@inproceedings {43256,
	title = {Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence},
	journal = {IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023)},
	year = {2023},
	abstract = {Meibomian gland dysfunction is the most common cause of dry eye disease, which is a prevalent condition that can damage the ocular surface and cause reduced vision and substantial pain. Meibum secreted from the meibomian glands makes up the majority of the outer, protective lipid layer of the tear film. Changes in the secreted meibum and markers of glandular damage can be detected through tear sampling.Several studies have investigated the tear film protein expression in meibomian gland dysfunction, but less work apply machine learning to analyze the protein patterns.\&nbsp;We use machine learning and methods from explainable artificial intelligence to detect potential clinically relevant proteins in meibomian gland dysfunction. Two different explainable artificial intelligence methods are compared. Several of the proteins found important in the models have been linked to dry eye disease in the past, while some are novel. Consequently, explainable artificial intelligence methods serve as a promising tool for screening for proteins that are relevant for meibomian gland dysfunction. By doing so, one may be able to discover new biomarkers and treatments, and gain a better understanding of how diseases develop.},
	keywords = {Dry eye disease, Explainable artificial intelligence, Machine learning, meibomian gland dysfunction, proteomics},
	author = {Stor{\r a}s, Andrea and Magn{\o}, Morten and Fineide, Fredrik and Thiede, Bernd and Chen, Xiangjun and Str{\"u}mke, Inga and Halvorsen, P{\r a}l and Utheim, Tor and Riegler, Michael}
}
@inproceedings {43319,
	title = {Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence},
	journal = {IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023)},
	year = {2023},
	abstract = {Dry eye disease is a common and potentially debilitating medical condition. Meibum secreted from the meibomian glands is the largest contributor to the outermost, protective lipid layer of the tear film. Dysfunction of the meibomian glands is the most common cause of dry eye disease. As meibomian gland dysfunction progresses, gradual atrophy of the glands is observed. The meibomian glands are commonly visualized through meibography, a technique requiring specialist equipment and knowledge that might not be available to the physician. In the present project we use machine learning on clinical tabular data to predict the degree of meibomian gland dropout. Moreover, we employ explainable artificial intelligence on the best performing algorithms for feature importance evaluation. The best performing algorithms were AdaBoost, multilayer perceptron\&nbsp;and LightGBM\&nbsp;which outperformed the majority vote baseline classifier in every included evaluation metric for both multioutput and binary classification. Through explainable artificial intelligence known associations are validated and novel connections identified and discussed.},
	keywords = {Dry eye disease, Explainable artificial intelligence, Machine learning, meibography, meibomian gland dysfunction},
	author = {Fineide, Fredrik and Stor{\r a}s, Andrea and Riegler, Michael and Utheim, Tor}
}
@article {28224,
	title = {Artificial intelligence in dry eye disease},
	journal = {The Ocular Surface},
	volume = {23},
	year = {2022},
	month = {Jan-01-2022},
	pages = {74 - 86},
	publisher = {Elsevier},
	abstract = {Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term {\textquoteleft}AI{\textquoteright} is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of\&nbsp;interferometry, slit-lamp and\&nbsp;meibography\&nbsp;images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.},
	keywords = {artificial intelligence, Dry eye disease, Machine learning},
	issn = {15420124},
	doi = {10.1016/j.jtos.2021.11.004},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1542012421001324},
	author = {Stor{\r a}s, Andrea and Str{\"u}mke, Inga and Riegler, Michael and Grauslund, Jakob and Hugo Lewi {Hammer} and Yazidi, Anis and Halvorsen, P{\r a}l and Kjell G. {Gundersen} and Tor P. {Utheim} and Catherine J. {Jackson}}
}
@article {43121,
	title = {Deep learning to predict power output from respiratory inductive plethysmography data},
	journal = {Applied AI Letters},
	volume = {3},
	year = {2022},
	month = {04/2022},
	pages = {e65},
	publisher = {Wiley},
	keywords = {breathing, deep learning, Machine learning, power estimation, respiratory inductive plethysmography},
	doi = {https://doi.org/10.1002/ail2.65},
	author = {Erik Johannes {Husom} and Bernab{\'e}, Pierre and Sen, Sagar}
}
@misc {42696,
	title = {Explainable Artificial Intelligence in Medicine},
	howpublished = {Nordic AI Meet 2022},
	year = {2022},
	abstract = {Machine learning (ML) has shown outstanding abilities to solve a large variety of tasks such as image recognition and natural language processing, which has huge relevance for the medical field. Complex ML models, including convolutional neural networks (CNNs), are used to analyse high dimensional data such as images and videos from medical examinations.\&nbsp;With increasing model complexity, the demand for techniques improving human understanding of the ML models also increases. If medical doctors do not understand how the models work, they might not know when the models are actually wrong or even refuse to use them. This can hamper the implementation of ML systems in the clinic and negatively affect patients.\&nbsp;To promote successful integration of ML systems in the clinic, it is important to provide explanations that establish trust in the models among healthcare personnel.\&nbsp;Explainable artificial intelligence (XAI) aims to provide explanations about ML models and their predictions. Several techniques have already been developed.\&nbsp;Existing XAI methods often fail to meet the requirements of medical doctors, probably because they are not sufficiently involved in the development of the methods.\&nbsp;We develop ML models solving tasks in various medical domains. The resulting models are explained using a selection of existing XAI methods, and the explanations are evaluated by medical experts. Their feedback is used to develop improved XAI methods.\&nbsp;We have investigated established techniques for making ML systems more transparent in the fields of gastroenterology, assisted reproductive technology, organ transplantation and cardiology.\&nbsp;Experiences from our projects will be used to develop new explanation techniques for clinical practice in close collaboration with medical experts.},
	keywords = {Explainable artificial intelligence, Machine learning, medicine},
	author = {Stor{\r a}s, Andrea and Str{\"u}mke, Inga and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {42542,
	title = {Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning},
	journal = {35th IEEE CBMS International Symposium on Computer-Based Medical Systems},
	year = {2022},
	pages = {38-43},
	publisher = { IEEE},
	abstract = {Tacrolimus is one of the cornerstone immunosuppressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that can accurately estimate the drug exposure for individual dose adaptions would be of high clinical value. In this work, we propose a new technique using machine learning to estimate the tacrolimus exposure in kidney transplant recipients. Our models achieve predictive errors that are at the same level as an established population pharmacokinetic model, but are faster to develop and require less knowledge about the pharmacokinetic properties of the drug.},
	keywords = {Machine learning, personalized medicine, transplantation},
	doi = {10.1109/CBMS55023.2022.00014},
	author = {Stor{\r a}s, Andrea and {\r A}sberg, Anders and Halvorsen, P{\r a}l and Riegler, Michael and Str{\"u}mke, Inga}
}
@misc {28053,
	title = {Anomaly Detection in Optical Links Using State of Polarization Monitoring},
	year = {2021},
	address = {2021 Joint European Conference on Networks and Communications \& 6G Summit, Porto, Portugal},
	keywords = {Anomaly detection, Machine learning, Optical Fibre, State of Polarization},
	url = {https://www.eucnc.eu/poster-a/},
	author = {Bj{\o}rnstad, Steinar and Gulistan, Aamir}
}
@inproceedings { EuCNC2021,
	title = {An Exposed Closed-Loop Model for Customer-Driven Service Assurance Automation},
	journal = {2021 Joint European Conference on Networks and Communications \& 6G Summit (EuCNC/6G Summit)},
	year = {2021},
	month = {06/2021},
	pages = {419{\textendash}424},
	publisher = {IEEE Computer Society},
	address = {Porto, Portugal},
	abstract = {Artificial Intelligence (AI) is widely applied in telecommunications to enable zero-touch automation in network operation and service management. Due to the high complexity, deploying advanced AI mechanisms is not always feasible inside the operator{\textquoteright}s network domains. Instead, via service exposures, it becomes possible for vertical customers to integrate their external AI solutions with the network and service management system to form a closed loop (CL) and contribute to the automation process. In this paper, we propose an exposed CL model based on service exposure and apply it to automate service assurance tasks like autoscaling in a network function virtualization (NFV) system orchestrated by ETSI Open Source MANO (OSM). A testbed is built to validate the model. It collects monitoring data from the OSM monitoring module and external monitoring tools. Vertical customers drive and customize their AI solutions to aggregate these data sets and run analytics to detect and predict anomalies prepared for scaling. Preliminary analysis demonstrates the added values of customer-driven monitoring and analysis via the exposed CL.},
	keywords = {Autonomous Management, Closed Loop, Machine learning, Monitoring, Service Exposure},
	isbn = {978-1-6654-1526-2},
	doi = {10.1109/EuCNC/6GSummit51104.2021.9482533},
	author = {Xie, Min and Foivos Ioannis {Michelinakis} and Dreibholz, Thomas and Pujol-Roig, Joan and Malacarne, Sara and Majumdar, Sayantini and Wint Yi {Poe} and Elmokashfi, Ahmed}
}
@mastersthesis {42516,
	title = {Sammenligning av kardiologisk og algoritmebasert EKG-tolkning p{\r a} idrettsut{\o}vere: Kan kunstig intelligens forbedre algoritmene?},
	journal = {The University of Oslo},
	number = {1},
	year = {2021},
	month = {02/2021},
	pages = {139},
	abstract = {Hjertestans er den vanligste {\r a}rsaken til plutselig d{\o}d blant idrettsut{\o}vere. Dette skyldes ofte arvelige hjertesykdommer som blant annet kan oppdages p{\r a} et elektrokardiogram (EKG). I denne studien ble to problemstillinger unders{\o}kt: (1) Om tolkningsalgoritmene i en klinisk anvendt elektrokardiograf er bedre tilpasset EKG fra den generelle befolkning enn idrettsut{\o}vere? (2) Om moderne metoder, som kunstig intelligens (KI), kunne brukes til {\r a} forbedre dagens kliniske anvendte tolkningsalgoritmer slik at de kan bli bedre kalibrert for idrettsut{\o}vere?For {\r a} unders{\o}ke (1) ble det registrert EKG fra 28 idrettsut{\o}vere ved bruk av en moderne elektrokardiograf. EKGene ble tolket av tolkningsalgoritmen i elektrokardiografen, og sammenlignet med en kardiologs tolkning. Tolkningsalgoritmen klassifiserte 46\% av EKGene som normale, 25\% i grenseomr{\r a}det og 29\% som unormale. Kardiologen fulgte retningslinjene fra\&nbsp;International Criteria, og klassifiserte 93\% som normale og 7\% i grenseomr{\r a}det. Resultatene viser at tolkningsalgoritmen feilvurderer noen av omr{\r a}dene i EKGet. Dette ga i noen tilfeller alvorlige feilklassifiseringer, som akutt hjerteinfarkt (STEMI).For {\r a} unders{\o}ke (2) ble et stort {\r a}pent datasett av EKGer fra den generelle befolkning tatt i bruk. Ni ulike modeller ble trent, og validert p{\r a} datasettet, deretter ble fire prediksjoner fra de to beste modellene forklart ved bruk av forklarbar KI. Den beste modellen fikk en PhysioNet/CinC Challenge sk{\r a}r = 0,512{\textpm}0,006 og en F1-sk{\r a}r=0,405{\textpm}0,008 ved 10-ganger kryssvalidering. Modellarkitekturene lar seg forklare ved bruk av forklarbar KI. Forklarbarheten er viktig for modellens kliniske relevans. For videre arbeid med disse problemstillingene trengs tilgang til flere EKG fra idrettsut{\o}vere.},
	keywords = {Athletes, Dyp l{\ae}ring, ECG, Electrocardiogram, Elektrokardiogram, Heart, Idrettshjerter, Machine learning, Maskinl{\ae}ring},
	doi = {10.5281/zenodo.4445257},
	url = {https://www.duo.uio.no/handle/10852/86019},
	author = {Singstad, Bj{\o}rn-Jostein}
}
@inproceedings {10.1145/3394171.3416287,
	title = {ACM Multimedia BioMedia 2020 Grand Challenge Overview},
	journal = {Proceedings of the 28th ACM International Conference on Multimedia},
	year = {2020},
	pages = {4655{\textendash}4658},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {The BioMedia 2020 ACM Multimedia Grand Challenge is the second in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year{\textquoteright}s challenge, participants are asked to develop algorithms that automatically predict the quality of a given human semen sample using a combination of visual, patient-related, and laboratory-analysis-related data. Compared to last year{\textquoteright}s challenge, participants are provided with a fully multimodal dataset (videos, analysis data, study participant data) from the field of assisted human reproduction. The tasks encourage the use of the different modalities contained within the dataset and finding smart ways of how they may be combined to further improve prediction accuracy. For example, using only video data or combining video data and patient-related data. The ground truth was developed through a preliminary analysis done by medical experts following the World Health Organization{\textquoteright}s standard for semen quality assessment. The task lays the basis for automatic, real-time support systems for artificial reproduction. We hope that this challenge motivates multimedia researchers to explore more medical-related applications and use their vast knowledge to make a real impact on people{\textquoteright}s lives.},
	keywords = {artificial intelligence, Machine learning, male fertility, semen analysis, spermatozoa},
	isbn = {9781450379885},
	doi = {10.1145/3394171.3416287},
	url = {https://doi.org/10.1145/3394171.3416287},
	author = {Hicks, Steven and Thambawita, Vajira and Hugo Lewi {Hammer} and Trine B. {Haugen} and Jorunn M. {Andersen} and Witczak, Oliwia and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {27472,
	title = {HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy},
	journal = {Scientific Data},
	volume = {7},
	year = {2020},
	month = {08/2020},
	pages = {1-14},
	publisher = {Springer Nature},
	abstract = {Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents\&nbsp;HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at B{\ae}rum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The\&nbsp;HyperKvasir\&nbsp;dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.},
	keywords = {dataset, GI, Machine learning},
	doi = {10.1038/s41597-020-00622-y},
	url = {http://www.nature.com/articles/s41597-020-00622-y},
	author = {Borgli, Hanna and Thambawita, Vajira and Pia H. {Smedsrud} and Hicks, Steven and Jha, Debesh and Sigrun L. {Eskeland} and Kristin Ranheim {Randel} and Pogorelov, Konstantin and Lux, Mathias and Duc Tien Dang {Nguyen} and Johansen, Dag and Griwodz, Carsten and H{\r a}kon Kvale {Stensland} and Garcia-Ceja, Enrique and Peter T. {Schmidt} and Hugo Lewi {Hammer} and Riegler, Michael and Halvorsen, P{\r a}l and de Lange, Thomas}
}
@article {naseer2020:mode,
	title = {Mode Inference using enhanced Segmentation and Pre-processing on raw Global Positioning System data},
	journal = {Measurement and Control},
	volume = {53},
	year = {2020},
	month = {08/2020},
	pages = {1144 - 1158},
	publisher = {SAGE journals},
	abstract = {Many applications use the Global Positioning System data that provide rich context information for multiple purposes. Easier availability and access of Global Positioning System data can facilitate various mobile applications, and one of such applications is to infer the mobility of a user. Most existing works for inferring users{\textquoteright} transportation modes need the combination of Global Positioning System data and other types of data such as accelerometer and Global System for Mobile Communications. However, the dependency of the applications to use data sources other than the Global Positioning System makes the use of applications difficult if the peer data source is not available. In this paper, we introduce a new generic framework for the inference of transportation mode by only using the Global Positioning System data. Our contribution is threefold. First, we propose a new method for Global Positioning System trajectory data preprocessing using the grid probability distribution function. Second, we introduce an algorithm for the change point{\textendash}based trajectory segmentation, to more effectively identify the single-mode segments from Global Positioning System trajectories. Third, we introduce new statistical-based topographic features that are more discriminative for transportation mode detection. Through extensive evaluation on the large trajectory data GeoLife, our approach shows significant performance improvement in terms of accuracy over state-of-the-art baseline models.},
	keywords = {classification, global positioning system trajectory, Machine learning, statistical analysis, transportation mode},
	issn = {0020-2940},
	doi = {10.1177/0020294020918324},
	url = {https://journals.sagepub.com/doi/10.1177/0020294020918324},
	author = {Nawaz, Asif and Zhiqiu, Huang and Senzhang, Wang and Hussain, Yasir and Naseer, Amara and Izhar, Muhammad and Khan, Zaheer},
	editor = {Onder, Mehmet}
}
@inproceedings {27612,
	title = {Scalable Infrastructure for Efficient Real-Time Sports Analytics},
	journal = {Companion Publication of the 2020 International Conference on Multimodal Interaction},
	year = {2020},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Recent technological advances are adapted in sports to improve performance, avoid injuries, and make advantageous decisions. In this paper, we describe our ongoing efforts to develop and deploy PMSys, our smartphone-based athlete monitoring and reporting system. We describe our first attempts to gain insight into some of the data we have collected. Experiences so far are promising, both on the technical side and for athlete performance development. Our initial application of artificial-intelligence methods for prediction is encouraging and indicative.},
	keywords = {algorithmic analysis, artificial intelligence, Machine learning, privacy-preserving data collection, Sports performance logging},
	isbn = {9781450380027},
	doi = {10.1145/339503510.1145/3395035.3425300},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3395035https://dl.acm.org/doi/10.1145/3395035.3425300https://dl.acm.org/doi/pdf/10.1145/3395035.3425300},
	author = {H{\r a}vard D. {Johansen} and Johansen, Dag and Kupka, Tomas and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Truong, Khiet and Heylen, Dirk and Czerwinski, Mary and Berthouze, Nadia and Chetouani, Mohamed and Nakano, Mikio}
}
@article {42749,
	title = {DILAF: A framework for distributed analysis of large-scale system logs for anomaly detection},
	journal = {Software: Practice and Experience},
	volume = {49},
	year = {2019},
	month = {02/2019},
	pages = {153-170},
	publisher = { Wiley},
	abstract = {System logs constitute a rich source of information for detection and prediction of anomalies. However, they can include a huge volume of data, which is usually unstructured or semistructured. We introduce DILAF, a framework for distributed analysis of large-scale system logs for anomaly detection. DILAF is comprised of several processes to facilitate log parsing, feature extraction, and machine learning activities. It has two distinguishing features with respect to the existing tools. First, it does not require the availability of source code of the analyzed system. Second, it is designed to perform all the processes in a distributed manner to support scalable analysis in the context of large-scale distributed systems. We discuss the software architecture of DILAF and we introduce an implementation of it. We conducted controlled experiments based on two datasets to evaluate the effectiveness of the framework. In particular, we evaluated the performance and scalability attributes under various degrees of parallelism. Results showed that DILAF can maintain the same accuracy levels while achieving more than 30\% performance improvement on average as the system scales, compared to baseline approaches that do not employ fully distributed processing.\&nbsp;},
	keywords = {Anomaly detection, Big Data, Distributed systems, log analysis, Machine learning, parallel processing, software architecture},
	author = {Astekin, Merve and Zengin, Harun and Sozer, Hasan}
}
@inproceedings {25794,
	title = {Deep Learning and Handcrafted Feature Based Approaches for Automatic Detection of Angiectasia},
	journal = {2018 IEEE Conference on Biomedical and Health Informatics (BHI)},
	year = {2018},
	pages = {365-368},
	publisher = {IEEE},
	abstract = {Angiectasia, formerly called angiodysplasia, is one of the most frequent vascular lesions and often the cause of gastrointestinal bleedings. Medical specialists assessing videos or images of examinations reach a detection performance of 16\% for the detection of bleeding to 69\% for the detection of angiectasia. This shows that automatic detection to support medical experts can be useful. In this paper, we present several machine learning-based approaches for angiectasia detection in wireless video capsule endoscopy frames. In summary, the most promising results for pixel-wise localization and framewise detection are obtained by the proposed deep learning method using generative adversarial networks (GANs). Using this approach, we achieve a sensitivity of 88\% and specificity of 99.9\% for pixel-wise localization, and a sensitivity of 98\% and a specificity of 100\% for frame-wise detection. Thus, the results demonstrate the capability of using deep learning for automatic angiectasia detection in real clinical settings.\&nbsp;},
	keywords = {Angiectasia, computer aided diagnosis, deep learning, Machine learning, video capsular endoscopy},
	doi = {10.1109/BHI.2018.8333444},
	author = {Pogorelov, Konstantin and Ostroukhova, Olga and Petlund, Andreas and Halvorsen, P{\r a}l and de Lange, Thomas and Espeland, H{\r a}vard and Kupka, Tomas and Griwodz, Carsten and Riegler, Michael}
}
@techreport {26111,
	title = {Employing Multi-Objective Search and Machine Learning to Mine Cross Product Line Rules: A Technical Report},
	year = {2018},
	note = {This work was supported by the Zen-Configurator project funded by the Research Council of Norway (Grant No. 240024/F20) under the category of Young Research Talents of the FRIPO funding scheme. Tao Yue and Shaukat Ali are also supported by the Co-evolver project funded by the Research Council of Norway (grant no. 286898/LIS) under the category of Researcher Projects of the FRIPO funding scheme.},
	month = {05/2018},
	pages = {1-59},
	publisher = {Simula Research Laboratory},
	address = {Oslo, Norway},
	abstract = {Modern systems are being developed by integrating multiple products within/across product lines that communicate with each other through information networks. Runtime behaviors of such systems are related to product configurations and information networks. Cost-effectively supporting Product Line Engineering (PLE) of such systems is challenging mainly because of lacking the support of automation of the configuration process. Capturing rules is the key for automating the configuration process in PLE. However, there does not exist explicitly-specified rules constraining configurable parameter values of such products and product lines. Manually specifying such rules is tedious and time-consuming. To address this challenge, in this paper, we present an improved version (named as SBRM+) of our previously proposed Search-based Rule Mining (SBRM) approach. SBRM+ incorporates two machine learning algorithms (i.e., C4.5 and PART) and two multi-objective search algorithms (i.e., NSGA-II and NSGA-III), employs a clustering algorithm (i.e., k-means) for classifying rules as high or low confidence rules, which are used for defining three objectives to guide the search. To evaluate SBRM+ (i.e., SBRM+NSGA-II-C45, SBRM+NSGA-III-C45, SBRM+NSGA-II-PART, and SBRM+NSGA-III-PART), we performed two case studies (Cisco and Jitsi) and conducted three types of analyses of results: difference analysis, correlation analysis, and trend analysis. Results of the analyses show that all the SBRM+ approaches performed significantly better than two Random Search-based approaches (RBRM+-C45 and RBRM+-PART) in terms of fitness values, six quality indicators, and 17 machine learning quality measurements (MLQMs). As compared to RBRM+ approaches, SBRM+ approaches have improved the quality of rules based on MLQMs up to 27\% for the Cisco case study and 28\% for the Jitsi case study.},
	keywords = {Configuration, Interacting Products, Machine learning, Multi-Objective Search, Product Line, Rule Mining},
	url = {https://www.simula.no/sites/default/files/publications/files/employing_multi-objective_search_and_machine_learning_to_mine_cross_product_line_rules.pdf},
	author = {Safdar Aqeel {Safdar} and Yue, Tao and Ali, Shaukat and Lu, Hong}
}
@mastersthesis {26253,
	title = {Hyperparameter optimization using Bayesian optimization on transfer learning for medical image classification},
	journal = {The University of Oslo},
	year = {2018},
	abstract = {The field of medicine has a history of adopting new technology. Video equipment and sensors are used to visualize areas of interest in the human allowing for doctors to make diagnoses based on imagery observations. However, the detection rate of the doctors towards diseases and abnormalities is heavily dependent on the experience and state of mind of the doctor doing the examination. Computer-aided detection systems are systems designed to aid the doctor in improving the detection rate, and they are using or experimenting with machine learning. Deep convolutional neural networks, a type of machine learning, are shown to be highly efficient at image detection, classification, and analysis. However, these networks require large datasets to train properly. Transfer learning is a training technique where we use a pre-trained machine learning model and transfer some of the attained knowledge from other application domains over to a new model. This way, we can use small datasets and train a model in much shorter time. In this respect, transfer learning works fine but has many configurations called hyperparameters which are often not optimized. Our work aims to address the lack of automatic hyperparameter optimization for transfer learning by experiments utilizing a known hyperparameter optimization method and creating a system for running those experiments. First, we decided to focus on the field of gastroenterology by utilizing two publicly available datasets showing images from the gastrointestinal tract. Next, we used a specific transfer learning method and chose hyperparameters suitable for automatic optimization. The optimization method we chose was Bayesian optimization because of its reputation for being one of the best methods for hyperparameter optimization. However, Bayesian optimization has hyperparameters of its own, and there are also different versions of Bayesian optimization. We chose to limit the thesis, so we use standard Bayesian optimization with standard parameters. We created a system for running automatic experiments of three different hyperparameter optimization strategies. With the system, we ran a set of experiments for each dataset. Between the strategies, one was successful in achieving a high validation accuracy, while the others were considered failures. Compared to baselines, our best models was around 10\% better. With these experiments, we demonstrated that automatic hyperparameter optimization is an effective strategy for increasing performance in transfer learning and that the best hyperparameters are nontrivial to select manually.},
	keywords = {Bayesian optimization, convolutional networks, deep learning, hyperparameter optimization, Machine learning, nonconvergence filtering, Transfer Learning},
	url = {https://www.duo.uio.no/handle/10852/64146},
	author = {Borgli, Rune}
}
@article {25292,
	title = {Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge},
	journal = {IEEE Transactions on Medical Imaging},
	year = {2017},
	pages = {1-19},
	publisher = {IEEE},
	keywords = {Endoscopic vision, Handcrafted features, Machine learning, Polyp Detection, Validation Framework},
	issn = {0278-0062},
	doi = {10.1109/TMI.2017.2664042},
	author = {Bernal, Jorge and Tajbakhsh, Nima and Sanchez, Javier and Bogdan J {Matuszewski} and Chen, Hao and Yu, Lequan and Angermann, Quentin and Romain, Olivier and Rustad, Bjorn and Balasingham, Ilangko and Pogorelov, Konstantin and Choi, Sungbin and Debard, Quentin and Maier-Hein, Lena and Speidel, Stefanie and Stoyanov, Danail and Brandao, Patrick and C{\'o}rdova, Henry and S{\'a}nchez-Montes, Cristina and Suryakanth R. {Gurudu} and Fern{\'a}ndez-Esparrach, Gloria and Dray, Xavier and Liang, Jianming and Histace, Aymeric}
}
@inproceedings {Rolfsnes:2016:SANER,
	title = {Generalizing the Analysis of Evolutionary Coupling for Software Change Impact Analysis},
	journal = {23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER)},
	year = {2016},
	pages = {201-212},
	publisher = {IEEE},
	abstract = {Software change impact analysis aims to find artifacts potentially affected by a change. Typical approaches apply language-specific static or dynamic dependence analysis, and are thus restricted to homogeneous systems. This restriction is a major drawback given today\&rsquo;s increasingly heterogeneous software. Evolutionary coupling has been proposed as a language-agnostic alternative that mines relations between source-code entities from the system\&rsquo;s change history. Unfortunately, existing evolutionary coupling based techniques fall short. For example, using Singular Value Decomposition (SVD) quickly becomes computationally expensive. An efficient alternative applies targeted association rule mining, but the most widely known approach (ROSE) has restricted applicability: experiments on two large industrial systems, and four large open source systems, show that ROSE can only identify dependencies about 25\% of the time. To overcome this limitation, we introduce TARMAQ, a new algorithm for mining evolutionary coupling. Empirically evaluated on the same six systems, TARMAQ performs consistently better than ROSE and SVD, is applicable 100\% of the time, and runs orders of magnitude faster than SVD. We conclude that the proposed algorithm is a significant step forward towards achieving robust change impact analysis for heterogeneous systems.},
	keywords = {evolutionary coupling, Machine learning, software repository mining, targeted association rule mining},
	doi = {10.1109/SANER.2016.101},
	author = {Thomas Gramstad {Rolfsnes} and Di Alesio, Stefano and Behjati, Razieh and Moonen, Leon and Binkley, David}
}
@article {lutu2014bgp,
	title = {The BGP Visibility Toolkit: Detecting Anomalous Internet Routing Behavior},
	journal = {IEEE/ACM Transactions on Networking},
	year = {2015},
	month = {04/2015},
	publisher = {IEEE},
	abstract = {In this paper, we propose the BGP Visibility Toolkit, a system for detecting and analyzing anomalous behavior in the Internet. We show that interdomain prefix visibility can be used to single out cases of erroneous demeanors resulting from misconfiguration or bogus routing policies. The implementation of routing policies with BGP is a complicated process, involving fine-tuning operations and interactions with the policies of the other active ASes. Network operators might end up with faulty configurations or unintended routing policies that prevent the success of their strategies and impact their revenues. As part of the Visibility Toolkit, we propose the BGP Visibility Scanner, a tool which identifies limited visibility prefixes in the Internet. The tool enables operators to provide feedback on the expected visibility status of prefixes. We build a unique set of ground-truth prefixes qualified by their ASes as intended or unintended to have limited visibility. Using a machine learning algorithm, we train on this unique dataset an alarm system that separates with 95\% accuracy the prefixes with unintended limited visibility. Hence, we find that visibility features are generally powerful to detect prefixes which are suffering from inadvertent effects of routing policies. Limited visibility could render a whole prefix globally unreachable. This points towards a serious problem, as limited reachability of a non-negligible set of prefixes undermines the global connectivity of the Internet. We thus verify the correlation between global visibility and global connectivity of prefixes.},
	keywords = {Anomaly detection, BGP, Internet measurements, Machine learning},
	doi = {10.1109/TNET.2015.2413838},
	author = {Lutu, Andra and Bagnulo, Marcelo and Pelsser, Cristel and Maennel, Olaf and Cid-Sueiro, Jesus}
}
