tomato Cultivation:...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Grasielli B. Zimmermann Marcelo E. Pellenz Alceu De Souza Britto Yandre M. G. da Costa Graduate Program in Computer Science (PPGIa) Pontifical Catholic University of Parana (PUCPR) Curitiba PR Brazil State University of Maringá (UEM) Maringá Brazil

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Plant diseases are one of the factors that compro-mise food production goals. Tomatoes are one of the world's most consumed vegetables and are widely affected by various diseases. Tomato cultivation in greenhouses enables continuous production. In this context, this research focuses on identifying diseases in greenhouse tomato cultivation scenarios. For this study, new datasets were created with two image sizes: the Tomato Leaf Image Dataset (TLID) with image sizes of 256 x 256 pixels and 15,256 images, and the Patch Tomato Leaf Image Dataset (PTLID) with patch sizes of 32 x 32 pixels and 227,218 images. Both datasets comprise seven classes, including four types of diseases, two combinations of diseases on the same leaf, and the healthy leaf. Machine Learning techniques have been widely used to identify plant diseases. This work presents two machine learning methods tested with both datasets. In the proposed models, three convolutional neural networks were combined: a customized CNN, VGG19, and Resnet50, along with two voting classification methods using Hard and Soft de-cisions. The evaluation conducted on the datasets demonstrated that using patches significantly improves results, achieving an accuracy of 90.48%. This technique enables the identification of the disease stage.

关键词： Plant diseases Image segmentation Accuracy Databases Noise Greenhouses Machine learning Network architecture Convolutional neural networks Residual neural networks

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Music Recommendation System for Shared Environments

Music Recommendation System for Shared Environments

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： João G. Bracaioli Vitório Carlos N. Silla Graduate Program in Computer Science (PPGIA) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Brazil

The increasing amount of information available nowadays has led to a growth in research on recommendation systems. In the context of music, these systems help individuals filter and discover new music and styles based on their common tastes. However, while most music recommender solutions used by streaming platforms are based on individual characteristics, group recommendations are still underexplored. This paper provides a brief analysis of group recommendation systems and their testing, as well as proposes a real-time music recommendation system for groups that consider shared environment contexts, such as time of day and location, to investigate whether contextual information can improve the selection of songs for a group. The study evaluates different aggregation strategies for individual preferences and uses experiments to test the effectiveness of the proposed system. The results indicate that taking into account contextual information improves user satisfaction and song selection for playlists, making the proposed system promising for music recommendation in shared environments.

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Bringing More Girls to STEM: Initiatives within the Frontiers in Education Conference

Bringing More Girls to STEM: Initiatives within the Frontier...

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Frontiers in Education (FIE) Conference

作者： Isabela M. Montingelli Andreia Malucelli Sheila Reinehr Carlos N. Silla Graduate Program in Computer Science (PPGIa) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba PR Brazil

STEM (science, Technology, Engineering, and Math) courses are male-dominated spaces where women are the minority most of the time. Even now, when women are the majority in universities, the computer science and engineering degrees are still uneven when it comes to gender. There have been interesting initiatives in the past Frontiers In Education (FIE) conferences about several of the issues related to bringing more girls into the computer science and engineering fields of study. Some of these works typically address one or more challenges associated with increasing the number of women in computer science and engineering by using different approaches and targeting different age groups. The majority of the studied works focus on raising awareness through the development of boot camps, workshops, programs and other initiatives. Other works investigate the attitude towards computing careers and the lack of interest in these areas by female students, regardless of age. There are also works that are focused on female student's retention and success in their academic degrees. All of the reviewed works have one major goal in common: bringing more girls into STEM fields, such as engineering and computer science. In order to achieve this goal, the existing initiatives employ disciplinary and interdisciplinary approaches. Although the majority of the work's main technical focus is on teaching programming, this is achieved by using a variety of approaches and resources. For example, there are works aimed at developing digital games, approaches that use robotics with Arduino and also approaches that integrate computer programming with musical instruments, among others. We did not find a study that aggregated and analyzed these initiatives. The main goal of this paper is to analyze past FIE editions to identify and aggregate studies related to attracting girls to STEM areas. The method was based on systematic review procedures considering FIE 2010 thru FIE 2022. We analyze and ev

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A Machine Learning-Based Digital Twin Model for Pressure Prediction in the Fuel Injection System 48

A Machine Learning-Based Digital Twin Model for Pressure Pre...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Duarte, Edwin P. Viegas, Eduardo K. Santin, Altair O. Graduate Program in Computer Science PPGIa Pontificia Universidade Catolica Do Parana-Pucpr Brazil Robert Bosch GmbH Engineering Systems Curitiba Brazil Secure Systems Research Center Abu Dhabi United Arab Emirates

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Over the last years, the engine calibration task has mostly been conducted based on the engineers' knowledge. As a result, considering the complexity of modern engines, finding the most suitable configuration for each situation has become an impractical and expensive task. Apart from causing engines to be produced with inadequate calibration configuration, it can also decrease the lifespan of their components, degrading their efficiency. This paper proposes a machine learning-based digital twin model for pressure prediction in a fuel injection system, split into two steps. First, we extract statistical engine features based on a predefined time window to represent the engine behavior over time. Second, a digital twin implemented through a machine learning model is used to predict pressure levels in the fuel injection system. As a result, the predicted values can be used to assist the engine common rail system module in avoiding undesired engine states. Experiments performed on a new dataset, built over a real diesel-based engine, consisting of 208 features and over 1.3 million instances, have shown the feasibility of our proposal. The proposed scheme can predict in an advance time of 0.1 seconds the pressure levels for a fuel injection system with only 0.057 RMSE. Moreover, it increases its error rate by only 10.6% if a 0.5-second time advance is required. © 2022 IEEE.

关键词： Machine learning

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Towards a Reliable Hierarchical Android Malware Detection Through Image-based CNN

Towards a Reliable Hierarchical Android Malware Detection Th...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Jhonatan Geremias Eduardo K. Viegas Altair O. Santin Alceu Britto Pedro Horchulhack Pontificia Universidade Catolica do Parana (PUCPR) - Graduate Program in Computer Science (PPGIa) Brazil Secure Systems Research Center - Technology Innovation Institute (TII) Abu Dhabi

The number of Android malicious applications keeps growing as time passes, even paving their way to official app markets. In recent years, a promising malware detection approach makes use of the compiled app source codes (dex), through convolutional neural networks (CNN) as an image classification task. Unfortunately, current proposals often rely on unrealistic datasets, focusing their detection on the mal-ware families, while neglecting the detection of malware apps in the first place. In this paper, we propose a reliable and hierarchical Android malware detection through an image-based CNN scheme, implemented twofold. First, Android malware classification is performed in a hierarchically-structured local manner, initially identifying malware apps, then, their related family. Second, to ensure reliability and improve classification accuracy, only highly confident classified apps are reported, in a classification with reject option rationale. Experiments performed in a new dataset with over 26 thousand Android apps, divided into 29 malware families, compounding over 13 GB of app dex images, have shown that current image-based CNN for malware detection is unable to provide high detection accuracies. In contrast, our proposed model is able to reliably detect malware apps, improving the true-negative rates by up to 5.5%, and the average true-positive rate of the malware families of accepted apps by up to 12.7%, while rejecting only 10% of Android apps.

关键词： Source coding Focusing Malware Convolutional neural networks Reliability Proposals Task analysis

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Multi-Modal Music Mood Classification with Dynamic Classifier Selection

Multi-Modal Music Mood Classification with Dynamic Classifie...

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： João G. Bracaioli Vitório Alceu S. Britto Junior Costa Yandre M. G. Carlos N. Silla Graduate Program in Computer Science (PPGIA) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Brazil Department of Informatics State University of Maringá (UEL) Maringá – Paraná Brazil

In this paper we investigate the suitability of different types of Dynamic Classifier Selection approaches for the task of multimodal music mood classification. The dynamic selection methods evaluated were: KNORA-UNION, KNORAELIMINATE, Dynamic Ensemble Selection Performance, Overall Local Accuracy, Local Class Accuracy, Multiple Classifier Behaviour, A Priori and A Posteriori. The experiments were performed using the Brazilian Music Mood Database, which is a multimodal database, containing the audio signal itself, beyond their visual representation (i.e. spectrogram) and the lyrics. The obtained results have shown that the use of dynamic classifier selection methods can improve the classification results for the task at hand.

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Intrusion Detection Model Updates Through GAN Data Augmentation and Transfer Learning

Intrusion Detection Model Updates Through GAN Data Augmentat...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Pedro Horchulhack Eduardo K. Viegas Altair O. Santin Jhonatan Geremias Pontificia Universidade Catolica do Parana (PUCPR) ― Graduate Program in Computer Science (PPGIa) Brazil Secure Systems Research Center ― Technology Innovation Institute (TII) Abu Dhabi

ISBN: (纸本)9781665435413

Current machine learning techniques for network-based intrusion detection cannot handle the evolving behavior of network traffic, requiring periodic model updates to be conducted. Besides requiring huge amounts of labeled network traffic to be provided, traditional model updates demand expressive computational costs. This paper proposes a new feasible model update procedure implemented in two steps. First, we use a Generative Adversarial Network (GAN) to augment the sampled network traffic. Next, we use the augmented dataset to perform model updates through a transfer learning-based approach. Thus, our model can decrease both the number of instances that must be labeled and the computational costs during model updates. Our experiments on a one-year dataset with over 8 TB of data show that literature techniques cannot handle changes in network traffic behavior. In contrast, the proposed model without updates improved true-positive rates by up to 25.6%. With monthly model updates, it requires only 14% of computational costs and 2.3% of instances to be provided.

关键词： Computational modeling Transfer learning Intrusion detection Telecommunication traffic Generative adversarial networks Data models Computational efficiency

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Towards Multi-view Android Malware Detection Through Image-based Deep Learning

Towards Multi-view Android Malware Detection Through Image-b...

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International Wireless Communications and Mobile Computing Conference, IWCMC

Over the last years, several works have proposed highly accurate Android malware detection techniques. Surprisingly, modern malware apps can still pave their way to official markets, thus, demanding the provision of more robust and accurate detection approaches. This paper proposes a new multi-view Android malware detection through image-based deep learning, implemented threefold. First, apps are evaluated according to several feature sets in a multi-view setting, thus, increasing the information provided for the classification task. Second, extracted feature sets are converted to an image format while maintaining the principal components of the data distribution, keeping the information for the classification task. Third, built images are jointly represented in a single shot, each in a predefined image channel, enabling the application of deep learning architectures. Experiments on a new version of a publicly available Android malware dataset composed of over 11 thousand Android apps have shown our proposal's feasibility. It reaches true-negative rates of up to 99.5% when implemented with a single-view approach with our new image-building technique. In addition, if our proposed multi-view scheme is used, the classification accuracies of malware families become more stable, reaching a true-positive rate of up to 98.7%.

关键词： Deep learning Wireless communication Buildings computer architecture Feature extraction Malware Proposals

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Automatic Music Score Difficulty Classification

Automatic Music Score Difficulty Classification

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Diogo Steinke Deconto Erick Luiz Fontoura Valenga Carlos N. Silla Graduate Program in Computer Science (PPGIA) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Brazil Computer Music Technology Laboratory (Onloop) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Brazil School of Information Technology Universidade Positivo Curitiba Brazil

Learning a musical instrument is challenging in many different aspects. One important aspect is the motivation of the music students. One common practice to keep music students engaged is to use songs that the students like. However, identifying an appropriate music score for the music students that is compatible with the specific skill level and music taste of each student can be a challenging task. Defining the difficulty of each score manually is a complex task, as it requires theoretical knowledge and mastery of a particular musical instrument and music theory. In this context, the automatic music score difficulty classification task can help music teachers and students by automatically classifying music scores according to their difficulty. The main contribution of this work is to evaluate the use of two approaches based on the musicXML files and different classification algorithms for this task concerning three different music instruments, namely the violin, the piano and the acoustic guitar. Our experiments show interesting results for all the musical instruments.

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