Background: Background: Supervision is one of the important tools to help people working in assisting professions. There are many literature on this subject, but it is a problem to find a clear specification of the notion of supervision where its limits go. For a person who does not know the supervisor, it is also good practice to know practical lessons for a better idea of how a supervisor really works. On the contrary, one who knows the supervisor might wish to extend his / her knowledge and compare it with his / her experience. Objectives: The main objective of the research was to provide theoretically available information on the concept of supervision and to compare this knowledge with experiences of supervisor. Another goal was to find out what they think they are supervising supervisors, how they think they are perceived, how they can be useful, and if at all. Finally, the third objective was to find out what viewers have on the current course of supervision. Methodology: The thesis was divided into the theoretical and practical part. The theoretical part completes the practical part and vice versa. In this qualitative research, semi- standardized rohovory was used. The sample was composed of 4 supervisors - three of the adiktological practice and one of the supervisor's practice in social...

Master of Science / Department of Computing and Information Sciences / William H. Hsu / The goal of this research is to present a comparison between different clustering and segmentation techniques, both supervised and unsupervised, to detect plant and crop rows. Aerial images, taken by an Unmanned Aerial Vehicle (UAV), of a corn field at various stages of growth were acquired in RGB format through the Agronomy Department at the Kansas State University. Several segmentation and clustering approaches were applied to these images, namely K-Means clustering, Excessive Green (ExG) Index algorithm, Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and a deep learning approach based on Fully Convolutional Networks (FCN), to detect the plants present in the images. A Hough Transform (HT) approach was used to detect the orientation of the crop rows and rotate the images so that the rows became parallel to the x-axis. The result of applying different segmentation methods to the images was then used in estimating the location of crop rows in the images by using a template creation method based on Green Pixel Accumulation (GPA) that calculates the intensity profile of green pixels present in the images. Connected component analysis was then applied to find the centroids of the detected plants. Each centroid was associated with a crop row, and centroids lying outside the row templates were discarded as being weeds. A comparison between the various segmentation algorithms based on the Dice similarity index and average run-times is presented at the end of the work.

Artificial intelligence has made a big impact on the world in recent years, and more knowledge inthe subject seems to be of vital importance as the possibilities seems endless. Is it possible to teacha computer to drive a car in a virtual environment, by training a neural network to act intelligentlythrough the usage of supervised machine learning? With less than 2 hours of data collected whenpersonally driving the car, I show that yes, it is indeed possible. This is done by applying thetechniques of supervised machine learning combined in conjunction with a deep convolutional neuralnetwork. This were applied through software developed to interact between the network and the agentinside the virtual environment. I believe the dataset could have been cut down to about 10 percentof the size and still achieve the research goal. This shows not just the possibility of teaching aneural network a good policy in stochastic environments with supervised machine learning, but alsothat it can draw accurate (enough) conclusions to imitate human behavior when driving a car.

This master thesis is a collaboration with Jeppesen, a Boeing company to attempt applying machine learning techniques to predict “When does Operator manually solve the disruption? If he chooses to use Optimiser, then which option would he choose? And why?”. Through the course of this project, various techniques are employed to study, analyze and understand the historical labeled data of airline consisting of alerts during disruptions and tries to classify each data point into one of the categories: manual or optimizer option. This is done using various supervised machine learning classification methods.

abstract: Feature embeddings differ from raw features in the sense that the former obey certain properties like notion of similarity/dissimilarity in it's embedding space. word2vec is a preeminent example in this direction, where the similarity in the embedding space is measured in terms of the cosine similarity. Such language embedding models have seen numerous applications in both language and vision community as they capture the information in the modality (English language) efficiently. Inspired by these language models, this work focuses on learning embedding spaces for two visual computing tasks, 1. Image Hashing 2. Zero Shot Learning. The training set was used to learn embedding spaces over which similarity/dissimilarity is measured using several distance metrics like hamming / euclidean / cosine distances. While the above-mentioned language models learn generic word embeddings, in this work task specific embeddings were learnt which can be used for Image Retrieval and Classification separately. Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin. Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore. / Dissertation/Thesis / Masters Thesis Computer Engineering 2019

abstract: Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large corpora of annotated data in order to train task-specific visual recognition models. Despite significant advances made over the past decade, the fact remains collecting and annotating the data needed to successfully train a model is a prohibitively expensive endeavor. Moreover, these models are prone to rapid performance degradation when applied to data sampled from a different domain. Recent works in the development of deep adaptation networks seek to overcome these challenges by facilitating transfer learning between source and target domains. In parallel, the unification of dominant semi-supervised learning techniques has illustrated unprecedented potential for utilizing unlabeled data to train classification models in defiance of discouragingly meager sets of annotated data. In this thesis, a novel domain adaptation algorithm -- Domain Adaptive Fusion (DAF) -- is proposed, which encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of this hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks. / Dissertation/Thesis / Masters Thesis Computer Science 2019

As unlabeled data becomes increasingly available, the need for robust data mining techniques increases as well. Clustering is a common data mining tool which seeks to find related, independent patterns in data called clusters. The cluster validation problem addresses the question of how well a given clustering fits the data set. We present CVIC (cluster validation using instance-based confidences) which assigns confidence scores to each individual instance, as opposed to more traditional methods which focus on the clusters themselves. CVIC trains supervised learners to recreate the clustering, and instances are scored based on output from the learners which corresponds to the confidence that the instance was clustered correctly. One consequence of individually validated instances is the ability to direct users to instances in a cluster that are either potentially misclustered or correctly clustered. Instances with low confidences can either be manually inspected or reclustered and instances with high confidences can be automatically labeled. We compare CVIC to three competing methods for assigning confidence scores and show results on CVIC's ability to successfully assign scores that result in higher average precision and recall for detecting misclustered and correctly clustered instances across five clustering algorithms on twenty data sets including handwritten historical image data provided by Ancestry.com.

Stack Overflow is a rich source of questions and answers—discussions—about software development. One topic of discussion is software design, such as the correct use of design patterns or best practices in data access. Since design is a more abstract topic in software engineering, researchers have long sought to characterize and model design knowledge. However, these approaches typically require significant expert input to contextualize the abstract design information. In this study, we explore how combining expert input with Stack Overflow might serve as an effective way to identify design topics. Being able to identify and classify this design knowledge would enable the discovery and sharing of this knowledge, enabling developers better leverage Stack Overflow for crowd-sourcing their design decisions. We first perform inductive coding of design-tagged Stack Overflow questions and answers to identify the design concepts that developers discuss. We report on areas where inter-rater agreement was a challenge, including abstraction levels. Since inductive coding is expensive, we apply a semi-supervised (Anchored CorEx) approach. We find that it outperforms LDA and offers superior interpretability and the ability to incorporate expert domain knowledge. We leverage Anchored CorEx to identify how design is discussed on Stack Overflow and leveraged in GitHub projects. We conclude by describing how our experience using the semi-supervised CorEx approach leads us to believe that approaches like Anchored CorEx that combine domain knowledge and scalability are key for analyzing large SE text repositories. / Graduate

This paper explores alternative ways for smaller actors on the energy market to identify potential customers using publicly available data and different machine learning algorithms. During recent years, price has been considered to have the biggest impact on the behaviour of the consumers on the energy market. Since the bigger actors on the market can use their economies of scale to lower their prices, smaller actors need to find alternative ways to reach out to consumers. The machine learning algorithms in this paper will use the sales data from a small energy company, operating in Sweden and attempt to find a connection between existing customers using their demographic properties. By acquiring a deeper knowledge of what differentiates consumers that are willing to purchase energy from the energy company and the other consumers, the energy company may increase their rate of successful sales. Due to the lack of customer data avilable coupled with a lack of relevant public data, the results in this paper are not conclusive. However, it provides a baseline for future research as the results may be more reliable when the number of customers purchasing energy from The Energy Company increases. / Det här arbetet utforskar alternativa tillvägagångssätt för för mindre aktörer på energimarknaden att identifiera nya potentiella kunder, baserat på publikt tillgänglig data som analyseras med hjälp av maskininlärningsalgoritmer. På senare år har pris ansetts vara den faktor som påverkar val av leverantör mest. Eftersom större aktörer på marknaden kan utnyttja skalfördelar kan de pressa priserna hårt, medans mindre aktörer måste finna andra vägar att vinna nya kunder. Maskininlärningsalgoritmerna i den här uppsatsen kommer att använda försäljningsdata från ett litet energibolag, som bedriver verksamhet i Sverige, med målet att hitta ett mönster mellan existerande kunder och deras demografiska data. Genom att förskaffa sig djupare kunskap om vad som differentierar kunder kan energibolaget förbättra sin försäljning. På grund av en förhållandevis liten mängd kunddata och brist på publik data gick det inte att hitta ett betydande samband mellan kunderna och deras demografiska data. Resultaten utgör dock en bra grund för fortsatt forskning då resultaten blir mer pålitliga då mer kunddata införskaffas, vilket blir en naturlig följd av att energibolagets försäljning fortsätter utvecklas.

There are many challenges facing society today, both socially and industrially. Whether it is to improve productivity in factories or with the intention of improving the quality of life of people in their homes, technological advances in robotics and computing have led to solutions to many problems in modern society. These areas are of great interest and are in constant development, especially in societies with a relatively ageing population. In this thesis, we address different challenges in which robotics, artificial intelligence and computer vision are used as tools to propose solutions oriented to home assistance. These tools can be organised into three main groups: “Grasping Challenges”, where we have addressed the problem of performing robot grasping in domestic environments; “Hand Interaction Challenges”, where we have addressed the detection of static and dynamic hand gestures, using approaches based on DeepLearning and GeometricLearning; and finally, “Human Behaviour Recognition”, where using a machine learning model based on hyperbolic geometry, we seek to group the actions that performed in a video sequence.