Journals
- Cross-Instance Regulatory Compliance Checking of Business Process Event Logs (), In IEEE Transactions on Software Engineering, volume , 2023.
Abstract
Event logs capture the execution of business processes, such that each task is represented by an event and each individual execution is a chronological sequence of events, called an event trace. Event logs allow after-the-act and runtime analysis of deployed business processes to verify whether their execution complies with regulations and business requirements. Checking the compliance of a single sequence of events in a trace is straightforward and a number of approaches have been proposed to address this. However, some regulations or business rules span multiple process instances and a cross-instance analysis is required. In order to check whether such requirements are maintained at all times, multiple traces need to be analysed together, which can result in a combinatorial computational complexity. In this paper, we present a novel approach that efficiently checks runtime regulatory compliance based on event logs, while supporting cross-instance rule evaluation and extensible function evaluation over sequences of attribute data values. The efficiency and applicability of the proposed method is tested in a two-pronged evaluation, showing a significant improvement over existing techniques with respect to capabilities as well as computational complexity. The approach presented in this paper is subject to a patent application, with patent number WO2021/248201.
Keywords: Business process, Event log, Compliance, Regulations, Cross-instance, Instance-spanning, Runtime verificationBibTeX
doi - Enough Hot Air: The Role of Immersion Cooling (), In Energy Informatics, 2023.
Abstract
Air cooling is the traditional solution to chill servers in data centers. However, the continuous increase in global data center energy consumption combined with the increase of the racks’ power dissipation calls for the use of more efficient alternatives. Immersion cooling is one such alternative. In this paper, we quantitatively examine and compare air cooling and immersion cooling solutions. The examined characteristics include power usage efficiency (PUE), computing and power density, cost, and maintenance overheads. A direct comparison shows a reduction of about 50% in energy consumption and a reduction of about two-thirds of the occupied space, by using immersion cooling. In addition, the higher heat capacity of used liquids in immersion cooling compared to air allows for much higher rack power densities. Moreover, immersion cooling requires less capital and operational expenditures. However, challenging maintenance procedures together with the increased number of IT failures are the main downsides. By selecting immersion cooling, cloud providers must trade-off the decrease in energy and cost and the increase in power density with its higher maintenance and reliability concerns. Finally, we argue that retrofitting an air-cooled data center with immersion cooling will result in high costs and is generally not recommended.
BibTeX
url - Carbon Emission-Aware Job Scheduling for Kubernetes Deployments (), In The Journal of Supercomputing, 2023.
Abstract
Decreasing carbon emissions of data centers while guaranteeing Quality of Service (QoS) is one of the major challenges for efficient resource management of large-scale cloud infrastructures and societal sustainability. Previous works in the area of carbon reduction mostly focus on decreasing overall energy consumption, replacing energy sources with renewable ones, and migrating workloads to locations where lower emissions are expected. These measures do not consider the energy mix of the power used for the data center. In other words, all KWh of energy are considered the same from the point of view of emissions, which is rarely the case in practice. In this paper, we overcome this deficit by proposing a novel practical CO2-aware workload scheduling algorithm implemented in the Kubernetes orchestrator to shift non-critical jobs in time. The proposed algorithm predicts future CO2 emissions by using historical data of energy generation, selects time-shiftable jobs, and creates job schedules utilizing greedy sub-optimal CO2 decisions. The proposed algorithm is implemented using Kubernetes’ scheduler extender solution due to its ease of deployment with little overheads. The algorithm is evaluated with real-world workload traces and compared to the default Kubernetes scheduling implementation on several actual scenarios.
BibTeX
url - On Memristors for Enabling Energy Efficient and Enhanced Cognitive Network Functions (), In IEEE Access, IEEE, volume 10, 2022.
Abstract
The high performance requirements of nowadays computer networks are limiting their ability to support important requirements of the future. Two important properties essential in assuring cost-efficient computer networks and supporting new challenging network scenarios are operating energy efficient and supporting cognitive computational models. These requirements are hard to fulfill without challenging the current architecture behind network packet processing elements such as routers and switches. Notably, these are currently dominated by the use of traditional transistor-based components. In this article, we contribute with an in-depth analysis of alternative architectural design decisions to improve the energy footprint and computational capabilities of future network packet processors by shifting from transistor-based components to a novel component named Memristor . A memristor is a computational component characterized by non-volatile operations on a physical state, mostly represented in form of (electrical) resistance. Its state can be read or altered by input signals, e.g. electrical pulses, where the future state always depends on the past state. Unlike in traditional von Neumann architectures, the principles behind memristors impose that memory operations and computations are inherently colocated. In combination with the non-volatility, this allows to build memristors at nanoscale size and significantly reduce the energy consumption. At the same time, memristors appear to be highly suitable to model cognitive functionality due to the state dependence transitions in the memristor. In cognitive architectures, our survey contributes to the study of memristor-based Ternary Content Addressable Memory (TCAM) used for storage of cognitive rules inside packet processors. Moreover, we analyze the memristor-based novel cognitive computational architectures built upon self-learning capabilities by harnessing from non-volatility and state-based response of memristors (including reconfigurable architectures, reservoir computation architectures, neural network architectures and neuromorphic computing architectures).
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urldoi - Correlating the Community Structure of Constraint Satisfaction Problems with Search Time (), In International Journal on Artificial Intelligence Tools, 2022.
Abstract
A constraint satisfaction problem (CSP) is, in its most general form, an NP-complete problem. One of the several classes of tractable problems that exist contains all the problems with a restricted structure of the constraint scopes. This paper studies community structure, a particular type of restricted structure underpinning a class of tractable SAT problems with potentially similar relevance to CSPs. Using the modularity, it explores the community structure of a wide variety of problems with both academic and industrial relevance. Its impact on the search times of several general solvers, as well as one that uses tree-decomposition, is also analysed to determine whether constraint solvers exploit this type of structure. Nearly all CSP instances have a strong community structure, and those belonging to the same class have comparable modularity values. For the general solvers, strong correlations between the community structure and the search times are not apparent. A more definite correlation exists between the modularity and the search times of the tree-decomposition, suggesting that it might, in part, be able to take advantage of the community structure. However, combined with the relatively strong correlation between the modularity and the tree-width, it could also indicate a similarity between these two measures.
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urldoi - Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand (), In Energies 2022, Vol. 15, Page 3425, Multidisciplinary Digital Publishing Institute, volume 15, 2022.
Abstract
This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1 6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73 2.98%; Summer: 8.41 14.44%). The coefficient of determination (R2) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts.
Keywords: Bayesian optimization algorithm, NARX, SARIMAX, electricity demand, short, term forecastBibTeX
urldoi - Optimal Joint Operation of Coupled Transportation and Power Distribution Urban Networks (), In Energy Informatics, 2022.
Abstract
The number of Electric Vehicles (EVs) and consequently their penetration level into urban society is increasing which has imperatively reinforced the need for a joint stochastic operational planning of Transportation Network (TN) and Power Distribution Network (PDN). This paper solves a stochastic multi-agent simulation-based model with the objective of minimizing the total cost of interdependent TN and PDN systems. Capturing the temporally dynamic inter-dependencies between the coupled networks, an equilibrium solution results in optimized system cost. In addition, the impact of large-scale EV integration into the PDN is assessed through the mutual coupling of both networks by solving the optimization problems, i.e., optimal EV routing using traffic assignment problem and optimal power flow using branch flow model. Previous works in the area of joint operation of TN and PDN networks fall short in considering the time-varying and dynamic nature of all effective parameters in the coupled TN and PDN system. In this paper, a Dynamic User Equilibrium (DUE) network model is proposed to capture the optimal traffic distribution in TN as well as optimal power flow in PDN. A modified IEEE 30 bus system is adapted to a low voltage power network to examine the EV charging impact on the power grid. Our case study demonstrates the enhanced operation of the joint networks incorporating heterogeneous EV characteristics such as battery State of Charge (SoC), charging requests as well as PDN network’s marginal prices. The results of our simulations show how solving our defined coupled optimization problem reduces the total cost of the defined case study by 36% compared to the baseline scenario. The results also show a 45% improvement on the maximum EV penetration level with only minimal voltage deviation (less than 0.3%).
BibTeX
url - Network Testing Utilizing ProgrammableNetworking Hardware. (), In IEEE Communications Magazine, IEEE, 2022.
- Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks (), In Processes, volume 9, 2021.
Abstract
By virtue of the steady societal shift to the use of smart technologies built on the increasingly popular smart grid framework, we have noticed an increase in the need to analyze household electricity consumption at the individual level. In order to work efficiently, these technologies rely on load forecasting to optimize operations that are related to energy consumption (such as household appliance scheduling). This paper proposes a novel load forecasting method that utilizes a clustering step prior to the forecasting step to group together days that exhibit similar energy consumption patterns. Following that, we attempt to classify new days into pre-generated clusters by making use of the available context information (day of the week, month, predicted weather). Finally, using available historical data (with regard to energy consumption) alongside meteorological and temporal variables, we train a CNN-LSTM model on a per-cluster basis that specializes in forecasting based on the energy profiles present within each cluster. This method leads to improvements in forecasting performance (upwards of a 10% increase in mean absolute percentage error scores) and provides us with the added benefit of being able to easily highlight and extract information that allows us to identify which external variables have an effect on the energy consumption of any individual household.
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urldoi - TCEP: Transitions in Operator Placement to Adapt to Dynamic Network Environments. (), In In Journal of Computer and Systems Sciences (JCSS), Special Issue on Algorithmic Theory of Dynamic Networks and its Applications., Elsevier, volume 122, 2021.
- Employability prediction: a survey of current approaches, research challenges and applications (), In Journal of Ambient Intelligence and Humanized Computing, 2021.
Abstract
Student employability is crucial for educational institutions as it is often used as a metric for their success. The job market landscape, however, more than ever dynamic, is evolving due to the globalization, automation, and recent advances in Artificial Intelligence. Identifying the significant factors affecting employability, as well as the requirements of the new job market can tremendously help all stakeholders. Knowing their weaknesses and strengths, students might better plan their career. Instructors can focus on more appropriate skill sets to meet the requirements of rapidly evolving labor markets. Program managers can anticipate and improve their curriculum to build new competencies, both for educating, training and reskilling current and future workers. All these combined efforts certainly can contribute to increasing employability. Data driven and machine learning techniques have been extensively used in various fields of educational data mining. More and more studies are investigating data mining techniques for the prediction of employability. Yet, these studies show a lot of variation, for instance, with respect to the data used, the methods adopted, or even the research questions posed. In this paper, we aim to depict a clear picture of the art, clarifying for each standard step of data mining process, the differences, and similarities of these studies, along with further suggestions. Thus, this survey provides a comprehensive roadmap, enabling the application of data mining for employability.
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doi - OpenBNG: Central office network functions on programmable data plane hardware (), In International Journal of Network Management, Wiley, volume 31, 2021.
- SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks (), In BMC bioinformatics, BioMed Central, volume 22, 2021.
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- Adaptive On-the-fly Changes in Distributed Processing Pipelines (), In Frontiers in Big Data, Frontiers, 2021.
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- A multi-robot allocation model for multi-object based on Global Optimal Evaluation of Revenue (), In International Journal of Advanced Robotic Systems, volume 18, 2021.
Abstract
The problem of global optimal evaluation for multi-robot allocation has gained attention constantly, especially in a multi-objective environment, but most algorithms based on swarm intelligence are difficult to give a convergent result. For solving the problem, we established a Global Optimal Evaluation of Revenue method of multi-robot for multi-tasks based on the real textile combing production workshop, consumption, and different task characteristics of mobile robots. The Global Optimal Evaluation of Revenue method could traversal calculates the profit of each robot corresponding to different tasks with global traversal over a finite set, then an optimization result can be converged to the global optimal value avoiding the problem that individual optimization easy to fall into local optimal results. In the numerical simulation, for fixed set of multi-object and multi-task, we used different numbers of robots allocation operation. We then compared with other methods: Hungarian, the auction method, and the method based on game theory. The results showed that Global Optimal Evaluation of Revenue reduced the number of robots used by at least 17%, and the delay time could be reduced by at least 16.23%.
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urldoi - Unsupervised approach towards analysing the public transport bunching swings formation phenomenon (), In Public Transport, 2020.
- Prediction-Based Underutilized and Destination Host Selection Approaches for Energy-Efficient Dynamic VM Consolidation in Data Centers (), In The Journal of Supercomputing, 2020.
Abstract
Improving the energy efficiency while guaranteeing quality of services (QoS) is one of the main challenges of efficient resource management of large-scale data centers. Dynamic virtual machine (VM) consolidation is a promising approach that aims to reduce the energy consumption by reallocating VMs to hosts dynamically. Previous works mostly have considered only the current utilization of resources in the dynamic VM consolidation procedure, which imposes unnecessary migrations and host power mode transitions. Moreover, they select the destinations of VM migrations with conservative approaches to keep the service-level agreements , which is not in line with packing VMs on fewer physical hosts. In this paper, we propose a regression-based approach that predicts the resource utilization of the VMs and hosts based on their historical data and uses the predictions in different problems of the whole process. Predicting future utilization provides the opportunity of selecting the host with higher utilization for the destination of a VM migration, which leads to a better VMs placement from the viewpoint of VM consolidation. Results show that our proposed approach reduces the energy consumption of the modeled data center by up to 38% compared to other works in the area, guaranteeing the same QoS. Moreover, the results show a better scalability than all other approaches. Our proposed approach improves the energy efficiency even for the largest simulated benchmarks and takes less than 5% time overhead to execute for a data center with 7600 physical hosts.
BibTeX
url - Efficient conditional compliance checking of business process models (), In Computers in Industry, ELSEVIER SCIENCE BV, volume 115, 2020.
Abstract
When checking compliance of business processes against a set of business rules or regulations, the ability to handle and verify conditions in both the model and the rules is essential. Existing design-time verification approaches, however, either completely lack support for the verification of conditions or propose costly verification methods that also consider the full data perspective. This paper proposes a novel light-weight verification method, which is preferable over expensive approaches that include the data perspective when considering structural properties of a business process model. This novel approach generates partial models that capture only relevant execution states to the conditions under investigation. The resulting model can be verified using existing model checking techniques. The computation of such partial models fully abstracts conditions from the full models and specifications, thus avoiding the analysis of the full data perspective. The proposed method is complete with respect to the analyzed execution paths, while significantly reducing the state space complexity by pruning unreachable states given the conditions under investigation. This approach offers the ability to check if a process is compliant with rules and regulations on a much more fine-grained level, and it enables a more precise formulation of the conditions that should and should not hold in the processes. The approach is particularly useful in dynamic environments where processes are constantly changing and efficient conditional compliance checking is a necessity. The approach – implemented in Java and publicly available – is evaluated in terms of performance and practicability, and tested over both synthetic datasets and a real-life case from the Australian telecommunications sector.
Keywords: Business process models, Formal verification, Conditional compliance, Data perspective, Temporal logicBibTeX
urlpdfdoi - Infrastructure Aware Heterogeneous-Workloads Scheduling for Data Center Energy Cost Minimization (), In IEEE Transactions on Cloud Computing, volume 10, 2020.
Abstract
A huge amount of energy consumption, the cost of this usage and environmental effects have become serious issues for commercial cloud providers. Solar energy is a promising clean energy source, to provide some portion of the Internet data center's (IDC's) energy usage which can reduce environmental effects and total energy costs. Moreover, due to the high energy consumption of the cooling system, considering cooling power in job scheduling can provide efficient solutions to reduce total energy consumption. In this article, we investigate the problem of minimizing the energy cost of an IDC and propose an algorithm which schedules heterogeneous IDC workloads, by considering available renewable energy, cooling subsystem, and electricity rate structure. We evaluate the effectiveness and feasibility of our algorithm using real and synthetic workload traces. The simulation results illustrate how our proposed solution reduces the data center's energy cost by up to 46 percent compared to previous solutions. Moreover, results show that our solution is capable of reducing energy cost of data centers under different weather conditions, and rate structures.
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urldoi - Prediction of academic performance at undergraduate graduation: Course grades or grade point average? (), In Applied Sciences (Switzerland), volume 10, 2020.
Abstract
Predicting the academic standing of a student at the graduation time can be very useful, for example, in helping institutions select among candidates, or in helping potentially weak students in overcoming educational challenges. Most studies use individual course grades to represent college performance, with a recent trend towards using grade point average (GPA) per semester. It is unknown however which of these representations can yield the best predictive power, due to the lack of a comparative study. To answer this question, a case study is conducted that generates two sets of classification models, using respectively individual course grades and GPAs. Comprehensive sets of experiments are conducted, spanning different student data, using several well-known machine learning algorithms, and trying various prediction window sizes. Results show that using course grades yields better accuracy if the prediction is done before the third term, whereas using GPAs achieves better accuracy otherwise. Most importantly, variance analysis on the experiment results reveals interesting insights easily generalizable: individual course grades with short prediction window induces noise, and using GPAs with long prediction window causes over-simplification. The demonstrated analytical approach can be applied to any dataset to determine when to use which college performance representation for enhanced prediction.
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doi - The Internet of Everything: Smart things and their impact on business models (), In Journal of Business Research, 2020.
- Predicting academic success in higher education: literature review and best practices (), In International Journal of Educational Technology in Higher Education, volume 17, 2020.
Abstract
© 2020, The Author(s). Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success, through which student attributes to focus on, up to which machine learning method is more appropriate to the given problem. This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.
Keywords: Data mining, Guidelines, Higher education, Prediction, Review, Student successBibTeX
doi - Workload Scheduling on heterogeneous Mobile Edge Cloud in 5G networks to Minimize SLA Violation (), In arXiv preprint arXiv:2003.02820, 2020.
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- OpenBNG: Central office network functions on programmable data plane hardware (), In International Journal of Network Management, Wiley, 2020.
- Grußwort der Gastherausgeber zum Thema Fog Computing (), In Informatik Spektrum, Springer Science and Business Media LLC, volume 42, 2020.
- MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers (), In IEEE Transactions on Services Computing, volume 15, 2019.
Abstract
Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource management of large-scale cloud infrastructures. Previous works in the area of dynamic Virtual Machine (VM) consolidation are mostly focused on addressing the energy challenge, but fall short in proposing comprehensive, scalable, and low-overhead approaches that jointly tackle energy efficiency and performance variability. Moreover, they usually assume over-simplistic power models, and fail to accurately consider all the delay and power costs associated with VM migration and host power mode transition. These assumptions are no longer valid in modern servers executing heterogeneous workloads and lead to unrealistic or inefficient results. In this paper, we propose a centralized-distributed low-overhead failure-aware dynamic VM consolidation strategy to minimize energy consumption in large-scale data centers. Our approach selects the most adequate power mode and frequency of each host during runtime using a distributed multi-agent Machine Learning (ML) based strategy, and migrates the VMs accordingly using a centralized heuristic. Our Multi-AGent machine learNing-based approach for Energy efficienT dynamIc Consolidation (MAGNETIC) is implemented in a modified version of the CloudSim simulator, and considers the energy and delay overheads associated with host power mode transition and VM migration, and is evaluated using power traces collected from various workloads running in real servers and resource utilization logs from cloud data center infrastructures. Results show how our strategy reduces data center energy consumption by up to 15 percent compared to other works in the state-of-the-art (SoA), guaranteeing the same QoS and reducing the number of VM migrations and host power mode transitions by up to 86 and 90 percent, respectively. Moreover, it shows better scalability than all other approaches, taking less than 0.7 percent time overhead to execute for a data center with 1,500 VMs. Finally, our solution is capable of detecting host performance variability due to failures, automatically migrating VMs from failing hosts and draining them from workload.
BibTeX
urldoi - Variability in business processes: Automatically obtaining a generic specification (), In Information Systems, PERGAMON-ELSEVIER SCIENCE LTD, volume 80, 2019.
Abstract
The existence of different process variants is inevitable in many modern organizations. However, variability in business process support has proven to be a challenge as it requires a flexible business process specification that supports the required process variants, while at the same time being compliant with policies and regulations. Declarative approaches could support variability, by providing rules constraining process behavior and thereby allowing different variants. However, manual specification of these rules is complicated and error-prone. As such, tools are required to ensure that duplication and overlap of rules is avoided as much as possible, while retaining maintainability. In this paper, we present an approach to represent different process variants in a single compound prime event structure, and provide a method to subsequently derive variability rules from this compound prime event structure. The approach is evaluated by conducting an exploratory evaluation on different sets of real-life business process variants, including a real-life case from the Dutch eGovernment, to demonstrate the effectiveness and applicability of the approach.
Keywords: Business Process Model, Declarative Variability Modeling, Event Structure, Temporal Logic, PROCESS MODELS, CORRECTNESSBibTeX
urlpdfdoi - Analytical tool for the modelling and simulation of curriculum: Towards automated design, assessment, and improvement (), In International Journal of Engineering Education, volume 35, 2019.
Abstract
© 2019 TEMPUS Publications. Continuous quality improvement cycle is essential in educational systems allowing institutions to meet the evolving needs of the market. As such, it is required by all accreditation agencies. Curriculum revision is a critical step of this cycle. This study proposes a modelling paradigm to automate the design, analysis and improvement of curriculum. Based on proven theoretical principles, this novel graph-based approach captures both pre-requisite and cognitive dependencies among courses, enabling an optimal learning environment for students. The presented tool allows an easy and fast analysis of the impact of potential course revisions on all other courses, hence enabling a better continuous quality improvement process, thus providing benefits to many stakeholders in the education system, namely managers, instructors, students and employers. The proposed modelling paradigm is explained and illustrated on a capstone project course offered in the College of Computer Science and IT.
Keywords: Accreditation, Automated tool, Curriculum design, Curriculum development, Engineering education, Quality assuranceBibTeX
- Fostering higher cognitive skills through design thinking in digital hardware course: A case study (), In ICIC Express Letters, volume 13, 2019.
Abstract
© 2019 ICIC International. All rights reserved. Computer Science students are reportedly facing many issues in acquiring higher cognitive skills (e.g., analysis, and design). Digital hardware is one of the first courses in a typical Computer Science curriculum where students need to master these skills while analyzing and designing sequential circuits. This study investigates the pedagogical effectiveness of the Design Thinking methodology in improving students' higherorder cognitive skills in the digital hardware course. Design Thinking was embedded in the digital hardware course through a real-world design challenge where teams of students iteratively collaborated. The design problem was purposely set to necessitate knowledge and skills yet to be covered hence fostering in students' curiosity and eagerness to learn new topics, thus engaging students as active learners and meaning creators. The study demonstrates a significant gain in test scores. It also describes how to easily embed the Design Thinking process in the digital hardware curriculum.
Keywords: Analysis, Continuous quality improvement, Design, Design Thinking, Digital Logic, Higherorder cognitive skills, Student learning outcomesBibTeX
doi - The u-can-act Platform: A Tool to Study Intra-individual Processes of Early School Leaving and Its Prevention Using Multiple Informants (), In Frontiers in Psychology, volume 10, 2019.
- Energy management for user's thermal and power needs: A survey (), In Energy Reports, volume 5, 2019.
- IMOS: improved meta-aligner and Minimap2 on spark (), In BMC bioinformatics, Springer, volume 20, 2019.
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- Shedding Light on the Dark Corners of the Internet: A Survey of Tor Research (), In Journal of Network and Computer Applications, Elsevier, volume 114, 2018.
Abstract
Anonymity services have seen high growth rates with increased usage in the past few years. Among various services, Tor is one of the most popular peer-to-peer anonymizing service. In this survey paper, we summarize, analyze, classify and quantify 26 years of research on the Tor network. Our research shows that ‘security’ and ‘anonymity’ are the most frequent keywords associated with Tor research studies. Quantitative analysis shows that the majority of research studies on Tor focus on ‘deanonymization’ the design of a breaching strategy. The second most frequent topic is analysis of path selection algorithms to select more resilient paths. Analysis shows that the majority of experimental studies derived their results by deploying private testbeds while others performed simulations by developing custom simulators. No consistent parameters have been used for Tor performance analysis. The majority of authors performed throughput and latency analysis.
BibTeX
urldoi - A Formal Model for Compliance Verification of Service Compositions (), In Ieee transactions on services computing, volume 11, 2018.
Abstract
Business processes design and execution environments increasingly need support from modular services in service compositions to offer the flexibility required by rapidly changing requirements. With each evolution, however, the service composition must continue to adhere to laws and regulations, resulting in a demand for automated compliance checking. Existing approaches, if at all, either offer only verification after the fact or linearize models to such an extent that parallel information is lost. We propose a mapping of service compositions to Kripke structures by using colored Petri nets. The resulting model allows preventative compliance verification using well-known temporal logics and model checking techniques while providing full insight into parallel executing branches and the local next invocation. Furthermore, the mapping causes limited state explosion, and allows for significant further model reduction. The approach is validated on a case study from a telecom company in Australia and evaluated with respect to performance and expressiveness. We demonstrate that the proposed mapping has increased expressiveness while being less vulnerable to state explosion than existing approaches, and show that even large service compositions can be verified preventatively with existing model checking techniques.
Keywords: Service Composition, Business process, Compliance, Verification, Temporal Logic, Colored Petri net, Kripke structure, COMPLIANCE-CHECKING, BUSINESS, SPECIFICATION, SUPPORTBibTeX
urlpdfdoi - Fast and Energy-Efficient CNFET Adders with CDM and Sensitivity-Based Device-Circuit Co-Optimization (), In IEEE Transactions on Nanotechnology, volume 17, 2018.
Abstract
Since integrated circuit technology entered into the nanoscale regime, energy efficiency has become one of the most significant challenges. The carbon nanotube field effect transistor (CNFET) is one of the highly appreciated nanoscale devices for replacement due to its similar process to the current CMOS technology. The big question in this paper is what are the other specific controllable parameters in CNFET technology for designers to design high-performance and energy-efficient circuits and how much these parameters impact the circuit characteristics? In this regard, two energy-efficient full adders, as the crucial building blocks of digital systems, in 32 nm CNFET technology are designed. Cell design methodology as an efficient logic style is used for the new designs, and CNFET-SEA is used for the optimization. The CNFET-SEA, which is a modification of simple exact algorithm (SEA), is proposed as an appropriate sizing algorithm for circuits in CNFET technology. The sensitivity analysis, as a new approach, is used in the CNFET-SEA algorithm to obtain better sizing results in shorter runtime. The number of tubes, the diameter of tubes, and pitch are considered as the three specific device parameters in the CNFET technology for device-circuit co-optimization, and their effect on the circuit characteristics is investigated. The simulation results show a 15-97% delay, 8-87% power-delay product (PDP), and 22-99% energy-delay product improvement for the proposed full adders compared with the referenced ones. The PDP optimization with CNFET-SEA in comparison with SEA shows 11-20% improvement with a significant runtime reduction for selected adders.
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urldoi - Zero-queue ethernet congestion control protocol based on available bandwidth estimation (), In Journal of Network and Computer Applications, Elsevier, volume 105, 2018.
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- Multi-User Low Intrusive Occupancy Detection (), In Sensors, MDPI, volume 18, 2018.
- Topological Considerations on Decentralised Energy Exchange in the Smart Grid (), In Procedia Computer Science, volume 130, 2018.
- Learning behind glass walls: learning style and partition-room, is there a correlation? (), In International Journal of Innovation Science, 2018.
Abstract
© 2018, Emerald Publishing Limited. Purpose: This study aims to investigate how a very particular learning environment, namely, partition rooms, affect students’ teaching experience and further explore if students’ learning styles is a pertinent determinant. Partition rooms are very common in Saudi Arabia when lectures are held by male instructors for female students. The male instructor delivers his lesson behind a glass wall, creating an environment of limited visual and auditory interaction. Various digital tools are present, meant to overcome the gap caused by the lack of direct student–teacher contact. Design/methodology/approach: The researchers collected data from a sample of 109 female students who are studying at Level 4 Computer Science Department, College of Computer Sciences and Information Technology, at a public university in Saudi Arabia. All of them experienced a minimum of two courses undertaken in a partition room. The survey consists of two parts with a total of 53 questions. The first 20 questions were adopted from the perceptual learning style preference questionnaire (PLSP). Findings: Research findings reveal that students are affected differently by the various dimensions of the partition room depending on their learning style. Originality/value: There are fewer results in the literature that study learners of our particular group, namely, Saudi females. The study focuses on students studying IT and related fields. This study is almost unique, as most studies of the kind are related to the experience of females learning English as a foreign language. Therefore, the authors’ research gives much-needed insight into the conditions and perceptions of female students studying toward their degree in a technical field.
Keywords: Cultural specific education, Educational technology, Female education, Learning environment, Partition room, Saudi education, Technology efficacyBibTeX
doi - A smarter electricity grid for the Eastern Province of Saudi Arabia: Perceptions and policy implications (), In Utilities Policy, volume 50, 2018.
Abstract
© 2017 Elsevier Ltd Saudi Arabia aspires to transition toward a smarter electricity grid with increased reliance on renewable energy, where customers will use or produce green energy and where smart meters will enable customers to tailor their behavior and decrease their carbon footprint. The success of the transition is dependent on householder acceptance. This research studies the public's disposition toward a smarter grid. The Eastern Province of Saudi Arabia is taken as a case study through a field questionnaire to assess public knowledge about energy sources and environmental impacts on the environments, people's disposition toward a smarter electric grid, and the main motivations for undergoing this transition. A logit model is used to investigate determinants. Stated willingness is taken as a variable representing an individual's disposition. We found that the public is willing to use green energy, accept smart meters, or become co-producers. However, their fear of unknown technologies and perceptions about their high cost are major obstacles to their adoption. Enhancive knowledge, especially about ecological sensitivity, and governmental incentives will help to win public acceptance. Also, government subsidies that lower prices should be cut and dynamic pricing should be implemented to motivate electricity saving behavior.
Keywords: Kingdom of Saudi Arabia, Renewable energy, Residential area, Smart grid, Smart metering, Social acceptance, Solar energyBibTeX
doi - Exploring the emotional dynamics of subclinically depressed individuals with and without anhedonia: An experience sampling study (), In Journal of Affective Disorders, volume 228, 2018.
- Topological Considerations on the Use of Batteries to Enhance the Reliability of HV-Grids (), In Journal of Energy Storage, volume 18, 2018.
- A task-based greedy scheduling algorithm for minimizing energy of mapreduce jobs (), In Journal of grid computing, Springer, volume 16, 2018.
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- Adaptive Provisioning of Heterogeneous Cloud Resources for Big Data Processing (), In Big Data and Cognitive Computing, volume 2, 2018.
- The impact of digital technology on female students' learning experience in partition-rooms: Conditioned by social context (), In IEEE Transactions on Education, volume 61, 2018.
Abstract
Contribution: As expected, a partition-room environment negatively affects students' learning. An unexpected result of this study is that female students occasionally choose not to use the technology available in partition-rooms, to avoid undesirable facial exposure. Background: The main purpose of partition-rooms is to prevent male instructors from seeing female students' faces. In learning environments where instructors and students are physically separated, technology is expected to play an integral role in bridging the gap. In one side of partition-rooms, female students use their own mobile devices, such as laptops, tablets and mobile phones, for course activities and communication; in the other side, the instructor has various digital teaching equipment provided by the institution. Research Question: What effect does a partition-room's physical environment have on female students' academic performance, satisfaction, technology efficacy, and perceived learning? What effect does a partition-room's social environment have on female students' academic performance, satisfaction, technology efficacy, and perceived learning? Methodology: Both quantitative and qualitative approaches were followed. Quantitative results were obtained from a student questionnaire. Qualitative data was gathered in a focus group session. Findings: The communication benefits offered by technology are impaired by both the physical context and the cultural-social context. The latter emerged during focus group discussions where students said that their faces might by revealed in the light emitted by their devices. Thus, local culture and social context limit the benefits of using digital technology in the classroom.
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doi - Personalized Physical Activity Coaching: A Machine Learning Approach (), In Sensors, volume 18, 2018.
- Planning meets activity recognition: Service coordination for intelligent buildings (), In Pervasive and Mobile Computing, Elsevier, volume 38, 2017.
- Automated Generation Algorithm for Synthetic Medium Voltage Radial Distribution Systems (), In IEEE Journal on Emerging and Selected Topics in Circuits and Systems, volume 7, 2017.
- Sizing and Siting of Large-Scale Batteries in Transmission Grids to Optimize the Use of Renewables (), In IEEE Journal on Emerging and Selected Topics in Circuits and Systems, volume 7, 2017.
- MuGKeG: Secure Multi-channel Group Key Generation Algorithm for Wireless Networks (), In Wireless Personal Communications, Springer, volume 96, 2017.
Abstract
The broadcast nature of communication channels in infrastructureless wireless networks poses challenges to security. In this paper, we propose a novel technique namely secure multi-channel group key generation (MuGKeG) algorithm. We utilize the available channels switching behaviour between multiple nodes to hide our key from eavesdropper. We provide descriptions for an illustrative base case of three users and one eavesdropper and expand it for the case of N users with C channels and M eavesdroppers. Repeated application of the MuGKeG algorithm on the order of O(logN) allows scaling the size of the group in the order of millions. We provide an analytical closed-form solution for the entropy of the secret group key generated when eavesdroppers follow an optimal attack strategy, and verify it by ns-3 simulations. Comparison with previous state-of-the-art schemes suggests that MuGKeG can provide upto 20 kbps increase in secrecy rate with a scalable key size.
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urldoi - A Stochastic Model for Transit Latency in OpenFlow SDNs (), In Computer Networks, Elsevier, volume 113, 2017.
Abstract
Software defined networks (SDNs) introduced the concept of decoupling control and data planes which is a paradigm shift. The OpenFlow protocol is one of a number of technologies that enables this decoupling and, in effect, commodifies network equipment. As of now, there is still limited work that has been done towards modeling the transit delay across OpenFlow switches experienced by network traffic. In this work we develop a stochastic model for the path latency in Open vSwitch (used together with a POX controller) based on measurements made in experiments performed on three different platforms which include 1) Mininet, 2) MikroTik RouterBoard 750GL and 3) GENI testbed softswitch. We propose a log-normal mix model (LNMM) and show that it offers a R2 value of greater than 0.90 for most of our experiments. We also demonstrate how the M/M/1 models proposed in earlier studies is a poor fit.
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urldoi - Indoor self-localization via bluetooth low energy beacons (), In IDRBT JOURNAL OF IJBT, volume 1, 2017.
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- Metrics for Sustainable Data Centers (), In IEEE Transactions on Sustainable Computing, 2017.
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- Analytical Modeling of End-to-End Delay in OpenFlow Based Networks (), In IEEE Access, IEEE, volume 5, 2016.
Abstract
OpenFlow enabled networks split and separate the data and control planes of traditional networks. This design commodifies network switches and enables centralized control of the network. Control decisions are made by an OpenFlow controller, and locally cached by switches, as directed by controllers. This can significantly impact the forwarding delay incurred by packets in switches, because controllers are not necessarily co-located with switches. Only very few studies have been conducted to evaluate the performance of OpenFlow in terms of end-to-end delay. In this paper, we develop a stochastic model for the end to end delay in OpenFlow switches based on measurements made in Internet-scale experiments performed on three different platforms, i.e., Mininet, the GENI testbed, and the OF@TEIN testbed.
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urldoi - A Novel Strategy for Optimising Decentralised Energy Exchange for Prosumers (), In Energies, MDPI, volume 9, 2016.
- Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms (), In Applied Soft Computing, Elsevier, volume 40, 2016.
- Domain-Independent Planning for Services in Uncertain and Dynamic Environments (), In Artificial Intelligence, Elsevier, volume 236, 2016.
- Let's get Physiqual - an intuitive and generic method to combine ssensor technology with ecological momentary assessments (), In Journal of Biomedical Informatics, volume 63, 2016.
- From the grid to the smart grid, topologically (), In Physica A, Elsevier, volume 449, 2016.
- Automatic RDF-ization of big data semi-structured datasets (), In Maskana, volume 7, 2016.
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- Automated planning for ubiquitous computing (), In ACM Comput. Surv., ACM, volume 49, 2016.
- Design and implementation of a residential energy monitoring system prototype tailored to meet local needs (), In International Journal of Computing and Digital Systems, volume 5, 2016.
Abstract
The Kingdom of Saudi Arabia, like many other Gulf Council Countries, is lately experiencing a very rapid population and industrial growth, which results in an increasing demand for energy. To meet this growing demand, the GCC too is transitioning towards a smarter electricity grid with increased penetration of renewable sources. However, all agree that the success of such a shift in paradigm also depends on demand side management, most of energy demands coming for residential area. Providing residents with real-time feedback on their energy consumption is a promising way to promote energy saving behavior through an increased awareness. This paper outlines the design and development phases of a residential energy monitoring system that has been tailored to meet local needs, that is to say a non-intrusive system with a user friendly interface available both in English and Arabic endowed with an alert system providing real-time consumption information, as well as energy saving and awareness tips.
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doi - Temporal dynamics of health and well-being: A crowdsourcing approach to momentary assessments and automated generation of personalized feedback (), In Psychosomatic Medicine, 2016.
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- Detecting similar areas of knowledge using semantic and data mining technologies (), In Electronic Notes in Theoretical Computer Science, Elsevier, volume 329, 2016.
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- Automating vector autoregression on electronic patient diary data (), In IEEE Journal of Biomedical and Health Informatics, IEEE, volume 20, 2015.
- Integrating Transactions into BPEL Service Compositions: An Aspect-Based Approach (), In ACM Transactions on the Web, ACM, volume 9, 2015.
- Characterizing topological bottlenecks for data delivery in CTP using simulation-based stress testing with natural selection (), In Ad Hoc Networks, Elsevier, volume 30, 2015.
- A complex network approach for identifying vulnerabilities of the medium and low voltage grid (), In International Journal of Critical Infrastructures, Inderscience, volume 11, 2015.
- HTN planning: Overview, comparison, and beyond (), In Artificial Intelligence, Elsevier, volume 222, 2015.
- HowNutsAreTheDutch (HoeGekIsNL): A crowdsourcing study of mental symptoms and strengths (), In International Journal of Methods in Psychiatric Research, 2015.
- Improving QoS of IPTV and VoIP over IEEE 802.11n (), In Computers & Electrical Engineering, Elsevier, volume 43, 2015.
Abstract
Tremendous growth rates of Internet Protocol Television (IPTV) and Voice over Internet Protocol (VoIP) have demanded the shift of paradigm from wired to wireless applications. Increased packet loss with continuously varying wireless conditions make the transmission a challenging task in wireless environment. Our study investigates and proposes improvement in the transmission of combined IPTV and VoIP over the IEEE 802.11n WLAN. Our major contributions include the analytical and experimental investigations of (1) transport layer protocol UDP/TFRC for IPTV and VoIP, (2) optimal physical layer parameters for IPTV and VoIP, (3) proposition of wireless enhancement of TFMCC (W-TFMCC) to enhance the capacity and Quality of Service (QoS) of wireless IPTV and VoIP. Analytical and experimental evaluations show a 25% increase in capacity using TFRC with 167% more bandwidth share to TCP. Our study shows that use of W-TFMCC with optimal parameters can enhance IPTV and VoIP capacity by 44%.
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urldoi - Integration and massive storage of hydro-meteorological data combining big data & semantic web technologies (), In Maskana, volume 6, 2015.
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- The Impact of Topology on Energy Consumption for Collection Tree Protocols: An Experimental Assessment through Evolutionary Computation (), In Applied Soft Computing, volume 16, 2014.
- Smart Education Modes and E-Learning Market: The Needs of the Next Generation (), In Smart Learning Environments Journal, The State of the Art in Smart Learning Issue, Springer, 2014.
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- Leefplezier: Personalized Well-being (), In IEEE Intelligent Informatics Bulletin, volume 15, 2014.
- Power Grid Complex Network Evolutions for the Smart Grid (), In Physica A: Statistical Mechanics and its Applications, volume 396, 2014.
- Generating Realistic Dynamic Prices and Services for the Smart Grid (), In IEEE Systems Journal, volume 9, 2014.
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- E-Mental Health Self-Management for Psychotic Disorders: State of the Art and Future Perspectives (), In Psychiatric Services, volume 65, 2014.
- Automated Runtime Repair of Business Processes (), In Inf. Syst., volume 39, 2014.
- Dynamic Constraint Satisfaction with Space Reduction in Smart Environments (), In International Journal on Artificial Intelligence Tools, volume 23, 2014.
- Generating personalized advice for schizophrenia patients (), In Artificial Intelligence in Medicine, volume 58, 2013.
- Coordinating the Web of Services for a Smart Home (), In ACM Transactions on the Web, volume 7, 2013.
- An Interplatform Service-Oriented Middleware for the Smart Home (), In International Journal of Smart Home, volume 7, 2013.
- The Management of Elearning at University of KKU, ABHA (), In International Journal of Emerging Technologies in Learning, volume 8, 2013.
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- The Power Grid as a Complex Network: a Survey (), In Physica A: Statistical Mechanics and its Applications, volume 392, 2013.
- E-health self-management in psychotic disorders: state of the art and future perspectives (), In Psychiatric Services, volume 65, 2013.
- Aware homes (), In Awareness Magazine: Self-awareness in autonomic systems, 2013.
- Energy Intelligent Buildings based on User Activity: A Survey (), In Energy and Buildings, volume 56, 2013.
- Ontology-based Office Activity Recognition with Applications for Energy Savings (), In Journal of Ambient Intelligence and Humanized Computing, 2013.
- Usability Evaluation of a Web-Based Support System for People With a Schizophrenia Diagnosis (), In Journal of Medical Internet Research, volume 14, 2012.
- What IS can do for Environmental Sustainability (), In Communications of the Association for Information Systems, volume 30, 2012.
- Policy-Based Scheduling of Cloud Services (), In Scalable Computing: Practice and Experience, volume 13, 2012.
- Service-Orientation and the Smart Grid: State and Trend (), In Service Oriented Computing and Applications, volume 6, 2012.
- Optimizing Energy Costs for Offices Connected to the Smart Grid (), In IEEE Transactions on Smart Grid, volume 3, 2012.
- Logic for physical space (), In Synthese, volume 18, 2012.
- Forward (), In Journal of System Assurance Engineering and Management, volume 3, 2012.
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- Reduced Context Consistency Diagrams for Resolving Inconsistent Data (), In ICST Transactions on Ubiquitous Environments, volume 12, 2012.
- Modeling Dynamic Reconfigurations in Reo using High-Level Replacement Systems (), In Science of Computer Programming, volume 76, 2011.
- Transaction management in Service-Oriented Systems: requirements and a proposal (), In IEEE Transactions on Service Computing, volume 2, 2011.
- Towards Decentralized Trading: A Topological Investigation of the Medium and Low Voltage Grids (), In IEEE Transactions on Smart Grid, volume 2, 2011.
- Deriving Business Processes with Service Level Agreements from Early Requirements (), In Journal of Systems and Software, volume 84, 2011.
- An Online Portal on Outcomes for Dutch Service Users (), In Psychiatric Services, volume 62, 2011.
- Modelling and Managing the Variability of Web Service-based Systems (), In Journal of Systems and Software, volume 83, 2010.
- Channel-based Coordination via Constraint Satisfaction (), In Science of Computer Programming, 2010.
- Automated graph-based methodology for fault detection and location in power systems (), In IEEE Transactions on Power Delivery, volume 25, 2010.
Abstract
This study investigates how the model-based fault detection and location approach of structural analysis can be adapted to meet the needs of power systems, where challenges associated with increased system complexity make conventional protection schemes impractical. With a global view of the protected system and the systematic and automated use of the system's analytical redundancy, faults are detected and located by more than one means. This redundancy can be used as a confirmation mechanism within a wide-area protection scheme to avoid unnecessary or false tripping due to protection component failure or disturbance. Furthermore, this redundancy turns the sensor configuration problem into an optimization problem with regard to fault detection and location. The effectiveness of different system topologies can then be compared on the basis of the optimal number of sensors they require. The principle of structural analysis is described in detail and illustrated on a simple power system model. Pertinence of the approach is demonstrated through simulation. © 2010 IEEE.
Keywords: Fault diagnosis, Power system protection, Structural analysis, Wide-area protectionBibTeX
doi - Are our homes ready for services? A domotic infrastructure based on the Web service stack (), In Pervasive and Mobile Computing, volume 4, 2008.
- Fault diagnosis of a water for injection system using enhanced structural isolation (), In International Journal of Applied Mathematics and Computer Science, volume 18, 2008.
Abstract
A water for injection system supplies chilled sterile water as a solvent for pharmaceutical products. There are ultimate requirements for the quality of the sterile water, and the consequence of a fault in temperature or in flow control within the process may cause a loss of one or more batches of the production. Early diagnosis of faults is hence of considerable interest for this process. This study investigates the properties of multiple matchings with respect to isolability, and it suggests to explore the topologies of multiple use-modes for the process and to employ active techniques for fault isolation to enhance structural isolability of faults. The suggested methods are validated on a high-fidelity simulation of the process.
Keywords: Fault diagnosis, Fault isolation, Matching, Structural analysisBibTeX
doi - Structural analysis of fault isolability in the DAMADICS benchmark (), In Control Engineering Practice, volume 14, 2006.
Abstract
Structural analysis is a powerful tool for early determination of fault detectability/fault isolability possibilities. It is shown how different levels of knowledge about faults can be incorporated in a structural fault isolability analysis and how they result in different isolability properties. The results are evaluated on the DAMADICS valve benchmark model. It is also shown how to determine which faults in the benchmark need further modelling to get desired isolability properties of the diagnosis system. © 2005 Elsevier Ltd. All rights reserved.
Keywords: Fault detection and isolation, Fault modelling, Structural analysisBibTeX
doi - Structural isolability of faults and breakdowns: Application to a valve model (), In Journal Europeen des Systemes Automatises, volume 38, 2004.
Abstract
Structural analysis is a powerful tool for early determination of many underlying properties of a system. This tool is widely used for the diagnosis of complex systems for the early analysis of structural detectability and isolability of faults. The contribution of this paper is twofold: different points such as faults representation or how to handle differential variables are clarified. It is then shown how different levels of knowledge about faults can be incorporated in a structural fault isolability analysis and how they result in different isolability properties. The results are evaluated on a valve model that constitutes a benchmark for the European DAMADICS 1 Research and Training network. It is also shown how to determine which faults in a system need further modelling investigation to get desired isolability properties.
Keywords: Diagnosis, Fault isolability, Modelling, Structural analysisBibTeX
- Isolabilité structurelle des défaillances. Application à un modèle de vanne (), In Journal Européen des Systèmes Automatisés, volume 38, 2004.
Abstract
Structural analysis is a powerful tool for early determination of many underlying properties of a system. This tool is widely used for the diagnosis of complex systems for the early analysis of structural detectability and isolability of faults. The contribution of this paper is twofold: different points such as faults representation or how to handle differential variables are clarified. It is then shown how different levels of knowledge about faults can be incorporated in a structural fault isolability analysis and how they result in different isolability properties. The results are evaluated on a valve model that constitutes a benchmark for the European DAMADICS1 Research and Training network. It is also shown how to determine which faults in a system need further modelling investigation to get desired isolability properties.
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