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Saguna, Saguna
Publications (10 of 25) Show all publications
Palm, E., Mitra, K., Saguna, S. & Åhlund, C. (2017). A Bayesian system for cloud performance diagnosis and prediction. In: Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom: . Paper presented at 8th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2016, Luxembourg, 12-15 December 2016 (pp. 371-374). Piscataway, NJ: IEEE Computer Society, Article ID 7830706.
Open this publication in new window or tab >>A Bayesian system for cloud performance diagnosis and prediction
2017 (English)In: Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, Piscataway, NJ: IEEE Computer Society, 2017, 371-374 p., 7830706Conference paper, Published paper (Refereed)
Abstract [en]

The stochastic nature of the cloud systems makes cloud quality of service (QoS) performance diagnosis and prediction a challenging task. A plethora of factors including virtual machine types, data centre regions, CPU types, time-of-the-day, and day-of-the-week contribute to the variability of the cloud QoS. The state-of-the-art methods for cloud performance diagnosis do not capture and model complex and uncertain inter-dependencies between these factors for efficient cloud QoS diagnosis and prediction. This paper presents ALPINE, a proof-of-concept system based on Bayesian networks. Using a real-life dataset, we demonstrate that ALPINE can be utilised for efficient cloud QoS diagnosis and prediction under stochastic cloud conditions

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Computer Society, 2017
Series
International Conference on Cloud Computing Technology and Science, ISSN 2330-2194
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-62202 (URN)10.1109/CloudCom.2016.0065 (DOI)000398536300049 ()2-s2.0-85013025438 (Scopus ID)9781509014453 (ISBN)
Conference
8th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2016, Luxembourg, 12-15 December 2016
Available from: 2017-02-28 Created: 2017-02-28 Last updated: 2017-11-24Bibliographically approved
Mitra, K., Saguna, S., Åhlund, C. & Ranjan, R. (2017). ALPINE: A Bayesian System For Cloud Performance Diagnosis And Prediction. In: 2017 IEEE International Conference on Services Computing (SCC): . Paper presented at 14th IEEE International Conference on Services Computing (SCC), Honolulu, HI, USA, 25-30 June 2017 (pp. 281-288). Piscataway, NJ: IEEE, Article ID 8034996.
Open this publication in new window or tab >>ALPINE: A Bayesian System For Cloud Performance Diagnosis And Prediction
2017 (English)In: 2017 IEEE International Conference on Services Computing (SCC), Piscataway, NJ: IEEE, 2017, 281-288 p., 8034996Conference paper, Published paper (Refereed)
Abstract [en]

Cloud performance diagnosis and prediction is a challenging problem due to the stochastic nature of the cloud systems. Cloud performance is affected by a large set of factors such as virtual machine types, regions, workloads, wide area network delay and bandwidth. Therefore, necessitating the determination of complex relationships between these factors. The current research in this area does not address the challenge of modeling the uncertain and complex relationships between these factors. Further, the challenge of cloud performance prediction under uncertainty has not garnered sufficient attention. This paper proposes, develops and validates ALPINE, a Bayesian system for cloud performance diagnosis and prediction. ALPINE incorporates Bayesian networks to model uncertain and complex relationships between several factors mentioned above. It handles missing, scarce and sparse data to diagnose and predict stochastic cloud performance efficiently. We validate our proposed system using extensive real data and show that it predicts cloud performance with high accuracy of 91.93%.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2017
Keyword
Bayesian Network, Cloud Computing, Diagnosis, Quality of Service, Prediction
National Category
Computer and Information Science Computer Science Other Computer and Information Science Computer Systems
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-64458 (URN)10.1109/SCC.2017.43 (DOI)2-s2.0-85032332116 (Scopus ID)978-1-5386-2005-2 (ISBN)
Conference
14th IEEE International Conference on Services Computing (SCC), Honolulu, HI, USA, 25-30 June 2017
Available from: 2017-06-25 Created: 2017-06-25 Last updated: 2017-11-24Bibliographically approved
Belyakhina, T., Zaslavsky, A., Mitra, K., Saguna, S. & Jayaraman, P. P. (2017). DisCPAQ: Distributed Context Acquisition and Reasoning for Personalized Indoor Air Quality Monitoring in IoT-Based Systems. In: Galinina O., Andreev S., Balandin S., Koucheryavy Y. (Ed.), Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 17th International Conference, NEW2AN 2017, 10th Conference, ruSMART 2017, Third Workshop NsCC 2017, St. Petersburg, Russia, August 28–30, 2017, Proceedings. Paper presented at 17th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2017, 10th Conference on Internet of Things and Smart Spaces, ruSMART 2017 and 3rd International Workshop on Nano-scale Computing and Communications, NsCC 2017, St. Petersburg, Russia, August 28–30, 2017 (pp. 75-86). Cham: Springer.
Open this publication in new window or tab >>DisCPAQ: Distributed Context Acquisition and Reasoning for Personalized Indoor Air Quality Monitoring in IoT-Based Systems
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2017 (English)In: Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 17th International Conference, NEW2AN 2017, 10th Conference, ruSMART 2017, Third Workshop NsCC 2017, St. Petersburg, Russia, August 28–30, 2017, Proceedings / [ed] Galinina O., Andreev S., Balandin S., Koucheryavy Y., Cham: Springer, 2017, 75-86 p.Conference paper, Published paper (Refereed)
Abstract [en]

The rapidly emerging Internet of Things supports many diverse applications including environmental monitoring. Air quality, both indoors and outdoors, proved to be a significant comfort and health factor for people. This paper proposes a smart context-aware system for indoor air quality monitoring and prediction called DisCPAQ. The system uses data streams from air quality measurement sensors to provide real-time personalised air quality service to users through a mobile app. The proposed system is agnostic to sensor infrastructure. The paper proposes a context model based on Context Spaces Theory, presents the architecture of the system and identifies challenges in developing large scale IoT applications. DisCPAQ implementation, evaluation and lessons learned are all discussed in the paper.

Place, publisher, year, edition, pages
Cham: Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10531
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-66097 (URN)10.1007/978-3-319-67380-6_7 (DOI)2-s2.0-85031425522 (Scopus ID)978-3-319-67379-0 (ISBN)978-3-319-67380-6 (ISBN)
Conference
17th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2017, 10th Conference on Internet of Things and Smart Spaces, ruSMART 2017 and 3rd International Workshop on Nano-scale Computing and Communications, NsCC 2017, St. Petersburg, Russia, August 28–30, 2017
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2017-11-24Bibliographically approved
Zhalgasbekova, A., Zaslavsky, A., Mitra, K., Saguna, S. & Jayaraman, P. P. (2017). Opportunistic Data Collection for IoT-Based Indoor Air Quality Monitoring. In: Galinina O., Andreev S., Balandin S., Koucheryavy Y. (Ed.), Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 17th International Conference, NEW2AN 2017, 10th Conference, ruSMART 2017, Third Workshop NsCC 2017, St. Petersburg, Russia, August 28–30, 2017, Proceedings. Paper presented at 17th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2017, 10th Conference on Internet of Things and Smart Spaces, ruSMART 2017 and 3rd International Workshop on Nano-scale Computing and Communications, NsCC 2017, St. Petersburg, Russia, August 28–30, 2017 (pp. 53-65). Cham: Springer.
Open this publication in new window or tab >>Opportunistic Data Collection for IoT-Based Indoor Air Quality Monitoring
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2017 (English)In: Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 17th International Conference, NEW2AN 2017, 10th Conference, ruSMART 2017, Third Workshop NsCC 2017, St. Petersburg, Russia, August 28–30, 2017, Proceedings / [ed] Galinina O., Andreev S., Balandin S., Koucheryavy Y., Cham: Springer, 2017, 53-65 p.Conference paper, Published paper (Refereed)
Abstract [en]

Opportunistic sensing advance methods of IoT data collection using the mobility of data mules, the proximity of transmitting sensor devices and cost efficiency to decide when, where, how and at what cost collect IoT data and deliver it to a sink. This paper proposes, develops, implements and evaluates the algorithm called CollMule which builds on and extends the 3D kNN approach to discover, negotiate, collect and deliver the sensed data in an energy- and cost-efficient manner. The developed CollMule software prototype uses Android platform to handle indoor air quality data from heterogeneous IoT devices. The CollMule evaluation is based on performing rate, power consumption and CPU usage of single algorithm cycle. The outcomes of these experiments prove the feasibility of CollMule use on mobile smart devices.

Place, publisher, year, edition, pages
Cham: Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10531
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-66098 (URN)10.1007/978-3-319-67380-6_5 (DOI)2-s2.0-85031411967 (Scopus ID)978-3-319-67379-0 (ISBN)978-3-319-67380-6 (ISBN)
Conference
17th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2017, 10th Conference on Internet of Things and Smart Spaces, ruSMART 2017 and 3rd International Workshop on Nano-scale Computing and Communications, NsCC 2017, St. Petersburg, Russia, August 28–30, 2017
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2017-11-24Bibliographically approved
Idowu, S., Saguna, S., Åhlund, C. & Schelén, O. (2016). Applied Machine Learning: Forecasting Heat Load in District Heating System. Energy and Buildings, 133, 478-488.
Open this publication in new window or tab >>Applied Machine Learning: Forecasting Heat Load in District Heating System
2016 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 133, 478-488 p.Article in journal (Refereed) Published
Abstract [en]

Forecasting energy consumption in buildings is a key step towards the realization of optimized energy production, distribution and consumption. This paper presents a data driven approach for analysis and forecast of aggregate space and water thermal load in buildings. The analysis and the forecast models are built using district heating data unobtrusively collected from ten residential and commercial buildings located in Skellefteå, Sweden. The load forecast models are generated using supervised machine learning techniques, namely, support vector machine, regression tree, feed forward neural network, and multiple linear regression. The model takes the outdoor temperature, historical values of heat load, time factor variables and physical parameters of district heating substations as its input. A performance comparison among the machine learning methods and identification of the importance of models input variables is carried out. The models are evaluated with varying forecast horizons of every hour from 1 up to 48 hours. Our results show that support vector machine, feed forward neural network and multiple linear regression are more suitable machine learning methods with lower performance errors than the regression tree. Support vector machine has the least normalized root mean square error of 0.07 for a forecast horizon of 24 hour.

National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-59596 (URN)10.1016/j.enbuild.2016.09.068 (DOI)000389087300045 ()2-s2.0-84992362157 (Scopus ID)
Note

Validerad; 2016; Nivå 2; 2016-11-08 (andbra)

Available from: 2016-10-10 Created: 2016-10-10 Last updated: 2017-11-30Bibliographically approved
Nanda, R., Saguna, S., Mitra, K. & Åhlund, C. (2016). BayesForSG:: A Bayesian Model for Forecasting Thermal Load in Smart Grids (ed.). In: (Ed.), (Ed.), SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing. Paper presented at ACM Symposium on Applied Computing : 04/04/2016 - 07/04/2016 (pp. 2135-2141). New York: ACM Digital Library.
Open this publication in new window or tab >>BayesForSG:: A Bayesian Model for Forecasting Thermal Load in Smart Grids
2016 (English)In: SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing, New York: ACM Digital Library, 2016, 2135-2141 p.Conference paper, Published paper (Refereed)
Abstract [en]

Forecasting the thermal load demand for residential buildings assists in optimizing energy production and developing demand response strategies in a smart grid system. However, the presence of a large number of factors such as outdoor temperature, district heating operational parameters, building characteristics and occupant behavior, make thermal load forecasting a challenging task. This paper presents an efficient model for thermal load forecast in buildings with different variations of heat load consumption across both winter and spring seasons using a Bayesian Network. The model has been validated by utilizing the realistic district heating data of three residential buildings from the district heating grid of the city of Skellefteå, Sweden over a period of four months. The results from our model shows that the current heat load consumption and outdoor temperature forecast have the most influence on the heat load forecast. Further, our model outperforms state-of-the-art methods for heat load forecasting by achieving a higher average accuracy of 77.97% by utilizing only 10% of the training data for a forecast horizon of 1 hour.

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2016
Keyword
Smart Grid, smart city, District Heating System, bayesian network, machine learning, forecasting, Information technology - Computer science, Informationsteknik - Datorvetenskap
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-30238 (URN)10.1145/2851613.2853127 (DOI)400b9534-955c-47d1-812d-fe87e9b05c73 (Local ID)978-1-4503-3739-7 (ISBN)400b9534-955c-47d1-812d-fe87e9b05c73 (Archive number)400b9534-955c-47d1-812d-fe87e9b05c73 (OAI)
Conference
ACM Symposium on Applied Computing : 04/04/2016 - 07/04/2016
Note
Godkänd; 2016; 20151203 (karan)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved
Ngo, K., Saguna, S., Mitra, K. & Åhlund, C. (2016). IReHMo: An efficient IoT-Based Remote health Monitoring System for Smart Regions (ed.). In: (Ed.), 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015: Boston, United States, 13 - 17 October 2015. Paper presented at International Conference on E-health Networking, Applications & Services : IEEE HealthCom 2015 14/10/2015 - 17/10/2015 (pp. 563-568). Piscataway, NJ: IEEE Communications Society, Article ID 7454565.
Open this publication in new window or tab >>IReHMo: An efficient IoT-Based Remote health Monitoring System for Smart Regions
2016 (English)In: 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015: Boston, United States, 13 - 17 October 2015, Piscataway, NJ: IEEE Communications Society, 2016, 563-568 p., 7454565Conference paper, Published paper (Refereed)
Abstract [en]

The ageing population worldwide is constantly rising, both in urban and regional areas. There is a need for IoT-based remote health monitoring systems that take care of the health of elderly people without compromising their convenience and preference of staying at home. However, such systems may generate large amounts of data. The key research challenge addressed in this paper is to efficiently transmit healthcare data within the limit of the existing network infrastructure, especially in remote areas. In this paper, we identified the key network requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, we studied the network communication protocols such as CoAP, MQTT and HTTP to understand the needs of such a system, in particular the bandwidth requirements and the volume of generated data. Subsequently, we have proposed IReHMo - an IoT-based remote health monitoring architecture that efficiently delivers healthcare data to the servers. The CoAP-based IReHMo implementation helps to reduce up to 90% volume of generated data for a single sensor event and up to 56% required bandwidth for a healthcare scenario. Finally, we conducted a scalability analysis to determine the feasibility of deploying IReHMo in large numbers in regions of north Sweden.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2016
Keyword
IoT, Cloud Computing, remote health monitoring, sensor, coap, perfomance, pervasive computing, smart city, smart region, Information technology - Computer engineering, Informationsteknik - Datorteknik
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-32413 (URN)10.1109/HealthCom.2015.7454565 (DOI)6e8414cf-1df1-4450-ab2f-bd84f0a140cb (Local ID)978-1-4673-8325-7 (ISBN)6e8414cf-1df1-4450-ab2f-bd84f0a140cb (Archive number)6e8414cf-1df1-4450-ab2f-bd84f0a140cb (OAI)
Conference
International Conference on E-health Networking, Applications & Services : IEEE HealthCom 2015 14/10/2015 - 17/10/2015
Note

Validerad; 2016; Nivå 1; 20150917 (karan)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved
Shah, T., Ali, Y., Mitra, K., Saguna, S., Jayaraman, P. P., Rabhi, F. & Ranjan, R. (2016). Remote health care cyber-physical system: quality of service (QoS) challenges and opportunities. IET Cyber-Physical Systems: Theory & Applications, 1(1), 40-48.
Open this publication in new window or tab >>Remote health care cyber-physical system: quality of service (QoS) challenges and opportunities
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2016 (English)In: IET Cyber-Physical Systems: Theory & Applications, ISSN 2398-3396, Vol. 1, no 1, 40-48 p.Article in journal (Refereed) Published
Abstract [en]

There is a growing emphasis to find alternative non-traditional ways to manage patients to ease the burden on health care services largely fuelled by a growing demand from sections of population that is ageing. In-home remote patient monitoring applications harnessing technological advancements in the area of Internet of things (IoT), semantic web, data analytics, and cloud computing have emerged as viable alternatives. However, such applications generate large amounts of real-time data in terms of volume, velocity, and variety thus making it a big data problem. Hence, the challenge is how to combine and analyse such data with historical patient data to obtain meaningful diagnoses suggestions within acceptable time frames (considering quality of service (QoS)). Despite the evolution of big data processing technologies (e.g. Hadoop) and scalable infrastructure (e.g. clouds), there remains a significant gap in the areas of heterogeneous data collection, real-time patient monitoring, and automated decision support (semantic reasoning) based on well-defined QoS constraints. In this study, the authors review the state-of-the-art in enabling QoS for remote health care applications. In particular, they investigate the QoS challenges required to meet the analysis and inferencing needs of such applications and to overcome the limitations of existing big data processing tools.

Place, publisher, year, edition, pages
IET, 2016
Keyword
cloud computing, cyber physical system, quality of service, remote health care, health care
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-61029 (URN)10.1049/iet-cps.2016.0023 (DOI)
Available from: 2016-12-12 Created: 2016-12-12 Last updated: 2017-11-24Bibliographically approved
Shah, T., Yavari, A., Mitra, K., Saguna, S., Jayaraman, P. P., Rabhi, F. & Ranjan, R. (2016). Remote Healthcare Big Data Processing in Real-Time: Quality of Service Challenges and Opportunities (ed.). In: (Ed.), (Ed.), Handbook on Big Data Technologies: . Paper presented at . : Springer-Verlag GmbH.
Open this publication in new window or tab >>Remote Healthcare Big Data Processing in Real-Time: Quality of Service Challenges and Opportunities
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2016 (English)In: Handbook on Big Data Technologies, Springer-Verlag GmbH , 2016Chapter in book (Refereed)
Place, publisher, year, edition, pages
Springer-Verlag GmbH, 2016
Keyword
Big Data, Remote Healthcare, Cloud Computing, Internet of Things, IoT, Information technology - Computer science, Informationsteknik - Datorvetenskap
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-20975 (URN)9e3b327b-7860-44e5-b056-87b7e527e25b (Local ID)9e3b327b-7860-44e5-b056-87b7e527e25b (Archive number)9e3b327b-7860-44e5-b056-87b7e527e25b (OAI)
Note
Upprättat; 2016; 20160624 (karan)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24
Louis, B., Mitra, K., Saguna, S. & Åhlund, C. (2015). CloudSimDisk: Energy-Aware Storage Simulation in CloudSim (ed.). In: (Ed.), (Ed.), 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC): Limassol, 7-10 Dec. 2015. Paper presented at IEEE/ACM International Conference on Utility and Cloud Computing : 07/12/2015 - 07/12/2015 (pp. 11-15). Piscataway, NJ: IEEE Communications Society, Article ID 7431390.
Open this publication in new window or tab >>CloudSimDisk: Energy-Aware Storage Simulation in CloudSim
2015 (English)In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC): Limassol, 7-10 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, 11-15 p., 7431390Conference paper, Published paper (Refereed)
Abstract [en]

The cloud computing paradigm is continually evolving, and with it, the size and the complexity of its infrastructure. Assessing the performance of a cloud environment is an essential but an arduous task. Further, the energy consumed by data centers is steadily increasing and major components such as the storage systems need to be more energy efficient. Cloud simulation tools have proved quite useful to study these issues. However, these simulation tools lack mechanisms to study energy efficient storage in cloud systems. This paper contributes in the area of cloud computing by extending the widely used cloud simulator CloudSim. In this paper, we propose CloudSimDisk, a scalable module for modeling and simulation of energy-aware storage in cloud systems. We show how CloudSimDisk can be used to simulate energy-aware storage, and can be extended to study new algorithms for energy-awareness in cloud systems. Our simulation results proved to be in accordance with the analytical models that were developed to model energy consumption of hard disk drives in cloud systems. The source code of CloudSimDisk is also made available for the research community for further testing and development.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015
Keyword
Cloud Computing, simulation, Energy efficiency, Storage, cloudsim, Information technology - Computer engineering, Informationsteknik - Datorteknik
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-32408 (URN)10.1109/UCC.2015.15 (DOI)6e79bb29-f127-4b5a-acf0-21cd894f562d (Local ID)9780769556970 (ISBN)6e79bb29-f127-4b5a-acf0-21cd894f562d (Archive number)6e79bb29-f127-4b5a-acf0-21cd894f562d (OAI)
Conference
IEEE/ACM International Conference on Utility and Cloud Computing : 07/12/2015 - 07/12/2015
Note
Validerad; 2016; Nivå 1; 20150919 (karan)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved
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