Westerdals Oslo School of Arts, Communication and Technology

Sleep disorder detection has greatly improved with the integration of machine learning, offering enhanced accuracy and effectiveness. However, the labor-intensive nature of diagnosis still presents challenges. To address these, we propose a novel coordination model aimed at improving detection accuracy and reliability through a multi-model ensemble approach. The proposed method employs a multi-layered ensemble model, starting with the careful selection of N models to capture essential features. Techniques such as thresholding, predictive scoring, and the conversion of Softmax labels into multidimensional feature vectors improve interpretability. Ensemble methods like voting and stacking are used to ensure collaborative decision-making across models. Both the original dataset and one modified using the Synthetic Minority Oversampling Technique (SMOTE) were evaluated to address data imbalance issues. The ensemble model demonstrated superior performance, achieving 96.88% accuracy on the SMOTE-implemented dataset and 95.75% accuracy on the original dataset. Moreover, an eight-fold cross-validation yielded an impressive 99.5% accuracy, indicating the reliability of the model in handling unbalanced data and ensuring precise detection of sleep disorders. Compared to individual models, the proposed ensemble method significantly outperformed traditional models. The combination of models not only enhanced accuracy but also improved the system's ability to handle unbalanced data, a common limitation in traditional methods. This study marks a significant advancement in sleep disorder detection through the integration of innovative ensemble techniques. The proposed approach, combining multiple models and advanced interpretability methods, promises improved patient outcomes and greater diagnostic accuracy, paving the way for future applications in medical diagnostics.

Objective To gain the knowledge needed to develop a cognitive behavioral intervention and reliable psychoeducation applicable for conversational artificial intelligence models, we investigated the underlying constructs of thoughts common to cognitive distortions found in online questions written by adolescents with symptoms of depression. Methods From June 30, 2020, to October 30, 2020, we analyzed a sample of 100 written questions from adolescents about depression posted on an online information service using a qualitative analysis guided by cognitive behavioral theory and informed by the neuroscience of adolescence. Results Four types of cognitive distortions (CDs) were found: (1) emotional reasoning, (2) mind reading, (3) catastrophizing, and (4) labeling. Our analysis suggested 3 underlying constructs common to the different CDs: (1) emotional states appearing as reality, (2) experiencing this emotional reality as something others think, and (3) generalizing such beliefs to every relation and the future. These constructs may signify events leading to a ruminative state that seems hard to escape. Conclusion The 4 different CDs originate from 3 underlying constructs possibly associated with adolescent neurodevelopment. This study indicates a potential to reveal the underlying constructs of thought common to different CDs, thus making CDs more useful as a target point in artificial intelligence–based technological information and intervention tools.

Background: Depression is common during adolescence. Early intervention can prevent it from developing into more progressive mental disorders. Combining information technology and clinical psychoeducation is a promising way to intervene at an earlier stage. However, data-driven research on the cognitive response to health information targeting adolescents with symptoms of depression is lacking. Objective: This study aimed to fill this knowledge gap through a new understanding of adolescents' cognitive response to health information about depression. This knowledge can help to develop population-specific information technology, such as chatbots, in addition to clinical therapeutic tools for use in general practice. Methods: The data set consists of 1870 depression-related questions posted by adolescents on a public web-based information service. Most of the posts contain descriptions of events that lead to depression. On a sample of 100 posts, we conducted a qualitative thematic analysis based on cognitive behavioral theory investigating behavioral, emotional, and symptom responses to beliefs associated with depression. Results: Results were organized into four themes. (1) Hopelessness, appearing as a set of negative beliefs about the future, possibly results from erroneous beliefs about the causal link between risk factors and the course of depression. We found beliefs about establishing a sturdy therapy alliance as a responsibility resting on the patient. (2) Therapy hesitancy seemed to be associated with negative beliefs about therapy prognosis and doubts about confidentiality. (3) Social shame appeared as a consequence of impaired daily function when the cause is not acknowledged. (4) Failing to attain social interaction appeared to be associated with a negative symptom response. In contrast, actively obtaining social support reduces symptoms and suicidal thoughts. Conclusions: These results could be used to meet the clinical aims stated by earlier psychoeducation development, such as instilling hope through direct reattribution of beliefs about the future; challenging causal attributions, thereby lowering therapy hesitancy; reducing shame through the mechanisms of externalization by providing a tentative diagnosis despite the risk of stigmatizing; and providing initial symptom relief by giving advice on how to open up and reveal themselves to friends and family and balance the message of self-management to fit coping capabilities. An active counseling style advises the patient to approach the social environment, demonstrating an attitude toward self-action.

From the early days of computer systems to the present, software testing has been considered as a crucial process that directly affects the quality and reliability of software-oriented products and services. Accordingly, there is a huge amount of literature regarding the improvement of software testing approaches. However, there are limited reviews that show the whole picture of the software testing studies covering the topics and trends of the field. This study aims to provide a general figure reflecting topics and trends of software testing by analyzing the majority of software testing articles published in the last 40 years. A semi-automated methodology is developed for the analysis of software testing corpus created from core publication sources. The methodology of the study is based on the implementation of probabilistic topic modeling approach to discover hidden semantic patterns in the 14,684 published articles addressing software testing issues between 1980 and 2019. The results revealed 42 topics of the field, highlighting five software development ages, namely specification, detection, generation, evaluation, and prediction. The recent accelerations of the topics also showed a trend toward prediction-based software testing actions. Additionally, a higher trend on the topics concerning “Security Vulnerability”, “Open Source” and “Mobile Application” was identified. This study showed that the current trend of software testing is towards prediction-based testing strategies. Therefore, the findings of this study may provide valuable insights for the industry and software communities to be prepared for the possible changes in the software testing procedures using prediction-based approaches.

This article explores Swedish Parliamentarians' Twitter practices during the 2014 general elections. For individual candidates, the political party is important for positions within the party and on the ballot, especially in a party-centered democracy. A previous qualitative (n)ethnographic research project during the previous elections in 2010, in which one campaigning politician was studied in-depth, found that her social media practices to a large extent were inward-facing, focusing on the own party network. But does this result resonate among all Swedish Parliamentarians? Specifically, the authors ask: is Twitter primarily used interactively, for intra-party communication, to interact with strategic voter groups or voters in general? By analyzing all Parliamentarians tweets two weeks up to the elections the authors conclude that retweeting was done within a party political network while @messaging was directed towards political opponents. Mass media journalists and editorial writers were important in Parliamentarians' Twitter practices, while so-called ordinary voters were more absent.

Healthcare professionals are increasingly working with data in their care delivery practices. However, there is limited understanding of how data work is enabling novel practices. This study focuses on novel nursing practices emerging in the context of remote monitoring of chronic patients. Specifically, we analyze how personalization of care is achieved in practice through data work. The study is based on a case of a pilot center in Norway where nurses provide remote care to patients by using a specialized system. We examine the practices of the nurses and how data in the form of graphs, alerts, questionnaires and messages are used to personalize care. We identify three main practices of data work for personalization: preparatory work, continuous adjustment and question fine-tuning. Finally, we discuss the pivotal role of nurses’ data work for personalized care in remote care.

As a sovereign wealth fund, the $1 trillion Norwegian Government Pension Fund-Global (‘the Oil Fund’), which is managed by Norges Bank Investment Management on behalf of the welfare of Norway’s citizens, is supposed to be a flagship for socially responsible investments through its Council of Ethics. However, its investment in Delta Topco, the holding company of Formula 1 world championship that, through Formula One Group, brokered a deal with Russia to host a Formula 1 Grand Prix in 2014, raises the question of whether the Oil Fund should enhance its due diligence processes. Although no evidence of corruption related to the race is introduced, the complex relation between financial logic and the world of sports still raises questions about the ethical solidity of the Oil Fund’s investment. Drawing upon reports of the relationship between political economy and sporting events, this paper therefore analyses, in light of the Oil Fund’s ethical guidelines, the complexities of its investment in Delta Topco. As a result, it is argued that a new set of examination methods by the Council of Ethics may be warranted.

In a global context which promotes the use of explicit semantics for sharing information and developing new services, the MAchine Readable Cataloguing (MARC) format that is commonly used by libraries worldwide has demonstrated its many limitations. The conceptual reference model for bibliographic information presented in the Functional Requirements for Bibliographic Records (FRBR) is expected to be the foundation for a new generation of catalogs that will replace MARC and the digital card catalog. The need for transformation of legacy MARC records to FRBR representation (FRBRization) has led to the proposal of various tools and approaches. However, these projects and the results they achieve are difficult to compare due to lack of common datasets and well defined and appropriate metrics. Our contributions fill this gap by proposing BIB-R, the first public benchmark for the FRBRization process. It is composed of two datasets that enable the identification of the strengths and weaknesses of a FRBRization tool. It also defines a set of well defined metrics that evaluate the different steps of the FRBRization process. Those resources, as well as the results of a large experiment involving three FRBRization tools tested against our benchmark, are available to the community under an open licence.

Modelling 3D objects is challenging; often special software skills are required. This paper explores a new method for experimenting with 3D modelling using two-dimensional drawings. These drawings use coloured areas to dictate the rate of curvature. The curvature images are rendered in a radial manner from the centre to the sides. The method allows complex 3D shapes to be modelled. There is no need to employ any new software program as any arbitrary 2D painting application can be used to sketch objects. KeywordsSketching3D-Modelling2D hand drawingsDesignIdeation

Some individuals with reduced motor function rely on scanning keyboards to operate computers. A problem observed with scanning keyboards is that errors typically occur during the first group or first cell of a group. This paper proposes to reduce such errors by introducing longer dwell-times for the first element in scan sequences. The paper theoretically explores several designs and evaluates their effect on overall text entry performance.