Increased consumer awareness together with the importance of sustainable consumption is currently a highly debated topic. Frequently, the media reports scandals from strong international brands, revealing information regarding deficiencies linked to the brands social sustainability efforts. As a consequence, some consumers are starting to put higher demands on social sustainability by spreading condemnations. This is to prevent injustices such as child labour and modern slavery. The condemnation can be in the form of negative word-of- mouth or by boycotting the brand. This is something that can affect the brand strongly by damaging the consumers perception of the brand, which can be fatal to the consumer-brand relationship. However, this is something that seems to affect some brands to a greater extent than other brands This study explores the strength of Keller’s brand resonance level as a possible explanation to the above mentioned anomality. It shows that brands resonance operates as a countermeasure against negative information in relation to the brands indiscretions regarding social sustainability. Brand resonance can contradict the consumers ethical values and self-imagery, leaving the consumers perception of the brand unscathed. The results showed that brand resonance can become so strong that it can make the consumer abandon their ethical values, even for consumers whom consider themselves as highly ethical. This study has been conducted by a hypothetically deductive methodology. To determine the significance of the result it has been verified with a Z-test that applied a 5% significance level.

<p> </p> <p>Satellite imagery is becoming increasingly available due to a large number of commercial satellite operators. Many fields use satellite images, including meteorology, forestry, natural disaster analysis, and agriculture. These images can be changed or tampered with image manipulation tools that can cause issues in many applications. Manipulation detection techniques designed for images captured by ``consumer cameras'' tend to fail when used on satellite images. In this thesis we examine methods for detecting splices where an object or area is inserted into a satellite image. Three semi-supervised one-class methods are proposed for the detection and localization of manipulated images. A supervised and supervised fusion approach are also describe to detect spliced forgeries. The semi-supervised one-class method does not require any prior knowledge of the type of manipulations that an adversary could insert in the satellite imagery. First, a new method known as Satellite SVDD (Sat-SVDD) which adapts the Deep SVDD technique is described. Another semi-supervised one-class one-class detection technique based on deep belief networks (DBN) for splicing detection and localization is then discussed. Multiple configurations of the Deep Belief network were compared to other common one-class classification methods. Finally, a semi-supervised one-class technique that uses a Vision Transformer to detect spliced areas within satellite images is introduced. The supervised method does not require prior knowledge of the type of manipulations inserted into the satellite imagery. A supervised method known as Nested Attention U-Net, to detect spliced. The supervised fusion approach known as Sat U-Net fuses the results of two exiting forensic splicing localization methods to increase their overall accuracy. Sat U-Net is a U-Net based architecture exploiting several Transformers to enhance the splicing detection performance. Sat U-Net fuses the outputs of two semi-supervised one-class splicing detection methods, Gated PixelCNN Ensemble and Vision Transformer, to produce a heatmap highlighting the manipulated image region. The supervised fusion approach trained on images from one satellite can be lightly retrained on few images from another satellite to detect spliced regions. In this thesis I introduce five datasets of manipulated satellite images that contain spliced objects. Three of the datasets contains images with spliced objects generated by a generative adversarial network (GAN).</p>

Throughout history, geographical information has been vital in different contexts, such as national security matters, economics, geopolitics, military, and natural resources. Due to the various applications, geographical information has been handled as valuable and sensitive information. As technology evolves, geographical information is becoming increasingly more available. This thesis investigates the data attributes relevant to its sensitivity and creates an information security classification model suitable for the satellite imagery produced, analyzed, and maintained by Maxar Technologies Ltd. All geographical information is of value. Everything from terrain information to protected areas where features such as roads, critical infrastructure, and buildings are of extra interest. Other factors that affect the sensitivity of the imagery are the resolution, amount of information, type of files (3D or other processed data), legislation, and more. The methodology used to achieve this consisted of two major parts, a risk assessment procedure and translating risk contexts and parameters into a classification model. The classification levels identified are PUBLIC, VALUABLE, SENSITIVE, and CLASSIFIED. A classification model is defined for individual imagery and a separate model for projects. A project gets at least the same classification as the highest classed file and other contexts that may affect the sensitivity.   Lastly, the thesis explore automation possibilities and a supervised learning approach is tested on the model created for the classification of files. Various machine learning models are fitted to a dataset that is collected from the satellite imagery products of Maxar and manually classed using the defined classification levels. F-score and MCC are used to evaluate the automation. These are metrics based on the occurrences of false positives and negatives. Furthermore, the thesis discusses topics related to the sensitivity of geographical information and how to handle such information. This thesis tries to lay the foundation for many future work possibilities.

The traditional diagnosis of patients with disorders of consciousness relies solely on behavioral responses. In 1996 it was estimated that 43% of patients diagnosed with unresponsive wakefulness syndrome (vegetative state) receive the wrong diagnosis. Assessing consciousness is perhaps the most crucial part of the diagnostic process. The challenging task of identifying covert consciousness in this patient group seems to be the biggest issue. In 2006 willful modulation of brain activity in response to a mental imagery task was discovered in a patient with unresponsive wakefulness syndrome. The brain activity was measured with functional magnetic resonance imaging. It was concluded that consciousness was preserved in this patient and new research investigating this novel method began to take place. The aim of this thesis was to conduct a systematic review of the literature and thereby arrive at the best current estimate of the proportion of patients who receive a diagnosis that wrongfully defines them as “unconscious” although they in fact are “covertly conscious”. In this review, 11 studies were examined. The results showed that patients with unresponsive wakefulness syndrome, who still receive the wrong diagnosis, decreased to 22-28% by the use of neuroimaging. This improvement points to the possible use of neuroimaging methods in the diagnosis of disorders of consciousness. However, this result cannot be taken without reservations. The limitations of the studies have to be taken into consideration. For example, most studies included a limited sample size and healthy controls did not always give the expected response to mental imagery tasks.

Generic visual object tracking is the task of tracking one or several objects in all frames in a video, knowing only the location and size of the target in the initial frame. Visual tracking can be carried out in both the infrared and the visual spectrum simultaneously, this is known as multi-modal tracking. Utilizing both spectra can result in a more diverse tracker since visual tracking in infrared imagery makes it possible to detect objects even in poor visibility or in complete darkness. However, infrared imagery lacks the number of details that are present in visual images. A common method for visual tracking is to use discriminative correlation filters (DCF). These correlation filters are then used to detect an object in every frame of an image sequence. This thesis focuses on investigating aspects of a DCF based tracker, operating in the two different modalities, infrared and visual imagery. First, it was investigated whether the tracking benefits from using two channels instead of one and what happens to the tracking result if one of those channels is degraded by an external cause. It was also investigated if the addition of image features can further improve the tracking. The result shows that the tracking improves when using two channels instead of only using a single channel. It also shows that utilizing two channels is a good way to create a robust tracker, which is still able to perform even though one of the channels is degraded. Using deep features, extracted from a pre-trained convolutional neural network, was the image feature improving the tracking the most, although the implementation of the deep features made the tracking significantly slower.

This paper will be focusing on sound mixing in film by using a comparative study. This article will try to find out if there is a way to use Michel Chion’s and Walter Murch’s sound design theories as design methods and compare them with each other. To test their theories we did our own sound design and sound mixing to a short film. During the mixing process we split up the project and worked on two different mixes. Strictly following the theories of Murch and Chions theories. During the comparison which contains the questions about effectivity, quality and priority in the mix we figured out that both theories could be used as a design perspective since they are both able to be divided into methods. / Denna undersökning kommer att fokusera på ljudmixning inom film med användandet av komparativ metod. Artikeln kommer försöka ta reda på om det går att använda sig utav Michel Chion och Walter Murch ljuddesignteorier som designmetoder och jämföra dem med varandra. För att testa deras teorier gjorde vi vår egna ljuddesign och mixning till en kortfilm. Under mixningsprocessen delade vi upp projektet och arbetade med två mixningar och strikt följde deras teorier. Under jämförelsen vilket innehåller frågor om effektivitet, kvalitet och prioritet i mixen fick vi reda på att de båda går att använda som designperspektiv då respektive teorier gör det möjligt att dela upp de i metoder.

<p>Urban models are of growing importance today for urban and environmental planning, geographic information systems, urban simulations, and as content for entertainment applications. Various methods have addressed aerial or ground scale image-based and sensor-based reconstruction. However, few, if any, approaches have automatically produced urban models from satellite images due to difficulties of data noise, data sparsity, and data uncertainty. Our key observations are that many structures in urban areas exhibit regular properties, and a second or more satellite views for urban structures are usually available. Hence, we can overcome the aforementioned issues obtained from satellite imagery by synthesizing the underlying structure layout. In addition, recent advances in deep learning allow the development of novel algorithms that was not possible several years ago. We leverage relevant deep learning techniques for classifying/predicting urban structure parameters and modeling urban areas that address the problem of satellite data quality and uncertainty. In this dissertation, we present a machine learning-based procedural generation framework to automatically and quickly reconstruct urban areas by using regularities of urban structures (e.g., cities, buildings, facades, roofs, etc.) from satellite imagery, which can be applied to not only multiple resolutions ranging from low resolution (e.g., 3 meters) to high resolutions (e.g., WV3 0.3 meter) of satellite images but also the different scales (e.g., cities, blocks, parcels, buildings, facades) of urban environments. Our method is fully automatic and generates procedural structures in urban areas given satellite imagery. Experimental results show that our method outperforms previous state-of-the-art methods quantitatively and qualitatively for multiple datasets. Furthermore, by applying our framework to multiple urban structures, we demonstrate our approach can be generalized to various pattern types. We also have preliminary results applying this for flooding, archaeological sites, and more.</p>

From 2019 to 2020, greenhouse and field research was conducted at North Dakota State University to investigate the canopy temperature response of waterhemp (Amaranthus rudis), kochia (Kochia scoparia), common ragweed (Ambrosia artemisiifolia), horseweed (Conyza canadensis), Palmer amaranth (Amaranthus palmeri), and red root pigweed (Amaranthus retroflexus) after glyphosate application to identify glyphosate resistance. In these experiments, thermal images were captured of randomized glyphosate resistant populations and glyphosate susceptible populations of each weed species. The weed canopies' thermal values were extracted and submitted to statistical testing and various classifiers in an attempt to discriminate between resistant and susceptible populations. Glyphosate resistant horseweed, when collected within greenhouse conditions, was the only biotype reliably classified using significantly cooler temperature signatures than its susceptible counterpart. For field conditions, image based machine learning classifiers using thermal data were outperformed by classifiers made using additional multispectral data, suggesting thermal is not a reliable predictor of glyphosate resistance.

Background. In computer graphics, it can be a time-consuming process to render photorealistic images. This rendering process, called “physically based rendering” uses complex algorithms to calculate the behavior of light. Fortunately, most renderers offer the possibility to alter the render-settings, allowing for a decrease in render time, but this usually comes at the cost of a lower quality image. Objectives. This study aims to identify what setting has the highest impact on the rendering process in the V-Ray renderer. It also examines if a perceived difference can be seen when reducing this setting. Methods. To achieve this, an experiment was done where 22 participants would indicate their preference for rendered images. The images were rendered in V-Ray with different settings, which affected their respective render time differently. Additionally, an objective image metric was used to analyze the images and try to form a correlation with the subjective results. Results. The results show that the anti-aliasing setting had the highest impact on render time as well as user preference. It was found that participants preferred images with at least 25% to 50% anti-aliasing depending on the scene. The objective results also coincided well enough with the subjective results that it could be used as a faster analytical tool to measure the quality of a computer-generated image. Prior knowledge of rendering was also taken into account but did not give conclusive results about user preferences. Conclusions. From the results it can be concluded that anti-aliasing is the most important setting for achieving good subjective image quality in V-Ray. Additionally, the use of an objective image assessment tool can drastically speed up the process for targeting a specific visual quality goal.

The purpose of this dissertation was to test the significance of objective l y measured imagery ability on the learning of self-controled relaxation of autonomic nervous system activity. Imagery is discussed in terms of its interaction with Autogenic vs. Jacobsonian methods of training clinical relaxation. Thirty-six female subjects from a college population, representing extreme highs and lows on "spatial ability" tests were given a series of three six-session sequences of Baseline, Treatment 1, and Treatment 2, which contained silent relaxation as a control, plus Jacobsonian and Autogenic relaxation. High and low spatial ability subjects were divided into split groups (A & B) which were given Jacobsonian and Autogenic relaxation treatment in different sequence orders. Skin temperature biofeedback was used to monitor the little fingers on both hands as a general indicator of autonomic clinical relaxation. Mean temperature; temperature change within sessions; and temperature change between sessions, were analyzed by different treatment periods and spatial ability groups. The data from these groups were analyzed using an ANOVA design. There were no significant differences in mean temperature data. A nearly significant two-way interaction was found between imagery ability and treatment order during Autogenic training. Also a significant interaction was found in skin temperature change between sessions for, "Sensory" vs. "Intuitive" personality types, and a nearly significant difference for Autogenic vs. Jacobsonian treatment. It was concluded that Jacobsonian training was generally more effective than Autogenic training for inducing vascular relaxation in both high and low imagery subjects. Also it was found that Sensory perceptual types are significantly more stable in terms of day to day skin temperature variation during relaxation training, than are Intuitive perceptual types.