| 1. |
- Abid, Nosheen, 1993-, et al.
(author)
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UCL: Unsupervised Curriculum Learning for Utility Pole Detection from Aerial Imagery
- 2022
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In: Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA). - : IEEE. - 9781665456425
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Conference paper (peer-reviewed)abstract
- This paper introduces a machine learning-based approach for detecting electric poles, an essential part of power grid maintenance. With the increasing popularity of deep learning, several such approaches have been proposed for electric pole detection. However, most of these approaches are supervised, requiring a large amount of labeled data, which is time-consuming and labor-intensive. Unsupervised deep learning approaches have the potential to overcome the need for huge amounts of training data. This paper presents an unsupervised deep learning framework for utility pole detection. The framework combines Convolutional Neural Network (CNN) and clustering algorithms with a selection operation. The CNN architecture for extracting meaningful features from aerial imagery, a clustering algorithm for generating pseudo labels for the resulting features, and a selection operation to filter out reliable samples to fine-tune the CNN architecture further. The fine-tuned version then replaces the initial CNN model, thus improving the framework, and we iteratively repeat this process so that the model learns the prominent patterns in the data progressively. The presented framework is trained and tested on a small dataset of utility poles provided by “Mention Fuvex” (a Spanish company utilizing long-range drones for power line inspection). Our extensive experimentation demonstrates the progressive learning behavior of the proposed method and results in promising classification scores with significance test having p−value<0.00005 on the utility pole dataset.
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| 2. |
- Abid, Nosheen, 1993-, et al.
(author)
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UCL: Unsupervised Curriculum Learning for Water Body Classification from Remote Sensing Imagery
- 2021
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In: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 105
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Journal article (peer-reviewed)abstract
- This paper presents a Convolutional Neural Networks (CNN) based Unsupervised Curriculum Learning approach for the recognition of water bodies to overcome the stated challenges for remote sensing based RGB imagery. The unsupervised nature of the presented algorithm eliminates the need for labelled training data. The problem is cast as a two class clustering problem (water and non-water), while clustering is done on deep features obtained by a pre-trained CNN. After initial clusters have been identified, representative samples from each cluster are chosen by the unsupervised curriculum learning algorithm for fine-tuning the feature extractor. The stated process is repeated iteratively until convergence. Three datasets have been used to evaluate the approach and show its effectiveness on varying scales: (i) SAT-6 dataset comprising high resolution aircraft images, (ii) Sentinel-2 of EuroSAT, comprising remote sensing images with low resolution, and (iii) PakSAT, a new dataset we created for this study. PakSAT is the first Pakistani Sentinel-2 dataset designed to classify water bodies of Pakistan. Extensive experiments on these datasets demonstrate the progressive learning behaviour of UCL and reported promising results of water classification on all three datasets. The obtained accuracies outperform the supervised methods in domain adaptation, demonstrating the effectiveness of the proposed algorithm.
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| 3. |
- Agües Paszkowsky, Núria, et al.
(author)
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Vegetation and Drought Trends in Sweden’s Mälardalen Region – Year-on-Year Comparison by Gaussian Process Regression
- 2020
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In: 2020 Swedish Workshop on Data Science (SweDS). - : IEEE. - 9781728192048
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Conference paper (peer-reviewed)abstract
- This article describes analytical work carried out in a pilot project for the Swedish Space Data Lab (SSDL), which focused on monitoring drought in the Mälardalen region in central Sweden. Normalized Difference Vegetation Index (NDVI) and the Moisture Stress Index (MSI) – commonly used to analyse drought – are estimated from Sentinel 2 satellite data and averaged over a selection of seven grassland areas of interest. To derive a complete time-series over a season that interpolates over days with missing data, we use Gaussian Process Regression, a technique from multivariate Bayesian analysis. The analysis show significant differences at 95% confidence for five out of seven areas when comparing the peak drought period in the dry year 2018 compared to the corresponding period in 2019. A cross-validation analysis indicates that the model parameter estimates are robust for temporal covariance structure (while inconclusive for the spatial dimensions). There were no signs of over-fitting when comparing in-sample and out-of-sample RMSE.
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| 4. |
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| 5. |
- Al-Azzawi, Sana Sabah Sabry, et al.
(author)
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Innovative Education Approach Toward Active Distance Education: a Case Study in the Introduction to AI course
- 2022
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In: Conference Proceedings. The Future of Education 2022.
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Conference paper (peer-reviewed)abstract
- In this paper, we first describe various synchronous and asynchronous methods for enhancing student engagement in big online courses. We showcase the implementation of these methods in the “Introduction to Artificial Intelligence (AI)” course at Luleå University of Technology, which has attracted around 500 students in each of its iterations (twice yearly, since 2019). We also show that these methods can be applied efficiently, in terms of the teaching hours required. With the increase in digitization and student mobility, the demand for improved and personalized content delivery for distance education has also increased. This applies not only in the context of traditional undergraduate education, but also in the context of adult education and lifelong learning. This higher level of demand, however, introduces a challenge, especially as it is typically combined with a shortage of staff and needs for efficient education. This challenge is further amplified by the current pandemic situation, which led to an even bigger risk of student-dropout. To mitigate this risk, as well as to meet the increased demand, we applied various methods for creating engaging interaction in our pedagogy based on Moor’s framework: learner-to-learner, learner-to-instructor, and learner-to-content engagement strategies. The main methods of this pedagogy are as follows: short, and interactive videos, active discussions in topic-based forums, regular live sessions with group discussions, and the introduction of optional content at many points in the course, to address different target groups. In this paper, we show how we originally designed and continuously improved the course, without requiring more than 500 teaching hours per iteration (one hour per enrolled student), while we also managed to increase the successful completion rate of the participants by 10%, and improved student engagement and feedback for the course by 50%. We intend to share a set of best-practices applicable to many other e-learning courses in ICT.
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| 6. |
- Alonso, Pedro, 1986-, et al.
(author)
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Hate Speech Detection using Transformer Ensembles on the HASOC Dataset
- 2020
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In: Speech and Computer. - Cham : Springer. ; , s. 13-21
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Conference paper (peer-reviewed)abstract
- With the ubiquity and anonymity of the Internet, the spread of hate speech has been a growing concern for many years now. The language used for the purpose of dehumanizing, defaming or threatening individuals and marginalized groups not only threatens the mental health of its targets, as well as their democratic access to the Internet, but also the fabric of our society. Because of this, much effort has been devoted to manual moderation. The amount of data generated each day, particularly on social media platforms such as Facebook and twitter, however makes this a Sisyphean task. This has led to an increased demand for automatic methods of hate speech detection.Here, to contribute towards solving the task of hate speech detection, we worked with a simple ensemble of transformer models on a twitter-based hate speech benchmark. Using this method, we attained a weighted F1-score of 0.8426, which we managed to further improve by leveraging more training data, achieving a weighted F1-score of 0.8504. Thus markedly outperforming the best performing system in the literature.
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| 7. |
- Alonso, Pedro, 1986-, et al.
(author)
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TheNorth at HASOC 2019 : Hate Speech Detection in Social Media Data
- 2019
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In: Working Notes of FIRE 2019 - Forum for Information Retrieval Evaluation. - : RWTH Aachen University. ; , s. 293-299
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Conference paper (peer-reviewed)abstract
- The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate speech can be detrimental to maintaining the peace and harmony in society. Particularly when hate speech is spread with the intention to defame people, or spoil the image of a person, a community, or a nation. A major ground for spreading hate speech is that of social media. This significantly contributes to the difficultyof the task, as social media posts not only include paralinguistic tools (e.g. emoticons, and hashtags), their linguistic content contains plenty of poorly written text that does not adhere to grammar rules. With the recent development in Natural Language Processing (NLP), particularly with deep architecture, it is now possible to anlayze unstructured composite natural language text. For this reason, we propose a deep NLP model for the detection of automatic hate speech in social media data. We have applied our model on the HASOC2019 hate speech corpus, and attained a macro F1 score of 0.63 in the detection of hate speech.
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| 8. |
- Alonso, Pedro, 1986-, et al.
(author)
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TheNorth at SemEval-2020 Task 12 : Hate Speech Detection using RoBERTa
- 2020
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In: The International Workshop on Semantic Evaluation. - : International Committee for Computational Linguistics. ; , s. 2197-2202
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Conference paper (peer-reviewed)abstract
- Hate speech detection on social media platforms is crucial as it helps to avoid severe harm to marginalized people and groups. The application of Natural Language Processing (NLP) and Deep Learning has garnered encouraging results in the task of hate speech detection. The expressionof hate, however, is varied and ever-evolving. Thus better detection systems need to adapt to this variance. Because of this, researchers keep on collecting data and regularly come up with hate speech detection competitions. In this paper, we discuss our entry to one such competition,namely the English version of sub-task A for the OffensEval competition. Our contribution can be perceived through our results, that was first an F1-score of 0.9087, and with further refinementsdescribed here climb up to 0.9166. It serves to give more support to our hypothesis that one ofthe variants of BERT, namely RoBERTa can successfully differentiate between offensive and non-offensive tweets, given the proper preprocessing steps
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| 9. |
- Brännvall, Rickard, et al.
(author)
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Cross-encoded meta embedding towards transfer learning
- 2020
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In: ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. - : ESANN (i6doc.com). - 9782875870742 ; , s. 631-636, s. 631-636
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Conference paper (peer-reviewed)abstract
- In this paper we generate word meta-embeddings from already existing embeddings using cross-encoding. Previous approaches can only work with words that exist in each source embedding, while the architecture presented here drops this requirement. We demonstrate the method using two pre-trained embeddings, namely GloVE and FastText. Furthermore, we propose additional improvements to the training process of the metaembedding. Results on six standard tests for word similarity show that the meta-embedding trained outperforms the original embeddings. Moreover, this performance can be further increased with the proposed improvements, resulting in a competitive performance with those reported earlier.
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| 10. |
- Brännvall, Rickard, et al.
(author)
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National Space Data Lab on Kubernetes
- 2019
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Conference paper (other academic/artistic)abstract
- The National Space Data Lab is a collaboration project between Swedish National Space Agency, RISE Research Institutes of Sweden, Luleå University of Technology and AI Sweden. It will be a national knowledge and data hub for Swedish authorities’ work on earth observation data and for the development of AI-based analysis of data, generated in space systems. The platform is deployed on Kubernetes.Purpose• Increase the availability of space data for the benefit of developing society and industry• Provide platform for accessing space data and analytical tools
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