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  • 1.
    A. Oliveira, Roger
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    S. Salles, Rafael
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Rönnberg, Sarah K.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Deep Learning for Power Quality with Special Reference to Unsupervised Learning2023In: 27th International Conference on Electricity Distribution (CIRED 2023), IEEE, 2023, p. 935-939, article id 10417Conference paper (Refereed)
  • 2.
    Abbasi, Jasim
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Predictive Maintenance in Industrial Machinery using Machine Learning2021Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Background: The gearbox and machinery faults prediction are expensive both in terms of repair and loss output in production. These losses or faults may lead to complete machinery or plant breakdown. 

    Objective: The goal of this study was to apply advanced machine learning techniques to avoid these losses and faults and replace them with predictive maintenance. To identify and predict the faults in industrial machinery using Machine Learning (ML)  and Deep Learning (DL) approaches. 

    Methods: Our study was based on two types of datasets which includes gearbox and rotatory machinery dataset. These datasets were analyzed to predict the faults using machine learning and deep neural network models. The performance of the model was evaluated for both the datasets with binary and multi-classification problems using the different machine learning models and their statistics.

    Results: In the case of the gearbox fault dataset with a binary classification problem, we observed random forest and deep neural network models performed equally well, with the highest F1-score and AUC score of around 0.98 and with the least error rate of 7%.  In addition to this, in the case of the multi-classification rotatory machinery fault prediction dataset, the random forest model outperformed the deep neural network model with an AUC score of 0.98. 

    Conclusions: In conclusion classification efficiency of the Machine Learning (ML) and Deep Neural Network (DNN) model were tested and evaluated. Our results show Random Forest (RF) and Deep Neural Network (DNN) models have better fault prediction ability to identify the different types of rotatory machinery and gearbox faults as compared to the decision tree and AdaBoost. 

    Keywords: Machine Learning, Deep Learning, Big Data, Predictive Maintenance, Rotatory Machinery Fault Prediction, Gearbox Fault Prediction, Machinery Fault Database, Internet of Things (IoT), Spectra quest machinery fault simulator, Cloud Computing, Industry 4.0

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    Predictive Maintenance in Industrial Machinery using Machine Learning
  • 3.
    Abdelaziz, Ahmed
    et al.
    Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur.
    Ang, Tanfong
    Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur.
    Sookhak, Mehdi
    Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur.
    Khan, Suleman
    Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Liew, Cheesun
    Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur.
    Akhunzada, Adnan
    Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur.
    Survey on network virtualization using openflow: Taxonomy, opportunities, and open issues2016In: KSII Transactions on Internet and Information Systems, ISSN 1976-7277, Vol. 10, no 10, p. 4902-4932Article in journal (Refereed)
    Abstract [en]

    The popularity of network virtualization has recently regained considerable momentum because of the emergence of OpenFlow technology. It is essentially decouples a data plane from a control plane and promotes hardware programmability. Subsequently, OpenFlow facilitates the implementation of network virtualization. This study aims to provide an overview of different approaches to create a virtual network using OpenFlow technology. The paper also presents the OpenFlow components to compare conventional network architecture with OpenFlow network architecture, particularly in terms of the virtualization. A thematic OpenFlow network virtualization taxonomy is devised to categorize network virtualization approaches. Several testbeds that support OpenFlow network virtualization are discussed with case studies to show the capabilities of OpenFlow virtualization. Moreover, the advantages of popular OpenFlow controllers that are designed to enhance network virtualization is compared and analyzed. Finally, we present key research challenges that mainly focus on security, scalability, reliability, isolation, and monitoring in the OpenFlow virtual environment. Numerous potential directions to tackle the problems related to OpenFlow network virtualization are likewise discussed

  • 4.
    Abdi, Mohamed
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Ali, Mahammed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Hur prototyper kan användas i arbetet med applikationer riktade till ungdomar2020Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Abstract Using prototypes when developing IT artifacts is not something new. With the help of prototypes, developers get a chance to show what the IT artifact can look like without investing too much time or money. The end user also gets the chance to see the IT artifact before it has been developed and gets a chance to add or remove features and requirements. This study examines how to use prototypes and how the use of prototypes improves the experience of mobile application interfaces for young people. In the study, young people from a leisure center were interviewed regarding the development of a mobile application. Three different prototypes have been presented and data has been collected on those prototypes to then see how the different prototypes can be used in a development process. After data collection, it turned out that the Lo-fi prototype did not contribute as much as the Hi-fi prototypes, so there is no need to develop Lo-fi prototypes in order to obtain data on user experience.

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  • 5.
    Abdukalikova, Anara
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Machine Learning assisted system for the resource-constrained atrial fibrillation detection from short single-lead ECG signals2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    An integration of ICT advances into a conventional healthcare system is spreading extensively nowadays. This trend is known as Electronic health or E-Health. E-Health solutions help to achieve the sustainability goal of increasing the expected lifetime while improving the quality of life by providing a constant healthcare monitoring. Cardiovascular diseases are one of the main killers yearly causing approximately 17.7 million deaths worldwide. The focus of this work is on studying the detection of one of the cardiovascular diseases – Atrial Fibrillation (AF) arrhythmia.  This type of arrhythmia has a severe influence on the heart health conditions and could cause congestive heart failure (CHF), stroke, and even increase the risk of death. Therefore, it is important to detect AF as early as possible. In this thesis we focused on studying various machine learning techniques for AF detection using only short single lead Electrocardiography recordings. A web-based solution was built as a final prototype, which first simulates the reception of a recorded signal, conducts the preprocessing, makes a prediction of the AF presence, and visualizes the result. For the AF detection the relatively high accuracy score was achieved comparable to the one of the state-of-the-art. The work was based on the investigation of the proposed architectures and the usage of the database of signals from the 2017 PhysioNet/CinC Challenge. However, an additional constraint was introduced to the original problem formulation, since the idea of a future deployment on the resource-limited devices places the restrictions on the complexity of the computations being performed for achieving the prediction. Therefore, this constraint was considered during the development phase of the project.

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  • 6.
    Abdukalikova, Anara
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Wiklund, Urban
    Umeå University, Umeå, Sweden.
    Detection of Atrial Fibrillation from Short ECGs: Minimalistic Complexity Analysis for Feature-Based Classifiers2018In: Computing in Cardiology 2018: Proceedings / [ed] Christine Pickett; Cristiana Corsi; Pablo Laguna; Rob MacLeod, IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    In order to facilitate data-driven solutions for early detection of atrial fibrillation (AF), the 2017 CinC conference challenge was devoted to automatic AF classification based on short ECG recordings. The proposed solutions concentrated on maximizing the classifiers F 1 score, whereas the complexity of the classifiers was not considered. However, we argue that this must be addressed as complexity places restrictions on the applicability of inexpensive devices for AF monitoring outside hospitals. Therefore, this study investigates the feasibility of complexity reduction by analyzing one of the solutions presented for the challenge.

  • 7.
    Abdulmumin, Idris
    et al.
    Ahmadu Bello University, Zaria, Nigeria; HausaNLP.
    Beukman, Michael
    University of the Witwatersrand, South Africa.
    Alabi, Jesujoba O.
    Saarland University, Germany.
    Emezue, Chris
    TUM, Germany; Mila - Quebec AI Institute.
    Asiko, Everlyn
    University of Cape Town, South Africa; African Institute for Mathematical Sciences.
    Adewumi, Oluwatosin
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Muhammad, Shamsuddeen Hassan
    HausaNLP; LIAAD-INESC TEC, Porto, Portugal.
    Adeyemi, Mofetoluwa
    Uppsala University, Sweden.
    Yousuf, Oreen
    Uppsala University, Sweden.
    Singh, Sahib
    Ford Motor Company.
    Gwadabe, Tajuddeen Rabiu
    HausaNLP; University of Chinese Academy of Sciences, China.
    Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages2022In: Proceedings of the Seventh Conference on Machine Translation (WMT) / [ed] Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri, Association for Computational Linguistics , 2022, p. 1001-1014Conference paper (Refereed)
    Abstract [en]

    We participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task. This work de-scribes our approach, which is based on filtering the given noisy data using a sentence-pair classifier that was built by fine-tuning a pre-trained language model. To train the classifier, we obtain positive samples (i.e. high-quality parallel sentences) from a gold-standard curated dataset and extract negative samples (i.e.low-quality parallel sentences) from automatically aligned parallel data by choosing sentences with low alignment scores. Our final machine translation model was then trained on filtered data, instead of the entire noisy dataset. We empirically validate our approach by evaluating on two common datasets and show that data filtering generally improves overall translation quality, in some cases even significantly.

  • 8.
    Abdunabiev, Isomiddin
    et al.
    Department of Computer and Software, Hanyang University.
    Lee, Choonhwa
    Department of Computer and Software, Hanyang University.
    Hanif, Muhammad
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems.
    An Auto-Scaling Architecture for Container Clusters Using Deep Learning2021In: 2021년도 대한전자공학회 하계종합학술대회 논문집, DBpia , 2021, p. 1660-1663Conference paper (Refereed)
    Abstract [en]

    In the past decade, cloud computing has become one of the essential techniques of many business areas, including social media, online shopping, music streaming, and many more. It is difficult for cloud providers to provision their systems in advance due to fluctuating changes in input workload and resultant resource demand. Therefore, there is a need for auto-scaling technology that can dynamically adjust resource allocation of cloud services based on incoming workload. In this paper, we present a predictive auto-scaler for Kubernetes environments to improve the quality of service. Being based on a proactive model, our proposed auto-scaling method serves as a foundation on which to build scalable and resource-efficient cloud systems.

  • 9.
    Abedin, Md. Zainal
    et al.
    University of Science and Technology Chittagong.
    Chowdhury, Abu Sayeed
    University of Science and Technology Chittagong.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Karim, Razuan
    University of Science and Technology Chittagong.
    An Interoperable IP based WSN for Smart Irrigation Systems2017Conference paper (Refereed)
    Abstract [en]

    Wireless Sensor Networks (WSN) have been highly developed which can be used in agriculture to enable optimal irrigation scheduling. Since there is an absence of widely used available methods to support effective agriculture practice in different weather conditions, WSN technology can be used to optimise irrigation in the crop fields. This paper presents architecture of an irrigation system by incorporating interoperable IP based WSN, which uses the protocol stacks and standard of the Internet of Things paradigm. The performance of fundamental issues of this network is emulated in Tmote Sky for 6LoWPAN over IEEE 802.15.4 radio link using the Contiki OS and the Cooja simulator. The simulated results of the performance of the WSN architecture presents the Round Trip Time (RTT) as well as the packet loss of different packet size. In addition, the average power consumption and the radio duty cycle of the sensors are studied. This will facilitate the deployment of a scalable and interoperable multi hop WSN, positioning of border router and to manage power consumption of the sensors.

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  • 10.
    Abedin, Md. Zainal
    et al.
    University of Science and Technology, Chittagong.
    Paul, Sukanta
    University of Science and Technology, Chittagong.
    Akhter, Sharmin
    University of Science and Technology, Chittagong.
    Siddiquee, Kazy Noor E Alam
    University of Science and Technology, Chittagong.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Selection of Energy Efficient Routing Protocol for Irrigation Enabled by Wireless Sensor Networks2017In: Proceedings of 2017 IEEE 42nd Conference on Local Computer Networks Workshops, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 75-81Conference paper (Refereed)
    Abstract [en]

    Wireless Sensor Networks (WSNs) are playing remarkable contribution in real time decision making by actuating the surroundings of environment. As a consequence, the contemporary agriculture is now using WSNs technology for better crop production, such as irrigation scheduling based on moisture level data sensed by the sensors. Since WSNs are deployed in constraints environments, the life time of sensors is very crucial for normal operation of the networks. In this regard routing protocol is a prime factor for the prolonged life time of sensors. This research focuses the performances analysis of some clustering based routing protocols to select the best routing protocol. Four algorithms are considered, namely Low Energy Adaptive Clustering Hierarchy (LEACH), Threshold Sensitive Energy Efficient sensor Network (TEEN), Stable Election Protocol (SEP) and Energy Aware Multi Hop Multi Path (EAMMH). The simulation is carried out in Matlab framework by using the mathematical models of those algortihms in heterogeneous environment. The performance metrics which are considered are stability period, network lifetime, number of dead nodes per round, number of cluster heads (CH) per round, throughput and average residual energy of node. The experimental results illustrate that TEEN provides greater stable region and lifetime than the others while SEP ensures more througput.

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  • 11.
    Abedin, Md. Zainal
    et al.
    University of Science and Technology, Chittagong.
    Siddiquee, Kazy Noor E Alam
    University of Science and Technology Chittagong.
    Bhuyan, M. S.
    University of Science & Technology Chittagong.
    Karim, Razuan
    University of Science and Technology Chittagong.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Performance Analysis of Anomaly Based Network Intrusion Detection Systems2018In: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Piscataway, NJ: IEEE Computer Society, 2018, p. 1-7Conference paper (Refereed)
    Abstract [en]

    Because of the increased popularity and fast expansion of the Internet as well as Internet of things, networks are growing rapidly in every corner of the society. As a result, huge amount of data is travelling across the computer networks that lead to the vulnerability of data integrity, confidentiality and reliability. So, network security is a burning issue to keep the integrity of systems and data. The traditional security guards such as firewalls with access control lists are not anymore enough to secure systems. To address the drawbacks of traditional Intrusion Detection Systems (IDSs), artificial intelligence and machine learning based models open up new opportunity to classify abnormal traffic as anomaly with a self-learning capability. Many supervised learning models have been adopted to detect anomaly from networks traffic. In quest to select a good learning model in terms of precision, recall, area under receiver operating curve, accuracy, F-score and model built time, this paper illustrates the performance comparison between Naïve Bayes, Multilayer Perceptron, J48, Naïve Bayes Tree, and Random Forest classification models. These models are trained and tested on three subsets of features derived from the original benchmark network intrusion detection dataset, NSL-KDD. The three subsets are derived by applying different attributes evaluator’s algorithms. The simulation is carried out by using the WEKA data mining tool.

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  • 12.
    Abid, Nosheen
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Deep Learning for Geo-referenced Data: Case Study: Earth Observation2021Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The thesis focuses on machine learning methods for Earth Observation (EO) data, more specifically, remote sensing data acquired by satellites and drones. EO plays a vital role in monitoring the Earth’s surface and modelling climate change to take necessary precautionary measures. Initially, these efforts were dominated by methods relying on handcrafted features and expert knowledge. The recent advances of machine learning methods, however, have also led to successful applications in EO. This thesis explores supervised and unsupervised approaches of Deep Learning (DL) to monitor natural resources of water bodies and forests. 

    The first study of this thesis introduces an Unsupervised Curriculum Learning (UCL) method based on widely-used DL models to classify water resources from RGB remote sensing imagery. In traditional settings, human experts labeled images to train the deep models which is costly and time-consuming. UCL, instead, can learn the features progressively in an unsupervised fashion from the data, reducing the exhausting efforts of labeling. Three datasets of varying resolution are used to evaluate UCL and show its effectiveness: SAT-6, EuroSAT, and PakSAT. UCL outperforms the supervised methods in domain adaptation, which demonstrates the effectiveness of the proposed algorithm. 

    The subsequent study is an extension of UCL for the multispectral imagery of Australian wildfires. This study has used multispectral Sentinel-2 imagery to create the dataset for the forest fires ravaging Australia in late 2019 and early 2020. 12 out of the 13 spectral bands of Sentinel-2 are concatenated in a way to make them suitable as a three-channel input to the unsupervised architecture. The unsupervised model then classified the patches as either burnt or not burnt. This work attains 87% F1-Score mapping the burnt regions of Australia, demonstrating the effectiveness of the proposed method. 

    The main contributions of this work are (i) the creation of two datasets using Sentinel-2 Imagery, PakSAT dataset and Australian Forest Fire dataset; (ii) the introduction of UCL that learns the features progressively without the need of labelled data; and (iii) experimentation on relevant datasets for water body and forest fire classification. 

    This work focuses on patch-level classification which could in future be expanded to pixel-based classification. Moreover, the methods proposed in this study can be extended to the multi-class classification of aerial imagery. Further possible future directions include the combination of geo-referenced meteorological and remotely sensed image data to explore proposed methods. Lastly, the proposed method can also be adapted to other domains involving multi-spectral and multi-modal input, such as, historical documents analysis, forgery detection in documents, and Natural Language Processing (NLP) classification tasks.

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  • 13.
    Abid, Nosheen
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Kovács, György
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Wedin, Jacob
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Paszkowsky, Nuria Agues
    Research Institutes of Sweden, Sweden.
    Shafait, Faisal
    Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan; School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    UCL: Unsupervised Curriculum Learning for Utility Pole Detection from Aerial Imagery2022In: Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA), IEEE, 2022Conference paper (Refereed)
    Abstract [en]

    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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  • 14.
    Abid, Nosheen
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan; School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan.
    Malik, Muhammad Imran
    Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan; School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan.
    Shahzad, Muhammad
    Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan; School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan; Technical University of Munich (TUM), Munich, Germany.
    Shafait, Faisal
    Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan; School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan.
    Ali, Haider
    Engineering, TU, Kaiserslautern, Germany.
    Ghaffar, Muhammad Mohsin
    Johns Hopkins University, USA.
    Weis, Christian
    Johns Hopkins University, USA.
    Wehn, Norbert
    Johns Hopkins University, USA.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Burnt Forest Estimation from Sentinel-2 Imagery of Australia using Unsupervised Deep Learning2021In: Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA), IEEE, 2021, p. 74-81Conference paper (Refereed)
    Abstract [en]

    Massive wildfires not only in Australia, but also worldwide are burning millions of hectares of forests and green land affecting the social, ecological, and economical situation. Widely used indices-based threshold methods like Normalized Burned Ratio (NBR) require a huge amount of data preprocessing and are specific to the data capturing source. State-of-the-art deep learning models, on the other hand, are supervised and require domain experts knowledge for labeling the data in huge quantity. These limitations make the existing models difficult to be adaptable to new variations in the data and capturing sources. In this work, we have proposed an unsupervised deep learning based architecture to map the burnt regions of forests by learning features progressively. The model considers small patches of satellite imagery and classifies them into burnt and not burnt. These small patches are concatenated into binary masks to segment out the burnt region of the forests. The proposed system is composed of two modules: 1) a state-of-the-art deep learning architecture for feature extraction and 2) a clustering algorithm for the generation of pseudo labels to train the deep learning architecture. The proposed method is capable of learning the features progressively in an unsupervised fashion from the data with pseudo labels, reducing the exhausting efforts of data labeling that requires expert knowledge. We have used the realtime data of Sentinel-2 for training the model and mapping the burnt regions. The obtained F1-Score of 0.87 demonstrates the effectiveness of the proposed model.

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  • 15.
    Abid, Nosheen
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan; Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan.
    Shahzad, Muhammad
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan; Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan; Data Science in Earth Observation, Department of Aerospace and Geodesy, Technical University of Munich (TUM), Munich, Germany.
    Malik, Muhammad Imran
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan; Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan.
    Schwanecke, Ulrich
    RheinMain University of Applied Sciences, Germany.
    Ulges, Adrian
    RheinMain University of Applied Sciences, Germany.
    Kovács, György
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Shafait, Faisal
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan; Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan.
    UCL: Unsupervised Curriculum Learning for Water Body Classification from Remote Sensing Imagery2021In: International Journal of Applied Earth Observation and Geoinformation, ISSN 1569-8432, E-ISSN 1872-826X, Vol. 105, article id 102568Article in journal (Refereed)
    Abstract [en]

    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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  • 16.
    Abrishambaf, Reza
    et al.
    Department of Engineering Technology, Miami University, Hamilton, OH.
    Bal, Mert
    Department of Engineering Technology, Miami University, Hamilton, OH.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Distributed home automation system based on IEC61499 function blocks and wireless sensor networks2017In: Proceedings of the IEEE International Conference on Industrial Technology, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1354-1359, article id 7915561Conference paper (Refereed)
    Abstract [en]

    In this paper, a distributed home automation system will be demonstrated. Traditional systems are based on a central controller where all the decisions are made. The proposed control architecture is a solution to overcome the problems such as the lack of flexibility and re-configurability that most of the conventional systems have. This has been achieved by employing a method based on the new IEC 61499 function block standard, which is proposed for distributed control systems. This paper also proposes a wireless sensor network as the system infrastructure in addition to the function blocks in order to implement the Internet-of-Things technology into the area of home automation as a solution for distributed monitoring and control. The proposed system has been implemented in both Cyber (nxtControl) and Physical (Contiki-OS) level to show the applicability of the solution

  • 17.
    Acampora, Giovanni
    et al.
    Department of Physics “Ettore Pancini”, University of Naples Federico II, Naples, Italy.
    Pedrycz, WitoldDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.Vasilakos, AthanasiosLuleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.Vitiello, AutiliaDepartment of Physics “Ettore Pancini”, University of Naples Federico II, Naples, Italy.
    Computational Intelligence for Semantic Knowledge Management: New Perspectives for Designing and Organizing Information Systems2020Collection (editor) (Other academic)
    Abstract [en]

    This book provides a comprehensive overview of computational intelligence methods for semantic knowledge management. Contrary to popular belief, the methods for semantic management of information were created several decades ago, long before the birth of the Internet. In fact, it was back in 1945 when Vannevar Bush introduced the idea for the first protohypertext: the MEMEX (MEMory + indEX) machine. In the years that followed, Bush’s idea influenced the development of early hypertext systems until, in the 1980s, Tim Berners Lee developed the idea of the World Wide Web (WWW) as it is known today. From then on, there was an exponential growth in research and industrial activities related to the semantic management of the information and its exploitation in different application domains, such as healthcare, e-learning and energy management. 

    However, semantics methods are not yet able to address some of the problems that naturally characterize knowledge management, such as the vagueness and uncertainty of information. This book reveals how computational intelligence methodologies, due to their natural inclination to deal with imprecision and partial truth, are opening new positive scenarios for designing innovative semantic knowledge management architectures.

  • 18.
    Acampora, Giovanni
    et al.
    Department of Physics “Ettore Pancini”, University of Naples Federico II, Naples, Italy.
    Pedrycz, Witold
    Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vitiello, Autilia
    Department of Physics “Ettore Pancini”, University of Naples Federico II, Naples, Italy.
    Preface2020In: Computational Intelligence for Semantic Knowledge Management: New Perspectives for Designing and Organizing Information Systems / [ed] Giovanni Acampora; Witold Pedrycz; Athanasios V. Vasilakos; Autilia Vitiello, Springer Nature, 2020, Vol. 837, p. vii-xChapter in book (Other academic)
  • 19.
    Acharya, Soam
    et al.
    Cornell University, Ithaca.
    Smith, Brian P
    Cornell University, Ithaca.
    Parnes, Peter
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Characterizing user access to videos on the World Wide Web1999In: Multimedia computing and networking 2000 / [ed] Klara Nahrstedt, Bellingham, Wash: SPIE - International Society for Optical Engineering, 1999, p. 130-141Conference paper (Refereed)
    Abstract [en]

    Despite evidence of rising popularity of video on the web (or VOW), little is known about how users access video. However, such a characterization can greatly benefit the design of multimedia systems such as web video proxies and VOW servers. Hence, this paper presents an analysis of trace data obtained from an ongoing VOW experiment in Lulea University of Technology, Sweden. This experiment is unique as video material is distributed over a high bandwidth network allowing users to make access decisions without the network being a major factor. Our analysis revealed a number of interesting discoveries regarding user VOW access. For example, accesses display high temporal locality: several requests for the same video title often occur within a short time span. Accesses also exhibited spatial locality of reference whereby a small number of machines accounted for a large number of overall requests. Another finding was a browsing pattern where users preview the initial portion of a video to find out if they are interested. If they like it, they continue watching, otherwise they halt it. This pattern suggests that caching the first several minutes of video data should prove effective. Lastly, the analysis shows that, contrary to previous studies, ranking of video titles by popularity did not fit a Zipfian distribution.

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  • 20.
    Adalat, Mohsin
    et al.
    COSMOSE Research Group, Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
    Niazi, Muaz A.
    COSMOSE Research Group, Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Variations in power of opinion leaders in online communication networks2018In: Royal Society Open Science, E-ISSN 2054-5703, Vol. 5, no 10, article id 180642Article in journal (Refereed)
    Abstract [en]

    Online social media has completely transformed how we communicate with each other. While online discussion platforms are available in the form of applications and websites, an emergent outcome of this transformation is the phenomenon of ‘opinion leaders’. A number of previous studies have been presented to identify opinion leaders in online discussion networks. In particular, Feng (2016 Comput. Hum. Behav. 54, 43–53. (doi:10.1016/j.chb.2015.07.052)) has identified five different types of central users besides outlining their communication patterns in an online communication network. However, the presented work focuses on a limited time span. The question remains as to whether similar communication patterns exist that will stand the test of time over longer periods. Here, we present a critical analysis of the Feng framework both for short-term as well as for longer periods. Additionally, for validation, we take another case study presented by Udanor et al. (2016 Program 50, 481–507. (doi:10.1108/PROG-02-2016-0011)) to further understand these dynamics. Results indicate that not all Feng-based central users may be identifiable in the longer term. Conversation starter and influencers were noted as opinion leaders in the network. These users play an important role as information sources in long-term discussions. Whereas network builder and active engager help in connecting otherwise sparse communities. Furthermore, we discuss the changing positions of opinion leaders and their power to keep isolates interested in an online discussion network.

  • 21.
    Adelani, David Ifeoluwa
    et al.
    Spoken Language Systems Group (LSV), Saarland University, Germany; Masakhane NLP.
    Abbott, Jade
    Retro Rabbit, South Africa; Masakhane NLP.
    Neubig, Graham
    Language Technologies Institute, Carnegie Mellon University, United States.
    D'souza, Daniel
    ProQuest, United States; Masakhane NLP.
    Kreutzer, Julia
    Google Research, Canada; Masakhane NLP.
    Lignos, Constantine
    Brandeis University, United States; Masakhane NLP.
    Palen-Michel, Chester
    Brandeis University, United States; Masakhane NLP.
    Buzaaba, Happy
    Graduate School of Systems and Information Engineering, University of Tsukuba, Japan; Masakhane NLP.
    Rijhwani, Shruti
    Language Technologies Institute, Carnegie Mellon University, United States.
    Ruder, Sebastian
    DeepMind, United Kingdom.
    Mayhew, Stephen
    Duolingo, United States.
    Abebe Azime, Israel
    African Institute for Mathematical Sciences (AIMS-AMMI), Ethiopia; Masakhane NLP.
    Muhammad, Shamsuddeen H.
    University of Porto, Nigeria; Bayero University, Kano, Nigeria.
    Emezue, Chris Chinenye
    Technical University of Munich, Germany; Masakhane NLP.
    Nakatuma-Nabende, Joyce
    Makerere University, Kampala, Uganda; Masakhane NLP.
    Ogayo, Perez
    African Leadership University, Rwanda; Masakhane NLP.
    Anuoluwapo, Aremu
    University of Lagos, Nigeria; Masakhane NLP.
    Gitau, Catherine
    Masakhane NLP.
    Mbaye, Derguene
    Masakhane NLP.
    Alabi, Jesujoba
    Max Planck Institute for Informatics, Germany; Masakhane NLP.
    Yimam, Seid Muhie
    LT Group, Universität Hamburg, Germany.
    Gwadabe, Tajuddeen Rabiu
    University of Chinese Academy of Science, China; Masakhane NLP.
    Ezeani, Ignatius
    Lancaster University, United Kingdom; Masakhane NLP.
    Niyongabo, Rubungo Andre
    University of Electronic Science and Technology of China, China; Masakhane NLP.
    Mukiibi, Jonathan
    Makerere University, Kampala, Uganda.
    Otiende, Verrah
    United States International University - Africa (USIU-A), Kenya; Masakhane NLP.
    Orife, Iroro
    Niger-Volta LTI; Masakhane NLP.
    David, Davis
    Masakhane NLP.
    Ngom, Samba
    Masakhane NLP.
    Adewumi, Tosin
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Masakhane NLP.
    Rayson, Paul
    Lancaster University, United Kingdom.
    Adeyemi, Mofetoluwa
    Masakhane NLP.
    Muriuki, Gerald
    Makerere University, Kampala, Uganda.
    Anebi, Emmanuel
    Masakhane NLP.
    Chukwuneke, Chimaka
    Masakhane NLP.
    Odu, Nkiruka
    African University of Science and Technology, Abuja, Nigeria.
    Wairagala, Eric Peter
    Makerere University, Kampala, Uganda.
    Oyerinde, Samuel
    Masakhane NLP.
    Siro, Clemencia
    Masakhane NLP.
    Bateesa, Tobius Saul
    Makerere University, Kampala, Uganda.
    Oloyede, Temilola
    Masakhane NLP.
    Wambui, Yvonne
    Masakhane NLP.
    Akinode, Victor
    Masakhane NLP.
    Nabagereka, Deborah
    Makerere University, Kampala, Uganda.
    Katusiime, Maurice
    Makerere University, Kampala, Uganda.
    Awokoya, Ayodele
    University of Ibadan, Nigeria; Masakhane NLP.
    Mboup, Mouhamadane
    Masakhane NLP.
    Gebreyohannes, Dibora
    Masakhane NLP.
    Tilaye, Henok
    Masakhane NLP.
    Nwaike, Kelechi
    Masakhane NLP.
    Wolde, Degaga
    Masakhane NLP.
    Faye, Abdoulaye
    Masakhane NLP.
    Sibanda, Blessing
    Namibia University of Science and Technology, Namibia; Masakhane NLP.
    Ahia, Orevaoghene
    Instadeep, Nigeria; Masakhane NLP.
    Dossou, Bonaventure F. P.
    Jacobs University Bremen, Germany; Masakhane NLP.
    Ogueji, Kelechi
    University of Waterloo, Canada; Masakhane NLP.
    Diop, Thierno Ibrahima
    Masakhane NLP.
    Diallo, Abdoulaye
    Masakhane NLP.
    Akinfaderin, Adewale
    Masakhane NLP.
    Marengereke, Tendai
    Masakhane NLP.
    Osei, Salomey
    African Institute for Mathematical Sciences (AIMS-AMMI), Ethiopia; Masakhane NLP.
    MasakhaNER: Named Entity Recognition for African Languages2021In: Transactions of the Association for Computational Linguistics, E-ISSN 2307-387X, Vol. 9, p. 1116-1131Article in journal (Refereed)
    Abstract [en]

    We take a step towards addressing the under-representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.

  • 22.
    Adelani, David Ifeoluwa
    et al.
    Masakhane NLP; Saarland University, Germany; University College London, UK.
    Neubig, Graham
    Carnegie Mellon University, USA.
    Ruder, Sebastian
    Google Research.
    Rijhwani, Shruti
    Carnegie Mellon University, USA.
    Beukman, Michael
    Masakhane NLP; University of the Witwatersrand, South Africa.
    Palen-Michel, Chester
    Masakhane NLP; Brandeis University, USA.
    Lignos, Constantine
    Masakhane NLP; Brandeis University, USA.
    Alabi, Jesujoba O.
    Masakhane NLP; Saarland University, Germany.
    Muhammad, Shamsuddeen H.
    Masakhane NLP; LIAAD-INESC TEC, Portugal.
    Nabende, Peter
    Masakhane NLP; Makerere University, Uganda.
    Bamba Dione, Cheikh M.
    Masakhane NLP; University of Bergen, Norway.
    Bukula, Andiswa
    SADiLaR, South Africa.
    Mabuya, Rooweither
    SADiLaR, South Africa.
    Dossou, Bonaventure F.P.
    Masakhane NLP; Mila Quebec AI Institute, Canada.
    Sibanda, Blessing
    Masakhane NLP.
    Buzaaba, Happy
    Masakhane NLP; RIKEN Center for AI Project, Japan.
    Mukiibi, Jonathan
    Masakhane NLP; Makerere University, Uganda.
    Kalipe, Godson
    Masakhane NLP.
    Mbaye, Derguene
    Masakhane NLP; Baamtu, Senegal.
    Taylor, Amelia
    Masakhane NLP; Malawi University of Business and Applied Science, Malawi.
    Kabore, Fatoumata
    Masakhane NLP; Uppsala University, Sweden.
    Emezue, Chris Chinenye
    Masakhane NLP; TU Munich, Germany.
    Aremu, Anuoluwapo
    Masakhane NLP.
    Ogayo, Perez
    Masakhane NLP; Carnegie Mellon University, USA.
    Gitau, Catherine
    Masakhane NLP.
    Munkoh-Buabeng, Edwin
    Masakhane NLP; TU Clausthal, Germany.
    Koagne, Victoire M.
    Masakhane NLP.
    Tapo, Allahsera Auguste
    Masakhane NLP; Rochester Institute of Technology, USA.
    Macucwa, Tebogo
    Masakhane NLP; University of Pretoria, South Africa.
    Marivate, Vukosi
    Masakhane NLP; University of Pretoria, South Africa.
    Mboning, Elvis
    Masakhane NLP.
    Gwadabe, Tajuddeen
    Masakhane NLP.
    Adewumi, Tosin
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Masakhane NLP.
    Ahia, Orevaoghene
    Masakhane NLP; University of Washington, USA.
    Nakatumba-Nabende, Joyce
    Masakhane NLP; Makerere University, Uganda.
    Mokono, Neo L.
    Masakhane NLP; University of Pretoria, South Africa.
    Ezeani, Ignatius
    Masakhane NLP; Lancaster University, UK.
    Chukwuneke, Chiamaka
    Masakhane NLP; Lancaster University, UK.
    Adeyemi, Mofetoluwa
    Masakhane NLP; University of Waterloo, Canada.
    Hacheme, Gilles Q.
    Masakhane NLP; Ai4innov, France.
    Abdulmumin, Idris
    Masakhane NLP; Ahmadu Bello University, Nigeria.
    Ogundepo, Odunayo
    Masakhane NLP; University of Waterloo, Canada.
    Yousuf, Oreen
    Masakhane NLP; Uppsala University, Sweden.
    Ngoli, Tatiana Moteu
    Masakhane NLP.
    Klakow, Dietrich
    Saarland University, Germany.
    MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition2022In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics (ACL) , 2022, p. 4488-4508Conference paper (Refereed)
    Abstract [en]

    African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settings where current methods are effective. In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER). We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English. Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages.

  • 23.
    Adewumi, Oluwatosin
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Vector Representations of Idioms in Data-Driven Chatbots for Robust Assistance2022Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis presents resources capable of enhancing solutions of some Natural Language Processing (NLP) tasks, demonstrates the learning of abstractions by deep models through cross-lingual transferability, and shows how deep learning models trained on idioms can enhance open-domain conversational systems. The challenges of open-domain conversational systems are many and include bland repetitive utterances, lack of utterance diversity, lack of training data for low-resource languages, shallow world-knowledge and non-empathetic responses, among others. These challenges contribute to the non-human-like utterances that open-domain conversational systems suffer from. They, hence,have motivated the active research in Natural Language Understanding (NLU) and Natural Language Generation (NLG), considering the very important role conversations (or dialogues) play in human lives. The methodology employed in this thesis involves an iterative set of scientific methods. First, it conducts a systematic literature review to identify the state-of-the-art (SoTA) and gaps, such as the challenges mentioned earlier, in current research. Subsequently, it follows the seven stages of the Machine Learning (ML) life-cycle, which are data gathering (or acquisition), data preparation, model selection, training, evaluation with hyperparameter tuning, prediction and model deployment. For data acquisition, relevant datasets are acquired or created, using benchmark datasets as references, and their data statements are included. Specific contributions of this thesis are the creation of the Swedish analogy test set for evaluating word embeddings and the Potential Idiomatic Expression (PIE)-English idioms corpus for training models in idiom identification and classification. In order to create a benchmark, this thesis performs human evaluation on the generated predictions of some SoTA ML models, including DialoGPT. As different individuals may not agree on all the predictions, the Inter-Annotator Agreement (IAA) is measured. A typical method for measuring IAA is Fleiss Kappa, however, it has a number of shortcomings, including high sensitivity to the number of categories being evaluated. Therefore, this thesis introduces the credibility unanimous score (CUS), which is more intuitive, easier to calculate and seemingly less sensitive to changes in the number of categories being evaluated. The results of human evaluation and comments from evaluators provide valuable feedback on the existing challenges within the models. These create the opportunity for addressing such challenges in future work. The experiments in this thesis test two hypothesis; 1) an open-domain conversational system that is idiom-aware generates more fitting responses to prompts containing idioms, and 2) deep monolingual models learn some abstractions that generalise across languages. To investigate the first hypothesis, this thesis trains English models on the PIE-English idioms corpus for classification and generation. For the second hypothesis, it explores cross-lingual transferability from English models to Swedish, Yorùbá, Swahili, Wolof, Hausa, Nigerian Pidgin English and Kinyarwanda. From the results, the thesis’ additional contributions mainly lie in 1) confirmation of the hypothesis that an open-domain conversational system that is idiom-aware generates more fitting responses to prompts containing idioms, 2) confirmation of the hypothesis that deep monolingual models learn some abstractions that generalise across languages, 3) introduction of CUS and its benefits, 4) insight into the energy-saving and time-saving benefits of more optimal embeddings from relatively smaller corpora, and 5) provision of public access to the model checkpoints that were developed from this work. We further discuss the ethical issues involved in developing robust, open-domain conversational systems. Parts of this thesis are already published in the form of peer-reviewed journal and conference articles.

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  • 24.
    Adewumi, Oluwatosin
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Word Vector Representations using Shallow Neural Networks2021Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This work highlights some important factors for consideration when developing word vector representations and data-driven conversational systems. The neural network methods for creating word embeddings have gained more prominence than their older, count-based counterparts.However, there are still challenges, such as prolonged training time and the need for more data, especially with deep neural networks. Shallow neural networks with lesser depth appear to have the advantage of less complexity, however, they also face challenges, such as sub-optimal combination of hyper-parameters which produce sub-optimal models. This work, therefore, investigates the following research questions: "How importantly do hyper-parameters influence word embeddings’ performance?" and "What factors are important for developing ethical and robust conversational systems?" In answering the questions, various experiments were conducted using different datasets in different studies. The first study investigates, empirically, various hyper-parameter combinations for creating word vectors and their impact on a few natural language processing (NLP) downstream tasks: named entity recognition (NER) and sentiment analysis (SA). The study shows that optimal performance of embeddings for downstream \acrshort{nlp} tasks depends on the task at hand.It also shows that certain combinations give strong performance across the tasks chosen for the study. Furthermore, it shows that reasonably smaller corpora are sufficient or even produce better models in some cases and take less time to train and load. This is important, especially now that environmental considerations play prominent role in ethical research. Subsequent studies build on the findings of the first and explore the hyper-parameter combinations for Swedish and English embeddings for the downstream NER task. The second study presents the new Swedish analogy test set for evaluation of Swedish embeddings. Furthermore, it shows that character n-grams are useful for Swedish, a morphologically rich language. The third study shows that broad coverage of topics in a corpus appears to be important to produce better embeddings and that noise may be helpful in certain instances, though they are generally harmful. Hence, relatively smaller corpus can show better performance than a larger one, as demonstrated in the work with the smaller Swedish Wikipedia corpus against the Swedish Gigaword. The argument is made, in the final study (in answering the second question) from the point of view of the philosophy of science, that the near-elimination of the presence of unwanted bias in training data and the use of foralike the peer-review, conferences, and journals to provide the necessary avenues for criticism and feedback are instrumental for the development of ethical and robust conversational systems.

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  • 25.
    Adewumi, Oluwatosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Brännvall, Rickard
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. RISE Research Institutes of Sweden.
    Abid, Nosheen
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Pahlavan, Maryam
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sabah Sabry, Sana
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning2022In: Proceedings of the Northern Lights Deep Learning Workshop 2022 / [ed] Sigurd Løkse, Benjamin Ricaud, Septentrio Academic Publishing , 2022, Vol. 3Conference paper (Refereed)
    Abstract [en]

    Building open-domain conversational systems (or chatbots) that produce convincing responses is a recognized challenge. Recent state-of-the-art (SoTA) transformer-based models for the generation of natural language dialogue have demonstrated impressive performance in simulating human-like, single-turn conversations in English.This work investigates, by an empirical study, the potential for transfer learning of such models to Swedish language. DialoGPT, an English language pre-trained model, is adapted by training on three different Swedish language conversational datasets obtained from publicly available sources: Reddit, Familjeliv and the GDC. Perplexity score (an automated intrinsic metric) and surveys by human evaluation were used to assess the performances of the fine-tuned models. We also compare the DialoGPT experiments with an attention-mechanism-based seq2seq baseline model, trained on the GDC dataset. The results indicate that the capacity for transfer learning can be exploited with considerable success. Human evaluators asked to score the simulated dialogues judged over 57% of the chatbot responses to be human-like for the model trained on the largest (Swedish) dataset. The work agrees with the hypothesis that deep monolingual models learn some abstractions which generalize across languages. We contribute the codes, datasets and model checkpoints and host the demos on the HuggingFace platform.

  • 26.
    Adewumi, Oluwatosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Conversational Systems in Machine Learning from the Point of View of the Philosophy of Science—Using Alime Chat and Related Studies2019In: Philosophies, ISSN 2409-9287, Vol. 4, no 3, article id 41Article in journal (Refereed)
    Abstract [en]

    This essay discusses current research efforts in conversational systems from the philosophy of science point of view and evaluates some conversational systems research activities from the standpoint of naturalism philosophical theory. Conversational systems or chatbots have advanced over the decades and now have become mainstream applications. They are software that users can communicate with, using natural language. Particular attention is given to the Alime Chat conversational system, already in industrial use, and the related research. The competitive nature of systems in production is a result of different researchers and developers trying to produce new conversational systems that can outperform previous or state-of-the-art systems. Different factors affect the quality of the conversational systems produced, and how one system is assessed as being better than another is a function of objectivity and of the relevant experimental results. This essay examines the research practices from, among others, Longino’s view on objectivity and Popper’s stand on falsification. Furthermore, the need for qualitative and large datasets is emphasized. This is in addition to the importance of the peer-review process in scientific publishing, as a means of developing, validating, or rejecting theories, claims, or methodologies in the research community. In conclusion, open data and open scientific discussion fora should become more prominent over the mere publication-focused trend.

  • 27.
    Adewumi, Oluwatosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Corpora Compared: The Case of the Swedish Gigaword & Wikipedia Corpora2020Conference paper (Refereed)
    Abstract [en]

    In this work, we show that the difference in performance of embeddings from differently sourced data for a given language can be due to other factors besides data size. Natural language processing (NLP) tasks usually perform better with embeddings from bigger corpora. However, broadness of covered domain and noise can play important roles. We evaluate embeddings based on two Swedish corpora: The Gigaword and Wikipedia, in analogy (intrinsic) tests and discover that the embeddings from the Wikipedia corpus generally outperform those from the Gigaword corpus, which is a bigger corpus. Downstream tests will be required to have a definite evaluation.

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  • 28.
    Adewumi, Oluwatosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Exploring Swedish & English fastText Embeddings2022In: Artificial Intelligence and Cognition 2022: Proceedings of the 8th International Workshop on Artificial Intelligence and Cognition / [ed] Hadi Banaee, Amy Loutfi, Alessandro Saffiotti, Antonio Lieto, 2022, Vol. 3400, p. 201-208Conference paper (Refereed)
    Abstract [en]

    In this paper, we show that embeddings from relatively smaller corpora sometimes outperform thosefrom larger corpora and we introduce a new Swedish analogy test set and make it publicly available.To achieve good performance in Natural Language Processing (NLP) downstream tasks, several factorsplay important roles: dataset size, the right hyper-parameters, and well-trained embeddings. We utilizethe fastText tool for our experiments. We evaluate both the Swedish and English embeddings that wecreated using intrinsic evaluation (including analogy & Spearman correlation) and compare them with2 common, publicly available embeddings. Our English continuous Bag-of-Words (CBoW)-negativesampling embedding shows better performance compared to the publicly available GoogleNews version.We also describe the relationship between NLP and cognitive science. We contribute the embeddings forresearch or other useful purposes by publicly releasing them.

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  • 29.
    Adewumi, Oluwatosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Exploring Swedish & English fastText Embeddings for NER with the TransformerManuscript (preprint) (Other academic)
    Abstract [en]

    In this paper, our main contributions are that embeddings from relatively smaller corpora can outperform ones from far larger corpora and we present the new Swedish analogy test set. To achieve a good network performance in natural language processing (NLP) downstream tasks, several factors play important roles: dataset size, the right hyper-parameters, and well-trained embeddings. We show that, with the right set of hyper-parameters, good network performance can be reached even on smaller datasets. We evaluate the embeddings at the intrinsic level and extrinsic level, by deploying them on the Transformer in named entity recognition (NER) task and conduct significance tests. This is done for both Swedish and English. We obtain better performance in both languages on the downstream task with far smaller training data, compared to recently released, common crawl versions; and character n-grams appear useful for Swedish, a morphologically rich language.

  • 30.
    Adewumi, Oluwatosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Word2Vec: Optimal hyperparameters and their impact on natural language processing downstream tasks2022In: Open Computer Science, E-ISSN 2299-1093, Vol. 12, no 1, p. 134-141Article in journal (Refereed)
    Abstract [en]

    Word2Vec is a prominent model for natural language processing tasks. Similar inspiration is found in distributed embeddings (word-vectors) in recent state-of-the-art deep neural networks. However, wrong combination of hyperparameters can produce embeddings with poor quality. The objective of this work is to empirically show that Word2Vec optimal combination of hyper-parameters exists and evaluate various combinations. We compare them with the publicly released, original Word2Vec embedding. Both intrinsic and extrinsic (downstream) evaluations are carried out, including named entity recognition and sentiment analysis. Our main contributions include showing that the best model is usually task-specific, high analogy scores do not necessarily correlate positively with F1 scores, and performance is not dependent on data size alone. If ethical considerations to save time, energy, and the environment are made, then relatively smaller corpora may do just as well or even better in some cases. Increasing the dimension size of embeddings after a point leads to poor quality or performance. In addition, using a relatively small corpus, we obtain better WordSim scores, corresponding Spearman correlation, and better downstream performances (with significance tests) compared to the original model, which is trained on a 100 billion-word corpus.

  • 31.
    Adewumi, Oluwatosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream TasksManuscript (preprint) (Other academic)
    Abstract [en]

    Word2Vec is a prominent model for natural language processing (NLP) tasks. Similar nspiration is found in distributed embeddings for new state-of-the-art (SotA) deep neural networks.  However, wrong combination of hyper-parameters can produce poor quality vectors. The objective of this work is to empirically show optimal combination of hyper-parameters exists and evaluate various combinations. We compare them with the released, pre-trained original word2vec model. Both intrinsic and extrinsic (downstream) evaluations, including named entity recognition (NER) and sentiment analysis (SA) were carried out. The downstream tasks reveal that the best model is usually task-specific, high analogy scores don’t necessarily correlate positively with F1 scores and the same applies to focus on data alone. Increasing vector dimension size after a point leads to poor quality or performance. If ethical considerations to save time, energy and the environment are made, then reasonably smaller corpora may do just as well or even better in some cases. Besides, using a small corpus, we obtain better human-assigned WordSim scores, corresponding Spearman correlation and better downstream performances (with significance tests) compared to the original model, trained on 100 billion-word corpus.

  • 32.
    Adewumi, Oluwatosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Inner For-Loop for Speeding Up Blockchain Mining2020In: Open Computer Science, E-ISSN 2299-1093, Vol. 10, no 1, p. 42-47Article in journal (Refereed)
    Abstract [en]

    In this paper, the authors propose to increase the efficiency of blockchain mining by using a population-based approach. Blockchain relies on solving difficult mathematical problems as proof-of-work within a network before blocks are added to the chain. Brute force approach, advocated by some as the fastest algorithm for solving partial hash collisions and implemented in Bitcoin blockchain, implies exhaustive, sequential search. It involves incrementing the nonce (number) of the header by one, then taking a double SHA-256 hash at each instance and comparing it with a target value to ascertain if lower than that target. It excessively consumes both time and power. In this paper, the authors, therefore, suggest using an inner for-loop for the population-based approach. Comparison shows that it’s a slightly faster approach than brute force, with an average speed advantage of about 1.67% or 3,420 iterations per second and 73% of the time performing better. Also, we observed that the more the total particles deployed, the better the performance until a pivotal point. Furthermore, a recommendation on taming the excessive use of power by networks, like Bitcoin’s, by using penalty by consensus is suggested.

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  • 33.
    Adewumi, Oluwatosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sabry, Sana Sabah
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Abid, Nosheen
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    T5 for Hate Speech, Augmented Data, and Ensemble2023In: Sci, E-ISSN 2413-4155, Vol. 5, no 4, article id 37Article in journal (Refereed)
    Abstract [en]

    We conduct relatively extensive investigations of automatic hate speech (HS) detection using different State-of-The-Art (SoTA) baselines across 11 subtasks spanning six different datasets. Our motivation is to determine which of the recent SoTA models is best for automatic hate speech detection and what advantage methods, such as data augmentation and ensemble, may have on the best model, if any. We carry out six cross-task investigations. We achieve new SoTA results on two subtasks—macro F1 scores of 91.73% and 53.21% for subtasks A and B of the HASOC 2020 dataset, surpassing previous SoTA scores of 51.52% and 26.52%, respectively. We achieve near-SoTA results on two others—macro F1 scores of 81.66% for subtask A of the OLID 2019 and 82.54% for subtask A of the HASOC 2021, in comparison to SoTA results of 82.9% and 83.05%, respectively. We perform error analysis and use two eXplainable Artificial Intelligence (XAI) algorithms (Integrated Gradient (IG) and SHapley Additive exPlanations (SHAP)) to reveal how two of the models (Bi-Directional Long Short-Term Memory Network (Bi-LSTM) and Text-to-Text-Transfer Transformer (T5)) make the predictions they do by using examples. Other contributions of this work are: (1) the introduction of a simple, novel mechanism for correcting Out-of-Class (OoC) predictions in T5, (2) a detailed description of the data augmentation methods, and (3) the revelation of the poor data annotations in the HASOC 2021 dataset by using several examples and XAI (buttressing the need for better quality control). We publicly release our model checkpoints and codes to foster transparency.

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  • 34.
    Adewumi, Tosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Masakhane.
    Adeyemi, Mofetoluwa
    Masakhane.
    Anuoluwapo, Aremu
    Masakhane.
    Peters, Bukola
    CIS.
    Buzaaba, Happy
    Masakhane.
    Samuel, Oyerinde
    Masakhane.
    Rufai, Amina Mardiyyah
    Masakhane.
    Ajibade, Benjamin
    Masakhane.
    Gwadabe, Tajudeen
    Masakhane.
    Koulibaly Traore, Mory Moussou
    Masakhane.
    Ajayi, Tunde Oluwaseyi
    Masakhane.
    Muhammad, Shamsuddeen
    Baruwa, Ahmed
    Masakhane.
    Owoicho, Paul
    Masakhane.
    Ogunremi, Tolulope
    Masakhane.
    Ngigi, Phylis
    Jomo Kenyatta University of Agriculture and Technology.
    Ahia, Orevaoghene
    Masakhane.
    Nasir, Ruqayya
    Masakhane.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    AfriWOZ: Corpus for Exploiting Cross-Lingual Transfer for Dialogue Generation in Low-Resource, African Languages2023In: IJCNN 2023 - International Joint Conference on Neural Networks, Conference Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper (Refereed)
    Abstract [en]

    Dialogue generation is an important NLP task fraught with many challenges. The challenges become more daunting for low-resource African languages. To enable the creation of dialogue agents for African languages, we contribute the first high-quality dialogue datasets for 6 African languages: Swahili, Wolof, Hausa, Nigerian Pidgin English, Kinyarwanda & Yorùbá. There are a total of 9,000 turns, each language having 1,500 turns, which we translate from a portion of the English multi-domain MultiWOZ dataset. Subsequently, we benchmark by investigating & analyzing the effectiveness of modelling through transfer learning by utilziing state-of-the-art (SoTA) deep monolingual models: DialoGPT and BlenderBot. We compare the models with a simple seq2seq baseline using perplexity. Besides this, we conduct human evaluation of single-turn conversations by using majority votes and measure inter-annotator agreement (IAA). We find that the hypothesis that deep monolingual models learn some abstractions that generalize across languages holds. We observe human-like conversations, to different degrees, in 5 out of the 6 languages. The language with the most transferable properties is the Nigerian Pidgin English, with a human-likeness score of 78.1%, of which 34.4% are unanimous. We freely provide the datasets and host the model checkpoints/demos on the HuggingFace hub for public access.

  • 35.
    Adewumi, Tosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Alkhaled, Lama
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Mokayed, Hamam
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizingand Condescending Language2022In: Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) / [ed] Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan, Association for Computational Linguistics , 2022, p. 473-478Conference paper (Refereed)
    Abstract [en]

    This paper describes the system used by the Machine Learning Group of LTU in subtask 1 of the SemEval-2022 Task 4: Patronizing and Condescending Language (PCL) Detection. Our system consists of finetuning a pretrained text-to-text transfer transformer (T5) and innovatively reducing its out-of-class predictions. The main contributions of this paper are 1) the description of the implementation details of the T5 model we used, 2) analysis of the successes & struggles of the model in this task, and 3) ablation studies beyond the official submission to ascertain the relative importance of data split. Our model achieves an F1 score of 0.5452 on the official test set.

  • 36.
    Adewumi, Tosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    State-of-the-Art in Open-Domain Conversational AI: A Survey2022In: Information, E-ISSN 2078-2489, Vol. 13, no 6, article id 298Article, review/survey (Refereed)
    Abstract [en]

    We survey SoTA open-domain conversational AI models with the objective of presenting the prevailing challenges that still exist to spur future research. In addition, we provide statistics on the gender of conversational AI in order to guide the ethics discussion surrounding the issue. Open-domain conversational AI models are known to have several challenges, including bland, repetitive responses and performance degradation when prompted with figurative language, among others. First, we provide some background by discussing some topics of interest in conversational AI. We then discuss the method applied to the two investigations carried out that make up this study. The first investigation involves a search for recent SoTA open-domain conversational AI models, while the second involves the search for 100 conversational AI to assess their gender. Results of the survey show that progress has been made with recent SoTA conversational AI, but there are still persistent challenges that need to be solved, and the female gender is more common than the male for conversational AI. One main takeaway is that hybrid models of conversational AI offer more advantages than any single architecture. The key contributions of this survey are (1) the identification of prevailing challenges in SoTA open-domain conversational AI, (2) the rarely held discussion on open-domain conversational AI for low-resource languages, and (3) the discussion about the ethics surrounding the gender of conversational AI.

  • 37.
    Adewumi, Tosin P.
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    The Challenge of Diacritics in Yorùbá Embeddings2020In: ML4D 2020 Proceedings / [ed] Tejumade Afonja; Konstantin Klemmer; Aya Salama; Paula Rodriguez Diaz; Niveditha Kalavakonda; Oluwafemi Azeez, Neural Information Processing Systems Foundation , 2020, article id 2011.07605Conference paper (Refereed)
    Abstract [en]

    The major contributions of this work include the empirical establishment of a better performance for Yoruba embeddings from undiacritized (normalized) dataset and provision of new analogy sets for evaluation.The Yoruba language, being a tonal language, utilizes diacritics (tonal marks) in written form. We show that this affects embedding performance by creating embeddings from exactly the same Wikipedia dataset but with the second one normalized to be undiacritized. We further compare average intrinsic performance with two other work (using analogy test set & WordSim) and we obtain the best performance in WordSim and corresponding Spearman correlation.

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  • 38.
    Adewumi, Tosin P.
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Vector Representations of Idioms in Chatbots2020In: Proceedings: SAIS Workshop 2020, Chalmers University of Technology , 2020Conference paper (Refereed)
    Abstract [en]

    Open-domain chatbots have advanced but still have many gaps. My PhD aims to solve a few of those gaps by creating vector representations of idioms (figures of speech) that will be beneficial to chatbots and natural language processing (NLP), generally. In the process, new, optimal fastText embeddings in Swedish and English have been created and the first Swedish analogy test set, larger than the Google original, for intrinsic evaluation of Swedish embeddings has also been produced. Major milestones have been attained and others are soon to follow. The deliverables of this project will give NLP researchers the opportunity to measure the quality of Swedish embeddings easily and advance state-of-the-art (SotA) in NLP.

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  • 39.
    Adewumi, Tosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Södergren, Isabella
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems.
    Alkhaled, Lama
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sabry, Sana Sabah
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Bipol: Multi-axes Evaluation of Bias with Explainability in BenchmarkDatasets2023In: Proceedings of Recent Advances in Natural Language Processing / [ed] Galia Angelova, Maria Kunilovskaya and Ruslan Mitkov, Incoma Ltd. , 2023, p. 1-10Conference paper (Refereed)
    Abstract [en]

    We investigate five English NLP benchmark datasets (on the superGLUE leaderboard) and two Swedish datasets for bias, along multiple axes. The datasets are the following: Boolean Question (Boolq), CommitmentBank (CB), Winograd Schema Challenge (WSC), Winogender diagnostic (AXg), Recognising Textual Entailment (RTE), Swedish CB, and SWEDN. Bias can be harmful and it is known to be common in data, which ML models learn from. In order to mitigate bias in data, it is crucial to be able to estimate it objectively. We use bipol, a novel multi-axes bias metric with explainability, to estimate and explain how much bias exists in these datasets. Multilingual, multi-axes bias evaluation is not very common. Hence, we also contribute a new, large Swedish bias-labeled dataset (of 2 million samples), translated from the English version and train the SotA mT5 model on it. In addition, we contribute new multi-axes lexica for bias detection in Swedish. We make the codes, model, and new dataset publicly available.

  • 40.
    Adewumi, Tosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Vadoodi, Roshanak
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Geosciences and Environmental Engineering.
    Tripathy, Aparajita
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Nikolaidou, Konstantina
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms2022In: Proceedings of the 13th Language Resources and Evaluation Conference / [ed] Nicoletta Calzolari; Frédéric Béchet; Philippe Blache; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Hélène Mazo; Jan Odijk; Stelios Piperidis, European Language Resources Association (ELRA) , 2022, p. 689-696Conference paper (Refereed)
    Abstract [en]

    We present a fairly large, Potential Idiomatic Expression (PIE) dataset for Natural Language Processing (NLP) in English. The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work. To the best of the authors’ knowledge, this is the first idioms corpus with classes of idioms beyond the literal and the general idioms classification. Inparticular, the following classes are labelled in the dataset: metaphor, simile, euphemism, parallelism, personification, oxymoron, paradox, hyperbole, irony and literal. We obtain an overall inter-annotator agreement (IAA) score, between two independent annotators, of 88.89%. Many past efforts have been limited in the corpus size and classes of samples but this dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses). The corpus may also be extended by researchers to meet specific needs. The corpus has part of speech (PoS) tagging from the NLTK library. Classification experiments performed on the corpus to obtain a baseline and comparison among three common models, including the state-of-the-art (SoTA) BERT model, give good results. We also make publicly available the corpus and the relevant codes for working with it for NLP tasks.

  • 41.
    Adolfsson, Teodor
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Sundin, Axel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Database Selection Process in Very Small Enterprises in Software Development: A Case Study examining Factors, Methods, and Properties2023Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This thesis investigates the database model selection process in VSEs, looking into how priorities and needs differ compared to what is proposed by existing theory in the area. 

    The study was conducted as a case study of a two-person company engaged in developing various applications and performing consulting tasks. Data was collected through two semi-structured interviews. The first interview aimed to understand the company's process for selecting a database model, while the second interview focused on obtaining their perspective on any differences in their selection process compared to the theoretical recommendations and suggested methodology. The purpose was to investigate the important factors involved in the process and explore why and how they deviated from what the theory proposes.

    The study concludes that VSEs have different priorities compared to larger enterprises. Factors like transaction amount does not have to be considered much at the scale of a VSE. It is more important to look into the total cost of the database solution, including making sure that the selected technology is sufficiently efficient to use in development and relatively easy to maintain.

    Regarding selection methodology it was concluded that the time investment required to decide what is the best available database solution can be better spent elsewhere in the enterprise, and finding a good enough solution to get the wheels of the ground is likely a more profitable aim.

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  • 42.
    Aehle, Max
    et al.
    University of Kaiserslautern-Landau (RPTU), Germany.
    Tung Nguyen, Xuan
    University of Kaiserslautern-Landau (RPTU), Germany; National Institute for Nuclear Physics (INFN), Italy.
    Novák, Mihály
    European Organization for Nuclear Research (CERN), Switzerland/France.
    Dorigo, Tommaso
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. National Institute for Nuclear Physics (INFN), Italy.
    Gauger, Nicolas R.
    University of Kaiserslautern-Landau (RPTU), Germany.
    Kieseler, Jan
    Karlsruhe Institute of Technology (KIT), Germany.
    Klute, Markus
    Karlsruhe Institute of Technology (KIT), Germany.
    Vassilev, Vassil
    Princeton University, USA.
    Efficient Forward-Mode Algorithmic Derivatives of Geant4Manuscript (preprint) (Other academic)
  • 43.
    Afremo, Adam
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Användbarhet i mobila investeringstjänster2020Independent thesis Basic level (degree of Bachelor), 180 HE creditsStudent thesis
    Abstract [sv]

    Användbarhet inom digitala tjänster är en faktor som påverkar användares upplevelse i systemet. Detta blir särskilt viktigt för mobila plattformar där skärmstorleken är begränsad och användaren befinner sig i olika miljöer. Det är därför betydelsefullt att system designas för att uppnå god användbarhet. Även om det förekommer generella principer för hur system ska designas för att uppnå god användbarhet saknas det principer för mobila investeringstjänster. I denna studie har en kvalitativ studie genomförts på fem testanvändare med olika investeringserfarenheter. Testanvändarna har blivit intervjuade och utvärderat en mobil investeringstjänst. Även en benchmarking av mobila investeringstjänster på marknaden har genomförts för att jämföra och utvärdera olika system. I analysen framställs sex stycken principer baserat på tidigare forskning och studiens empiri. Studien visar på hur användbarhet kan förbättras i mobila investeringstjänster baserat på utvärderingen av olika investeringstjänster samt testanvändarnas upplevelse med studiens artefakt. Detta vilket även leder till en förbättrad användarupplevelse.

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  • 44.
    Afroze, Tasnim
    et al.
    Department of Computer Science and Engineering, Port City International University, Chattogram 4202, Bangladesh.
    Akther, Shumia
    Department of Computer Science and Engineering, Port City International University, Chattogram 4202, Bangladesh.
    Chowdhury, Mohammed Armanuzzaman
    Department of Computer Science and Engineering, University of Chittagong, Chattogram 4331, Bangladesh.
    Hossain, Emam
    Department of Computer Science and Engineering, Port City International University, Chattogram 4202, Bangladesh.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Chattogram 4331, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Glaucoma Detection Using Inception Convolutional Neural Network V32021In: Applied Intelligence and Informatics: First International Conference, AII 2021, Nottingham, UK, July 30–31, 2021, Proceedings / [ed] Mufti Mahmud; M. Shamim Kaiser; Nikola Kasabov; Khan Iftekharuddin; Ning Zhong, Springer, 2021, p. 17-28Conference paper (Refereed)
    Abstract [en]

    Glaucoma detection is an important research area in intelligent system and it plays an important role to medical field. Glaucoma can give rise to an irreversible blindness due to lack of proper diagnosis. Doctors need to perform many tests to diagnosis this threatening disease. It requires a lot of time and expense. Sometime affected people may not have any vision loss, at the early stage of glaucoma. For detecting glaucoma, we have built a model to lessen the time and cost. Our work introduces a CNN based Inception V3 model. We used total 6072 images. Among this image 2336 were glaucomatous and 3736 were normal fundus image. For training our model we took 5460 images and for testing we took 612 images. After that we obtained an accuracy of 0.8529 and a value of 0.9387 for AUC. For comparison, we used DenseNet121 and ResNet50 algorithm and got an accuracy of 0.8153 and 0.7761 respectively.

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  • 45.
    Agbo, Friday Joseph
    et al.
    School of Computing, University of Eastern Finland, P.O. Box 111, FIN-80101, Joensuu, Finland.
    Oyelere, Solomon Sunday
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Suhonen, Jarkko
    School of Computing, University of Eastern Finland, P.O. Box 111, FIN-80101, Joensuu, Finland.
    Laine, Teemu H.
    Department of Digital Media, Ajou University, 16499, Suwon, Republic of Korea.
    Co-design of mini games for learning computational thinking in an online environment2021In: Education and Information Technologies: Official Journal of the IFIP technical committee on Education, ISSN 1360-2357, E-ISSN 1573-7608, Vol. 26, no 5, p. 5815-5849Article in journal (Refereed)
    Abstract [en]

    Understanding the principles of computational thinking (CT), e.g., problem abstraction, decomposition, and recursion, is vital for computer science (CS) students. Unfortunately, these concepts can be difficult for novice students to understand. One way students can develop CT skills is to involve them in the design of an application to teach CT. This study focuses on co-designing mini games to support teaching and learning CT principles and concepts in an online environment. Online co-design (OCD) of mini games enhances students’ understanding of problem-solving through a rigorous process of designing contextual educational games to aid their own learning. Given the current COVID-19 pandemic, where face-to-face co-designing between researchers and stakeholders could be difficult, OCD is a suitable option. CS students in a Nigerian higher education institution were recruited to co-design mini games with researchers. Mixed research methods comprising qualitative and quantitative strategies were employed in this study. Findings show that the participants gained relevant knowledge, for example, how to (i) create game scenarios and game elements related to CT, (ii) connect contextual storyline to mini games, (iii) collaborate in a group to create contextual low-fidelity mini game prototypes, and (iv) peer review each other’s mini game concepts. In addition, students were motivated toward designing educational mini games in their future studies. This study also demonstrates how to conduct OCD with students, presents lesson learned, and provides recommendations based on the authors’ experience.

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  • 46.
    Agbo, Friday Joseph
    et al.
    School of Computing, University of Eastern Finland, P.O. Box 111, N80101, Joensuu, Finland; School of Computing and Data Science, Willamette University, Salem, OR, 97301, USA.
    Oyelere, Solomon Sunday
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Suhonen, Jarkko
    School of Computing, University of Eastern Finland, P.O. Box 111, N80101, Joensuu, Finland.
    Tukiainen, Markku
    School of Computing, University of Eastern Finland, P.O. Box 111, N80101, Joensuu, Finland.
    Design, development, and evaluation of a virtual reality game-based application to support computational thinking2023In: Educational technology research and development, ISSN 1042-1629, E-ISSN 1556-6501, Vol. 71, no 2, p. 505-537Article in journal (Refereed)
    Abstract [en]

    Computational thinking (CT) has become an essential skill nowadays. For young students, CT competency is required to prepare them for future jobs. This competency can facilitate students’ understanding of programming knowledge which has been a challenge for many novices pursuing a computer science degree. This study focuses on designing and implementing a virtual reality (VR) game-based application (iThinkSmart) to support CT knowledge. The study followed the design science research methodology to design, implement, and evaluate the first prototype of the VR application. An initial evaluation of the prototype was conducted with 47 computer science students from a Nigerian university who voluntarily participated in an experimental process. To determine what works and what needs to be improved in the iThinkSmart VR game-based application, two groups were randomly formed, consisting of the experimental (n = 21) and the control (n = 26) groups respectively. Our findings suggest that VR increases motivation and therefore increase students’ CT skills, which contribute to knowledge regarding the affordances of VR in education and particularly provide evidence on the use of visualization of CT concepts to facilitate programming education. Furthermore, the study revealed that immersion, interaction, and engagement in a VR educational application can promote students’ CT competency in higher education institutions (HEI). In addition, it was shown that students who played the iThinkSmart VR game-based application gained higher cognitive benefits, increased interest and attitude to learning CT concepts. Although further investigation is required in order to gain more insights into students learning process, this study made significant contributions in positioning CT in the HEI context and provides empirical evidence regarding the use of educational VR mini games to support students learning achievements.

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  • 47.
    Agbo, Friday Joseph
    et al.
    School of Computing, University of Eastern Finland, Joensuu, Finland.
    Oyelere, Solomon Sunday
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Suhonen, Jarkko
    School of Computing, University of Eastern Finland, Joensuu, Finland.
    Tukiainen, Markku
    School of Computing, University of Eastern Finland, Joensuu, Finland.
    iThinkSmart: Immersive Virtual Reality Mini Games to Facilitate Students’ Computational Thinking Skills2021In: Koli Calling '21: 21st Koli Calling International Conference on Computing Education Research / [ed] Otto Seppälä; Andrew Petersen, Association for Computing Machinery , 2021, article id 33Conference paper (Refereed)
    Abstract [en]

    This paper presents iThinkSmart, an immersive virtual reality-based application to facilitate the learning of computational thinking (CT) concepts. The tool was developed to supplement the traditional teaching and learning of CT by integrating three virtual mini games, namely, River Crossing, Tower of Hanoi, and Mount Patti treasure hunt, to foster immersion, interaction, engagement, and personalization for an enhanced learning experience. iThinkSmart mini games can be played on a smartphone with a Goggle Cardboard and hand controller. This first prototype of the game accesses players' competency of CT and renders feedback based on learning progress.  

     

  • 48.
    Agbo, Friday Joseph
    et al.
    School of Computing, University of Eastern Finland, P.O. Box 111, FIN-80101, Joensuu, Finland.
    Oyelere, Solomon Sunday
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Suhonen, Jarkko
    School of Computing, University of Eastern Finland, P.O. Box 111, FIN-80101, Joensuu, Finland.
    Tukiainen, Markku
    School of Computing, University of Eastern Finland, P.O. Box 111, FIN-80101, Joensuu, Finland.
    Scientific production and thematic breakthroughs in smart learning environments: a bibliometric analysis2021In: Smart Learning Environments, E-ISSN 2196-7091, Vol. 8, article id 1Article, review/survey (Refereed)
    Abstract [en]

    This study examines the research landscape of smart learning environments by conducting a comprehensive bibliometric analysis of the field over the years. The study focused on the research trends, scholar’s productivity, and thematic focus of scientific publications in the field of smart learning environments. A total of 1081 data consisting of peer-reviewed articles were retrieved from the Scopus database. A bibliometric approach was applied to analyse the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of smart learning environments. The result from this bibliometric analysis indicates that the first paper on smart learning environments was published in 2002; implying the beginning of the field. Among other sources, “Computers & Education,” “Smart Learning Environments,” and “Computers in Human Behaviour” are the most relevant outlets publishing articles associated with smart learning environments. The work of Kinshuk et al., published in 2016, stands out as the most cited work among the analysed documents. The United States has the highest number of scientific productions and remained the most relevant country in the smart learning environment field. Besides, the results also showed names of prolific scholars and most relevant institutions in the field. Keywords such as “learning analytics,” “adaptive learning,” “personalized learning,” “blockchain,” and “deep learning” remain the trending keywords. Furthermore, thematic analysis shows that “digital storytelling” and its associated components such as “virtual reality,” “critical thinking,” and “serious games” are the emerging themes of the smart learning environments but need to be further developed to establish more ties with “smart learning”. The study provides useful contribution to the field by clearly presenting a comprehensive overview and research hotspots, thematic focus, and future direction of the field. These findings can guide scholars, especially the young ones in field of smart learning environments in defining their research focus and what aspect of smart leaning can be explored.

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  • 49.
    Agbo, Friday Joseph
    et al.
    School of Computing, University of Eastern Finland, FIN-80101 Joensuu, Finland.
    Sanusi, Ismaila Temitayo
    School of Computing, University of Eastern Finland, FIN-80101 Joensuu, Finland.
    Oyelere, Solomon Sunday
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Suhonen, Jarkko
    School of Computing, University of Eastern Finland, FIN-80101 Joensuu, Finland.
    Application of Virtual Reality in Computer Science Education: A Systemic Review Based on Bibliometric and Content Analysis Methods2021In: Education Sciences, E-ISSN 2227-7102, Vol. 11, no 3, article id 142Article, review/survey (Refereed)
    Abstract [en]

    This study investigated the role of virtual reality (VR) in computer science (CS) education over the last 10 years by conducting a bibliometric and content analysis of articles related to the use of VR in CS education. A total of 971 articles published in peer-reviewed journals and conferences were collected from Web of Science and Scopus databases to conduct the bibliometric analysis. Furthermore, content analysis was conducted on 39 articles that met the inclusion criteria. This study demonstrates that VR research for CS education was faring well around 2011 but witnessed low production output between the years 2013 and 2016. However, scholars have increased their contribution in this field recently, starting from the year 2017. This study also revealed prolific scholars contributing to the field. It provides insightful information regarding research hotspots in VR that have emerged recently, which can be further explored to enhance CS education. In addition, the quantitative method remains the most preferred research method, while the questionnaire was the most used data collection technique. Moreover, descriptive analysis was primarily used in studies on VR in CS education. The study concludes that even though scholars are leveraging VR to advance CS education, more effort needs to be made by stakeholders across countries and institutions. In addition, a more rigorous methodological approach needs to be employed in future studies to provide more evidence-based research output. Our future study would investigate the pedagogy, content, and context of studies on VR in CS education.

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  • 50.
    Agbo-ola, Adedoyin
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Motivating Cybersecurity Awareness within an Organisation: An explorative study from an awareness practitioner’s perspective2022Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Security awareness has been a popular topic in the last few years for both information systems researchers and organisations. News broadcasts has brought attention to the increase in cyber-attacks, with these reports noting that a significant number of these breaches have been caused by human error, linked to employee’s lack of engagement with their organisations security policies and awareness campaigns. Whilst there is existing research in human factorsand the barriers of security behaviours effect on cybersecurity awareness; in practice we know very little about how employees can be motivated to engage in cybersecurity awareness programs.

    This study aims to explore how information security practitioners motivate interest in cybersecurity awareness. It does this through an exploratory case study approach using qualitative data collected from in-depth interviews of four cybersecurity awareness practitioners that were conducted. From an application perspective, the findings suggest that these practitioners do use a variety of techniques to motivate employee interest in cybersecurity awareness. The study identified four factors used by practitioners to motivate cybersecurity awareness which are 1) using different engaging techniques, 2) making it personable & relatable, 3) utilising leadership commitment and 4) embracing technical controls. This paper discusses these factors and implications for practitioners.

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