A database for publications published by researchers and students at SimulaMet.
Status
Research area
Books
Influence of delay on cloud gaming QoE
Springer Nature Switzerland AG: Springer Nature, 2022.Status: Accepted
Influence of delay on cloud gaming QoE
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Book |
| Year of Publication | 2022 |
| Publisher | Springer Nature |
| Place Published | Springer Nature Switzerland AG |
Book chapters
Eliciting and Prioritizing Services for Accessible Information - for Residential Real Estate Transactions
In Recent Developments in Universal Accessibility. Springer International Publishing,, 2022.Status: Accepted
Eliciting and Prioritizing Services for Accessible Information - for Residential Real Estate Transactions
A number of initiatives are underway for digitalizing real estate transaction processes. Public and private sector bodies are working to automate information retrieval and processing of the financial, ordinance and fiscal aspects of such transactions. Other initiatives, such as ours, are targeted toward helping stakeholders directly involved in selling and buying real estate. We present the results from a set of group sessions, where the focus was on improving the presentation of salient information to sellers and buyers of property. Based on an earlier conceptualization of perceived information difficulties, we elicited user stories for facilitating a better generation, provision and consumption of relevant information for the residential real estate transaction process. A total of ten services were aggregated from the user stories. We then asked a set of stakeholders to rate the effect of the services on functional objectives; i.e., on how they will affect the transaction process. We asked stakeholders at the managerial level to rate the functional objectives on strategic objectives. Combining the two sets of ratings, one obtains a rating of perceived benefit for the services, which can help in prioritzing which services to start developing first. In the outset, real estate transactions involve stakeholders with opposing interests. We conclude that multi-stakeholder group sessions can help generate services that serve these conflicting interests on a common ground.
| Afilliation | Software Engineering |
| Project(s) | EDOS: Effective Digitalization of Public Sector |
| Publication Type | Book Chapter |
| Year of Publication | 2022 |
| Book Title | Recent Developments in Universal Accessibility |
| Publisher | Springer International Publishing, |
Journal articles
Complexity and Variability Analyses of Motor Activity Distinguish Mood States in Bipolar Disorder
PLOS ONE (2022).Status: Accepted
Complexity and Variability Analyses of Motor Activity Distinguish Mood States in Bipolar Disorder
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | PLOS ONE |
| Publisher | PLOS ONE |
When should we (not) use the mean magnitude of relative error (MMRE) as an error measure in software development effort estimation?
Information and Software Technology 143 (2022).Status: Accepted
When should we (not) use the mean magnitude of relative error (MMRE) as an error measure in software development effort estimation?
Context: The mean magnitude of relative error (MMRE) is an error measure frequently used to evaluate and compare the estimation performance of prediction models and software professionals.
Objective: This paper examines conditions for proper use of MMRE in effort estimation contexts.
Method: We apply research on scoring functions to identify the type of estimates that minimizes the expected value of the MMRE.
Results: We show that the MMRE is a proper error measure for estimates of the most likely (mode) effort, but not for estimates of the median or mean effort, provided that the effort usage is approximately log-normally distributed, which we argue is a reasonable assumption in many software development contexts. The relevance of the findings is demonstrated on real-world software development data.
Conclusion: MMRE is not a proper measure of the accuracy of estimates of the median or mean effort, but may be used for the accuracy evaluation of estimates of most likely effort.
| Afilliation | Software Engineering |
| Project(s) | Department of IT Management, EDOS: Effective Digitalization of Public Sector |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Information and Software Technology |
| Volume | 143 |
| Date Published | 03/2022 |
| Publisher | Elsevier |
When 2 + 2 should be 5: The summation fallacy in time prediction
Journal of Behavioral Decision Making (2022).Status: Accepted
When 2 + 2 should be 5: The summation fallacy in time prediction
Predictions of time (e.g., work hours) are often based on the aggregation of estimates of elements (e.g., activities, subtasks). The only types of estimates that can be safely aggregated by summation are those reflecting predicted average outcomes (expected values). The sums of other types of estimates, such as bounds of confidence intervals or estimates of the mode, do not have the same interpretation as their components (e.g., the sum of the 90% upper bounds is not the appropriate 90% upper bound of the sum). This can be a potential source of bias in predictions of time, as shown in Studies 1 and 2, where professionals with experience in estimation provided total estimates of time that were inconsistent with their estimates of individual tasks. Study 3 shows that this inconsistency can be attributed to improper aggregation of time estimates and demonstrates how this can produce both over- and underestimation—and also time prediction intervals that are far too wide. Study 4 suggests that the results may reflect a more general fallacy in the aggregation of probabilistic quantities. Our observations are consistent with that inconsistencies and biases are driven by a tendency towards applying a naïve summation (2+2=4) of probabilistic (stochastic) values, in situations where this is not appropriate. This summation fallacy may be in particular consequential in a context where informal estimation methods (expert-judgment based estimation) are used.
| Afilliation | Software Engineering |
| Project(s) | Department of IT Management |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Journal of Behavioral Decision Making |
| Publisher | Wiley |
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
IEEE Transactions on Neural Networks and Learning Systems (2022): 1-14.Status: Accepted
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to the success of deep learning approaches. However, although deep learning models are being trained on large datasets, existing methods do not use the information from different learning epochs effectively. In this work, we leverage the information of each training epoch to prune the prediction maps of the subsequent epochs. We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch. The previous epoch mask is then used to provide hard attention to the learned feature maps at different convolutional layers. The network also allows rectifying the predictions in an iterative fashion during the test time. We show that our proposed feedback attention model provides a substantial improvement on most segmentation metrics tested on seven publicly available biomedical imaging datasets demonstrating the effectiveness of FANet. The source code is available at https://github.com/nikhilroxtomar/FANet.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Pagination | 1 - 14 |
| Date Published | Jan-01-2022 |
| Publisher | IEEE |
| ISSN | 2162-237X |
| URL | https://ieeexplore.ieee.org/document/9741842 |
| DOI | 10.1109/TNNLS.2022.3159394 |
Cell exclusion during human embryo development result in altered morphokinetic patterns up to morula formation
Human Reproduction (2022).Status: Accepted
Cell exclusion during human embryo development result in altered morphokinetic patterns up to morula formation
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Human Reproduction |
| Publisher | Human Reproduction |
Automating tracking of cell division for human embryo development in time lapse videos
Human Reproduction (2022).Status: Accepted
Automating tracking of cell division for human embryo development in time lapse videos
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Human Reproduction |
| Publisher | Human Reproduction |
Bias i kvantitativ analyse innen velferd
Tidsskrift for velferdsforskning (2022).Status: Accepted
Bias i kvantitativ analyse innen velferd
Ifølge Norges nasjonale strategi for kunstig intelligens (2020) er offentlig forvaltning og helse blant Norges satsningsområder for bruk av kunstig intelligens. Maskinlæring er en undergruppe av kunstig intelligens med potensiale for å løse en rekke utfordringer, men som også gir opphav til utfordringer. En slik utfordring er bias, eller skjevhet. Et eksempel på skjevhet er at tilstedeværende ulikheter i samfunnet representeres i datagrunnlaget maskinlæringsmodeller utvikles på. De resulterende modellene står dermed i fare for å adoptere og videreføre disse ulikhetene. En utfordring er at skjevhet har ulike definisjoner innen ulike fagområder, og kan ha mange ulike opphav. Vi bidrar til å løse denne utfordringen ved å gi en oversikt over ulike typer skjevhet og deres opphav med illustrasjoner fra et velferdsperspektiv, samt avklarer forskjellen til det nærliggende konseptet rettferdighet. Vi demonstrerer utfordringer relatert til databaserte modellers oppførsel ved å benytte maskinlæring til å predikere fremtidig ressursbehov i helsevesenet, spesifikt antall legebesøk i kommuner. Vi demonstrerer ulike typer skjevheter, diskuterer mulige løsninger og bruker metoder fra forklarbar kunstig intelligens for å analysere opphavet til skjevheter i forklaringsvariablene. Det finnes ingen universell løsning for å håndtere alle typer skjevheter, men skjevhet må tas høyde for i alle deler av en kvantitativ analyse.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Tidsskrift for velferdsforskning |
| Publisher | Universitetsforlaget |
| Place Published | Tidsskrift for velferdsforskning |
| Keywords | forklarbar kunstig intelligens, Maskinlæring, skjevhetsbegreper, velferdsforskning, XGBoost |
AI Anomaly Detection for Cloudified Mobile Core Architectures
Transactions on Network and Service Management (2022).Status: Accepted
AI Anomaly Detection for Cloudified Mobile Core Architectures
IT systems monitoring is a crucial process for managing and orchestrating network resources, allowing network providers to rapidly detect and react to most impediment causing network degradation. However, the high growth in size and complexity of current operational networks (2022) demands new solutions to process huge amounts of data (including alarms) reliably and swiftly. Further, as the network becomes progressively more virtualized, the hosting of nfv on cloud environments adds a magnitude of possible bottlenecks outside the control of the service owners. In this paper, we propose two deep learning anomaly detection solutions that leverage service exposure and apply it to automate the detection of service degradation and root cause discovery in a cloudified mobile network that is orchestrated by ETSI OSM. A testbed is built to validate these AI models. The testbed collects monitoring data from the OSM monitoring module, which is then exposed to the external AI anomaly detection modules, tuned to identify the anomalies and the network services causing them. The deep learning solutions are tested using various artificially induced bottlenecks. The AI solutions are shown to correctly detect anomalies and identify the network components involved in the bottlenecks, with certain limitations in a particular type of bottlenecks. A discussion of the right monitoring tools to identify concrete bottlenecks is provided.
| Afilliation | Communication Systems |
| Project(s) | 5G-VINNI: 5G Verticals INNovation Infrastructure , The Center for Resilient Networks and Applications, Simula Metropolitan Center for Digital Engineering, Simula Metropolitan Center for Digital Engineering, NorNet |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Transactions on Network and Service Management |
| Publisher | IEEE |
| Place Published | Los Alamitos, California/U.S.A. |
| ISSN | 1932-4537 |
| Keywords | 5G, AI, Anomaly detection, Autoencoders, deep learning, Mobile networks, Smart Networks |
