A database for publications published by researchers and students at SimulaMet.
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Proceedings, refereed
Measurement of software development effort estimation bias: Avoiding biased measures of estimation bias
In 11th International Conference on Software Engineering and Applications (SEA 2022), 2022.Status: Accepted
Measurement of software development effort estimation bias: Avoiding biased measures of estimation bias
In this paper, we propose improvements in how estimation bias, e.g., the tendency towards under-estimating the effort, is measured. The proposed approach emphasizes the need to know what the estimates are meant to represent, i.e., the type of estimate we evaluate and the need for a match between the type of estimate given and the bias measure used. We show that even perfect estimates of the mean effort will not lead to an expectation of zero estimation bias when applying the frequently used bias measure: (actual effort – estimated effort)/actual effort. This measure will instead reward under-estimates of the mean effort. We also provide examples of bias measures that match estimates of the mean and the median effort, and argue that there are, in general, no practical bias measures for estimates of the most likely effort. The paper concludes with implications for the evaluation of bias of software development effort estimates.
Afilliation | Software Engineering |
Project(s) | Department of IT Management |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | 11th International Conference on Software Engineering and Applications (SEA 2022) |
Investigative Interviews using a Multimodal Virtual Avatar
In American Psychology-Law Society Conference 2022. Denver USA,: American Psychology-Law Society, 2022.Status: Accepted
Investigative Interviews using a Multimodal Virtual Avatar
To meet best-practice standards, we are developing an interactive virtual avatar aiming as a training tool to raise interviewing skills of child-welfare and law-enforcement professionals. Therefore, we present the “Ilma” avatar that recognizes interviewers’ behavior during open-ended, closed and leading questions, and which can automatically respond to the conversation. We conducted a user study in which master students (N=3) and child protective workers (N=8) interviewed “Ilma” and rated their perception of the interaction. The results show that the participants valued the interaction and found the avatar useful. Thus, it has great potential to be an effective training tool.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | American Psychology-Law Society Conference 2022 |
Publisher | American Psychology-Law Society |
Place Published | Denver USA, |
Transfer Learning Based Joint Resource Allocation for Underlay D2D Communications
In IEEE Wireless Communications and Networking Conference (WCNC). IEEE Wireless Communications and Networking Conference (WCNC): IEEE, 2022.Status: Published
Transfer Learning Based Joint Resource Allocation for Underlay D2D Communications
In this paper, we investigate the application of transfer learning to train a DNN model for joint channel and power allocation in underlay Device-to-Device communication. Based on the traditional optimization solutions, generating training dataset for scenarios with perfect CSI is not computationally demanding, compared to scenarios with imperfect CSI. Thus, a transfer learning-based approach can be exploited to transfer the DNN model trained for the perfect CSI scenarios to the imperfect CSI scenarios. We also consider the issue of defining the similarity between two types of resource allocation tasks. For this, we first determine the value of outage probability for which two resource allocation tasks are same, that is, for which our numerical results illustrate the minimal need of relearning from the transferred DNN model. For other values of outage probability, there is a mismatch between the two tasks and our results illustrate a more efficient relearning of the transferred DNN model. Our results show that the learning dataset required for relearning of the transferred DNN model is significantly smaller than the required training dataset for a DNN model without transfer learning.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Signal and Information Processing for Intelligent Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | IEEE Wireless Communications and Networking Conference (WCNC) |
Publisher | IEEE |
Place Published | IEEE Wireless Communications and Networking Conference (WCNC) |
Load Distribution for Mobile Edge Computing with Reliable Server Pooling
In Proceedings of the 4th International Workshop on Recent Advances for Multi-Clouds and Mobile Edge Computing (M2EC) in conjunction with the 36th International Conference on Advanced Information Networking and Applications (AINA). Sydney, New South Wales/Australia: Springer, 2022.Status: Accepted
Load Distribution for Mobile Edge Computing with Reliable Server Pooling
Energy-efficient computing model is a popular choice for high performance as well as throughput oriented computing ecosystems. Mobile (computing) devices are becoming increasingly ubiquitous to our computing domain, but with limited resources (true both for computation as well as for energy). Hence, workload offloading from resource-constrained mobile devices to the Edge and maybe (later) to the cloud become necessary as well as useful. Thanks to the persistent technical breakthroughs in global wireless standards (or in mobile networks) together with the almost limitless amount of resources in public cloud platforms, workload offloading is possible and cheaper. In such scenarios, Mobile Edge Computing (MEC) resources could be provisioned in proximity to the users for supporting latency-sensitive applications. Here, two relevant problems could be: i) How to distribute workload to the resource pools of MEC as well as public (multi-)clouds? ii) How to manage such resource pools effectively? To answer these problems in this paper, we examine the performance of our proposed approach using the Reliable Server Pooling (RSerPool) framework in more detail. We also have outlined the resource pool management policies to effectively use RSerPool for workload offloading from mobile devices into the cloud/MEC ecosystem.
Afilliation | Communication Systems |
Project(s) | 5G-VINNI: 5G Verticals INNovation Infrastructure , NorNet, The Center for Resilient Networks and Applications, Simula Metropolitan Center for Digital Engineering, Simula Metropolitan Center for Digital Engineering, SMIL: SimulaMet Interoperability Lab, MELODIC: Multi-cloud Execution-ware for Large-scale Optimised Data-Intensive Computing |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | Proceedings of the 4th International Workshop on Recent Advances for Multi-Clouds and Mobile Edge Computing (M2EC) in conjunction with the 36th International Conference on Advanced Information Networking and Applications (AINA) |
Publisher | Springer |
Place Published | Sydney, New South Wales/Australia |
Keywords | Cloud computing, Load Distribution, Mobile Edge Computing (MEC), Multi-Cloud Computing, Reliable Server Pooling (RSerPool), Serverless Computing |
Multi-task FMRI Data Fusion using IVA and PARAFAC2
In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022.Status: Accepted
Multi-task FMRI Data Fusion using IVA and PARAFAC2
Afilliation | Machine Learning |
Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
GMSRF-Net: An Improved generalizability with Global Multi-Scale Residual Fusion Network for Polyp Segmentation
In 26TH International Conference on Pattern Recognition. Springer, 2022.Status: Accepted
GMSRF-Net: An Improved generalizability with Global Multi-Scale Residual Fusion Network for Polyp Segmentation
Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Widely used computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy. However, polyp segmentation datasets collected from varied centers can follow different imaging protocols leading to difference in data distribution. As a result, most methods suffer from performance drop when trained and tested on different distributions and therefore, require re-training for each specific dataset. We address this generalizability issue by proposing a global multi-scale residual fusion network (GMSRF-Net). Our proposed network maintains high-resolution representations by performing multi-scale fusion operations across all resolution scales through dense connections while preserving low-level information. To further leverage scale information, we design cross multi-scale attention (CMSA) module that uses multi-scale features to identify, keep, and propagate informative features. Additionally, we introduce multi-scale feature selection (MSFS) modules to perform channel-wise attention that gates irrelevant features gathered through global multi-scale fusion within the GMSRF-Net. The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of our network. Experiments conducted on two different polyp segmentation datasets show that our proposed GMSRF-Net outperforms the previous top-performing state-of-the-art method by 8.34% and 10.31% on unseen CVC-ClinicDB and on unseen Kvasir-SEG, in terms of dice coefficient. Additionally, when tested on unseen CVC-ColonDB, we surpass the state-of-the-art method by 9.38% and 4.04% in terms of dice coefficient, when source dataset is Kvasir-SEG and CVC-ClinicDB, respectively.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | 26TH International Conference on Pattern Recognition |
Publisher | Springer |
HOST-ATS: Automatic Thumbnail Selection with Dashboard-Controlled ML Pipeline and Dynamic User Survey
In ACM Multimedia Systems (MMSys) Conference. The ACM Multimedia Systems Conference (MMSys): ACM, 2022.Status: Accepted
HOST-ATS: Automatic Thumbnail Selection with Dashboard-Controlled ML Pipeline and Dynamic User Survey
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | ACM Multimedia Systems (MMSys) Conference |
Publisher | ACM |
Place Published | The ACM Multimedia Systems Conference (MMSys) |
DOI | 10.1145/3524273.3532908 |
Huldra: A Framework for Collecting Crowdsourced Feedback on Multimedia Assets
In ACM Multimedia Systems (MMSys) Conference. The ACM Multimedia Systems Conference (MMSys): ACM, 2022.Status: Accepted
Huldra: A Framework for Collecting Crowdsourced Feedback on Multimedia Assets
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | ACM Multimedia Systems (MMSys) Conference |
Publisher | ACM |
Place Published | The ACM Multimedia Systems Conference (MMSys) |
DOI | 10.1145/3524273.3532887 |
Automatic Thumbnail Selection for Soccer Videos using Machine Learning
In ACM Multimedia Systems (MMSys) Conference. The ACM Multimedia Systems Conference (MMSys): ACM, 2022.Status: Accepted
Automatic Thumbnail Selection for Soccer Videos using Machine Learning
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | ACM Multimedia Systems (MMSys) Conference |
Publisher | ACM |
Place Published | The ACM Multimedia Systems Conference (MMSys) |
DOI | 10.1145/3524273.3528182 |
Multimedia streaming analytics: quo vadis?
In MHV '22: Mile-High Video Conference. Denver, Colorado, USA: ACM, 2022.Status: Published
Multimedia streaming analytics: quo vadis?
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | MHV '22: Mile-High Video Conference |
Pagination | 62 - 69 |
Publisher | ACM |
Place Published | Denver, Colorado, USA |
ISBN Number | 9781450392228 |
URL | https://dl.acm.org/doi/10.1145/3510450.3517321 |
DOI | 10.1145/3510450.3517321 |