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  • 1.
    Havukainen, Martti
    et al.
    University of Eastern Finland, Joensuu, Finland.
    Laine, Teemu H.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Martikainen, Timo
    University of Eastern Finland, Joensuu, Finland.
    Sutinen, Erkki
    University of Turku, Turku, Finland.
    A Case Study on Co-designing Digital Games with Older Adults and Children: Game Elements, Assets, and Challenges2020In: The Computer Games Journal, E-ISSN 2052-773XArticle in journal (Refereed)
    Abstract [en]

    Digital games have traditionally been targeted at younger generations, although the proportion of older adult players is increasing. However, the design processes of digital games often do not consider the special needs of older adults. Co-design is a potential method to address this, but there is little research on co-designing games with older adults. In our study, we proposed a co-design process model that considers the intergenerational perspective. Using this model, eight older adults (two males and six females aged 47–80) and 22 sixth graders (11 males and 11 females aged 12–13) co-designed a digital game. The content of the game was based on old concepts used by the designers during their childhood. Similarly, game content involving new words and concepts were produced by the sixth graders. We collected data using semi-structured interviews and observations during the co-design process over a period of 24 months and then processed the data using grounded theory. The results indicated that the older adults identified seven game elements as essential to make games fun—appearance and aesthetics, competition, manageability of gameplay, social impact, familiarity, unpredictability, and intergenerational gameplay. Furthermore, we identified six assets that older adults have as game co-designers and five challenges that co-designing games with older adults may entail.

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  • 2.
    Islam, Raihan Ul
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Centre for Critical Infrastructure and Societal Security.
    A learning mechanism for BRBES using enhanced Belief Rule-Based Adaptive Differential Evolution2020Conference paper (Refereed)
    Abstract [en]

    Nowadays, Belief rule-based expert systems (BRBESs) are widely used in various domains which provides a framework to handle qualitative and quantitative data by addressing several kinds of uncertainty. Learning plays an important role in BRBES to upgrade its knowledge base and parameters values, necessary for the improvement of  the prediction accuracy. Different optimal training procedures such as Particle Swarm Optimisation (PSO), Differential Evolution (DE), and Genetic Algorithm (GA) have been used as learning mechanisms. Among these procedures, DE performs comparatively better than others. However, DE's performance depends significantly in assigning near optimal values to its control parameters including cross over and mutation factors. Therefore, the objective of this article is to present a novel optimal training procedure by integrating DE with BRBES. This is named as enhanced belief rule-based adaptive differential evolution (eBRBaDE) algorithm because it has the ability to determine the near-optimal values of both the control parameters while ensuring the balanced exploitation and exploration in the search space.  In addition, a new joint optimization learning mechanism by using eBRBaDE is presented where both parameter and structure of BRBES are considered.  The reliability of the eBRBaDE has been compared with evolutionary optimization algorithms such as GA, PSO, BAT, DE and L-SHADE. This comparison has been carried out by taking account of both conjunctive and disjunctive BRBESs while predicting the Power Usage Effectiveness (PUE) of a datacentre. The comparison demonstrates that the eBRBaDE provides higher prediction accuracy of PUE than from other evolutionary optimization algorithms.

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  • 3.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    An Improved Belief Rule-Based Expert System with an Enhanced Learning Mechanism2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Belief rule-based expert systems (BRBESs) are widely used in various domains which provide an integrated framework to handle qualitative and quantitative data by addressing several kinds of uncertainty. The correctness of the data significantly affects the accuracy of the BRBESs. Learning plays an important role in BRBESs to upgrade their knowledge base and parameters values, necessary to improve the accuracy of prediction. In addition, comparatively larger datasets hinder the accuracy of BRBESs.

    Therefore, this doctoral thesis focuses on four different aspects of BRBESs, namely, the accuracy of data, multi-level complex problem, learning of BRBES, and accuracy of prediction for comparatively large dataset.

    First, the accuracy of data acquisition plays an important role, necessary to ensure accurate prediction in BRBESs. Therefore, the data coming from sensors contain anomaly due to various types of uncertainty, which hampers the accuracy of prediction. Hence, anomalous data needs to be filtered out. A novel algorithm based on belief rule base for detecting the anomaly from sensor data has been proposed in this thesis.

    Second, BRBESs can be considered to handle the multi-level complex problem like the prediction of a flood as they address different types of uncertainty. A web based BRBES was developed for predicting flood which provides better usability, allows handling of larger numbers of rule bases, and facilitates scalability. In addition, a learning mechanism for multi-level BRBESs has been developed by taking account of flooding, considered as an example of a complex problem. This learning mechanism for multi-level BRBES demonstrates promising results in comparison to other machine learning techniques including, Long Short-term Memory (LSTM), Artificial neural network (ANN), Support Vector Machine (SVM), and Linear regression.

    Third, different optimal training procedures used to support learning in BRBESs. Among these, Differential Evolution (DE) appears performing better in comparison to other evolution algorithms, including Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). However, DE's performance depends considerably in assigning near-optimal values to its control parameters. Therefore, an enhanced belief rule-based adaptive differential evolution (eBRBaDE) proposed in this thesis with the capability of ensuring balanced exploitation and exploration in the search space by providing near-optimal values to the DE's control parameters. The capability of accurate prediction of eBRBaDE has been demonstrated by taking account of power usage effectiveness (PUE) of datacentre in comparison to other evolutionary algorithms used in BRBESs optimal training procedures.

    Fourth, the recent advancement of sensor technologies enabled acquiring of a huge amount of data. In this context, deep learning appears as an effective method to process this huge amount of data. However, this high volume of data contains various types of uncertainties, including vagueness, imprecision, randomness, ignorance and incompleteness. Hence, an enhanced deep learning approach, named BRB-DL, has been developed by integrating BRBES, allowing the improvement of prediction accuracy, especially in case of a large dataset. The applicability of this BRB-DL has been carried out by considering a large amount of air pollution data to predict the air quality index (AQI) of different Chinese cities.

    In the light of the above, it can be argued that the novel anomaly detection algorithm proposed in this thesis enables the removing of anomalous data. The proposed learning mechanism for multi-level BRBES allows handling of the multi-level complex problem. The optimal training procedure, named eBRBaDE, enabling determination of optimal learning parameters of BRBESs and finally, the integration of deep learning with BRBES allows to handle large data set.

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  • 4.
    Kabir, Sami
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    Department of Computer Science & Engineering, University of Chittagong, Chattogram, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution2020In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 20, no 7, p. 1-25, article id 1956Article in journal (Refereed)
    Abstract [en]

    Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

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  • 5.
    Zhang, Jixian
    et al.
    School of Information Science and Engineering, Yunnan University, Kunming, Yunnan 650504, PR China.
    Yang, Xutao
    School of Information Science and Engineering, Yunnan University, Kunming, Yunnan 650504, PR China.
    Xie, Ning
    School of Information Science and Engineering, Yunnan University, Kunming, Yunnan 650504, PR China.
    Zhang, Xuejie
    School of Information Science and Engineering, Yunnan University, Kunming, Yunnan 650504, PR China.
    Vasilakos, Athanasios V.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Li, Weidong
    School of Mathematics and Statistics, Yunnan University, Kunming, Yunnan 650504, PR China.
    An online auction mechanism for time-varying multidimensional resource allocation in clouds2020In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 111, p. 27-38Article in journal (Refereed)
    Abstract [en]

    Multidimensional resource allocation is a hot topic in cloud computing, but current cloud platforms support only fixed resource allocation, that is, the user resource requirements are consistent throughout the usage period, which may cause a waste of resources and reduce the revenue of resource providers. Therefore, time-varying multidimensional resource allocation and the corresponding pricing mechanism represent a new challenge in cloud computing. We address the problem of online time-varying multidimensional resource allocation and pricing in clouds. Specifically, (1) we propose a novel integer programming model for the time-varying multidimensional resource allocation problem and (2) we design a truthful online auction mechanism for resource allocation in a competitive environment. For the resource allocation algorithm, we propose a waiting period strategy and dominant-resource-based strategy to improve the social welfare and resource utilization. Simultaneously, a payment pricing algorithm based on critical value theory is proposed. Finally, we prove that the mechanism is truthful and individual rationality. Compared with existing research, our approach is characterized by high social welfare, high resource utilization and short execution time.

  • 6.
    Palm, Emanuel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Bodin, Ulf
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Schelén, Olov
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Approaching Non-Disruptive Distributed Ledger Technologies via the Exchange Network Architecture2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 12379-12393Article in journal (Refereed)
    Abstract [en]

    The rise of distributed ledger technologies, such as R3 Corda, Hyperledger Fabric and Ethereum, has lead to a surge of interest in digitalizing different forms of contractual cooperation. By allowing for ledgers of collaboration-critical data to be reliably maintained between stakeholders without intermediaries, these solutions might enable unprecedented degrees of automation across organizational boundaries, which could have major implications for supply chain integration, medical journal sharing and many other use cases. However, these technologies tend to break with prevailing business practices by relying on code-as-contracts and distributed consensus algorithms , which can impose disruptive requirements on contract language, cooperation governance and interaction privacy. In this paper, we show how our Exchange Network architecture could be applied to avoid these disruptors. To be able to reason about the adequacy of our architecture, we present six requirements for effective contractual collaboration, which notably includes negotiable terms and effective adjudication . After outlining the architecture and our implementation of it, we describe how the latter meets our requirements by facilitating (1) negotiation, (2) user registries, (3) ownership ledgers and (4) definition sharing, as well as by only replicating ledgers between stakeholder pairs. To show how our approach compares to other solutions, we also consider how Corda, Fabric and Ethereum meet our requirements. We conclude that digital negotiation and ownership could replace many proposed uses of code-as-contracts for better compatibility with current contractual practices, as well as noting that distributed consensus algorithms are not mandatory for digital cooperation.

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  • 7.
    Yi,, J.-H.
    et al.
    School of Mathematics and Big Data, Foshan University, Foshan, China; School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China.
    Xing, L. -N.
    School of Mathematics and Big Data, Foshan University, Foshan, China.
    Wang, G.-G.
    Department of Computer Science and Technology, Ocean University of China, Qingdao, China.
    Dong, J.
    Department of Computer Science and Technology, Ocean University of China, Qingdao, China.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Alavi, A.H.
    Department of Civil and Environmental Engineering, University of Missouri, Columbia, United States.
    Wang, L.
    Department of Automation, Tsinghua University, Beijing, China.
    Behavior of crossover operators in NSGA-III for large-scale optimization problems2020In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 509, p. 470-487Article in journal (Refereed)
    Abstract [en]

    Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algorithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.

  • 8.
    Poirot, Valentin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Ericson, Mårten
    Ericsson Research, 977 53, Luleå, Sweden.
    Nordberg, Mats
    Ericsson Research, 977 53, Luleå, Sweden.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Energy efficient multi-connectivity algorithms for ultra-dense 5G networks2020In: Wireless networks, ISSN 1022-0038, E-ISSN 1572-8196, Vol. 26, no 3, p. 2207-2222Article in journal (Refereed)
    Abstract [en]

    Two radio air interfaces, Evolved-LTE and New Radio, coexist in new 5G systems. New Radio operates in the millimeter band and provides a better bandwidth, but the higher frequencies also imply worse radio conditions. Multi-connectivity, a feature of 5G that allows users to connect to more than one base station simultaneously, can offer the advantages of both interfaces. In this paper, we investigate how multi-connectivity can improve user reliability and the system’s energy efficiency. Five algorithms for secondary cell association are presented and evaluated. We show a decrease in the radio link failure rate of up to 50% at high speeds and improvements of the energy efficiency of up to 20% at low speeds.

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  • 9.
    Kumar, Neeraj
    et al.
    Thapar University, Patiala, Punjab, India.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Choo, Kim-Kwang Raymond
    University of South Australia, Australia.
    Yang, Laurence T.
    St. Francis Xavier University, Canada.
    Energy Management for Cyber-Physical Cloud Systems2020In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 105, p. 754-756Article in journal (Refereed)
  • 10.
    Sun, Gang
    et al.
    Key Lab of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, Chengdu, China.
    Zhou, Run
    Key Lab of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, Chengdu, China.
    Sun, Jian
    Key Lab of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, Chengdu, China.
    Yu, Hongfang
    Key Lab of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, Chengdu, China. Peng Cheng Laboratory, Shenzhen, China.
    Vasilakos, Athanasios V.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Computer Science and Technology, Fuzhou University, China.
    Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization2020In: IEEE Internet of Things Journal, ISSN 2327-4662Article in journal (Refereed)
    Abstract [en]

    The efficient deployment of virtual network functions (VNFs) for network service provisioning is key for achieving network function virtualization (NFV); however, most existing studies address only offline or one-off deployments of service function chains (SFCs) while neglecting the dynamic (i.e., online) deployment and expansion requirements. In particular, many methods of energy/resource cost reduction are achieved by merging VNFs. However, the energy waste and device wear for large-scale collections of servers (e.g., cloud networks and data centers) caused by sporadic request updating are ignored. To solve these problems, we propose an energy-aware routing and adaptive delayed shutdown (EAR-ADS) algorithm for dynamic SFC deployment, which includes the following features. 1) Energy-aware routing (EAR): By considering a practical deployment environment, a flexible solution is developed based on reusing open servers and selecting paths with the aims of balancing energy and resources and minimizing the total cost. 2) Adaptive delayed shutdown (ADS): The delayed shutdown time of the servers can be flexibly adjusted in accordance with the usage of each device in each time slot, thus eliminating the no-load wait time of the servers and frequent on/off switching. Therefore, EAR-ADS can achieve dual energy savings by both decreasing the number of open servers and reducing the idle/switching energy consumption of these servers. Simulation results show that EAR-ADS not only minimizes the cost of energy and resources but also achieves an excellent success rate and stability. Moreover, EAR-ADS is efficient compared with an improved Markov algorithm (SAMA), reducing the average deployment time by more than a factor of 40.

  • 11.
    Islam, Raihan Ul
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Centre for Critical Infrastructure and Societal Security.
    Inference and Multi-level Learning in a Belief Rule-Based Expert System to Predict Flooding2020Conference paper (Refereed)
    Abstract [en]

    Floods are one of the most dangerous catastrophic events. By the year 2050 flooding due to rise of ocean level  may  cost  one  trillion USD to coastal cities. Since flooding involves multi-dimensional elements, its accurate prediction is difficult. In addition, the elements cannot be measured with 100% accuracy. Belief rule-based expert systems (BRBESs) can be considered as an appropriate approach to handle  this  type  of  problem  because they are capable of addressing  uncertainty. However, BRBESs need to be equipped with the capacity to handle multi- level learning and inference to improve its accuracy of flood prediction. Therefore, this paper proposes a new learning and inference mechanism, named joint optimization using belief rule- based adaptive differential evolution (BRBaDE) for multi-level BRBES, which has the capability to handle multi-level learning and inference. Various machine learning methods, including Artificial Neural Networks (ANN), Support Vector Machine (SVM), Linear Regression and Long Short Term Memory have been compared with BRBaDE. The result exhibits that our proposed learning mechanism performs betters than learning techniques as mentioned above in terms of accuracy in flood prediction.

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  • 12.
    Chude-Okonkwo, Uche A. K.
    et al.
    Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa. Institute of Intelligent Systems, University of Johannesburg, Gauteng 2006, South Africa.
    Maharaj, B. T.
    Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa.
    Vasilakos, A.V.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Malekian, Reza
    Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa.
    Information-Theoretic Model and Analysis of Molecular Signaling in Targeted Drug Delivery2020In: IEEE Transactions on Nanobioscience, ISSN 1536-1241, E-ISSN 1558-2639, Vol. 19, no 2, p. 270-284Article in journal (Refereed)
    Abstract [en]

    Targeted drug delivery (TDD) modality promises a smart localization of appropriate dose of therapeutic drugs to the targeted part of the body at reduced system toxicity. To achieve the desired goals of TDD, accurate analysis of the system is important. Recent advances in molecular communication (MC) present prospects to analyzing the TDD process using engineering concepts and tools. Specifically, the MC platform supports the abstraction of TDD process as a communication engineering problem in which the injection and transportation of drug particles in the human body and the delivery to a specific tissue or organ can be analyzed using communication engineering tools. In this paper we stand on the MC platform to present the information-theoretic model and analysis of the TDD systems. We present a modular structure of the TDD system and the probabilistic models of the MC-abstracted modules in an intuitive manner. Simulated results of information-theoretic measures such as the mutual information are employed to analyze the performance of the TDD system. Results indicate that uncertainties in drug injection/release systems, nanoparticles propagation channel and nanoreceiver systems influence the mutual information of the system, which is relative to the system’s bioequivalence measure.

  • 13.
    Wazid, Mohammad
    et al.
    Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India.
    Das, Ashok Kumar
    Center for Security, Theory and Algorithmic Research, International Institute of Information Technology, Hyderabad, India.
    Bhat K, Vivekananda
    Centre for Cryptography, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Computer Science and Technology, Fuzhou University, Fujian, China.
    LAM-CIoT: Lightweight authentication mechanism in cloud-based IoT environment2020In: Journal of Network and Computer Applications, ISSN 1084-8045, E-ISSN 1095-8592, Vol. 150, article id 102496Article in journal (Refereed)
    Abstract [en]

    Internet of Things (IoT) becomes a new era of the Internet, which consists of several connected physical smart objects (i.e., sensing devices) through the Internet. IoT has different types of applications, such as smart home, wearable devices, smart connected vehicles, industries, and smart cities. Therefore, IoT based applications become the essential parts of our day-to-day life. In a cloud-based IoT environment, cloud platform is used to store the data accessed from the IoT sensors. Such an environment is greatly scalable and it supports real-time event processing which is very important in several scenarios (i.e., IoT sensors based surveillance and monitoring). Since some applications in cloud-based IoT are very critical, the information collected and sent by IoT sensors must not be leaked during the communication. To accord with this, we design a new lightweight authentication mechanism in cloud-based IoT environment, called LAM-CIoT. By using LAM-CIoT, an authenticated user can access the data of IoT sensors remotely. LAM-CIoT applies efficient “one-way cryptographic hash functions” along with “bitwise XOR operations”. In addition, fuzzy extractor mechanism is also employed at the user's end for local biometric verification. LAM-CIoT is methodically analyzed for its security part through the formal security using the broadly-accepted “Real-Or-Random (ROR)” model, formal security verification using the widely-used “Automated Validation of Internet Security Protocols and Applications (AVISPA)” tool as well as the informal security analysis. The performance analysis shows that LAM-CIoT offers better security, and low communication and computation overheads as compared to the closely related authentication schemes. Finally, LAM-CIoT is evaluated using the NS2 network simulator for the measurement of network performance parameters that envisions the impact of LAM-CIoT on the network performance of LAM-CIoT and other schemes.

  • 14.
    Chen, Jialu
    et al.
    Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China.
    Zhou, Jun
    Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China.
    Cao, Zhenfu
    Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Computer Science and Technology, Fuzhou University, China.
    Dong, Xiaolei
    Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China..
    Choo, Kim-Kwang Raymond
    Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX, USA.
    Lightweight Privacy-preserving Training and Evaluation for Discretized Neural Networks2020In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 7, no 4, p. 2663-2678Article in journal (Refereed)
    Abstract [en]

    Machine learning, particularly the neural network, is extensively exploited in dizzying applications. In order to reduce the burden of computing for resource-constrained clients, a large number of historical private datasets are required to be outsourced to the semi-trusted or malicious cloud for model training and evaluation. To achieve privacy preservation, most of the existing work either exploited the technique of public key fully homomorphic encryption (FHE) resulting in considerable computational cost and ciphertext expansion, or secure multiparty computation (SMC) requiring multiple rounds of interactions between user and cloud. To address these issues, in this paper, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized neural networks is proposed. Firstly, we put forward an efficient single key fully homomorphic data encapsulation mechanism (SFH-DEM) without exploiting public key FHE. Based on SFH-DEM, a series of atomic calculations over the encrypted domain including multivariate polynomial, nonlinear activation function, gradient function and maximum operations are devised as building blocks. Furthermore, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized neural networks is proposed, which can also be extended to convolutional neural network. Finally, we give the formal security proofs for dataset privacy, model training privacy and model evaluation privacy under the semi-honest environment and implement the experiment on real dataset MNIST for recognizing handwritten numbers in discretized neural network to demonstrate the high efficiency and accuracy of our proposed LPTE.

  • 15.
    Laato, Samuli
    et al.
    University of Turku.
    Hyrynsalmi, Sonja
    LUT University.
    Rauti, Sampsa
    University of Turku.
    Islam, A.K.M. Najmul
    University of Turku.
    Laine, Teemu H.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Location-based Games as Exergames: From Pokémon To The Wizarding World2020In: INTERNATIONAL JOURNAL OF SERIOUS GAMES, E-ISSN 2384-8766, Vol. 7, no 1, p. 79-95Article in journal (Refereed)
    Abstract [en]

    Exergames, i.e. games which aim to increase player’s physical activity, are a prominent sub-category of serious games (SGs). Recently, location-based games (LBGs) similar to Pokémon GO have gained the attention of exergame designers as they have been able to reach people who would otherwise not be motivated to exercise. Multiple studies have been conducted on Pokémon GO alone, identifying positive outcomes related to, for example, exercise and social well-being. However, with substantial findings derived from a single game, it is unclear whether the identified benefits of playing Pokémon GO are present in other similar games. In order to broaden the understanding of LBGs as exergames, this study investigates the gameplay features and initial reactions of early adopters to a game called Harry Potter: Wizards Unite (HPWU) which was launched in summer 2019. A questionnaire (N=346) was sent to HPWU players to measure the effects playing the game has on their physical activity. During the first week of play, an increase in mild physical activity was recorded among HPWU players, similar to what has been reported with Pokémon GO. Also almost half of respondents (46,82%) reported to play the game socially, showcasing how LBGs can generally have a positive impactalso on players’ social well-being.

  • 16.
    Minovski, Dimitar
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Modeling Quality of IoT Experience in Autonomous Vehicles2020In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 7, no 5, p. 1-17, article id IoT-7417-2019Article in journal (Refereed)
    Abstract [en]

    Today's research on Quality of Experience (QoE) mainly addresses multimedia services. With the introduction of the Internet of Things (IoT), there is a need for new ways of evaluating the QoE. Emerging IoT services, such as autonomous vehicles (AVs), are more complex and involve additional quality requirements, such as those related to machine-to-machine communication that enables self-driving. In fully autonomous cases, it is the intelligent machines operating the vehicles. Thus, it is not clear how intelligent machines will impact end-user QoE, but also how end users can alter and affect a self-driving vehicle. This article argues for a paradigm shift in the QoE area to cover the relationship between humans and intelligent machines. We introduce the term Quality of IoT-experience (QoIoT) within the context of AV, where the quality evaluation, besides end users, considers quantifying the perspectives of intelligent machines with objective metrics. Hence, we propose a novel architecture that considers Quality of Data (QoD), Quality of Network (QoN), and Quality of Context (QoC) to determine the overall QoIoT in the context of AVs. Finally, we present a case study to illustrate the use of QoIoT.

  • 17.
    Kim, Joo Chan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Multimodal Interaction with Internet of Things and Augmented Reality: Foundations, Systems and Challenges2020Report (Other academic)
    Abstract [en]

    The development of technology has enabled diverse modalities that can be used by humans or machines to interact with computer systems. In particular, the Internet of Things (IoT) and Augmented Reality (AR) are explored in this report due to the new modalities offered by these two innovations which could be used to build multimodal interaction systems. Researchers have utilized multiple modalities in interaction systems for providing better usability. However, the employment of multiple modalities introduces some challenges that need to be considered in the development of multimodal interaction systems to achieve high usability. In order to identify a number of remaining challenges in the research area of multimodal interaction systems with IoT and AR, we analyzed a body of literature on multimodal interaction systems from the perspectives of system architecture, input and output modalities, data processing methodology and use cases. The identified challenges are regarding of (i) multidisciplinary knowledge, (ii) reusability, scalability and security of multimodal interaction system architecture, (iii) usability of multimodal interaction interface, (iv) adaptivity of multimodal interface design, (v) limitation of current technology, and (vi) advent of new modalities. We are expecting that the findings of this report and future research can be used to nurture the multimodal interaction system research area, which is still in its infancy.

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  • 18.
    Minovski, Dimitar
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. InfoVista Sweden AB.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zhohov, Roman
    Quality of Experience for the Internet of Things2020In: IT Professional Magazine, ISSN 1520-9202, E-ISSN 1941-045X, p. 1-9, article id ITPro-2018-09-0068Article in journal (Refereed)
    Abstract [en]

    The Internet of Things (IoT) brings a set of unique and complex challenges to the field of Quality of Experience (QoE) evaluation. The state-of-the-art research in QoE mainly targets multimedia services, such as voice, video, and the Web, to determine quality perceived by end-users. Therein, main evaluation metrics involve subjective and objective human factors and network quality factors. Emerging IoT may also include intelligent machines within services, such as health-care, logistics, and manufacturing. The integration of new technologies such as machine-to-machine communications and artificial intelligence within IoT services may lead to service quality degradation caused by machines. In this article, we argue that evaluating QoE in the IoT services should also involve novel metrics for measuring the performance of the machines alongside metrics for end-users' QoE. This article extends the legacy QoE definition in the area of IoT and defines conceptual metrics for evaluating QoE using an industrial IoT case study.

  • 19.
    Vasudev, Harsha
    et al.
    Birla Institute of Technology and Science (BITS) Pilani, Goa, India.
    Das, Debasis
    Indian Institute of Technology Jodhpur, Rajasthan, India.
    Vasilakos, Athanasios V
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Secure message propagation protocols for IoVs communication components2020In: Computers & electrical engineering, ISSN 0045-7906, E-ISSN 1879-0755, Vol. 82, article id 106555Article in journal (Refereed)
    Abstract [en]

    With the development of Internet of Vehicles (IoVs), the smart transportation field has achieved a lot of attention by providing a wide variety of benefits, such as enhanced road safety, reduced traffic congestion, traveler safety, and less pollution. It made the concept of ‘intelligence on wheels’ into a real one. However, the highly dynamic nature of vehicles and insecure channels are the significant challenges in an IoV environment. In the existing researches, secure and lightweight protocols for the IoVs communication components are missing. In this paper, we design secure and lightweight communication protocols for different components of IoVs, such as V2V (Vehicle-to-Vehicle), V2P (Vehicle-to-Portable Device), V2R (Vehicle-to-Road Side Unit), V2I (Vehicle-to-Infrastructure), and V2S (Vehicle-to-Sensor). We have done in-depth security analysis to ensure the resistant power against different strong attacks. Moreover, we have implemented the protocols on a Desktop Computer and Raspberry Pi. From the results, it is observed that the proposed protocols perform well in the perspectives of communication, storage, computation, and battery consumption than other competitive protocols.

  • 20.
    Zhang, Yi-Qing
    et al.
    Department of Electronic Engineering, and the Center of Smart Networks and Systems, School of Information Science and Engineering, Adaptive Networks and Control Laboratory, Fudan University, Shanghai, China.
    Li, Xiang
    Department of Electronic Engineering, and the Center of Smart Networks and Systems, School of Information Science and Engineering, Adaptive Networks and Control Laboratory, Fudan University, Shanghai, China.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Spectral Analysis of Epidemic Thresholds of Temporal Networks2020In: IEEE Transactions on Cybernetics, ISSN 2168-2267, E-ISSN 2168-2275, Vol. 50, no 5, p. 1965-1977Article in journal (Refereed)
    Abstract [en]

    Many complex systems can be modeled as temporal networks with time-evolving connections. The influence of their characteristics on epidemic spreading is analyzed in a susceptible-infected-susceptible epidemic model illustrated by the discrete-time Markov chain approach. We develop the analytical epidemic thresholds in terms of the spectral radius of weighted adjacency matrix by averaging temporal networks, e.g., periodic, nonperiodic Markovian networks, and a special nonperiodic non-Markovian network (the link activation network) in time. We discuss the impacts of statistical characteristics, e.g., bursts and duration heterogeneity, as well as time-reversed characteristic on epidemic thresholds. We confirm the tightness of the proposed epidemic thresholds with numerical simulations on seven artificial and empirical temporal networks and show that the epidemic threshold of our theory is more precise than those of previous studies.

  • 21.
    Kalkofen, Denis
    et al.
    Graz University of Technology.
    Mori, Shohei
    Graz University of Technology.
    Ladinig, Tobias
    Montanuniversität Leoben.
    Daling, Lea
    RWTH Aachen University.
    Abdelrazeq, Anas
    RWTH Aachen University.
    Ebner, Markus
    Graz University of Technology.
    Ortega, Manuel
    Montanuniversität Leoben.
    Feiel, Susanne
    Montanuniversität Leoben.
    Gabl, Sebastian
    Graz University of Technology.
    Shepel, Taras
    TU Bergakademie Freiberg.
    Tibbett, James
    SeePilot.
    Laine, Teemu H.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hitch, Michael
    Tallinn University of Technology.
    Drebenstedt, Carsten
    TU Bergakademie Freiberg.
    Moser, Peter
    Montanuniversität Leoben.
    Tools for Teaching Mining Students in Virtual Reality based on 360° Video Experiences2020In: 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops: VRW 2020, IEEE, 2020, p. 455-459Conference paper (Other academic)
    Abstract [en]

    In recent years, Virtual Reality (VR) technology has found their way into higher education. Its power lays in its ability to provide immersive three-dimensional (3D) experiences that help conveying educational content whilst providing rich interaction possibilities. Especially in mining engineering education, VR has high potential to reshape the provided learning content. Field trips, i.e. mine visits, are an integral part of the education and necessary to transfer knowledge to students. However, field trips are time and cost intensive and mines often have tight entry regulations. As a result, the number of field trips is limited. VR-based field trips offer a considerable alternative presupposed they replicate the complex mining environment realistically. In addition, VR mines have the advantage of taking students close to events (e.g. explosions) that are impossible to demonstrate in a real mine. However, generating realistic 3D content for VR still involves complex, and thus time consuming tasks. Therefore, we present the design of a VR Framework for teaching mining students based on 360° video data, its evaluation in three different lectures, and its extension based on the feedback we received from students and teachers from four different universities.

  • 22.
    Minovski, Dimitar
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Towards Quality of Experience for Industrial Internet of Things2020Licentiate thesis, comprehensive summary (Other academic)
    Abstract [sv]

    Today’s research on Quality of Experience (QoE) mainly addresses multimedia services, where the end-users' subjective perception is the prime factor of determining the QoE. With the introduction of the Internet of Things (IoT), there is a need for new ways of evaluating the QoE. Emerging IoT services, such as remotely-controlled operations, autonomous vehicles (AVs), and energy management, are more complex, creating additional quality requirements emerging from the machine-to-machine (M2M) communication and autonomous processes. One challenge, as an extension of the legacy QoE concept, is understanding the perception of end-users QoE in the context of IoT services. For instance, within the current state of the art in QoE it is not clear how intelligent machines can impact end-users' QoE, but also how end-users can alter or affect an intelligent machine. Another challenge is the quality evaluation of the M2M and systems that enable the machines to run by themselves. Consider a self-driving vehicle, where multiple autonomous decisions are simultaneously made as a result of predictive models that reason on the vehicle's generated data. An evaluation of the predictive models is inevitable due to abundance of the potential sources of failures. A quality degradation of the IoT hardware, the software enabling autonomous decision, and the M2M communication can raise life-threatening concerns, directly impacting the end-users' QoE. In this thesis, we argue for a paradigm shift in the QoE area that understands the relationships between humans and intelligent machines, as well as within the machines. Our contributions are as follows: first, we introduce the term Quality of IoT-experience (QoIoT) to extend the conventional QoE approaches in covering IoT services. Within QoIoT, we consider a quality evaluation from the perspective of the end-users, as well as from intelligent machines. The end-user's perception is captured by following the conventional QoE approaches, while regarding intelligent machine we propose the usage of objective metrics to describe their experiences and performance. As our second contribution, we propose a novel QoIoT architecture that consists of a layered methodology in order to determine the overall QoIoT. The QoIoT architecture, firstly, models the data-sources of an IoT service, classified within four layers: physical, network, application, and virtual. Secondly, the architecture proposes three layers for measuring the QoIoT by considering Quality of Data (QoD), Quality of Network (QoN), and Quality of Context (QoC), with QoC being the prime layer in measuring the objective performance metrics. Finally, the third contribution considers a case-study of cellular IoT, involving autonomous mining vehicles, which we utilize to achieve a preliminary results that validate the proposed QoIoT architecture.

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  • 23.
    Gan, Wensheng
    et al.
    College of Cyber Security/College of Information Science and Technology, Jinan University, Guangzhou 510632 Chin.
    Lin, Jerry Chun-Wei
    Western Norway University of Applied Sciences 5063, Bergen Norway.
    Chao, Han-Chieh
    National Dong Hwa University, Hualien 97401 Taiwan.
    Vasilakos, Athanasios V.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Yu, Philip S.
    University of Illinois at Chicago, Chicago, IL 60607 USA.
    Utility-Driven Data Analytics on Uncertain Data2020In: IEEE Systems Journal, ISSN 1932-8184, E-ISSN 1937-9234Article in journal (Refereed)
    Abstract [en]

    Modern Internet of Things (IoT) applications generate massive amounts of data, much of them in the form of objects/items of readings, events, and log entries. Specifically, most of the objects in these IoT data contain rich embedded information (e.g., frequency and uncertainty) and different levels of importance (e.g., unit risk/utility of items, interestingness, cost, or weight). Many existing approaches in data mining and analytics have limitations, such as only the binary attribute is considered within a transaction, as well as all the objects/items having equal weights or importance. To solve these drawbacks, a novel utility-driven data analytics algorithm named HUPNU is presented in this article. As a general utility-driven uncertain data mining model, HUPNU can extract High-Utility patterns by considering both Positive and Negative unit utilities from Uncertain data. The qualified high-utility patterns can be effectively discovered for intrusion detection, risk prediction, manufacturing management, and decision-making, among others. By using the developed vertical Probability-Utility list with the positive and negative utilities structure, as well as several effective pruning strategies, experiments showed that the developed HUPNU approach with the pruning strategies performed great in mining the qualified patterns efficiently and effectively.

  • 24.
    Biswas, Munmun
    et al.
    BGC Trust University Bangladesh,Department of Computer Science and Engineering,Bidyanagar, Chandanaish,Bangladesh.
    Chowdury, Mohammad Salah Uddin
    BGC Trust University Bangladesh,Department of Computer Science and Engineering,Bidyanagar, Chandanaish,Bangladesh.
    Nahar, Nazmun
    BGC Trust University Bangladesh,Department of Computer Science and Engineering,Bidyanagar, Chandanaish,Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong University,Department of Computer Science and Engineering,Bangladesh,4331.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Belief Rule Base Expert System for staging Non-Small Cell Lung Cancer under Uncertainty2019In: BECITHCON 2019: 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), IEEE, 2019, p. 47-52Conference paper (Other academic)
    Abstract [en]

    Non small cell Lung cancer (NSCLC) is one of the most well-known types of Lung cancer which is reason for cancer related demise in Bangladesh. The early detection stage of NSCLC is required for improving the survival rate by taking proper decision for surgery and radiotherapy. The most common factors for staging NSCLC are age, tumor size, lymph node distance, Metastasis and Co morbidity. Moreover, physicians' diagnosis is unable to give more reliable outcome due to some uncertainty such as ignorance, incompleteness, vagueness, randomness, imprecision. Belief Rule Base Expert System (BRBES) is fit to deal with above mentioned uncertainty by applying both Belief Rule base and Evidential Reasoning approach. Therefore, this paper represents the architecture, development and interface for staging NSCLC by incorporating belief rule base as well as evidential reasoning with the capability of handling uncertainty. At last, a comparative analysis is added which indicate that the outcomes of proposed expert system is more reliable and efficient than the outcomes generated from traditional human expert as well as Support Vector Machine (SVM) or Fuzzy Rule Base Expert System (FRBES).

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  • 25.
    Jamil, Mohammad Newaj
    et al.
    Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Belief Rule Based Expert System for Evaluating Technological Innovation Capability of High-Tech Firms Under Uncertainty2019In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    Technological innovation capability (TIC) is a complicated and subtle concept which is based on multiple quantitative and qualitative criteria. The cores of a firm’s long-term competitive dominance are defined by technological innovation capability which is the incentive for a firm’s innovation. Various types of uncertainty can be noticed while considering multiple criteria for evaluating TIC. In order to evaluate TIC in a reliable way, a Belief Rule Base (BRB) Expert System can be used to handle both quantitative and qualitative data and their associated uncertainties. In this paper, a RESTful API-based BRB expert system is introduced to evaluate technological innovation capability by taking uncertainties into consideration. This expert system will facilitate firms’ managers to obtain a recapitulation of the TIC evaluation. It will help them to take essential steps to ensure corporate survival and strengthen their weak capabilities continuously to facilitate a competitive advantage. Other users can also use this API to apply BRB for a different domain. However, a comparison between the knowledge-driven approach (BRBES) and several data-driven models has been performed to find out the reliability in evaluating TIC. The result shows that the outcome of BRBES is better than other data-driven approaches.

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  • 26.
    Hossain, Mohammad Shahadat
    et al.
    University of Chittagong, Bangladesh.
    Tuj-Johora, Fatema
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Belief Rule Based Expert System to Assess Hypertension under Uncertainty2019In: Journal of Internet Services and Information Security (JISIS), ISSN 2182-2069, E-ISSN 2182-2077, Vol. 9, no 4, p. 18-38, article id 2Article in journal (Refereed)
    Abstract [en]

    Hypertension (HPT) plays an important role, especially for stroke and heart diseases. Therefore, theaccurate assessment of hypertension is becoming a challenge. However, the presence of uncertainties, associated with the signs and symptoms of HPT are becoming crucial to conduct the preciseassessment. This article presents a web-based expert system (web BRBES) by employing beliefrule based (BRB) methodology to assess HPT, allowing the generation of reliable results. In order tocheck the reliability of the system, a comparison has been performed among various approaches suchas decision tree, random forest, artificial neural networks, fuzzy rule based expert system and experts’opinion. Different performance metrics such as confusion matrix, accuracy, root mean square error,area under curve have been used to contrast the reliability of the approaches. The BRBES producesa more reliable result than from the other approaches. Moreover, the user friendliness of the webBRBES found high as obtained by using the PACT (People, Activities, Contexts, Technologies) approach over 200 people.

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  • 27.
    Nahar, Nazmun
    et al.
    BGC Trust University Bangladesh, Bidyanagar, Chandanaish, Bangladesh.
    Ara, Ferdous
    Department of Computer Science and Engineering, BGC Trust University, Bangladesh.
    Neloy, Md. Arif Istiek
    Department of Computer Science and Engineering, BGC Trust University, Bangladesh.
    Barua, Vicky
    Department of Computer Science and Engineering, BGC Trust University, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Comparative Analysis of the Ensemble Method for Liver Disease Prediction2019In: Proceedings of International Conference on Innovation in Engineering and Technology (ICIET), 2019Conference paper (Refereed)
    Abstract [en]

    Early diagnosis of liver disease is very important in order to save human lives and take appropriate measure to control the disease. In several fields, especially in the field of medical science, the ensemble method was successfully applied. This research work uses different ensemble methods to investigate the early detection of liver disease. The selected dataset for this analysis is made up of attributes such as total bilirubin, direct bilirubin, age, sex, total protein, albumin, and globulin ratio. This research mainly aims at measuring and comparing the efficiency of different ensemble methods. AdaBoost, LogitBoost, BeggRep, BeggJ48 and Random Forest are the ensemble method used in this research. The study shows that LogitBoost is the most accurate model than other ensemble approaches.

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  • 28.
    Noor, Ayman
    et al.
    Newcastle University. Taibah University.
    Jha, Devki Nandan
    Newcastle University.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Jayaraman, Prem Prakash
    Swinburne University of Technology.
    Souza, Arthur
    Federal University of Rio Grande do Norte.
    Ranjan, Rajiv
    Newcastle University.
    Dustdar, Schahram
    Vienna University of Technology.
    A Framework for Monitoring Microservice-Oriented Cloud Applications in Heterogeneous Virtualization Environments2019In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    Microservices have emerged as a new approach for developing and deploying cloud applications that require higher levels of agility, scale, and reliability. To this end, a microservice-based cloud application architecture advocates decomposition of monolithic application components into independent software components called "microservices". As the independent microservices can be developed, deployed, and updated independently of each other, it leads to complex run-time performance monitoring and management challenges. To solve this problem, we propose a generic monitoring framework, Multi-microservices Multi-virtualization Multi-cloud (M3) that monitors the performance of microservices deployed across heterogeneous virtualization platforms in a multi-cloud environment. We validated the efficacy and efficiency of M3 using a Book-Shop application executing across AWS and Azure.

  • 29.
    Huang, Mingfeng
    et al.
    School of Information Science and Engineering, Central South University, Changsha 410083, China..
    Liu, Anfeng
    School of Information Science and Engineering, Central South University, Changsha 410083, China.
    Xiong, Neal N.
    Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, USA.
    Wang, Tian
    School of Computer Science, National Huaqiao University, Quanzhou 362000, China..
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Low-Latency Communication Scheme for Mobile Wireless Sensor Control Systems2019In: IEEE Transactions on Systems, Man & Cybernetics. Systems, ISSN 2168-2216, E-ISSN 1349-2543, Vol. 49, no 2, p. 317-332Article in journal (Refereed)
    Abstract [en]

    Millions of dedicated sensors are deployed in smart cities to enhance quality of urban living. Communication technologies are critical for connecting these sensors and transmitting events to sink. In control systems of mobile wireless sensor networks (MWSNs), mobile nodes are constantly moving to detect events, while static nodes constitute the communication infrastructure for information transmission. Therefore, how to communicate with sink quickly and effectively is an important research issue for control systems of MWSNs. In this paper, a communication scheme named first relay node selection based on fast response and multihop relay transmission with variable duty cycle (FRAVD) is proposed. The scheme can effectively reduce the network delay by combining first relay node selection with node duty cycles setting. In FRAVD scheme, first, for the first relay node selection, we propose a strategy based on fast response, that is, select the first relay node from adjacent nodes in the communication range within the shortest response time, and guarantee that the remaining energy and the distance from sink of the node are better than the average. Then for multihop data transmission of static nodes, variable duty cycle is introduced novelty, which utilizes the residual energy to improve the duty cycle of nodes in far-sink area, because nodes adopt a sleep-wake asynchronous mode, increasing the duty cycle can significantly improve network performance in terms of delays and transmission reliability. Our comprehensive performance analysis has demonstrated that compared with the communication scheme with fixed duty cycle, the FRAVD scheme reduces the network delay by 24.17%, improves the probability of finding first relay node by 17.68%, while also ensuring the network lifetime is not less than the previous researches, and is a relatively efficient low-latency communication scheme.

  • 30.
    Akter, Shamima
    et al.
    International Islamic University, Chittagong, Bangladesh.
    Nahar, Nazmun
    University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A New Crossover Technique to Improve Genetic Algorithm and Its Application to TSP2019In: Proceedings of 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), IEEE, 2019, article id 18566123Conference paper (Refereed)
    Abstract [en]

    Optimization problem like Travelling Salesman Problem (TSP) can be solved by applying Genetic Algorithm (GA) to obtain perfect approximation in time. In addition, TSP is considered as a NP-hard problem as well as an optimal minimization problem. Selection, crossover and mutation are the three main operators of GA. The algorithm is usually employed to find the optimal minimum total distance to visit all the nodes in a TSP. Therefore, the research presents a new crossover operator for TSP, allowing the further minimization of the total distance. The proposed crossover operator consists of two crossover point selection and new offspring creation by performing cost comparison. The computational results as well as the comparison with available well-developed crossover operators are also presented. It has been found that the new crossover operator produces better results than that of other cross-over operators.

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  • 31.
    Safkhani, Masoumeh
    et al.
    Department of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A New Secure Authentication Protocol for Telecare Medicine Information Systemand Smart Campus2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 23514-23526Article in journal (Refereed)
    Abstract [en]

    Telecare Medicine Information System (TMIS)'s security importance attracts a lot of attention these days. Whatever the security of TMIS improves, its application becomes wider. To address this requirement, recently, Li et al. proposed a new privacy-preserving RFID authentication protocol for TMIS. After that, Zhou et al. and also Benssalah et al. presented their scheme, which is not secure, and they presented their new authentication protocol and claim that their proposal can provide higher security for TMIS applications. In this stream, Zheng et al. proposed a novel authentication protocol with application in smart campus, including TMIS. In this paper, we present an efficient impersonation and replay attacks against Zheng et al. with the success probability of 1 and a desynchronization attack which is applicable against all of the rest three mentioned protocols with the success probability of 1-2^{-n} , where n is the protocols parameters length. After that, we proposed a new protocol despite these protocols can resist the attacks presented in this paper and also other active and passive attacks. Our proposed protocol's security is also done both informally and formally through the Scyther tool.

  • 32.
    Lan, Yihua
    et al.
    School of Computer and Information Technology, Nanyang Normal University, Nanyang, China.
    Bai, Kun
    School of Computer and Information Technology, Nanyang Normal University, Nanyang, China..
    Hung, Chih-Cheng
    Laboratory for Machine Vision and Security Research, College of Computing and Software Engineering, Kennesaw State University, Marietta, USA.
    Alelaiwi, Abdulhameed
    Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Novel Definition of Equivalent Uniform Dose Based on Volume Dose Curve2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 45850-45857Article in journal (Refereed)
    Abstract [en]

    With the improvement of mobile device performance, the requirement of equivalent dose description in intensity-modulated radiation therapy is increasing in mobile multimedia for health-care. The emergence of mobile cloud computing will provide cloud servers and storage for IMRT mobile applications, thus realizing visualized radiotherapy in a real sense.Equivalent uniform dose (EUD) is a biomedical indicator based on the dose measure. In this study, the dose volume histogram is used to describe the dose distribution of different tissues in target and nontarget regions. The traditional definition of equivalent uniform dose such as the exponential form and the linear form has only a few parameters in the model for fast calculation. However, there is no close relationship between this traditional definition and the dose volume histogram.In order to establish the consistency between the equivalent uniform dose and the dose volume histogram, this paper proposes a novel definition of equivalent uniform dose based on the volume dose curve, called VD-EUD. By using a unique organic volume weight curve, it is easy to calculate VD-EUD for different dose distributions. In the definition, different weight curves are used to represent the biological effects of different organs. For the target area, we should be more careful about those voxels with low dose (cold point); thus, the weight curve is monotonically decreasing. While for the nontarget area, the curve is monotonically increasing. Furthermore, we present the curves for parallel, serial and mixed organs of nontarget areas separately, and we define the weight curve form with only two parameters. Medical doctors can adjust the curve interactively according to different patients and organs. We also propose a fluence map optimization model with the VD-EUD constraint, which means the proposed EUD constraint will lead to a large feasible solution space.We compare the generalized equivalent uniform dose (gEUD) and the proposed VD-EUD by experiments, which show that the VD-EUD has a closer relationship with the dose volume histogram. If the biological survival probability is equivalent to the VD-EUD, the feasible solution space would be large, and the target areas can be covered.By establishing a personalized organic weight curve, medical doctors can have a unique VD-EUD for each patient. By using the flexible and adjustable equivalent uniform dose definition, we can establish VD-EUD-based fluence map optimization model, which will lead to a larger solution space than the traditional dose volume constraint-based model. The VD-EUD is a new definition; thus, we need more clinical testing and verification.

  • 33.
    Lee, Cheng-Chi
    et al.
    Department of Library and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan, Republic of China; Department of Photonics and Communication Engineering, Asia University, Taichung, Taiwan, Republic of China.
    Li, Chun-Ta
    Department of Information Management, Tainan University of Technology, Tainan, Taiwan, Republic of China.
    Cheng, Chung-Lun
    Department of Library and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan, Republic of China.
    Lai, Yan-Ming
    Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Novel Group Ownership Delegate Protocol for RFID Systems2019In: Information Systems Frontiers, ISSN 1387-3326, E-ISSN 1572-9419, Vol. 21, no 5, p. 1153-1166Article in journal (Refereed)
    Abstract [en]

    In recent years, Radio Frequency Identification (RFID) applications of various kinds have been blooming. However, along with the stunning advancement have come all sorts of security and privacy issues, for RFID tags oftentimes store private data and so the permission to read a tag or any other kind of access needs to be carefully controlled. Therefore, of all the RFID-related researches released so far, a big portion focuses on the issue of authentication. There have been so many cases where the legal access to or control over a tag needs to be switched from one reader to another, which has encouraged the development of quite a number of different kinds of ownership transfer protocols. On the other hand, not only has the need for ownership transfer been increasing, but a part of it has also been evolving from individual ownership transfer into group ownership transfer. However, in spite of the growing need for practical group ownership transfer services, little research has been done to offer an answer to the need. In this paper, we shall present a new RFID time-bound group ownership delegate protocol based on homomorphic encryption and quadratic residues. In addition, in order to provide more comprehensive service, on top of mutual authentication and ownership delegation, we also offer options for the e-th time verification as well as the revocation of earlier delegation.

  • 34.
    Hasanov, Aziz
    et al.
    Ajou University.
    Laine, Teemu
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Chung, Tae-Sun
    Ajou University.
    A survey of adaptive context-aware learning environments2019In: Journal of Ambient Intelligence and Smart Environments, ISSN 1876-1364, E-ISSN 1876-1372, Vol. 11, no 5, p. 403-428Article, review/survey (Refereed)
    Abstract [en]

    Adaptive context-aware learning environments (ACALEs) can detect the learner’s context and adapt learning materi-als to match the context. The support for context-awareness and adaptation is essential in these systems so that they can makelearning contextually relevant. Previously, several related surveys have been conducted, but they are either outdated or they donot consider the important aspects of context-awareness, adaptation and pedagogy in the domain of ACALEs. To alleviate this,a comprehensive literature search on ACALEs was first performed. After filtering the results, 53 studies that were publishedbetween 2010 and 2018 were analyzed. The highlights of the results are: (i) mobile devices (PDAs, mobile phones, smartphones)are the most common client types, (ii) RFID/NFC are the most common sensors, (iii) ontology is the most common context mod-eling approach, (iv) context data typically originates from the learner profile or the learner’s location, (v) rule-based adaptationis the most used adaptation mechanism, and (vi) informative feedback is the most common feedback type. Additionally, we con-ducted a trend analysis on technology usage in ACALEs throughout the covered timespan, and proposed a taxonomy of contextcategories as well as several other taxonomies for describing various aspects of ACALEs. Finally, based on the survey results,directions for future research in the field were given. These results can be of interest to educational technology researchers andto developers of adaptive and context-aware applications.

  • 35.
    Gupta, Dipankar
    et al.
    Department of Computer Science and Engineering, Port City International University, Chattogram, Bangladesh.
    Hossain, Emam
    Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Sazzad
    Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh.
    A Digital Personal Assistant using Bangla Voice Command Recognition and Face Detection2019In: Proceedings of IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things 2019, 2019Conference paper (Refereed)
    Abstract [en]

    Though speech recognition has been a common interest of researchers over the last couple of decades, but very few works have been done on Bangla voice recognition. In this research, we developed a digital personal assistant for handicapped people which recognizes continuous Bangla voice commands. We employed the cross-correlation technique which compares the energy of Bangla voice commands with prerecorded reference signals. After recognizing a Bangla command, it executes a task specified by that command. Mouse cursor can also be controlled using the facial movement of a user. We validated our model in three different environments (noisy, moderate and noiseless) so that the model can act naturally. We also compared our proposed model with a combined model of MFCC & DTW, and another model which combines crosscorrelation with LPC. Results indicate that the proposed model achieves a huge accuracy and smaller response time comparing to the other two techniques.

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  • 36.
    Laato, Samuli
    et al.
    Department of Future Technologies, University of Turku, Finland.
    Laine, Teemu
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Sutinen, Erkki
    Department of Future Technologies, University of Turku, Finland.
    Affordances of music composing software for learning mathematics at primary schools2019In: Research in Learning Technology, ISSN 2156-7069, E-ISSN 2156-7077, Vol. 27, article id 2259Article in journal (Refereed)
    Abstract [en]

    Music composing is associated with various positive learning outcomes, but in several countries, such as Finland, it is not part of the primary school music curriculum. There are several issues as to why music composing is not taught at schools, such as beliefs that composing requires extensive knowledge of music theory, lack of teachers’ confidence, lack of evidence on the method’s effectiveness and difficulty of assessment. Composing software has the potential of solving some of these issues, as they are connected to mathematics via music theory and technology, and with practical opportunities arising from adopting phenomenon-based learning at schools, the affordances of music composing technologies for learning mathematics are investigated in this study. For this purpose, 57 music composing software were categorised and reviewed. Our analysis identified eight types of music visualisations and five types of note input methods. The music visualisations were compared to the mathematics content in the Finnish primary school curriculum and the note input methods were evaluated based on their relationship to the music visualisations. The coordinate grid-based piano roll was the most common visualisation and the tracker visualisation had the most affordances for learning primary school math. Music composing software were found to have affordances for teaching mathematical concepts, notations and basic calculus skills, among others. Composing methods involving direct interaction with visualisations support the experiential learning of music theory, and consequently, the learning of mathematics. Based on the findings of this study, we concluded that music composing is a promising activity through which mathematics and music theory can be learned at primary schools.

  • 37.
    Seo, Jungryul
    et al.
    Department of Computer Engineering, Ajou University, Suwon, Korea.
    Laine, Teemu H.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Sohn, Kyung-Ah
    Department of Computer Engineering, Ajou University, Suwon, Korea.
    An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 20, article id 4561Article in journal (Refereed)
    Abstract [en]

    In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted to detect the user’s emotions from physiological data. Among a myriad of target emotions, boredom, in particular, has been suggested to cause not only medical issues but also challenges in various facets of daily life. However, to the best of our knowledge, no previous studies have used electroencephalography (EEG) and galvanic skin response (GSR) together for boredom classification, although these data have potential features for emotion classification. To investigate the combined effect of these features on boredom classification, we collected EEG and GSR data from 28 participants using off-the-shelf sensors. During data acquisition, we used a set of stimuli comprising a video clip designed to elicit boredom and two other video clips of entertaining content. The collected samples were labeled based on the participants’ questionnaire-based testimonies on experienced boredom levels. Using the collected data, we initially trained 30 models with 19 machine learning algorithms and selected the top three candidate classifiers. After tuning the hyperparameters, we validated the final models through 1000 iterations of 10-fold cross validation to increase the robustness of the test results. Our results indicated that a Multilayer Perceptron model performed the best with a mean accuracy of 79.98% (AUC: 0.781). It also revealed the correlation between boredom and the combined features of EEG and GSR. These results can be useful for building accurate affective computing systems and understanding the physiological properties of boredom.

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  • 38.
    Uddin Ahmed, Tawsin
    et al.
    Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
    Alam, Md. Jahangir
    Department of Civil Engineering Chittagong, University of Engineering & Technology, Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    An Integrated CNN-RNN Framework to Assess Road Crack2019In: Proceedings of the 2019 22nd International Conference on Computer and Information Technology (ICCIT), IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    Road crack detection and road damage assessment are necessary to support driving safety in a route network. Several unexpected incidents (e.g. road accidents) take place all over the world due to unhealthy road infrastructure. This paper proposes a deep learning approach for road crack detection and road damage assessment which will contribute to the transport sector of a country like Bangladesh where a plethora of roads undergo the crack problem. The proposed model consists of two phases. In the first phase, the model is trained using transfer learning (VGG16) to detect the existence of crack on the road surface. In the second phase, an integrated framework, combining CNN(VGG16) and RNN(LSTM), is trained to classify the crack in one of the two categories-severe and slight. After experiments, the validation accuracies obtained by the proposed models (VGG16 and VGG16-LSTM) are respectively 99.67% and 97.66%.

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  • 39.
    Hossain, Mohammad Shahadat
    et al.
    University of Chittagong, Bangladesh.
    Sultana, Zinnia
    International Islamic University Chittagong, Bangladesh.
    Nahar, Lutfun
    International Islamic University Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    An Intelligent System to Diagnose Chikungunya under Uncertainty2019In: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 10, no 2, p. 37-54Article in journal (Refereed)
    Abstract [en]

    Chikungunya is a virus-related disease, bring about by the virus called CHIKV that spreads throughmosquito biting. This virus first found in Tanzania, while blood from patients was isolated. Thecommon signs and symptoms, associated with Chikungunya are considered as fever, joint swelling,joint pain, muscle pain and headache. The examination of these signs and symptoms by the physician constitutes the typical preliminary diagnosis of this disease. However, the physician is unable tomeasure them with accuracy. Therefore, the preliminary diagnosis in most of the cases could sufferfrom inaccuracy, which leads to wrong treatment. Hence, this paper introduces the design and implementation of a belief rule based expert system (BRBES) which is capable to represent uncertainknowledge as well as inference under uncertainty. Here, the knowledge is illustrated by employing belief rule base while deduction is carried out by evidential reasoning. The real patient data of250 have been considered to demonstrate the accuracy and the robustness of the expert system. Acomparison has been performed with the results of BRBES and Fuzzy Logic Based Expert System(FLBES) as well as with the expert judgment. Furthermore, the result of BRBES has been contrastedwith various data-driven machine learning approaches, including ANN (Artificial Neural networks)and SVM (Support Vector Machine). The reliability of BRBESs was found better than those of datadriven machine learning approaches. Therefore, the BRBES presented in this paper could enable thephysician to conduct the analysis of Chikungunya more accurately.

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  • 40.
    Minovski, Dimitar
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Johansson, Per
    InfoVista Sweden AB.
    Analysis and Estimation of Video QoE in Wireless Cellular Networks using Machine Learning2019In: 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    The use of video streaming services are increasing in the cellular networks, inferring a need to monitor video quality to meet users' Quality of Experience (QoE). The so-called no-reference (NR) models for estimating video quality metrics mainly rely on packet-header and bitstream information. However, there are situations where the availability of such information is limited due to tighten security and encryption, which necessitates exploration of alternative parameters for conducting video QoE assessment. In this study we collect real-live in-smartphone measurements describing the radio link of the LTE connection while streaming reference videos in uplink. The radio measurements include metrics such as RSSI, RSRP, RSRQ, and CINR. We then use these radio metrics to train a Random Forrest machine learning model against calculated video quality metrics from the reference videos. The aim is to estimate the Mean Opinion Score (MOS), PSNR, Frame delay, Frame skips, and Blurriness. Our result show 94% classification accuracy, and 85% model accuracy (R 2 value) when predicting the MOS using regression. Correspondingly, we achieve 89%, 84%, 85%, and 82% classification accuracy when predicting PSNR, Frame delay, Frame Skips, and Blurriness respectively. Further, we achieve 81%, 77%, 79%, and 75% model accuracy (R 2 value) regarding the same parameters using regression.

  • 41.
    Chowdhury, Rumman Rashid
    et al.
    Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
    Hossain, Sazzad
    Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Analyzing Sentiment of Movie Reviews in Bangla by Applying Machine Learning Techniques2019In: Proceedings of the International Conference on Bangla Speech and Language Processing, IEEE, 2019Conference paper (Other academic)
    Abstract [en]

    This paper proposes a process of sentiment analysis of movie reviews written in Bangla language. This process can automate the analysis of audience’s reaction towards a specific movie or TV show. With more and more people expressing their opinions openly in the social networking sites, analyzing the sentiment of comments made about a specific movie can indicate how well the movie is being accepted by the general public. The dataset used in this experiment was collected and labeled manually from publicly available comments and posts from social media websites. Using Support Vector Machine algorithm, this model achieves 88.90% accuracy on the test set and by using Long Short Term Memory network [1] the model manages to achieve 82.42% accuracy. Furthermore, a comparison with some other machine learning approaches is presented in this paper.

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  • 42.
    Chowdhury, Rumman Rashid
    et al.
    Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Sazzad
    Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh.
    Bangla Handwritten Character Recognition using Convolutional Neural Network with Data Augmentation2019In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 2019Conference paper (Refereed)
    Abstract [en]

    This paper proposes a process of Handwritten Character Recognition to recognize and convert images of individual Bangla handwritten characters into electronically editable format, which will create opportunities for further research and can also have various practical applications. The dataset used in this experiment is the BanglaLekha-Isolated dataset [1]. Using Convolutional Neural Network, this model achieves 91.81% accuracy on the alphabets (50 character classes) on the base dataset, and after expanding the number of images to 200,000 using data augmentation, the accuracy achieved on the test set is 95.25%. The model was hosted on a web server for the ease of testing and interaction with the model. Furthermore, a comparison with other machine learning approaches is presented.

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  • 43.
    Dai, Hong-Ning
    et al.
    Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau.
    Wong, Raymond Chi-Wing
    Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST), Clear Water Bay, Kowloon, Hong Kong.
    Wang, Hao
    Norwegian University of Science and Technology, Norway.
    Zheng, Zibin
    School of Data and Computer Science, Sun Yat-sen University, Xiaoguwei Island, Guangzhou, China.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Big Data Analytics for Large-scale Wireless Networks: Challenges and Opportunities2019In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 52, no 5, article id 99Article in journal (Refereed)
    Abstract [en]

    The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.

  • 44.
    Alzubaidi, Ali
    et al.
    Newcastle University.Umm-Al-Qura University.
    Solaiman, Ellis
    Newcastle University.
    Patel, Pankesh
    Fraunhofer USA-Center for Experimental Software Engineering.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Blockchain-Based SLA Management in the Context of IoT2019In: IT Professional Magazine, ISSN 1520-9202, E-ISSN 1941-045X, Vol. 21, no 4, p. 33-40Article in journal (Refereed)
    Abstract [en]

    In pursuit of effective service level agreement (SLA) monitoring and enforcement in the context of Internet of Things (IoT) applications, this article regards SLA management as a distrusted process that should not be handled by a single authority. Here, we aim to justify our view on the matter and propose a conceptual blockchain-based framework to cope with some limitations associated with traditional SLA management approaches.

  • 45.
    Islam, Raihan Ul
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Ruci, Xhesika
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Kor, Ah-Lian
    School of Computing, Creative Technologies and Engineering Leeds Beckett University, Leeds, UK.
    Capacity Management of Hyperscale Data Centers Using Predictive Modelling2019In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 12, no 18, article id 3438Article in journal (Refereed)
    Abstract [en]

    Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient.

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  • 46.
    Li, Jianjiang
    et al.
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Zhang, Kai
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Yang, Xiaolei
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Wei, Peng
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Wang, Jie
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Ranjan, Rajiv
    School of Computer Science, China University of Geosciences.
    Category Preferred Canopy-K-means based Collaborative Filtering algorithm2019In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 93, p. 1046-1054Article in journal (Refereed)
    Abstract [en]

    It is the era of information explosion and overload. The recommender systems can help people quickly get the expected information when facing the enormous data flood. Therefore, researchers in both industry and academia are also paying more attention to this area. The Collaborative Filtering Algorithm (CF) is one of the most widely used algorithms in recommender systems. However, it has difficulty in dealing with the problems of sparsity and scalability of data. This paper presents Category Preferred Canopy-K-means based Collaborative Filtering Algorithm (CPCKCF) to solve the challenges of sparsity and scalability of data. In particular, CPCKCF proposes the definition of the User-Item Category Preferred Ratio (UICPR), and use it to compute the UICPR matrix. The results can be applied to cluster the user data and find the nearest users to obtain prediction ratings. Our experimentation results performed using the MovieLens dataset demonstrates that compared with traditional user-based Collaborative Filtering algorithm, the proposed CPCKCF algorithm proposed in this paper improved computational efficiency and recommendation accuracy by 2.81%.

  • 47. Nurgazy, M.
    et al.
    Zaslavsky, A.
    Jayaraman, P.P
    Kubler, S.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    CAVisAP: Context-Aware Visualisation of Air Pollution with IoT Platforms2019Conference paper (Refereed)
  • 48.
    Alhamazani, Khalid
    et al.
    School of Computer Science and Engineering, University of New South Wales.
    Ranjan, Rajiv
    CSIRO Digital Productivity, Acton.
    Jayaraman, Prem
    CSIRO Digital Productivity, Acton.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Liu, Chang
    Sydney University of Technology.
    Rabhi, Fethi
    School of Computer Science and Engineering, University of New South Wales.
    Georgakopoulos, Dimitrios
    Royal Melbourne Institute of Technology, Melbourne.
    Wang, Lizhe
    Chinese Academy of Sciences, Beijing.
    Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework2019In: I E E E Transactions on Cloud Computing, ISSN 2168-7161, Vol. 7, no 1, p. 48-61Article in journal (Refereed)