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Recommender systems are widely used in e-commerce websites to improve the buying experience of the customer. In recent years, e-commerce has been quickly expanding and its growth has been accelerated during the COVID-19 pandemic, when customers and retailers were asked to keep their distance and do lockdowns. Therefore, there is an increasing demand for items and good recommendations to the users to improve their shopping experience. In this master’s thesis a recommender system for a real-estate website is built, based on Support Vector Machines (SVM). The main characteristic of the built model is that it is trained with a few labelled samples and the rest of unlabelled samples, using a semi-supervised machine learning paradigm. The model is constructed step-by-step from the simple SVM, until the semi-supervised Nested Cost-Sensitive Support Vector Machine (NCS-SVM). Then, we compare our model using four different kernel functions: gaussian, second-degree polynomial, fourth-degree polynomial, and linear. We also compare a user with strict housing requirements against a user with vague requirements. We finish with a discussion focusing principally on parameter tuning, and briefly in the model downsides and ethical considerations.

Parkinson’s disease (PD) is one of the most common neurodegenerative brain disorders, commonly diagnosed and monitored via clinical examinations, which can be imprecise and lead to a delayed or inaccurate diagnosis. Therefore, recent research has focused on finding biomarkers by analyzing brain networks’ neural activity to find abnormalities associated with PD pathology. Brain signals can be measured using Magnetoencephalography (MEG) or Electroencephalogram (EEG), which have demonstrated their practical use in decoding neural activity. Nevertheless, interpreting and labeling human neural activity measured using MEG/EEG is yet a challenging task requiring vast of time and expertise. In addition, there is a risk of introducing bias or omitting important information not recognizable by humans. This thesis investigates whether it is possible to find meaningful features relevant to PD by uncovering the brain signals’ underlying structure using self-supervised learning (SSL), requiring no labels or hand-crafted features. Four experiments on one EEG and one MEG dataset were conducted to evaluate if the features found during the SSL were meaningful, including t-SNE, silhouette coefficient, Kolmogorov-Smirnov test, and classification performance. Additionally, transfer learning between the two datasets was tested. The SSL model, TS-TCC, was employed in this thesis due to its outstanding performance on two other EEGdatasets and its training efficiency. The evaluation of the EEG dataset inferred it was feasible to find meaningful features to distinguish PD from healthy controls to some extent using SSL. However, more investigations of reusing the features in a downstream task are needed. The evaluation of the MEG dataset did not reach the same satisfying result, the proposed reason, among others, was the amount of data. Lastly, transfer learning was unsuccessful in the setting of transforming knowledge from the EEG to the MEG dataset. / Parkinsons sjukdom är en av de mest förekommande neurodegenerativa hjärnsjukdomarna. Vanligtvis diagnostiseras och övervakas sjukdomen via kliniska undersökningar, dessa kan vara diffusa och leda till en fördröjd eller en felaktig diagnos. Den senaste forskning har därför fokuserat på att hitta nya biomarkörer, bland annat genom att analysera hjärnnätverkens neurala aktivitet för att hitta abnormiteter associerade med parkinsons patologi. Magnetoencefalografi (MEG) och elektroencefalogram (EEG) har visat sig vara bra tekniker för att avkoda neural aktivitet och kan därmed användas för att mäta hjärnsignaler. Dessvärre är det en utmanande uppgift att tolka och märka hjärnsignaler, det kräver mycket tid och expertis. Det finns också en risk att märkningen inte blir helt objektiv eller att viktig information som inte är upptäckbar av människor utelämnas. Denna avhandling undersöker om det är möjligt att hitta meningsfulla särdrag relevanta för parkinsons sjukdom medhjälp av självövervakad inlärning (SSL), som varken kräver etiketter eller handgjorda särdrag. För att utvärdera om särdragen funna av SSL är meningsfulla utfördes fyra experiment på ett EEG och ett MEG-dataset. Experimenten inkluderade tSNE, siluettkoefficienten, Kolmogorov-Smirnov-testet och klassificeringsprestanda. Dessutom utvärderades möjligheten att överföra särdrag mellan de två dataseten för att nå bättre resultat. TS-TCC användes som SSL modell i denna avhandling på grund av dess prestanda på två andra EEG-dataset och dess effektivitet när det kommer till träning. Utvärderingen av EEG-datat visade på att det var möjligt att hitta meningsfulla särdrag för att till viss del skilja patienter från friska kontroller. Däremot så behövs vidare undersökning av användandet av särdragen i en klassificerare. Utvärderingen av MEG-datat nådde inte samma tillfredsställande resultat; anledningen kan bland annat vara mängden data. Slutligen, det var inte möjligt att överföra särdrag mellan EEG och MEG-datat för att nå ett bättre resultat.

This work proposes a new approach to fast detection of abnormal behaviour of cooling, IT, and power distribution systems in micro data centers based on machine learning techniques. Conventional protection of micro data centers focuses on monitoring individual parameters such as temperature at different locations and when these parameters reach certain high values, then an alarm will be triggered. This research employs machine learning techniques to extract normal and abnormal behaviour of the cooling and IT systems. Developed data acquisition system together with unsupervised learning methods quickly learns the physical dynamics of normal operation and can detect deviations from such behaviours. This provides an efficient way for not only producing health index for the micro data center, but also a rich label logging system that will be used for the supervised learning methods. The effectiveness of the proposed detection technique is evaluated on an micro data center placed at Computing Infrastructure Research Center (CIRC) in McMaster Innovation Park (MIP), McMaster University. / Thesis / Master of Science (MSc)

Recent progress on deep neural networks has shown remarkable action recognition performance from videos. The remarkable performance is often achieved by transfer learning: training a model on a large-scale labeled dataset (source) and then fine-tuning the model on the small-scale labeled datasets (targets). However, existing action recognition models do not always generalize well on new tasks or datasets because of the following two reasons. i) Current action recognition datasets have a spurious correlation between action types and background scene types. The models trained on these datasets are biased towards the scene instead of focusing on the actual action. This scene bias leads to poor generalization performance. ii) Directly testing the model trained on the source data on the target data leads to poor performance as the source, and target distributions are different. Fine-tuning the model on the target data can mitigate this issue. However, manual labeling small- scale target videos is labor-intensive. In this dissertation, I propose solutions to these two problems. For the first problem, I propose to learn scene-invariant action representations to mitigate the scene bias in action recognition models. Specifically, I augment the standard cross-entropy loss for action classification with 1) an adversarial loss for the scene types and 2) a human mask confusion loss for videos where the human actors are invisible. These two losses encourage learning representations unsuitable for predicting 1) the correct scene types and 2) the correct action types when there is no evidence. I validate the efficacy of the proposed method by transfer learning experiments. I trans- fer the pre-trained model to three different tasks, including action classification, temporal action localization, and spatio-temporal action detection. The results show consistent improvement over the baselines for every task and dataset. I formulate human action recognition as an unsupervised domain adaptation (UDA) problem to handle the second problem. In the UDA setting, we have many labeled videos as source data and unlabeled videos as target data. We can use already exist- ing labeled video datasets as source data in this setting. The task is to align the source and target feature distributions so that the learned model can generalize well on the target data. I propose 1) aligning the more important temporal part of each video and 2) encouraging the model to focus on action, not the background scene, to learn domain-invariant action representations. The proposed method is simple and intuitive while achieving state-of-the-art performance without training on a lot of labeled target videos. I relax the unsupervised target data setting to a sparsely labeled target data setting. Then I explore the semi-supervised video action recognition, where we have a lot of labeled videos as source data and sparsely labeled videos as target data. The semi-supervised setting is practical as sometimes we can afford a little bit of cost for labeling target data. I propose multiple video data augmentation methods to inject photometric, geometric, temporal, and scene invariances to the action recognition model in this setting. The resulting method shows favorable performance on the public benchmarks. / Doctor of Philosophy / Recent progress on deep learning has shown remarkable action recognition performance. The remarkable performance is often achieved by transferring the knowledge learned from existing large-scale data to the small-scale data specific to applications. However, existing action recog- nition models do not always work well on new tasks and datasets because of the following two problems. i) Current action recognition datasets have a spurious correlation between action types and background scene types. The models trained on these datasets are biased towards the scene instead of focusing on the actual action. This scene bias leads to poor performance on the new datasets and tasks. ii) Directly testing the model trained on the source data on the target data leads to poor performance as the source, and target distributions are different. Fine-tuning the model on the target data can mitigate this issue. However, manual labeling small-scale target videos is labor-intensive. In this dissertation, I propose solutions to these two problems. To tackle the first problem, I propose to learn scene-invariant action representations to mitigate background scene- biased human action recognition models for the first problem. Specifically, the proposed method learns representations that cannot predict the scene types and the correct actions when there is no evidence. I validate the proposed method's effectiveness by transferring the pre-trained model to multiple action understanding tasks. The results show consistent improvement over the baselines for every task and dataset. To handle the second problem, I formulate human action recognition as an unsupervised learning problem on the target data. In this setting, we have many labeled videos as source data and unlabeled videos as target data. We can use already existing labeled video datasets as source data in this setting. The task is to align the source and target feature distributions so that the learned model can generalize well on the target data. I propose 1) aligning the more important temporal part of each video and 2) encouraging the model to focus on action, not the background scene. The proposed method is simple and intuitive while achieving state-of-the-art performance without training on a lot of labeled target videos. I relax the unsupervised target data setting to a sparsely labeled target data setting. Here, we have many labeled videos as source data and sparsely labeled videos as target data. The setting is practical as sometimes we can afford a little bit of cost for labeling target data. I propose multiple video data augmentation methods to inject color, spatial, temporal, and scene invariances to the action recognition model in this setting. The resulting method shows favorable performance on the public benchmarks.

This thesis addresses the problem of domain shift in 3D point cloud perception. In the last decades, there has been tremendous progress in within-domain training and testing. However, the performance of perception models is affected when training on a source domain and testing on a target domain sampled from different data distributions. As a result, a change in sensor or geo-location can lead to a harmful drop in model performance. While solutions exist for image perception, addressing this problem in point clouds remains unresolved. The focus of this thesis is the study and design of solutions for mitigating domain shift in 3D point cloud perception. We identify several settings differing in the level of target supervision and the availability of source data. We conduct a thorough study of each setting and introduce a new method to solve domain shift in each configuration. In particular, we study three novel settings in domain adaptation and domain generalization and propose five new methods for mitigating domain shift in 3D point cloud perception. Our methods are used by the research community, and at the time of writing, some of the proposed approaches hold the state-of-the-art. In conclusion, this thesis provides a valuable contribution to the computer vision community, setting the groundwork for the development of future works in cross-domain conditions.

Background. With increased pressure from both regulatory bodies and end-users, interest in privacy preserving machine learning methods have increased among companies and researchers in the last few years. One of the main areas of research regarding this is federated learning. Further, with the current situation in the world, interest in cybersecurity is also at an all time high, where intrusion detection systems are one component of interest. With anomaly-based intrusion detection systems using machine learning methods, it is desirable that these can adapt automatically over time as the network patterns change, resulting in online learning being highly relevant for this application. Previous research has studied offline federated intrusion detection systems. However, there have been very little work performed in the study of online federated learning for intrusion detection systems. Objectives. The objective of this thesis is to evaluate the performance of online federated machine learning methods for intrusion detection systems. Furthermore, the thesis will study the performance relationship between offline and online models for both centralized and federated learning, in order to draw conclusions about the ability to extrapolate from results between the different types of models. Methods. This thesis uses a quasi-experiment to evaluate two different types of models, Naive Bayes and Semi-supervised Federated Learning on Evolving Data Streams (SFLEDS), on three different datasets, NSL-KDD, UNSW-NB15, and CIC-IDS2017. For each model, four variants are implemented: centralized offline, centralized online, federated offline and federated online, and in the federated setting the models are evaluated with 20, 30, and 40 clients. Results. The results show that the best performing model in general is the federated online SFLEDS. They also highlight an important problem with using imbalanced datasets without proper care for data preprocessing and model design. Finally, the results show that there are no general relationships between offline and online models that hold in both the centralized and federated settings in terms of prediction performance. Conclusions. The main conclusion of the thesis is that online federated learning has a lot of potential for the application of intrusion detection systems, but more research is required to find the optimal models and parameters that result in satisfactory performance. / Bakgrund. Med ökat tryck från både tillsynsorgan och slutanvändare har intresset för integritetsbevarande maskininlärning ökat hos företag och forskare under de senaste åren. Ett av huvudområdena där det forskas om detta är inom federerad inlärning. Vidare, med det nuvarande läget i världen är intresset för cybersäkerhet högre än någonsin, där bland annat intrångsdetekteringssystem är av intresse. Med avvikelsebaserade intrångsdetekteringssystem som använder sig av maskininlärning så är det önskvärt att dessa automatiskt kan anpassa sig över tid när nätverksmönster förändras, vilket resulterar i att online maskininlärning är högst relevant för området. Tidigare forskning har studerat federerade offline intrångsdetekteringssystem, men det finns väldigt lite forskning gällande federerad online maskininlärning för intrångsdetekteringssystem. Syfte. Syftet med det här arbetet är att utvärdera prestandan av federerad online maskininlärning för intrångsdetekteringssystem. Vidare kommer det här arbetet att studera prestandaförhållandet mellan offline och online modeller för både centraliserad och federerad inlärning, för att kunna dra slutsatser om förmågan att extrapolera resultat mellan olika typer av modeller. \newline\textbf{Metod.} Det här arbetet använder sig av ett kvasiexperiment för att utvärdera två olika modeller, Naive Bayes och Semi-supervised Federated Learning on Evolving Data Streams (SFLEDS), på tre olika dataset, NSL-KDD, UNSW-NB15 och CIC-IDS2017. För varje modell implementeras fyra varianter: centraliserad offline, centraliserad online, federerad offline och federerad online. De federerade modellerna utvärderas med 20, 30 och 40 klienter. Resultat. Resultaten visar att den generellt bästa modellen är online SFLEDS. De belyser även ett viktigt problem med att använda obalanserade dataset utan tillräcklig hänsyn till förbearbetning av datan och modelldesign. Slutligen visar resultaten att det inte finns något generellt samband mellan offline och online modeller som stämmer för både centraliserad och federerad inlärning när det gäller modellprestanda. Slutsatser. Den huvudsakliga slutsatsen från arbetet är att federerad online maskininlärning har stor potential för intrångsdetekteringssystem, men mer forskning krävs för att hitta den bästa modellen och de bästa parametrarna för att nå ett tillfredsställande resultat.