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Interdisciplinary Project-Based Learning: An Experience with Digital Games and Music Production Students

Interdisciplinary Project-Based Learning: An Experience with...

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Frontiers in Education (FIE) Conference

作者： Claudio Carvilhe Glaucio H. M. Moro Anderson Vermonde Jose G. Noronha Filho Carlos N. Silla Computer Music Technology Laboratory (Onloop) Pontifícia Universidade Católica do Paraná (PUCPR) Brazil Digtal Games Development Course Pontifícia Universidade Católica do Paraná (PUCPR) Brazil Graduate Program in Computer Science (PPGIA) Pontifícia Universidade Católica do Paraná (PUCPR) Brazil School of Information Technology Halmstad University Sweden

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice full paper presents an inter-disciplinary Project-Based Learning (IPBL) approach that relies on a challenge as a key part of the learning process. A project is proposed to the students from the perspective of creating a direct association between learning and doing. In this paper, we present a variation of PBL adding the interdisciplinary factor: we united students from different areas (digital games and music production) under the common objective of producing a complete sounded digital game. An additional difficulty was overcome since all classes took place remotely due to the Covid-19 pandemic. To accomplish this challenge, a remote structure of classes was created to facilitate interaction between students as much as possible. During one semester, 2 groups of undergraduates from these two areas studied different modules organized in parallel under the general objective of an integrative project. The integrated project spanned 17 weeks, with classes structured to accommodate the evolving needs of project development. Initially, the first three weeks operated independently, allowing for focused exploration within each module. Subsequently, collaborative efforts were emphasized during weeks 4, 6, 9, 10, 12, and 15, fostering synergy and collective progress across both domains. Game students were given the mission of producing a game from scratch. To music production students the mission was making a full package of sound and music to each respective game. Facing a common schedule, all students had periodic meetings to align production and deadlines. In the end 10 games were produced and sounded. Initial hypotheses were raised about what we expected because of this experience. To assist in the analysis of the results, all the students involved responded to surveys about what they experienced. The successes and the need for improvements observed are detailed in this paper based on the application of the proposed methodology during the period

关键词： Surveys COVID-19 Schedules Pandemics Music Collaboration Games Production

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A Reminiscent Intrusion Detection Model Based on Deep Autoencoders and Transfer Learning

A Reminiscent Intrusion Detection Model Based on Deep Autoen...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Roger R. dos Santos Eduardo K. Viegas Altair O. Santin Graduate Program in Computer Science (PPGIa) Pontifical Catholic University of Parana (Pontifícia Universidade Católica do Paraná) Brazil

ISBN: (纸本)9781728181059

Machine learning techniques for network-based intrusion detection often assume that network traffic does not change over time or that model updates can be easily performed. This paper proposes a novel, reminiscent intrusion detection model based on deep autoencoders and transfer learning to ease the model update burden in a twofold implementation. First, a deep autoencoder is used as an additional feature extraction stage to obtain a historical feature representation of network traffic. Second, at model updates, the deep autoencoder parameters are updated through a transfer learning procedure, thus, significantly decreasing the amount of needed labeled training data and the computational costs. Experiments performed on a 8TB dataset containing real and valid network traffic ranging for one year have shown that approaches in the literature cannot handle with the network traffic behavior changes over time, requiring impractical amounts of labeled data to be provided during model training tasks. In addition, if no model updates are performed, the proposed scheme can improve the true-negative rate by up to 23.9%. If done so, it can provide similar accuracy rates of traditional techniques while demanding only 22% of labeled training data and 28% of computational costs.

关键词： Training Computational modeling Transfer learning Intrusion detection Training data Telecommunication traffic Feature extraction

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Detection of Service Provider Hardware Over-commitment in Container Orchestration Environments

Detection of Service Provider Hardware Over-commitment in Co...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Pedro Horchulhack Eduardo K. Viegas Altair O. Santin Pontifícia Universidade Católica do Paraná (PUCPR) — Graduate Program in Computer Science (PPGIa) Curitiba Paraná Brazil Secure Systems Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665435413

The deployment of container-based services continues to increase as time passes, mainly due to its fast provision time and lower allocation overheads. Yet, the literature still neglects the performance degradation in containers due to multi-tenancy and service provider hardware over-commitment. This paper proposes a new hardware over-commitment detection for container orchestration environments, implemented twofold. First, the containerized hardware usage of deployed containers is continuously monitored in a non-intrusive manner, leveraging the container engine resource management interface. Second, collected features are used by a recurrent neural network model for detecting both container and service level hardware over-commitment, following a time-series rationale. Experiments run on a containerized Apache Spark distribution have shown that multi-tenancy and hardware over-commitment significantly affect its performance. In addition, our proposed model is able to detect hardware over-commitment with up to 91% of true-positive at the container level, and up to 93% true-positive at the service level.

关键词： Degradation Recurrent neural networks Cluster computing Containers Feature extraction Hardware Resource management

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Improving Intrusion Detection Confidence Through a Moving Target Defense Strategy

Improving Intrusion Detection Confidence Through a Moving Ta...

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2021 IEEE Global Communications Conference (GLOBECOM)

ISBN: (纸本)9781728181059

Despite the promising results reported in the literature, the intrusion detection schemes cannot deal with new network traffic behaviors making such proposals unfeasible to be deployed in production environments. This paper presents an intrusion detection model that relies on a moving target defense strategy to face new network traffic behavior in a two stage process. First, the system select the most suitable classifiers set to assign a class (normal or attack) according to the current event behavior. Second, we evaluate if the performed classification is reliable by validating its confidence values. The goal is to ensure that only the higher confident classifications from the most suitable classifiers are used to trigger intrusion detection alerts, keeping the system reliable over time. Experiments performed on a dataset that spans over 97GB of data with seven categories of network traffic shows that current machine learning techniques cannot cope with novel traffic behavior, failing to detect up to four new traffic categories. In contrast, the proposed model can select the most confident classifiers, reducing the average false-negative rates by up to 39%, regardless of the current network traffic category.

关键词： Adaptation models Conferences Intrusion detection Telecommunication traffic Production Machine learning Reliability

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Learning Game programming Through Narrative, Sound Effects, and Music in Text-Based Games: A Creative Approach to Digital Game Education

Learning Game Programming Through Narrative, Sound Effects, ...

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Frontiers in Education (FIE) Conference

作者： Glaucio H. M. Moro Claudio R. V. Carvilhe Bruno C. de Paula Carlos N. Silla Computer Music Technology Laboratory (Onloop) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba PR Brazil Polytechnic School and Digtal Games Development Course Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba PR Brazil Computer Music Technology Laboratory (Onloop) Graduate Program in Computer Science (PPGIa) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba PR Brazil

Within the tradition of Digital Game courses, the use and teaching of programming is seen as essential for students to work on skills and gain knowledge to develop an artifact. However, for students at a more basic level, this can be a major difficulty considering the complexity of understanding a language they have never seen before. Narrative, sound, and music are critical elements in game creation and can be used to teach programming concepts in a more intuitive and engaging way. Recently, the “Creative Experience: Text-Based Game Development” module has strengthened the use of narrative as a way of teaching programming concepts. Narrative creates contexts and stories for players, while sound provides feedback and guidance during the game. According to Kelleher (2006), the use of games to teach programming is an effective form of learning because it allows students to experiment with concepts in a fun and playful way and develops important skills such as problem-solving, critical thinking, and teamwork. The use of narrative, sound, and music in games can be an effective way to teach complex programming concepts clearly and engagingly. This is an article that addresses the strategies created in the digital game module “Creative Experience: Text-Based Game Development” exploring narrative, sound, and music as learning elements for programming and comparing the results within the period of two course offerings, one with only programming teaching and another with the introduction of narrative and sound as the basis for constructing this knowledge. It is hoped that these teaching strategies can contribute to the development of valuable skills in the field of education and programming.

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Towards Real-time Video Content Detection in Resource Constrained Devices

Towards Real-time Video Content Detection in Resource Constr...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jhonatan Geremias Altair O. Santin Eduardo K. Viegas Alceu S. Britto Graduate Program in Computer Science - PPGIa Pontifical Catholic University of Parana Curitiba Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Convolutional neural networks have been successfully applied for video content detection in the last years. However, such cognitive models usually demand the availability of several gigabytes of memory and present a low detection throughput, as a result, they are not feasible for resource-constrained devices, especially for real-time applications like video streaming. In this paper, we address real-time video content detection in resource-constrained devices in a threefold manner. First, we improve detection throughput by means of a frame sampling technique. Then, we propose a new evaluation measure towards proper deployment of convolutional neural networks in resource-constrained devices. Finally, we address the accuracy degradation caused by the porting of the convolutional neural network, applying a lightweight classification verification technique. The evaluation results, through a real-time demanding application, show that the proposed approach can detect up to 301 frames/sec, demanding only 9 megabytes of memory while reaching up to 89.3% of accuracy. Besides, we can increase the detection throughput by up to 10 times, with no effects on accuracy, and further increase accuracy without effects on processing demands.

关键词： Streaming media Throughput Real-time systems computer architecture Task analysis Feature extraction Performance evaluation

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Dossier: Decentralized Trust Model Towards a Decentralized Demand

Dossier: Decentralized Trust Model Towards a Decentralized D...

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International Conference on computer Supported Cooperative Work in Design

作者： Vanderson Botelho Kelvin V. Kredens Juliano V. Martins Bráulio C. Ávila Edson E. Scalabrin Graduate Program on Applied Computer Science PPGIA Pontifical Catholic University of Paraná Curitiba PR Brazil

ISBN: (纸本)9781538614839

This paper presents a trust model that aggregates multiple sources of information, such as: testimonies, context, social information, preconception, reputation, and proposes a new data structure, called Dossier. The aim is to raise the success rate that consumer agents have when contracting provider agents. To reach this objective we make two hypotheses: (i) every consumer agent is able to measure and inform the quality of a service they receive from a provider agent; (ii) the Dossier is a data structure that allows the evaluated agent himself to keep locally the history of feedbacks received without modification or omission that benefits him. The model-that uses the Dossier structure-reduces some weak points of trust models that rely on the direct interaction between provider and consumer agents (direct trust) or those that rely on testimony obtained from client agents (indirect trust). The Dossier model allows us effectively to use the agent approach to build decentralized IT solutions, where control and data-of every application encapsulated in an agent-can actually be distributed logically and physically. The comparative analysis showed that the Dossier model has a behavior equivalent to or superior to the considered models (FIRE, TRAVOS, MARSH and Central) and that such a model can make the agents more efficient in choosing their partners. The reason for this is that the reputation of a given service provider agent is based on the reputation it has among the totality of consumer agents that used its services.

关键词： History Data structures Data models Analytical models Games Multi-agent systems Mathematical model

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Exploring Data Augmentation to Improve Music Genre Classification with ConvNets

Exploring Data Augmentation to Improve Music Genre Classific...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rafael L. Aguiar Yandre M.G. Costa Carlos N. Silla Postgraduate Program in Informatics (PPGIa) Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil Graduate Program in Computer Science (PCC) State University of Maringá (UEM) Maringá Brazil

In this work we address the automatic music genre classification as a pattern recognition task. The content of the music pieces were handled in the visual domain, using spectrograms created from the audio signal. This kind of image has been successfully used in this task since 2011 by extracting handcrafted features based on texture, since it is the main visual attribute found in spectrograms. In this work, the patterns were described by representation learning obtained with the use of convolutional neural network (CNN). CNN is a deep learning architecture and it has been widely used in the pattern recognition literature. Overfitting is a recurrent problem when a classification task is addressed by using CNN, it may occur due to the lack of training samples and/or due to the high dimensionality of the space. To increase the generalization capability we propose to explore data augmentation techniques. In this work, we have carefully selected strategies of data augmentation that are suitable for this kind of application, which are: adding noise, pitch shifting, loudness variation and time stretching. Experiments were conducted on the Latin Music Database (LMD), and the best obtained accuracy overcame the state of the art considering approaches based only in CNN.

关键词： Spectrogram Music Machine learning Training Task analysis Visualization Multiple signal classification

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Girls, Music and computer science

Girls, Music and Computer Science

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Frontiers in Education (FIE) Conference

作者： Carlos N. Silla André L. Przybysz Andriano Rivolli Thayna Gimenez Carolina Barroso Jessika Machado Computer Music Technology Laboratory – Graduate Program in Computer Science (PPGIa) Pontifical Catholic University of Paraná (PUCPR) – Curitiba PR Brazil Federal University of Technology of Paraná (UTFPR) – Cornélio Procópio PR Brazil

ISBN: (纸本)9781538611753;9781538611746

In this Innovative Practice paper we describe our experience on using an updated methodology based on the paper “Music Education Meets computer science and Engineering” with a focus on pre-university female students. The main differences from this work are: (1) We taught the girls how to program and how to play the music instruments in parallel, having two classes per week, one for each subject instead of weekly meetings; (2) We started the project by using some of the games and puzzles from *** and *** and then move to the Greenfoot Initial Learning Environment; (3) We asked the students to survey some music games related to their music instrument and design three potential games that they would like to develop using the music instruments; (4) They had to present and pitch the projects they designed to the undergraduate and graduate students of our computer music technology laboratory. Given the time constrains of the project, we had to help them to select the most feasible idea; (5) We applied a questionnaire to capture the perception of the students at the end of the project. It should be noted that all students successfully developed their music-based games using the digital music instruments and the Greenfoot Initial Learning Environment. In total four games were developed: (1) A game that allows drummers to perform ear training where they list to a particular drumming sequence and must replicate the sequence; (2) A game that allows guitar players to practice the execution of musical scales; (3) A game that allows keyboard players to practice their sight reading based on the simultaneous of two or more notes at the same time; (4) A game that allows bass players to practice their sight reading. It should be noted that by participating in the project one of the students acquired the skills and confidence to join the school and church bands.

关键词： Games Instruments Software Music Keyboards programming

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Using simplified chords sequences to classify songs genres

Using simplified chords sequences to classify songs genres

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Rodolfo Miranda Pereira Carlos N. Silla Informatics Department State University of Maringa (UEM) Maringa PR Brazil Computer Music Technology Laboratory — Graduate Program in Computer Science (PPGIa) Pontifical Catholic University of Paraná (PUCPR) Curitiba PR Brazil

ISBN: (纸本)9781509060689

Music Genre Recognition (MGR) is one of the most important forms of music organization and has been an important research subject. Most MGR methods consider the audio content itself instead of the songs meta-data, such as its chords. However, the audio is not always available due to copyright issues, which makes the use of meta-data for MGR an important task. The main objective of this paper is to propose a new method for MGR using simplified chords sequences. We also propose a new public dataset with 8,994 songs containing audio and chords features from six different genres in order to evaluate our method. The experimental results reached an accuracy rate of 56.13% using only the chords sequence feature with the Random Forest classifier, and 78.40% combining audio and chords features with the SVM classifier.

关键词： Feature extraction Support vector machines Frequency modulation Databases Rhythm Histograms Radio frequency

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