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Pllana, Sabri
Publications (10 of 74) Show all publications
Viebke, A., Memeti, S., Pllana, S. & Abraham, A. (2019). CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi. Journal of Supercomputing, 75(1), 197-227
Open this publication in new window or tab >>CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi
2019 (English)In: Journal of Supercomputing, ISSN 0920-8542, E-ISSN 1573-0484, Vol. 75, no 1, p. 197-227Article in journal (Refereed) Published
Abstract [en]

Deep learning is an important component of big-data analytic tools and intelligent applications, such as, self-driving cars, computer vision, speech recognition, or precision medicine. However, the training process is computationally intensive, and often requires a large amount of time if performed sequentially. Modern parallel computing systems provide the capability to reduce the required training time of deep neural networks.In this paper, we present our parallelization scheme for training convolutional neural networks (CNN) named Controlled Hogwild with Arbitrary Order of Synchronization (CHAOS). Major features of CHAOS include the support for thread and vector parallelism, non-instant updates of weight parameters during back-propagation without a significant delay, and implicit synchronization in arbitrary order. CHAOS is tailored for parallel computing systems that are accelerated with the Intel Xeon Phi. We evaluate our parallelization approach empirically using measurement techniques and performance modeling for various numbers of threads and CNN architectures. Experimental results for the MNIST dataset of handwritten digits using the total number of threads on the Xeon Phi show speedups of up to 103x compared to the execution on one thread of the Xeon Phi, 14x compared to the sequential execution on Intel Xeon E5, and 58x compared to the sequential execution on Intel Core i5.

Place, publisher, year, edition, pages
Springer, 2019
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-60938 (URN)10.1007/s11227-017-1994-x (DOI)000456629400014 ()
Available from: 2017-02-25 Created: 2017-02-25 Last updated: 2019-02-07Bibliographically approved
Alsouda, Y., Pllana, S. & Kurti, A. (2019). IoT-based Urban Noise Identification Using Machine Learning: Performance of SVM, KNN, Bagging, and Random Forest. In: Proceedings of the International Conference on Omni-Layer Intelligent Systems (COINS '19): . Paper presented at International Conference on Omni-Layer Intelligent Systems (COINS '19), Crete, Greece — May 05 - 07, 2019 (pp. 62-67). New York: ACM Publications
Open this publication in new window or tab >>IoT-based Urban Noise Identification Using Machine Learning: Performance of SVM, KNN, Bagging, and Random Forest
2019 (English)In: Proceedings of the International Conference on Omni-Layer Intelligent Systems (COINS '19), New York: ACM Publications, 2019, p. 62-67Conference paper, Published paper (Refereed)
Abstract [en]

Noise is any undesired environmental sound. A sound at the same dB level may be perceived as annoying noise or as pleasant music. Therefore, it is necessary to go beyond the state-of-the-art approaches that measure only the dB level and also identify the type of noise. In this paper, we present a machine learning based method for urban noise identification using an inexpensive IoT unit. We use Mel-frequency cepstral coefficients for audio feature extraction and supervised classification algorithms (that is, support vector machine, k-nearest neighbors, bootstrap aggregation, and random forest) for noise classification. We evaluate our approach experimentally with a data-set of about 3000 sound samples grouped in eight sound classes (such as car horn, jackhammer, or street music). We explore the parameter space of the four algorithms to estimate the optimal parameter values for classification of sound samples in the data-set under study. We achieve a noise classification accuracy in the range 88% - 94%.

Place, publisher, year, edition, pages
New York: ACM Publications, 2019
Keywords
bootstrap aggregation (Bagging), internet of things (IoT), k-nearest neighbors (KNN), mel-frequency cepstral coefficients (MFCC), random forest, smart cities, support vector machine (SVM), urban noise
National Category
Computer Systems
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-81767 (URN)10.1145/3312614.3312631 (DOI)978-1-4503-6640-3 (ISBN)
Conference
International Conference on Omni-Layer Intelligent Systems (COINS '19), Crete, Greece — May 05 - 07, 2019
Funder
Knowledge Foundation, 20150088, 20150259
Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-04-16Bibliographically approved
Memeti, S., Pllana, S., Ferati, M., Kurti, A. & Jusufi, I. (2019). IoTutor: How Cognitive Computing Can Be Applied to Internet of Things Education. In: Leon Strous and Vinton G. Cerf (Ed.), : . Paper presented at IFIPIoT 2018 (pp. 1-16). Springer
Open this publication in new window or tab >>IoTutor: How Cognitive Computing Can Be Applied to Internet of Things Education
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We present IoTutor that is a cognitive computing solution for education of students in the IoT domain. We implement the IoTutor as a platform-independent web-based application that is able to interact with users via text or speech using natural language. We train the IoTutor with selected scientific publications relevant to the IoT education. To investigate users' experience with the IoTutor, we ask a group of students taking an IoT master level course at the Linnaeus University to use the IoTutor for a period of two weeks. We ask students to express their opinions with respect to the attractiveness, perspicuity, efficiency, stimulation, and novelty of the IoTutor. The evaluation results show a trend that students express an overall positive attitude towards the IoTutor with majority of the aspects rated higher than the neutral value.

Place, publisher, year, edition, pages
Springer, 2019
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238 ; 548
Keywords
Internet of Things (IoT), education, cognitive computing, IBM Watson
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-80835 (URN)10.1007/978-3-030-15651-0_18 (DOI)978-3-030-15651-0 (ISBN)978-3-030-15650-3 (ISBN)
Conference
IFIPIoT 2018
Funder
Knowledge Foundation, 20150088, 20150259
Available from: 2019-02-26 Created: 2019-02-26 Last updated: 2019-03-29Bibliographically approved
Vitabile, S., Marks, M., Stojanovic, D., Pllana, S., Molina, J., Krzyszton, M., . . . Salomie, I. (2019). Medical Data Processing and Analysis for Remote Health and Activities Monitoring. In: Joanna Kołodziej, Horacio González-Vélez (Ed.), High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet (pp. 186-220). Springer
Open this publication in new window or tab >>Medical Data Processing and Analysis for Remote Health and Activities Monitoring
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2019 (English)In: High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet / [ed] Joanna Kołodziej, Horacio González-Vélez, Springer, 2019, p. 186-220Chapter in book (Refereed)
Abstract [en]

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human’s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11400
Keywords
e-Health, Internet of Things (IoT), Remote health monitoring, Pervasive healthcare (PH)
National Category
Computer Systems
Identifiers
urn:nbn:se:lnu:diva-81344 (URN)10.1007/978-3-030-16272-6_7 (DOI)978-3-030-16271-9 (ISBN)978-3-030-16272-6 (ISBN)
Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-04-10Bibliographically approved
Achilleos, A., Mettouris, C., Yeratziotis, A., Papadopoulos, G., Pllana, S., Huber, F., . . . Dinnyés, A. (2019). SciChallenge: A Social Media Aware Platform for Contest-Based STEM Education and Motivation of Young Students. IEEE Transactions on Learning Technologies, 12(1), 98-111
Open this publication in new window or tab >>SciChallenge: A Social Media Aware Platform for Contest-Based STEM Education and Motivation of Young Students
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2019 (English)In: IEEE Transactions on Learning Technologies, ISSN 1939-1382, E-ISSN 1939-1382, Vol. 12, no 1, p. 98-111Article in journal (Refereed) Published
Abstract [en]

Scientific and technological innovations have become increasingly important as we face the benefits and challenges of both globalization and a knowledge-based economy. Still, enrolment rates in STEM degrees are low in many European countries and consequently there is a lack of adequately educated workforce in industries. We believe that this can be mainly attributed to pedagogical issues, such as the lack of engaging hands-on activities utilized for science and math education in middle and high schools. In this paper, we report our work in the SciChallenge European project, which aims at increasing the interest of pre-university students in STEM disciplines, through its distinguishing feature, the systematic use of social media for providing and evaluation of the student-generated content. A social media-aware contest and platform were thus developed and tested in a pan-European contest that attracted >700 participants. The statistical analysis and results revealed that the platform and contest positively influenced participants STEM learning and motivation, while only the gender factor for the younger study group appeared to affect the outcomes (confidence level – p<.05).

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Communication Systems Software Engineering Pedagogy
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-71232 (URN)10.1109/TLT.2018.2810879 (DOI)
Projects
SciChallenge, EU H2020, Grant Agreement No 665868
Funder
EU, Horizon 2020, 665868
Available from: 2018-03-02 Created: 2018-03-02 Last updated: 2019-04-17Bibliographically approved
Spolaor, S., Gribaudo, M., Iacono, M., Kadavy, T., Oplatková, Z., Mauri, G., . . . Nobile, M. (2019). Towards Human Cell Simulation. In: Joanna Kołodziej, Horacio González-Vélez (Ed.), High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet (pp. 221-249). Springer
Open this publication in new window or tab >>Towards Human Cell Simulation
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2019 (English)In: High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet / [ed] Joanna Kołodziej, Horacio González-Vélez, Springer, 2019, p. 221-249Chapter in book (Refereed)
Abstract [en]

The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extreme computational effort required. In this context, model reduction techniques and High-Performance Computing infrastructures could be leveraged to mitigate these issues. In addition, as cellular processes are characterized by multiple scales of temporal and spatial organization, novel hybrid simulators able to harmonize different modeling approaches (e.g., logic-based, constraint-based, continuous deterministic, discrete stochastic, spatial) should be designed. This chapter describes a putative unified approach to tackle these challenging tasks, hopefully paving the way to the definition of large-scale comprehensive models that aim at the comprehension of the cell behavior by means of computational tools.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11400
Keywords
Agent-based simulation, Big data, Biochemical simulation, Computational intelligence, Constraint-based modeling, Fuzzy logic, High-performance computing, Model reduction, Multi-scale modeling, Parameter estimation, Reaction-based modeling, Systems biology
National Category
Computer Systems
Identifiers
urn:nbn:se:lnu:diva-81345 (URN)10.1007/978-3-030-16272-6_8 (DOI)978-3-030-16271-9 (ISBN)978-3-030-16272-6 (ISBN)
Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-04-10Bibliographically approved
Memeti, S. & Pllana, S. (2018). A machine learning approach for accelerating DNA sequence analysis. The international journal of high performance computing applications, 32(3), 363-379
Open this publication in new window or tab >>A machine learning approach for accelerating DNA sequence analysis
2018 (English)In: The international journal of high performance computing applications, ISSN 1094-3420, E-ISSN 1741-2846, Vol. 32, no 3, p. 363-379Article in journal (Refereed) Published
Abstract [en]

The DNA sequence analysis is a data and computationally intensive problem and therefore demands suitable parallel computing resources and algorithms. In this paper, we describe an optimized approach for DNA sequence analysis on a heterogeneous platform that is accelerated with the Intel Xeon Phi. Such platforms commonly comprise one or two general purpose host central processing units (CPUs) and one or more Xeon Phi devices. We present a parallel algorithm that shares the work of DNA sequence analysis between the host CPUs and the Xeon Phi device to reduce the overall analysis time. For automatic worksharing we use a supervised machine learning approach, which predicts the performance of DNA sequence analysis on the host and device and accordingly maps fractions of the DNA sequence to the host and device. We evaluate our approach empirically using real-world DNA segments for human and various animals on a heterogeneous platform that comprises two 12-core Intel Xeon E5 CPUs and an Intel Xeon Phi 7120P device with 61 cores.

Place, publisher, year, edition, pages
Sage Publications, 2018
Keywords
DNA sequence analysis, machine learning, heterogeneous parallel computing
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-54385 (URN)10.1177/1094342016654214 (DOI)000432133100005 ()
Available from: 2016-06-29 Created: 2016-06-29 Last updated: 2018-12-13Bibliographically approved
Alsouda, Y., Pllana, S. & Kurti, A. (2018). A Machine Learning Driven IoT Solution for Noise Classification in Smart Cities. In: Machine Learning Driven Technologies and Architectures for Intelligent Internet of Things (ML-IoT), August 28, 2018, Prague, Czech Republic: . Paper presented at Machine Learning Driven Technologies and Architectures for Intelligent Internet of Things (ML-IoT), August 28, 2018, Prague, Czech Republic (pp. 1-6). Euromicro
Open this publication in new window or tab >>A Machine Learning Driven IoT Solution for Noise Classification in Smart Cities
2018 (English)In: Machine Learning Driven Technologies and Architectures for Intelligent Internet of Things (ML-IoT), August 28, 2018, Prague, Czech Republic, Euromicro , 2018, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

We present a machine learning based method for noise classification using a low-power and inexpensive IoT unit. We use Mel-frequency cepstral coefficients for audio feature extraction and supervised classification algorithms (that is, support vector machine and k-nearest neighbors) for noise classification. We evaluate our approach experimentally with a dataset of about 3000 sound samples grouped in eight sound classes (such as, car horn, jackhammer, or street music). We explore the parameter space of support vector machine and k-nearest neighbors algorithms to estimate the optimal parameter values for classification of sound samples in the dataset under study. We achieve a noise classification accuracy in the range 85% -- 100%. Training and testing of our k-nearest neighbors (k = 1) implementation on Raspberry Pi Zero W is less than a second for a dataset with features of more than 3000 sound samples.

Place, publisher, year, edition, pages
Euromicro, 2018
Keywords
urban noise, smart cities, support vector machine (SVM), k-nearest neighbors (KNN), mel-frequency cepstral coefficients (MFCC), internet of things (IoT)
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-81672 (URN)
Conference
Machine Learning Driven Technologies and Architectures for Intelligent Internet of Things (ML-IoT), August 28, 2018, Prague, Czech Republic
Available from: 2019-04-04 Created: 2019-04-04 Last updated: 2019-04-16Bibliographically approved
Memeti, S., Pllana, S., Binotto, A., Kołodziej, J. & Brandic, I. (2018). A Review of Machine Learning and Meta-heuristic Methods for Scheduling Parallel Computing Systems. In: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications LOPAL 2018: . Paper presented at International Conference on Learning and Optimization Algorithms: Theory and Applications (LOPAL'18), Rabat, Morocco, May 02 - 05, 2018. New York, NY, USA: Association for Computing Machinery (ACM), Article ID 5.
Open this publication in new window or tab >>A Review of Machine Learning and Meta-heuristic Methods for Scheduling Parallel Computing Systems
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2018 (English)In: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications LOPAL 2018, New York, NY, USA: Association for Computing Machinery (ACM), 2018, article id 5Conference paper, Published paper (Refereed)
Abstract [en]

Optimized software execution on parallel computing systems demands consideration of many parameters at run-time. Determining the optimal set of parameters in a given execution context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for scheduling parallel computing systems. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of scheduling parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2018
Keywords
Parallel computing, machine learning, meta-heuristics, scheduling
National Category
Computer Sciences
Identifiers
urn:nbn:se:lnu:diva-76933 (URN)10.1145/3230905.3230906 (DOI)978-1-4503-5304-5 (ISBN)
Conference
International Conference on Learning and Optimization Algorithms: Theory and Applications (LOPAL'18), Rabat, Morocco, May 02 - 05, 2018
Available from: 2018-07-17 Created: 2018-07-17 Last updated: 2018-09-11Bibliographically approved
Perez, D., Memeti, S. & Pllana, S. (2018). A simulation study of a smart living IoT solution for remote elderly care. In: 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC): . Paper presented at 3rd International Conference on Fog and Mobile Edge Computing (FMEC), 23-26 April, 2018, Barcelona, Spain. (pp. 227-232). Barcelona, Spain: IEEE
Open this publication in new window or tab >>A simulation study of a smart living IoT solution for remote elderly care
2018 (English)In: 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), Barcelona, Spain: IEEE, 2018, p. 227-232Conference paper, Published paper (Refereed)
Abstract [en]

We report a simulation study of a smart living IoT solution for elderly people living in their own houses. Our study was conducted in the context of BoIT project in Sweden that investigates the use of various IoT devices for remote housing and care-giving services. We focus on a carephone device that enables to establish a voice connection via IP with care givers or relatives. We have developed a simulation model to study the IoT solution for elderly care in the Vaxjo municipality in Sweden. The simulation model can be used to address various issues, such as determining the lack or excess of resources or long waiting times, and study the system behavior when the number of alarms is increased. Simulation results indicate that a 15% increase in the arrivals rate would cause unacceptable long waiting times for patients to receive the care.

Place, publisher, year, edition, pages
Barcelona, Spain: IEEE, 2018
Keywords
remote elderly care, smart living, simulation, Internet of Things (IoT)
National Category
Human Aspects of ICT
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-74872 (URN)10.1109/FMEC.2018.8364069 (DOI)000444770700037 ()978-1-5386-5896-3 (ISBN)978-1-5386-5897-0 (ISBN)
Conference
3rd International Conference on Fog and Mobile Edge Computing (FMEC), 23-26 April, 2018, Barcelona, Spain.
Available from: 2018-06-02 Created: 2018-06-02 Last updated: 2018-10-22Bibliographically approved
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