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Fiedler, Markus
Publications (10 of 200) Show all publications
Fotrousi, F., Fricker, S., Fiedler, M. & Wüest, D. (2020). A Method for Gathering Evidence from Software in Use to Support Software Evolution. Journal of Empirical Software Engineering
Open this publication in new window or tab >>A Method for Gathering Evidence from Software in Use to Support Software Evolution
2020 (English)In: Journal of Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616Article in journal (Refereed) Submitted
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-19474 (URN)
Available from: 2020-05-18 Created: 2020-05-18 Last updated: 2020-05-19Bibliographically approved
Fotrousi, F., Stade, M., Seyff, N., Fricker, S. & Fiedler, M. (2020). How do Users Characterise Feedback Features of an Embedded Feedback Channel?.
Open this publication in new window or tab >>How do Users Characterise Feedback Features of an Embedded Feedback Channel?
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2020 (English)In: Article in journal (Refereed) Submitted
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-19475 (URN)
Available from: 2020-05-18 Created: 2020-05-18 Last updated: 2020-05-19Bibliographically approved
Fiedler, M. (2020). Performance analytics of a virtual reality streaming model. In: Hermanns H. (Ed.), Lecture Notes in Computer Science: . Paper presented at 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems, MMB 2020; Saarbrücken; Germany; 16 March 2020 through 18 March 2020 (pp. 1-18). Springer, 12040
Open this publication in new window or tab >>Performance analytics of a virtual reality streaming model
2020 (English)In: Lecture Notes in Computer Science / [ed] Hermanns H., Springer , 2020, Vol. 12040, p. 1-18Conference paper, Published paper (Refereed)
Abstract [en]

This work focuses on post-analysis of performance results by means of Performance Analytics. The results to be post-analysed are provided by a Stochastic Fluid Flow Model (SFFM) of Virtual Reality (VR) streaming. Performance Analytics implies using the Machine Learning (ML) algorithm M5P for constructing model trees, which we examine amongst others for asymptotic behaviours and parameter impacts in both uni- and multivariate settings. We gain valuable insights into key parameters and related thresholds of importance for good VR streaming performance. © Springer Nature Switzerland AG 2020.

Place, publisher, year, edition, pages
Springer, 2020
Keywords
Asymptotic behaviour, M5P algorithm, Machine Learning, Multivariate analysis, Stochastic Fluid Flow Model, Asymptotic analysis, Flow of fluids, Learning algorithms, Learning systems, Multivariant analysis, Stochastic models, Stochastic systems, Trees (mathematics), Virtual reality, Constructing models, Fluid flow modeling, Multi variate analysis, Parameter impacts, Post analysis, Streaming model
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:bth-19369 (URN)10.1007/978-3-030-43024-5_1 (DOI)2-s2.0-85082331489 (Scopus ID)9783030430238 (ISBN)
Conference
20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems, MMB 2020; Saarbrücken; Germany; 16 March 2020 through 18 March 2020
Funder
Knowledge Foundation, 20170056
Available from: 2020-04-09 Created: 2020-04-09 Last updated: 2020-04-09Bibliographically approved
Ammar, D., Moor, K. D., Skorin-Kapov, L., Fiedler, M. & Heegaard, P. E. (2019). Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation. In: Andersson K.,Tan H.-P.,Oteafy S. (Ed.), Proceedings - Conference on Local Computer Networks, LCN: . Paper presented at 44th Annual IEEE Conference on Local Computer Networks, LCN 2019, 14 October 2019 through 17 October 2019 (pp. 406-413). IEEE Computer Society
Open this publication in new window or tab >>Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation
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2019 (English)In: Proceedings - Conference on Local Computer Networks, LCN / [ed] Andersson K.,Tan H.-P.,Oteafy S., IEEE Computer Society , 2019, p. 406-413Conference paper, Published paper (Refereed)
Abstract [en]

We address the challenge faced by service providers in monitoring Quality of Experience (QoE) related metrics for WebRTC-based audiovisual communication services. By extracting features from various application-layer performance statistics, we explore the potential of using machine learning (ML) models to estimate perceivable quality impairments and to identify root causes. We argue that such performance-related data can be valuable and informative from a QoE assessment point of view, by allowing to identify the party/parties in a call that is/are experiencing quality impairments, and to trace the origins and causes of the problem. The paper includes case studies of multi-party videoconferencing that are established in a laboratory environment and exposed to various network disturbances and CPU limitations. Our results show that perceivable quality impairments in terms of video blockiness and audio distortions may be estimated with a high level of accuracy, thus proving the potential of exploiting ML models for automated QoE-driven monitoring and estimation of WebRTC performance. © 2019 IEEE.

Place, publisher, year, edition, pages
IEEE Computer Society, 2019
Keywords
audio distortion, machine learning, Quality of Experience (QoE), video-blockiness, WebRTC, Computer networks, Learning systems, Video conferencing, Audiovisual communication services, Blockiness, Laboratory environment, Network disturbances, Performance estimation, Performance statistics, Quality of service
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-19319 (URN)10.1109/LCN44214.2019.8990677 (DOI)2-s2.0-85080932466 (Scopus ID)9781728110288 (ISBN)
Conference
44th Annual IEEE Conference on Local Computer Networks, LCN 2019, 14 October 2019 through 17 October 2019
Available from: 2020-03-16 Created: 2020-03-16 Last updated: 2020-04-30Bibliographically approved
Fiedler, M., Chapala, U. K. & Peteti, S. (2019). Modeling instantaneous quality of experience using machine learning of model trees. In: 2019 11th International Conference on Quality of Multimedia Experience, QoMEX 2019: . Paper presented at 11th International Conference on Quality of Multimedia Experience, QoMEX, Berlin, 5 June 2019 through 7 June 2019. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Modeling instantaneous quality of experience using machine learning of model trees
2019 (English)In: 2019 11th International Conference on Quality of Multimedia Experience, QoMEX 2019, Institute of Electrical and Electronics Engineers Inc. , 2019Conference paper, Published paper (Refereed)
Abstract [en]

For service providers and operators, successful root cause analysis is essential for satisfactory service provisioning. However, reasons for sudden trend changes of the instantaneous Quality of Experience (QoE) may not always be immediately visible from underlying service- or network-level monitoring data. Thus, there is the challenge to pinpoint such moments of change in provisioning, and model the impact on instantaneous QoE, as a lead in root cause analysis. This work investigates the potential of Machine Learning (ML) of deriving time-interval-based models for instantaneous QoE ratings, obtained from a set of publicly available rating traces. In particular, the paper demonstrates the capability of the ML algorithm M5P to model trends of instantaneous QoE through model trees, consisting of piecewise linear functions over time. It is shown how and to which extent these functions can be used to estimate moments of change. Furthermore, the model trees support earlier assumptions about exponential shapes of instantaneous QoE over time as reactions to sudden changes of provisioning, such as video freezes. © 2019 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
M5P algorithm, Mean Opinion Score (MOS), Root cause analysis, Time dependency, Video freezes, Forestry, Machine learning, Multimedia systems, Piecewise linear techniques, Trees (mathematics), Mean opinion scores, Piece-wise linear functions, Quality of experience (QoE), Service provider, Service provisioning, Time interval, Quality of service
National Category
Communication Systems Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-18604 (URN)10.1109/QoMEX.2019.8743250 (DOI)000482562000035 ()2-s2.0-85068689152 (Scopus ID)9781538682128 (ISBN)
Conference
11th International Conference on Quality of Multimedia Experience, QoMEX, Berlin, 5 June 2019 through 7 June 2019
Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-13Bibliographically approved
Fiedler, M., Zepernick, H.-J. & Kelkkanen, V. (2019). Network-induced temporal disturbances in virtual reality applications. In: 2019 11th International Conference on Quality of Multimedia Experience, QoMEX 2019: . Paper presented at 11th International Conference on Quality of Multimedia Experience, QoMEX, Berlin, 5 June 2019 through 7 June 2019. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Network-induced temporal disturbances in virtual reality applications
2019 (English)In: 2019 11th International Conference on Quality of Multimedia Experience, QoMEX 2019, Institute of Electrical and Electronics Engineers Inc. , 2019Conference paper, Published paper (Refereed)
Abstract [en]

Virtual Reality (VR) applications put high demands on software and hardware in order to enable an immersive experience for the user and avoid causing simulator sickness. As soon as networks become part of the Motion-To-Photon (MTP) path between rendering and display, there is a risk for extraordinary delays that may impair Quality of Experience (QoE). This short paper provides an overview of latency measurements and models that are applicable to the MTP path, complemented by demands on user and network levels. It specifically reports on freeze duration measurements using a commercial TPCAST wireless VR solution, and identifies a corresponding stochastic model of the freeze length distribution, which may serve as disturbance model for VR QoE studies. © 2019 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Application programs, Multimedia systems, Quality of service, Stochastic systems, Virtual reality, Disturbance modeling, High demand, Latency measurements, Length distributions, Network level, Quality of experience (QoE), Simulator sickness, Software and hardwares, Stochastic models
National Category
Communication Systems Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-18605 (URN)10.1109/QoMEX.2019.8743304 (DOI)000482562000056 ()2-s2.0-85068679022 (Scopus ID)9781538682128 (ISBN)
Conference
11th International Conference on Quality of Multimedia Experience, QoMEX, Berlin, 5 June 2019 through 7 June 2019
Funder
Knowledge Foundation, 20170056
Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-13Bibliographically approved
Fiedler, M. (2019). Performance Analytics by Means of the M5P Machine Learning Algorithm. In: Proceedings of the 31st International Teletraffic Congress, ITC 2019: . Paper presented at 31st International Teletraffic Congress, ITC, Budapest, 27 August 2019 through 29 August 2019 (pp. 104-105). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Performance Analytics by Means of the M5P Machine Learning Algorithm
2019 (English)In: Proceedings of the 31st International Teletraffic Congress, ITC 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 104-105Conference paper, Published paper (Refereed)
Abstract [en]

Machine Learning (ML) has shown its capability to analyse, classify, and make predictions based on large data sets. Network performance analysis and evaluation focuses on finding and expressing qualitative, quantitative and formal relationships between performance-related parameters, with specific interest in asymptotic behaviours. This work introduces the notion of performance analytics as performance modeling with help of ML. In particular, it demonstrates the applicablibility of the ML algorithm M5P to such performance analytics, as the parameters of the generated model trees allow for identifying approximations together the applicable parameter sub-spaces in a straightforward approach. We present a set of examples with focus on post-analysis of analytically obtained performance results for asymptotic behaviour. © 2019 ITC Press.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
approximation formulae, model tree, multivariate analysis, performance evaluation, Approximation algorithms, Asymptotic analysis, Classification (of information), Forestry, Learning algorithms, Multivariant analysis, Parameter estimation, Trees (mathematics), Asymptotic behaviour, Large datasets, Model trees, Multi variate analysis, Network performance analysis, Performance Model, Machine learning
National Category
Telecommunications
Identifiers
urn:nbn:se:bth-18937 (URN)10.1109/ITC31.2019.00023 (DOI)2-s2.0-85074777929 (Scopus ID)9780988304574 (ISBN)
Conference
31st International Teletraffic Congress, ITC, Budapest, 27 August 2019 through 29 August 2019
Note

Funding details: d-nr 2014-0032; Funding text 1: ACKNOWLEDGEMENTS The co-funding of the “BigData@BTH” project by the Swedish KKS Foundation (d-nr 2014-0032) is gratefully acknowledged.

Available from: 2019-11-21 Created: 2019-11-21 Last updated: 2019-11-21Bibliographically approved
Ickin, S., Vandikas, K. & Fiedler, M. (2019). Privacy Preserving QoE Modeling using Collaborative Learning. In: INTERNET-QOE'19: PROCEEDINGS OF THE 4TH INTERNET-QOE WORKSHOP: QOE-BASED ANALYSIS AND MANAGEMENT OF DATA COMMUNICATION NETWORKS: . Paper presented at 4th Workshop on QoE-Based Analysis and Management of Data Communication Networks, Internet-QoE 2019, co-located with MobiCom 2019, Los Cabos; Mexico, 21 October (pp. 13-18). Association for Computing Machinery
Open this publication in new window or tab >>Privacy Preserving QoE Modeling using Collaborative Learning
2019 (English)In: INTERNET-QOE'19: PROCEEDINGS OF THE 4TH INTERNET-QOE WORKSHOP: QOE-BASED ANALYSIS AND MANAGEMENT OF DATA COMMUNICATION NETWORKS, Association for Computing Machinery , 2019, p. 13-18Conference paper, Published paper (Refereed)
Abstract [en]

Machine Learning (ML) based Quality of Experience (QoE) models potentially suffer from over-fitting due to limitations including low data volume, and limited participant profiles. This prevents models from becoming generic. Consequently, these trained models may under-perform when tested outside the experimented population. One reason for the limited datasets, which we refer in this paper as small QoE data lakes, is due to the fact that often these datasets potentially contain user sensitive information and are only collected throughout expensive user studies with special user consent. Thus, sharing of datasets amongst researchers is often not allowed. In recent years, privacy preserving machine learning models have become important and so have techniques that enable model training without sharing datasets but instead relying on secure communication protocols. Following this trend, in this paper, we present Round-Robin based Collaborative Machine Learning model training, where the model is trained in a sequential manner amongst the collaborated partner nodes. We benchmark this work using our customized Federated Learning mechanism as well as conventional Centralized and Isolated Learning methods. © 2019 Association for Computing Machinery.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2019
Keywords
Distributed Learning, Federated Learning, QoE, Convolutional codes, Data communication systems, Data privacy, Machine learning, Collaborative learning, Learning mechanism, Machine learning models, Quality of experience (QoE), Sensitive informations, Sequential manners, Quality of service
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18936 (URN)10.1145/3349611.3355548 (DOI)000518379400003 ()2-s2.0-85074785270 (Scopus ID)9781450369275 (ISBN)
Conference
4th Workshop on QoE-Based Analysis and Management of Data Communication Networks, Internet-QoE 2019, co-located with MobiCom 2019, Los Cabos; Mexico, 21 October
Available from: 2019-11-21 Created: 2019-11-21 Last updated: 2020-04-02Bibliographically approved
Minhas, T. N., Nawaz, O., Fiedler, M. & Khatibi, S. (2019). The Effects of Additional Factors on Subjective Quality Assessments. In: 2nd International Conference on Advancements in Computational Sciences, ICACS 2019: . Paper presented at 2nd International Conference on Advancements in Computational Sciences, ICACS, Lahore, 18 February 2019 through 20 February 2019 (pp. 120-124). Institute of Electrical and Electronics Engineers Inc., Article ID 8689138.
Open this publication in new window or tab >>The Effects of Additional Factors on Subjective Quality Assessments
2019 (English)In: 2nd International Conference on Advancements in Computational Sciences, ICACS 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 120-124, article id 8689138Conference paper, Published paper (Refereed)
Abstract [en]

In Quality of Experience, the users' degrees of delight or annoyance are quantized by direct human interaction via subjective quality assessments. There are many factors that may influence the users' responses, and standards have been laid down to strictly control the monitoring conditions. In this paper, we analyze limitations of Mean Opinion Score (MOS) assessment to portray the influence of additional factors on user behavior while assessing multimedia content. We show that the frequency of watching online video content as well as the user delight with the content play a significant role in her final feedback. Our study emphasizes the need to use additional metrics along with MOS for content ratings to obtain a more accurate measure of the user experience. © 2019 The University of Lahore.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Multimedia communication, Quality of Experience, User Perception, Multimedia systems, Quality of service, Content ratings, Human interactions, Mean opinion scores, Multi-media communications, Multimedia contents, Quality of experience (QoE), Subjective quality assessments, User perceptions, Behavioral research
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-17906 (URN)10.23919/ICACS.2019.8689138 (DOI)000470736000018 ()2-s2.0-85065105387 (Scopus ID)9789699721014 (ISBN)
Conference
2nd International Conference on Advancements in Computational Sciences, ICACS, Lahore, 18 February 2019 through 20 February 2019
Available from: 2019-05-21 Created: 2019-05-21 Last updated: 2019-06-27Bibliographically approved
Fiedler, M., Moller, S., Reichl, P. & Xie, M. (2018). A Glance at the Dagstuhl Manifesto 'QoE Vadis?'. In: 2018 10th International Conference on Quality of Multimedia Experience, QoMEX 2018: . Paper presented at 10th International Conference on Quality of Multimedia Experience, QoMEX 2018, 29 May 2018 through 1 June 2018, Sardinia, Italy. Institute of Electrical and Electronics Engineers Inc., Article ID 8463374.
Open this publication in new window or tab >>A Glance at the Dagstuhl Manifesto 'QoE Vadis?'
2018 (English)In: 2018 10th International Conference on Quality of Multimedia Experience, QoMEX 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, article id 8463374Conference paper, Published paper (Refereed)
Abstract [en]

This short paper presents the recently published Dagstuhl Manifesto 'QoE Vadis?'. The Manifesto is the result of a set of three Dagstuhl Seminars and one Dagstuhl Perspectives Workshop, aimed at shaping understanding, development and application of the Quality of Experience (QoE) notion and concept. Its task is to convey the current status, promising developments and future projections for different stakeholders. The latter are summarised in a set of eleven recommendations to academia, industry and funding organisations. © 2018 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
Business Aspects, Multimedia, Quality Feedback, Quality Management, Quality of Experience, Recommendations, Socioeconomic Aspects, User Experience
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-17147 (URN)10.1109/QoMEX.2018.8463374 (DOI)2-s2.0-85054407120 (Scopus ID)9781538626054 (ISBN)
Conference
10th International Conference on Quality of Multimedia Experience, QoMEX 2018, 29 May 2018 through 1 June 2018, Sardinia, Italy
Available from: 2018-10-19 Created: 2018-10-19 Last updated: 2018-10-19Bibliographically approved
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