A Robust Portable Environment for First-Year Computer Science Students
- Brown, Dane L, Connan, James
- Authors: Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465113 , vital:76574 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_6"
- Description: Computer science education in both South African universities and worldwide often aim at making students confident at problem solving by introducing various programming exercises. Standardising a computer environment where students can apply their computational thinking knowledge on a more even playing field – without worrying about software issues – can be beneficial for problem solving in classroom of diverse students. Research shows that having consistent access to this exposes students to core concepts of Computer Science. However, with the diverse student base in South Africa, not everyone has access to a personal computer or expensive software. This paper describes a new approach at first-year level that uses the power of a modified Linux distro on a flash drive to enable access to the same, fully-fledged, free and open-source environment, including the convenience of portability. This is used as a means to even the playing field in a diverse country like South Africa and address the lack of consistent access to a problem solving environment. Feedback from students and staff at the Institution are effectively heeded and attempted to be measured.
- Full Text:
- Date Issued: 2021
- Authors: Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465113 , vital:76574 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_6"
- Description: Computer science education in both South African universities and worldwide often aim at making students confident at problem solving by introducing various programming exercises. Standardising a computer environment where students can apply their computational thinking knowledge on a more even playing field – without worrying about software issues – can be beneficial for problem solving in classroom of diverse students. Research shows that having consistent access to this exposes students to core concepts of Computer Science. However, with the diverse student base in South Africa, not everyone has access to a personal computer or expensive software. This paper describes a new approach at first-year level that uses the power of a modified Linux distro on a flash drive to enable access to the same, fully-fledged, free and open-source environment, including the convenience of portability. This is used as a means to even the playing field in a diverse country like South Africa and address the lack of consistent access to a problem solving environment. Feedback from students and staff at the Institution are effectively heeded and attempted to be measured.
- Full Text:
- Date Issued: 2021
Adaptive machine learning based network intrusion detection
- Chindove, Hatitye E, Brown, Dane L
- Authors: Chindove, Hatitye E , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464052 , vital:76471 , xlink:href="https://doi.org/10.1145/3487923.3487938"
- Description: Network intrusion detection system (NIDS) adoption is essential for mitigating computer network attacks in various scenarios. However, the increasing complexity of computer networks and attacks make it challenging to classify network traffic. Machine learning (ML) techniques in a NIDS can be affected by different scenarios, and thus the recency, size and applicability of datasets are vital factors to consider when selecting and tuning a machine learning classifier. The proposed approach evaluates relatively new datasets constructed such that they depict real-world scenarios. It includes analyses of dataset balancing and sampling, feature engineering and systematic ML-based NIDS model tuning focused on the adaptive improvement of intrusion detection. A comparison between machine learning classifiers forms part of the evaluation process. Results on the proposed approach model effectiveness for NIDS are discussed. Recurrent neural networks and random forests models consistently achieved high f1-score results with macro f1-scores of 0.73 and 0.87 for the CICIDS 2017 dataset; and 0.73 and 0.72 against the CICIDS 2018 dataset, respectively.
- Full Text:
- Date Issued: 2021
- Authors: Chindove, Hatitye E , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464052 , vital:76471 , xlink:href="https://doi.org/10.1145/3487923.3487938"
- Description: Network intrusion detection system (NIDS) adoption is essential for mitigating computer network attacks in various scenarios. However, the increasing complexity of computer networks and attacks make it challenging to classify network traffic. Machine learning (ML) techniques in a NIDS can be affected by different scenarios, and thus the recency, size and applicability of datasets are vital factors to consider when selecting and tuning a machine learning classifier. The proposed approach evaluates relatively new datasets constructed such that they depict real-world scenarios. It includes analyses of dataset balancing and sampling, feature engineering and systematic ML-based NIDS model tuning focused on the adaptive improvement of intrusion detection. A comparison between machine learning classifiers forms part of the evaluation process. Results on the proposed approach model effectiveness for NIDS are discussed. Recurrent neural networks and random forests models consistently achieved high f1-score results with macro f1-scores of 0.73 and 0.87 for the CICIDS 2017 dataset; and 0.73 and 0.72 against the CICIDS 2018 dataset, respectively.
- Full Text:
- Date Issued: 2021
Adaptive network intrusion detection using optimised machine learning models
- Chindove, Hatitye E, Brown, Dane L
- Authors: Chindove, Hatitye E , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465634 , vital:76627 , xlink:href="https://www.researchgate.net/publication/358046953_Adaptive_Network_Intrusion_Detection_using_Optimised_Machine_Learning_Models"
- Description: Network intrusion detection system (NIDS) adoption is essential for mitigating computer network attacks in various scenarios. However, the increasing complexity of computer networks and attacks make it challenging to classify network traffic. Machine learning (ML) techniques in a NIDS can be affected by different scenarios, and thus the recency, size and applicability of datasets are vital factors to consider when selecting and tuning a machine learning classifier. The proposed approach evaluates relatively new datasets constructed such that they depict real-world scenarios. It includes empirical analyses of practical, systematic ML-based NIDS with significant network traffic for improved intrusion detection. A comparison between machine learning classifiers, including deep learning, form part of the evaluation process. Results on how the proposed approach increased model effectiveness for NIDS in a more practical setting are discussed. Recurrent neural networks and random forests models consistently achieved the best results.
- Full Text:
- Date Issued: 2021
- Authors: Chindove, Hatitye E , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465634 , vital:76627 , xlink:href="https://www.researchgate.net/publication/358046953_Adaptive_Network_Intrusion_Detection_using_Optimised_Machine_Learning_Models"
- Description: Network intrusion detection system (NIDS) adoption is essential for mitigating computer network attacks in various scenarios. However, the increasing complexity of computer networks and attacks make it challenging to classify network traffic. Machine learning (ML) techniques in a NIDS can be affected by different scenarios, and thus the recency, size and applicability of datasets are vital factors to consider when selecting and tuning a machine learning classifier. The proposed approach evaluates relatively new datasets constructed such that they depict real-world scenarios. It includes empirical analyses of practical, systematic ML-based NIDS with significant network traffic for improved intrusion detection. A comparison between machine learning classifiers, including deep learning, form part of the evaluation process. Results on how the proposed approach increased model effectiveness for NIDS in a more practical setting are discussed. Recurrent neural networks and random forests models consistently achieved the best results.
- Full Text:
- Date Issued: 2021
An Evaluation of Machine Learning Methods for Classifying Bot Traffic in Software Defined Networks
- Van Staden, Joshua, Brown, Dane L
- Authors: Van Staden, Joshua , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465645 , vital:76628 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-19-7874-6_72"
- Description: Internet security is an ever-expanding field. Cyber-attacks occur very frequently, and so detecting them is an important aspect of preserving services. Machine learning offers a helpful tool with which to detect cyber attacks. However, it is impossible to deploy a machine-learning algorithm to detect attacks in a non-centralized network. Software Defined Networks (SDNs) offer a centralized view of a network, allowing machine learning algorithms to detect malicious activity within a network. The InSDN dataset is a recently-released dataset that contains a set of sniffed packets within a virtual SDN. These sniffed packets correspond to various attacks, including DDoS attacks, Probing and Password-Guessing, among others. This study aims to evaluate various machine learning models against this new dataset. Specifically, we aim to evaluate their classification ability and runtimes when trained on fewer features. The machine learning models tested include a Neural Network, Support Vector Machine, Random Forest, Multilayer Perceptron, Logistic Regression, and K-Nearest Neighbours. Cluster-based algorithms such as the K-Nearest Neighbour and Random Forest proved to be the best performers. Linear-based algorithms such as the Multilayer Perceptron performed the worst. This suggests a good level of clustering in the top few features with little space for linear separability. The reduction of features significantly reduced training time, particularly in the better-performing models.
- Full Text:
- Date Issued: 2021
- Authors: Van Staden, Joshua , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465645 , vital:76628 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-19-7874-6_72"
- Description: Internet security is an ever-expanding field. Cyber-attacks occur very frequently, and so detecting them is an important aspect of preserving services. Machine learning offers a helpful tool with which to detect cyber attacks. However, it is impossible to deploy a machine-learning algorithm to detect attacks in a non-centralized network. Software Defined Networks (SDNs) offer a centralized view of a network, allowing machine learning algorithms to detect malicious activity within a network. The InSDN dataset is a recently-released dataset that contains a set of sniffed packets within a virtual SDN. These sniffed packets correspond to various attacks, including DDoS attacks, Probing and Password-Guessing, among others. This study aims to evaluate various machine learning models against this new dataset. Specifically, we aim to evaluate their classification ability and runtimes when trained on fewer features. The machine learning models tested include a Neural Network, Support Vector Machine, Random Forest, Multilayer Perceptron, Logistic Regression, and K-Nearest Neighbours. Cluster-based algorithms such as the K-Nearest Neighbour and Random Forest proved to be the best performers. Linear-based algorithms such as the Multilayer Perceptron performed the worst. This suggests a good level of clustering in the top few features with little space for linear separability. The reduction of features significantly reduced training time, particularly in the better-performing models.
- Full Text:
- Date Issued: 2021
An Evaluation of YOLO-Based Algorithms for Hand Detection in the Kitchen
- Van Staden, Joshua, Brown, Dane L
- Authors: Van Staden, Joshua , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465134 , vital:76576 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519307"
- Description: Convolutional Neural Networks have offered an accurate method with which to run object detection on images. Specifically, the YOLO family of object detection algorithms have proven to be relatively fast and accurate. Since its inception, the different variants of this algorithm have been tested on different datasets. In this paper, we evaluate the performances of these algorithms on the recent Epic Kitchens-100 dataset. This dataset provides egocentric footage of people interacting with various objects in the kitchen. Most prominently shown in the footage is an egocentric view of the participants' hands. We aim to use the YOLOv3 algorithm to detect these hands within the footage provided in this dataset. In particular, we examine the YOLOv3 algorithm using two different backbones: MobileNet-lite and VGG16. We trained them on a mixture of samples from the Egohands and Epic Kitchens-100 datasets. In a separate experiment, average precision was measured on an unseen Epic Kitchens-100 subset. We found that the models are relatively simple and lead to lower scores on the Epic Kitchens 100 dataset. This is attributed to the high background noise on the Epic Kitchens 100 dataset. Nonetheless, the VGG16 architecture was found to have a higher Average Precision (AP) and is, therefore, more suited for retrospective analysis. None of the models was suitable for real-time analysis due to complex egocentric data.
- Full Text:
- Date Issued: 2021
- Authors: Van Staden, Joshua , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465134 , vital:76576 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519307"
- Description: Convolutional Neural Networks have offered an accurate method with which to run object detection on images. Specifically, the YOLO family of object detection algorithms have proven to be relatively fast and accurate. Since its inception, the different variants of this algorithm have been tested on different datasets. In this paper, we evaluate the performances of these algorithms on the recent Epic Kitchens-100 dataset. This dataset provides egocentric footage of people interacting with various objects in the kitchen. Most prominently shown in the footage is an egocentric view of the participants' hands. We aim to use the YOLOv3 algorithm to detect these hands within the footage provided in this dataset. In particular, we examine the YOLOv3 algorithm using two different backbones: MobileNet-lite and VGG16. We trained them on a mixture of samples from the Egohands and Epic Kitchens-100 datasets. In a separate experiment, average precision was measured on an unseen Epic Kitchens-100 subset. We found that the models are relatively simple and lead to lower scores on the Epic Kitchens 100 dataset. This is attributed to the high background noise on the Epic Kitchens 100 dataset. Nonetheless, the VGG16 architecture was found to have a higher Average Precision (AP) and is, therefore, more suited for retrospective analysis. None of the models was suitable for real-time analysis due to complex egocentric data.
- Full Text:
- Date Issued: 2021
Early dehydration detection using infrared imaging
- Poole, Louise C, Brown, Dane L, Connan, James
- Authors: Poole, Louise C , Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465656 , vital:76629 , xlink:href="https://www.researchgate.net/profile/Louise-Poole-3/publication/357578445_Early_Dehydration_Detection_Using_Infrared_Imaging/links/61d5664eb8305f7c4b231d50/Early-Dehydration-Detection-Using-Infrared-Imaging.pdf"
- Description: Crop loss and failure have devastating impacts on a country’s economy and food security. Developing effective and inexpensive systems to minimize crop loss has become essential. Recently, multispectral imaging—in particular visible and infrared imaging—have become popular for analyzing plants and show potential for early identification of plant stress. We created a directly comparable visible and infrared image dataset for dehydration in spinach leaves. We created and compared various models trained on both datasets and concluded that the models trained on the infrared dataset outperformed all of those trained on the visible dataset. In particular, the models trained to identify early signs of dehydration yielded 45% difference in accuracy, with the infrared model obtaining 70% accuracy and the visible model obtaining 25% accuracy. Infrared imaging thus shows promising potential for application in early plant stress and disease identification.
- Full Text:
- Date Issued: 2021
- Authors: Poole, Louise C , Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465656 , vital:76629 , xlink:href="https://www.researchgate.net/profile/Louise-Poole-3/publication/357578445_Early_Dehydration_Detection_Using_Infrared_Imaging/links/61d5664eb8305f7c4b231d50/Early-Dehydration-Detection-Using-Infrared-Imaging.pdf"
- Description: Crop loss and failure have devastating impacts on a country’s economy and food security. Developing effective and inexpensive systems to minimize crop loss has become essential. Recently, multispectral imaging—in particular visible and infrared imaging—have become popular for analyzing plants and show potential for early identification of plant stress. We created a directly comparable visible and infrared image dataset for dehydration in spinach leaves. We created and compared various models trained on both datasets and concluded that the models trained on the infrared dataset outperformed all of those trained on the visible dataset. In particular, the models trained to identify early signs of dehydration yielded 45% difference in accuracy, with the infrared model obtaining 70% accuracy and the visible model obtaining 25% accuracy. Infrared imaging thus shows promising potential for application in early plant stress and disease identification.
- Full Text:
- Date Issued: 2021
Handwriting Recognition using Deep Learning with Effective Data Augmentation Techniques
- Brown, Dane L, Lidzhade, Ipfi
- Authors: Brown, Dane L , Lidzhade, Ipfi
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465700 , vital:76633 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519359"
- Description: Machine learning techniques have been successfully used in deciphering handwritten text. Deep learning has made further improvements in this regard. However, they require substantial amounts of training data. This research aims to improve the effectiveness of classification accuracy in the presence of limited training data on handwriting recognition. The main focus thus involves enabling deep models to converge during training on smaller datasets using data augmentation. This will allow for broader use of these systems across more regions, greater accessibility, and future related systems to be less reliant on the amount of data available. Therefore, the proposed research includes an image processing and machine learning approach to handwriting recognition while generating more sample data in various ways. Applying random cropping as an augmentation technique resulted in higher accuracy than several other augmentation techniques examined in this paper. Some of these techniques performed worse than on unaugmented data.
- Full Text:
- Date Issued: 2021
- Authors: Brown, Dane L , Lidzhade, Ipfi
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465700 , vital:76633 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519359"
- Description: Machine learning techniques have been successfully used in deciphering handwritten text. Deep learning has made further improvements in this regard. However, they require substantial amounts of training data. This research aims to improve the effectiveness of classification accuracy in the presence of limited training data on handwriting recognition. The main focus thus involves enabling deep models to converge during training on smaller datasets using data augmentation. This will allow for broader use of these systems across more regions, greater accessibility, and future related systems to be less reliant on the amount of data available. Therefore, the proposed research includes an image processing and machine learning approach to handwriting recognition while generating more sample data in various ways. Applying random cropping as an augmentation technique resulted in higher accuracy than several other augmentation techniques examined in this paper. Some of these techniques performed worse than on unaugmented data.
- Full Text:
- Date Issued: 2021
Investigating popular CNN architectures for plant disease detection
- Poole, Louise C, Brown, Dane L
- Authors: Poole, Louise C , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465168 , vital:76579 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519341"
- Description: Food production and food security have become increasingly important due to climate change and rising population numbers. One method to prevent crop loss is to develop a system to allow for early, efficient and accurate identification of plant diseases. CNNs often outperform previously popular machine learning algorithms. There are many existing CNN architectures. We compared and analysed the popular state-of-the-art architectures, namely ResNet, GoogLeNet and VGG, when trained for plant disease classification. We found that ResNet performed the best on the balanced Mendeley Leaves and PlantVillage datasets, obtaining 91.95% and 95.80% accuracy respectively. However, the ResNet architecture was relatively computationally expensive and slow to train. GoogLeNet obtained accuracies very close to those of ResNet with 89.35% and 94.59% achieved on the Mendeley Leaves and PlantVillage datasets respectively and could be considered a less computationally expensive alternative.
- Full Text:
- Date Issued: 2021
- Authors: Poole, Louise C , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465168 , vital:76579 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519341"
- Description: Food production and food security have become increasingly important due to climate change and rising population numbers. One method to prevent crop loss is to develop a system to allow for early, efficient and accurate identification of plant diseases. CNNs often outperform previously popular machine learning algorithms. There are many existing CNN architectures. We compared and analysed the popular state-of-the-art architectures, namely ResNet, GoogLeNet and VGG, when trained for plant disease classification. We found that ResNet performed the best on the balanced Mendeley Leaves and PlantVillage datasets, obtaining 91.95% and 95.80% accuracy respectively. However, the ResNet architecture was relatively computationally expensive and slow to train. GoogLeNet obtained accuracies very close to those of ResNet with 89.35% and 94.59% achieved on the Mendeley Leaves and PlantVillage datasets respectively and could be considered a less computationally expensive alternative.
- Full Text:
- Date Issued: 2021
Mobile attendance based on face detection and recognition using OpenVINO
- Authors: Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465201 , vital:76582 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9395836"
- Description: The OpenVINO toolkit enables versatile computer vision with an Intel® Movidius ™ Neural Compute Stick 2 connected to a Raspberry Pi. This small portable platform provides new opportunities for innovative solutions in computer vision applications and beyond. This paper investigates its feasibility for mobile attendance systems for settings such as classrooms or other scenarios that require headcount or roll call. Related studies of face-based systems are explored, while the advantages of the proposed system are highlighted. Although there are some positioning constraints, the proof-of-concept system processes an approximate average of five faces per second. That means it can take attendance in a lecture room of 90 students in about 18 seconds. A recognition accuracy of 98.1% with an f-score of 96.9% was yielded on a private classroom dataset captured with a modest RPi camera. These promising results were achieved using a tiny ResNet-18 architecture, producing significantly better results than MobileNet. Furthermore, it outperformed the recognition accuracy of other ‘lightweight’ methods used in the literature that do not run off embedded devices on publicly available datasets.
- Full Text:
- Date Issued: 2021
- Authors: Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465201 , vital:76582 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9395836"
- Description: The OpenVINO toolkit enables versatile computer vision with an Intel® Movidius ™ Neural Compute Stick 2 connected to a Raspberry Pi. This small portable platform provides new opportunities for innovative solutions in computer vision applications and beyond. This paper investigates its feasibility for mobile attendance systems for settings such as classrooms or other scenarios that require headcount or roll call. Related studies of face-based systems are explored, while the advantages of the proposed system are highlighted. Although there are some positioning constraints, the proof-of-concept system processes an approximate average of five faces per second. That means it can take attendance in a lecture room of 90 students in about 18 seconds. A recognition accuracy of 98.1% with an f-score of 96.9% was yielded on a private classroom dataset captured with a modest RPi camera. These promising results were achieved using a tiny ResNet-18 architecture, producing significantly better results than MobileNet. Furthermore, it outperformed the recognition accuracy of other ‘lightweight’ methods used in the literature that do not run off embedded devices on publicly available datasets.
- Full Text:
- Date Issued: 2021
Using Technology to Teach a New Generation
- Connan, James, Brown, Dane L, Watkins, Caroline
- Authors: Connan, James , Brown, Dane L , Watkins, Caroline
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465223 , vital:76584 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_8"
- Description: Introductory programming courses attract students from diverse backgrounds in terms of ability, motivation and experience. This paper introduces two technological tools, Thonny and Runestone Academy, that can be used to enhance introductory courses. These tools enable instructors to track the progress of individual students. This allows for the early identification of students that are not keeping up with the course and allows for early intervention in such cases. Overall this leads to a better course with higher throughput and better student retention.
- Full Text:
- Date Issued: 2021
- Authors: Connan, James , Brown, Dane L , Watkins, Caroline
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465223 , vital:76584 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_8"
- Description: Introductory programming courses attract students from diverse backgrounds in terms of ability, motivation and experience. This paper introduces two technological tools, Thonny and Runestone Academy, that can be used to enhance introductory courses. These tools enable instructors to track the progress of individual students. This allows for the early identification of students that are not keeping up with the course and allows for early intervention in such cases. Overall this leads to a better course with higher throughput and better student retention.
- Full Text:
- Date Issued: 2021
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