Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- Deep Learning (3)
- Empirical software engineering (2)
- Gated Recurrent Units (2)
- Human factors in software engineering (2)
- IoT (2)
-
- Adaptive neural fuzzy inference system (ANFIS) (1)
- Autoencoder (1)
- Autonomous system (1)
- Big Data (1)
- Big data (1)
- Classification (1)
- Cochlear implants (1)
- Container-based virtualization (1)
- Cross-cultural studies (1)
- Cross-cultural study (1)
- Data Analytics (1)
- Edge Computing (1)
- Electromyography (EMG) (1)
- Energy Forecasting (1)
- Energy Load Forecasting (1)
- Energy forecasting; generative adversarial network; recurrent neural network; generative model; Fourier transform; ARIMA; energy data (1)
- Feature selection (1)
- Feed-forward neural network (FNN) (1)
- Fundamental analysis (FA) (1)
- GRU (1)
- Gaming engines (1)
- Human dimension (1)
- Industrial robots (1)
- Information extraction (1)
- Knowledge management (1)
Articles 1 - 15 of 15
Full-Text Articles in Entire DC Network
Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger
Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger
Electrical and Computer Engineering Publications
The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial …
Automatic Recall Of Software Lessons Learned For Software Project Managers, Tamer Mohamed Abdellatif Mohamed, Luiz Fernando Capretz, Danny Ho
Automatic Recall Of Software Lessons Learned For Software Project Managers, Tamer Mohamed Abdellatif Mohamed, Luiz Fernando Capretz, Danny Ho
Electrical and Computer Engineering Publications
Context: Lessons learned (LL) records constitute the software organization memory of successes and failures. LL are recorded within the organization repository for future reference to optimize planning, gain experience, and elevate market competitiveness. However, manually searching this repository is a daunting task, so it is often disregarded. This can lead to the repetition of previous mistakes or even missing potential opportunities. This, in turn, can negatively affect the organization’s profitability and competitiveness.
Objective: We aim to present a novel solution that provides an automatic process to recall relevant LL and to push those LL to project managers. This will dramatically …
An Open-Source Integration Platform For Multiple Peripheral Modules With Kuka Robots, Mahyar Abdeetedal, Mehrdad Kermani Ph.D., P.Eng.
An Open-Source Integration Platform For Multiple Peripheral Modules With Kuka Robots, Mahyar Abdeetedal, Mehrdad Kermani Ph.D., P.Eng.
Electrical and Computer Engineering Publications
This paper presents an open-source software interface for the integration of a Kuka robot with peripheral tools and sensors, KUI: Kuka User Interface. KUI is developed based on Kuka Fast Research Interface (FRI) which enables soft real-time control of the robot. Simulink Desktop Real-Time™ or any User Datagram Protocol (UDP) client can send real-time commands to Kuka robot via KUI. In KUI, third-party tools can be added and controlled synchronously with Kuka light-weight robot (LWR). KUI can send the control commands via serial communication to the attached devices. KUI can generate low-level commands using data acquisition (DAQ) boards. This feature …
Can We Rely On Smartphone Applications?, Sonia Meskini, Ali Bou Nassif, Luiz Fernando Capretz
Can We Rely On Smartphone Applications?, Sonia Meskini, Ali Bou Nassif, Luiz Fernando Capretz
Electrical and Computer Engineering Publications
Smartphones are becoming necessary tools in the daily lives of millions of users who rely on these devices and their applications. There are thousands of applications for smartphone devices such as the iPhone, Blackberry, and Android, thus their reliability has become paramount for their users. This work aims to answer two related questions: (1) Can we assess the reliability of mobile applications by using the traditional reliability models? (2) Can we model adequately the failure data collected from many users? Firstly, it has been proved that the three most used software reliability models have fallen short of the mark when …
Ml4iot: A Framework To Orchestrate Machine Learning Workflows On Internet Of Things Data, Jose Miguel Alves, Leonardo Honorio, Miriam A M Capretz
Ml4iot: A Framework To Orchestrate Machine Learning Workflows On Internet Of Things Data, Jose Miguel Alves, Leonardo Honorio, Miriam A M Capretz
Electrical and Computer Engineering Publications
Internet of Things (IoT) applications generate vast amounts of real-time data. Temporal analysis of these data series to discover behavioural patterns may lead to qualified knowledge affecting a broad range of industries. Hence, the use of machine learning (ML) algorithms over IoT data has the potential to improve safety, economy, and performance in critical processes. However, creating ML workflows at scale is a challenging task that depends upon both production and specialized skills. Such tasks require investigation, understanding, selection, and implementation of specific ML workflows, which often lead to bottlenecks, production issues, and code management complexity and even then may …
Similarity-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian, Ljubisa Sehovac, Katarina Grolinger
Similarity-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian, Ljubisa Sehovac, Katarina Grolinger
Electrical and Computer Engineering Publications
Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building or a single aggregated load to predict future consumption for that same building or aggregated load. With hundreds of thousands of meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Similarity-Based Chained Transfer Learning (SBCTL), an approach for building neural network-based models for many meters by …
Development Of An Emg-Based Muscle Health Model For Elbow Trauma Patients, Emma Farago, Shrikant Chinchalkar, Daniel J. Lizotte, Ana Luisa Trejos
Development Of An Emg-Based Muscle Health Model For Elbow Trauma Patients, Emma Farago, Shrikant Chinchalkar, Daniel J. Lizotte, Ana Luisa Trejos
Electrical and Computer Engineering Publications
Wearable robotic braces have the potential to improve rehabilitative therapies for patients suffering from musculoskeletal (MSK) conditions. Ideally, a quantitative assessment of health would be incorporated into rehabilitative devices to monitor patient recovery. The purpose of this work is to develop a model to distinguish between the healthy and injured arms of elbow trauma patients based on electromyography (EMG) data. Surface EMG recordings were collected from the healthy and injured limbs of 30 elbow trauma patients while performing 10 upper-limb motions. Forty-two features and five feature sets were extracted from the data. Feature selection was performed to improve the class …
An Empirical Study Of User Support Tools In Open Source Software, Arif Raza, Luiz Fernando Capretz, Shuib Basri
An Empirical Study Of User Support Tools In Open Source Software, Arif Raza, Luiz Fernando Capretz, Shuib Basri
Electrical and Computer Engineering Publications
End users’ positive response is essential for the success of any software. This is true for both commercial and Open Source Software (OSS). OSS is popular not only because of its availability, which is usually free but due to the user support it provides, generally through public platforms. The study model of this research establishes a relationship between OSS user support and available support tools. To conduct this research, we used a dataset of 100 OSS projects in different categories and examined five user support tools provided by different OSS projects. The results show that project trackers, user mailing lists, …
Forecasting Building Energy Consumption With Deep Learning: A Sequence To Sequence Approach, Ljubisa Sehovac, Cornelius Nesen, Katarina Grolinger
Forecasting Building Energy Consumption With Deep Learning: A Sequence To Sequence Approach, Ljubisa Sehovac, Cornelius Nesen, Katarina Grolinger
Electrical and Computer Engineering Publications
Energy Consumption has been continuously increasing due to the rapid expansion of high-density cities, and growth in the industrial and commercial sectors. To reduce the negative impact on the environment and improve sustainability, it is crucial to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely used smart meters, have created possibilities for energy monitoring as well as for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting such as feedforward neural networks are not well-suited for interpreting the time dimensionality of a signal. Consequently, this paper uses Recurrent Neural Networks (RNN) to capture time …
Studies On The Software Testing Profession, Luiz Fernando Capretz, Pradeep Waychal, Jingdong Jia, Daniel Varona, Yadira Tejeda Saldaña
Studies On The Software Testing Profession, Luiz Fernando Capretz, Pradeep Waychal, Jingdong Jia, Daniel Varona, Yadira Tejeda Saldaña
Electrical and Computer Engineering Publications
This paper attempts to understand motivators and de-motivators that influence the decisions of software professionals to take up and sustain software testing careers across four different countries, i.e. Canada, China, Cuba, and India. The research question can be framed as “How many software professionals across different geographies are keen to take up testing careers, and what are the reasons for their choices?” Towards that, we developed a cross-sectional but simple survey-based instrument. In this study we investigated how software testers perceived and valued what they do and their environmental settings. The study pointed out the importance of visualizing software testing …
Comparing The Popularity Of Testing Career Among Canadian, Chinese, And Indian Students, Luiz Fernando Capretz, Pradeep Waychal, Jingdong Jia
Comparing The Popularity Of Testing Career Among Canadian, Chinese, And Indian Students, Luiz Fernando Capretz, Pradeep Waychal, Jingdong Jia
Electrical and Computer Engineering Publications
Despite its importance, software testing is, arguably, the least understood part of the software life cycle and still the toughest to perform correctly. Many researchers and practitioners have been working to address the situation. However, most of the studies focus on the process and technology dimensions and only a few on the human dimension of testing, in spite of the reported relevance of human aspects of software testing. Testers need to understand various stakeholders’ explicit and implicit requirements, be aware of how developers work individually and in teams, and develop skills to report test results wisely to stakeholders. These multifaceted …
Design And Job Rotation In Software Engineering: Results From An Industrial Study, Ronnie Santos, Maria Teresa Baldassarre, Fabio Q. B. Silva Dr., Cleyton Magalhaes, Luiz Fernando Capretz, Jorge Correia-Neto
Design And Job Rotation In Software Engineering: Results From An Industrial Study, Ronnie Santos, Maria Teresa Baldassarre, Fabio Q. B. Silva Dr., Cleyton Magalhaes, Luiz Fernando Capretz, Jorge Correia-Neto
Electrical and Computer Engineering Publications
Job rotation is a managerial practice to be applied in the organizational environment to reduce job monotony, boredom, and exhaustion resulting from job simplification, specialization, and repetition. Previous studies have identified and discussed the use of project-to-project rotations in software practice, gathering empirical evidence from qualitative and field studies and pointing out set of work-related factors that can be positively or negatively affected by this practice. Goal: We aim to collect and discuss the use of job rotation in software organizations in order to identify the potential benefits and limitations of this practice supported by the statement of existing theories …
Neural Network Models For Stock Selection Based On Fundamental Analysis, Yuxuan Huang, Luiz Fernando Capretz, Danny Ho
Neural Network Models For Stock Selection Based On Fundamental Analysis, Yuxuan Huang, Luiz Fernando Capretz, Danny Ho
Electrical and Computer Engineering Publications
Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial ratios. The study is designed to evaluate the performance of each architecture based on the relative return of the selected portfolios with respect to the benchmark stock index. The results show that both architectures possess the ability to separate winners and losers from a sample universe of stocks, and the selected portfolios outperform the benchmark. Our study argues …
Deep Learning: Edge-Cloud Data Analytics For Iot, Katarina Grolinger, Ananda M. Ghosh
Deep Learning: Edge-Cloud Data Analytics For Iot, Katarina Grolinger, Ananda M. Ghosh
Electrical and Computer Engineering Publications
Sensors, wearables, mobile and other Internet of Thing (IoT) devices are becoming increasingly integrated in all aspects of our lives. They are capable of collecting massive quantities of data that are typically transmitted to the cloud for processing. However, this results in increased network traffic and latencies. Edge computing has a potential to remedy these challenges by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex data analytics tasks. Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based …
A Virtual-Reality Training Simulator For Cochlear Implant Surgery, Blake Jones, Seyed Alireza Rohani, Nelson Ong, Tarek Tayeh, Hanif M. Ladak, Ahmad Chalabi, Sumit K. Agrawal
A Virtual-Reality Training Simulator For Cochlear Implant Surgery, Blake Jones, Seyed Alireza Rohani, Nelson Ong, Tarek Tayeh, Hanif M. Ladak, Ahmad Chalabi, Sumit K. Agrawal
Electrical and Computer Engineering Publications
No abstract provided.