Distributed Systems

Publications

    2023


      2022


      1. On Memristors for Enabling Energy Efficient and Enhanced Cognitive Network Functions ( and ), In IEEE Access, IEEE, volume 10, .

        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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      2. Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand (, , and ), In Energies 2022, Vol. 15, Page 3425, Multidisciplinary Digital Publishing Institute, volume 15, .

        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 forecast


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      3. Network Testing Utilizing ProgrammableNetworking Hardware. (, , , and ), In IEEE Communications Magazine, IEEE, .

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      4. TCAmMCogniGron: Energy Efficient Memristor-Based TCAM for Match-Action Processing (, , and ), In Proceedings of the 7th International Conference on Rebooting Computing (ICRC 2022), IEEE, .

        Abstract

        The Internet relies heavily on programmable match-action processors for matching network packets against locally available network rules and taking actions, such as forwarding and modification of network packets. This match-action process must be performed at high speed, i.e., commonly within one clock cycle, using a specialized memory unit called Ternary Content Addressable Memory (TCAM). Building on transistor-based CMOS designs, state-of-the-art TCAM architectures have high energy consumption and lack resilient designs for incorporating novel technologies for performing appropriate actions. In this article, we motivate the use of a novel fundamental component, the ‘Memristor’, for the development of TCAM architecture for match-action processing. Memristors can provide energy efficiency, non-volatility and better resource density as compared to transistors. We have proposed a novel memristor-based TCAM architecture called TCAmMCogniGron, built upon the voltage divider principle and requiring only two memristors and five transistors for storage and search operations compared to sixteen transistors in the traditional TCAM architecture. We analyzed its performance over an experimental data set of Nb-doped SrTiO3-based memristor. The analysis of TCAmMCogniGron showed promising power consumption statistics of 16 uW and 1 uW for match and mismatch operations along with twice the improvement in resources density as compared to the traditional architectures.


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      5. Towards Energy Efficient Memristor-based TCAM for Match-Action Processing (, , and ), In Proceedings of the 13th International Green and Sustainable Computing Conference (IGSC 2022), IEEE, .

        Abstract

        Match-action processors play a crucial role of communicating end-users in the Internet by computing network paths and enforcing administrator policies. The computation process uses a specialized memory called Ternary Content Addressable Memory (TCAM) to store processing rules and use header information of network packets to perform a match within a single clock cycle. Currently, TCAM memories consume huge amounts of energy resources due to the use of traditional transistor-based CMOS technology. In this article, we motivate the use of a novel component, the memristor, for the development of a TCAM architecture. Memristors can provide energy efficiency, non-volatility, and better resource density as compared to transistors. We have proposed a novel memristor-based TCAM architecture built upon the voltage divider principle for energy efficient match-action processing. Moreover, we have tested the performance of the memristor-based TCAM architecture using the experimental data of a novel Nb-doped SrTiO3 memristor. Energy analysis of the proposed TCAM architecture for given memristor shows promising power consumption statistics of 16 μW for a match operation and 1 μW for a mismatch operation.


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      6. On the Use of the Conformance and Compliance Keywords During Verification of Business Processes (, and ), In Business Process Management Forum (C. Di Ciccio, R. Dijkman, A. del Río Ortega, S. Rinderle-Ma, eds.), Springer, .

        Abstract

        A wealth of techniques have been developed to help organizations understand their processes, verify correctness against requirements and diagnose potential problems. In general, these verification techniques allow us to check whether a business process conforms or complies with some specification, and each of them is specifically designed to solve a particular business problem at a stage of the BPM lifecycle. However, the terms conformance and compliance are often used as synonyms and their distinct differences in verification goals is blurring. As a result, the terminology used to describe the techniques or the corresponding verification activity does not always match with the precise meaning of the terms as they are defined in the area of verification. Consequently, confusion of these terms may hamper the application of the different techniques and the correct positioning of research. In this position paper, we aim to provide comprehensive definitions and a unified terminology throughout the BPM lifecycle. Moreover, we explore the consequences when these terms are used incorrectly. In doing so, we aim to improve adoption from research to practical applications by clarifying the relation between techniques and the intended verification goals.


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      7. Window-based Parallel Operator Execution with In-Network Computing: Proceedings (, and ), In Proceedings of the 16th ACM International Conference on Distributed and Event-based Systems (DEBS '22), ACM New York, NY, USA, .

        Abstract

        Data parallel processing is a key concept to increase the scalability and elasticity in event streaming systems. Often data parallelism is accomplished in a splitter-merger architecture where the splitter divides incoming streams into partitions and forwards them to parallel operator instances. The splitter performance is a limiting factor to the system throughput and the parallelization degree.This work studies how to leverage novel methods of in-network computing to accelerate the splitter functionality by implementing it as an in-network function. While dedicated hardware for in-network computing has a high potential to enhance the splitter performance, in-network programming models like the P4 language are also highly limited in their expressiveness to support corresponding parallelization models. We propose P4SS which supports overlapping and non-overlapping count-based windows for multiple independent data streams and parallelizes them to a dynamically configurable number of operator instances. We validate in the context of a prototypical implementation our splitting strategy and its scalability in terms of switch resource consumption.


        Keywords: Data Parallelism, In-network Computing, Load Balancing, Complex Event Processing (CEP), P4 Language, Data Plane Programming


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      8. Enhancing Flexibility for Dynamic Time-Sensitive Network Configurations (, , , , and ), In Proceedings of the 3rd KuVS Fachgespräch on Network Softwarization, Universität Tübingen, .

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      9. On the Incremental Reconfiguration of Time-sensitive Networks at Runtime (, , , , and ), In Proceedings of the IFIP Networking Conference., IFIP, .

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      10. Travel light: state shedding for efficient operator migration (, , and ), In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems (DEBS'22), ACM press, .

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      11. FA2: Fast, Accurate Autoscaling for Serving Deep Learning Inference with SLA Guarantees (, , , and ), In Proceedings of the 28th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2022), IEEE, .

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      12. Towards adaptive quality-aware Complex Event Processing in the Internet of Things (, and ), In The 18th International Conference on Mobility, Sensing and Networking (MSN 2022), IEEE, .

        Abstract

        This paper investigates how to complement Complex Event Processing (CEP) with dynamic quality monitoring mechanisms and support the dynamic integration of suitable sensory data sources. In the proposed approach, queries to detect complex events are annotated with consumer-definable quality policies that are evaluated and used to autonomously assign (or even configure) suitable data sources of the sensing infrastructure. We present and study different forms of expressing quality policies and explore how they affect the process of quality monitoring including different modes of assessing and applying quality-related adaptations. A performance study in an IoT scenario shows that the proposed mechanisms in supporting quality policy monitoring and adaptively selecting suitable data sources succeed in enhancing the acquired quality of results while fulfilling consumers' quality requirements. We show that the quality-based selection of sensor sources also extends the network's lifetime by optimizing the data sources' energy consumption.


        Keywords: Complex Event Processing, Adaptation, Quality, Internet of Things


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      13. PANDA: performance prediction for parallel and dynamic stream processing (, , and ), In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems, ACM press, .

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      14. Using Memristors for Energy Efficient Cognitive Network Functions (, , and ), In Symposium on Physics of Information in Matter [Poster Session], AMOLF, .

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      15. In-Network Computing Over Memristor-Based Cognitive Network Functions (, , and ), In Brain-Inspired Concepts and Materials for Information Processing (Brainspiration) Conference [Poster Session], University of Twente, .

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      16. Memristor-Based Cognitive Network Packet Processors ( and ), In Neuromorphic Computing Netherlands (NCN 2022) Workshop [Abstracts, Talks and Posters], Radboud University, .

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      17. Memristor-Based Cognitive and Energy Efficient In-Network Processing (, , and ), In Workshop on Bio-Inspired Information Pathways [Abstracts and Posters], CRC-1461 Neurotronics, University of Kiel, .

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      2021


      1. Multi-Energy Management of Buildings in Smart Grids (), University of Groningen, .

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      2. Automated Service Composition Using AI Planning and Beyond (, and ), Chapter in (M. Aiello, A. Bouguettaya, D. A. Tamburri, W. J. van den Heuvel, eds.), Springer International Publishing, .

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      3. A multi-robot allocation model for multi-object based on Global Optimal Evaluation of Revenue (, , , and ), In International Journal of Advanced Robotic Systems, volume 9, .

        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 percent, and the delay time could be reduced by at least 16.23 percent.


        Keywords: global optimal, multi-robot, path planning, response time, task allocation


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      4. TCEP: Transitions in Operator Placement to Adapt to Dynamic Network Environments. (, , , , and ), In In Journal of Computer and Systems Sciences (JCSS), Special Issue on Algorithmic Theory of Dynamic Networks and its Applications., Elsevier, volume 122, .

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      5. Employability prediction: a survey of current approaches, research challenges and applications (, , , and ), In Journal of Ambient Intelligence and Humanized Computing, .

        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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      6. OpenBNG: Central office network functions on programmable data plane hardware (, , , , , , , , , and ), In International Journal of Network Management, Wiley, volume 31, .

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      7. SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks (, and ), In BMC bioinformatics, BioMed Central, volume 22, .

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      8. Adaptive On-the-fly Changes in Distributed Processing Pipelines (, , and ), In Frontiers in Big Data, Frontiers, .

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      9. Digital Twins: an enabler for digital transformation ( and ), Chapter in The Digital Transformation handbook, .

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      10. Accelerating the Performance of Data Analytics using Network-centric Processing (), In The 15th ACM International Conference on Distributed and Event-based Systems (DEBS '21), June 28-July 2, 2021, Virtual Event, Italy, ACM New York, NY, USA, .

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      11. Leveraging Flexibility of Time-Sensitive Networks for dynamic Reconfigurability (, , , , and ), In Proceedings of IFIP Networking 2021, IFIP, .

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      12. Leveraging PIFO Queues for Scheduling in Time-Sensitive Networks (, , , , , and ), In In the Proceedings of the IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN 2021)., IEEE, .

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      13. P4-CoDel: Experiences on Programmable Data Plane Hardware (, , , , and ), In Proceedings of the IEEE International Conference on Communications (ICC 2021): Next-Generation Networking and Internet Symposium, IEEE, .

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      14. Towards QoE-Driven Optimization of Multi-Dimensional Content Streaming (, , , , , , and ), In Proceedings of the Conference on Networked Systems 2021 (NetSys 2021), European Association of Software Science and Technology, .

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      15. Iot based smart water quality monitoring: Recent techniques, trends and challenges for domestic applications (, and ), volume 13, .

        Abstract

        Safe water is becoming a scarce resource, due to the combined effects of increased population, pollution, and climate changes. Water quality monitoring is thus paramount, especially for domestic water. Traditionally used laboratory-based testing approaches are manual, costly, time consuming, and lack real-time feedback. Recently developed systems utilizing wireless sensor network (WSN) technology have reported weaknesses in energy management, data security, and communication coverage. Due to the recent advances in Internet-of-Things (IoT) that can be applied in the development of more efficient, secure, and cheaper systems with real-time capabilities, we present here a survey aimed at summarizing the current state of the art regarding IoT based smart water quality monitoring systems (IoT-WQMS) especially dedicated for domestic applications. In brief, this study probes into common water-quality monitoring (WQM) parameters, their safe-limits for drinking water, related smart sensors, critical review, and ratification of contemporary IoT-WQMS via a proposed empirical metric, analysis, and discussion and, finally, design recommendations for an efficient system. No doubt, this study will benefit the developing field of smart homes, offices, and cities.


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      2020


      1. Office Occupancy Detection based on Power Meters and BLE Beaconing (), University of Groningen, .

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      2. Optimization of energy distribution in smart grids (), University of Groningen, .

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      3. Unsupervised approach towards analysing the public transport bunching swings formation phenomenon (, , , and ), In Public Transport, .

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      4. Efficient conditional compliance checking of business process models (, and ), In Computers in Industry, ELSEVIER SCIENCE BV, volume 115, .

        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 logic


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      5. Prediction of academic performance at undergraduate graduation: Course grades or grade point average? ( and ), In Applied Sciences (Switzerland), volume 10, .

        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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      6. The Internet of Everything: Smart things and their impact on business models (, , , , and ), In Journal of Business Research, .

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      7. Predicting academic success in higher education: literature review and best practices ( and ), In International Journal of Educational Technology in Higher Education, volume 17, .

        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 success


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      8. Workload Scheduling on heterogeneous Mobile Edge Cloud in 5G networks to Minimize SLA Violation (, , and ), In arXiv preprint arXiv:2003.02820, .

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      9. OpenBNG: Central office network functions on programmable data plane hardware (, , , , , , , , , and ), In International Journal of Network Management, Wiley, .

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      10. Grußwort der Gastherausgeber zum Thema Fog Computing (, , and ), In Informatik Spektrum, Springer Science and Business Media LLC, volume 42, .

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      11. Theorie: processen van voortijdig schoolverlaten en begeleiding om dat te voorkomen (, , , and ), Chapter in Voortijdig schoolverlaten voorkomen Perspectieven van wetenschap, praktijk en beleid (M. A. E. van der Gaag, N. R. Snell, G. G. Bron, E. S. Kunnen, eds.), Uitgeverij Acco, .

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      12. Procesonderzoek: processen van uitvallen, blijven en begeleiding (, , , , , and ), Chapter in Voortijdig schoolverlaten voorkomen Perspectieven van wetenschap, praktijk en beleid (M. A. E. van der Gaag, N. R. Snell, G. G. Bron, E. S. Kunnen, eds.), Uitgeverij Acco, .

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      13. Towards Service-Oriented and Intelligent Microgrids (, and ), In Proceedings of the 3rd International Conference on Applications of Intelligent Systems, Association for Computing Machinery, .

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      14. Predictive Multi-Objective Scheduling with Dynamic Prices and Marginal CO2-Emission Intensities ( and ), In ACM e-Energy 2020, .

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      15. Sustainability Choices when Cooking Pasta (, and ), In ACM e-Energy 2020, .

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      16. Operator as a Service: Stateful Serverless Complex Event Processing (, , , and ), In Proceedings of the 2020 IEEE International Conference on Big Data, IEEE, .

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      17. The Community Structure of Constraint Satisfaction Problems and Its Correlation with Search Time ( and ), In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), .

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      18. Microbursts in Software and Hardware-based Traffic Load Generation (, and ), In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, .

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      19. Flexible Content-based Publish/Subscribe over Programmable Data Planes (, , , and ), In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, .

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      20. P4STA: High Performance Packet Timestamping with Programmable Packet Processors (, , , and ), In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, .

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      2019


      1. Variability in business processes: Automatically obtaining a generic specification (, , and ), In Information Systems, PERGAMON-ELSEVIER SCIENCE LTD, volume 80, .

        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, CORRECTNESS


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      2. Analytical tool for the modelling and simulation of curriculum: Towards automated design, assessment, and improvement (), In International Journal of Engineering Education, volume 35, .

        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 assurance


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      3. Fostering higher cognitive skills through design thinking in digital hardware course: A case study (, and ), In ICIC Express Letters, volume 13, .

        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 outcomes


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      4. The u-can-act Platform: A Tool to Study Intra-individual Processes of Early School Leaving and Its Prevention Using Multiple Informants (, , , , and ), In Frontiers in Psychology, volume 10, .

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      5. Energy management for user's thermal and power needs: A survey ( and ), In Energy Reports, volume 5, .

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      6. IMOS: improved meta-aligner and Minimap2 on spark (, and ), In BMC bioinformatics, Springer, volume 20, .

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      7. Development of a decision-aid for patients with depression considering treatment options: prediction of treatment response using a data-driven approach (, , , , , , , , , and ), In ISPOR Europe 2019, Copenhagen, Denmark, .

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      8. Predictive CO2-Efficient Scheduling of Hybrid Electric and Thermal Loads ( and ), In 2019 IEEE International Conference on Energy Internet (ICEI), .

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      9. ECiDA: Evolutionary Changes in Data Analysis (, , , , , , , and ), In ICT.Open, Hilversum, The Netherlands, .

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      10. Office Multi-Occupancy Detection using BLE Beacons and Power Meters (, and ), In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics, and Mobile Communication Conference, .

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      11. Temporal Analysis of 911 Emergency Calls Through Time Series Modeling (, , and ), In The International Conference on Advances in Emerging Trends and Technologies, .

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      12. Prediction of Imports of Household Appliances in Ecuador Using LSTM Networks (, , and ), In Conference on Information Technologies and Communication of Ecuador, .

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      13. Time to get personal? The impact of researchers’ choices on the selection of treatment targets using the experience sampling methodology (, , , , , , , , , and ), PsyArXiv, .

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      2018


      1. The Web Was Done by Amateurs: A Reflection on One of the Largest Collective Systems Ever Engineered (), Springer, .

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      2. The non-existent average individual: Automated personalization in psychopathology research by leveraging the capabilities of data science (), University of Groningen, .

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      3. Shedding Light on the Dark Corners of the Internet: A Survey of Tor Research (, and ), In Journal of Network and Computer Applications, Elsevier, volume 114, .

        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.


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      4. A Formal Model for Compliance Verification of Service Compositions (, and ), In Ieee transactions on services computing, volume 11, .

        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, SUPPORT


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      5. Multi-User Low Intrusive Occupancy Detection (, , and ), In Sensors, MDPI, volume 18, .

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        urldoi
      6. Topological Considerations on Decentralised Energy Exchange in the Smart Grid ( and ), In Procedia Computer Science, volume 130, .

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        urldoi
      7. Zero-queue ethernet congestion control protocol based on available bandwidth estimation (, and ), In Journal of network and computer applications, Elsevier, volume 105, .

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      8. Learning behind glass walls: learning style and partition-room, is there a correlation? (, , and ), In International Journal of Innovation Science, .

        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 efficacy


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        doi
      9. A smarter electricity grid for the Eastern Province of Saudi Arabia: Perceptions and policy implications (, , and ), In Utilities Policy, volume 50, .

        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 energy


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        doi
      10. Exploring the emotional dynamics of subclinically depressed individuals with and without anhedonia: An experience sampling study (, , , , and ), In Journal of Affective Disorders, volume 228, .

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      11. Topological Considerations on the Use of Batteries to Enhance the Reliability of HV-Grids (, , and ), In Journal of Energy Storage, volume 18, .

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      12. A task-based greedy scheduling algorithm for minimizing energy of mapreduce jobs ( and ), In Journal of grid computing, Springer, volume 16, .

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      13. Adaptive Provisioning of Heterogeneous Cloud Resources for Big Data Processing (, , , and ), In Big Data and Cognitive Computing, volume 2, .

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      14. The impact of digital technology on female students' learning experience in partition-rooms: Conditioned by social context (, , and ), In IEEE Transactions on Education, volume 61, .

        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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      15. One for All, All for One: A Heterogeneous Data Plane for Flexible P4 Processing (, , , and ), In arXiv e-prints, .

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      16. Personalized Physical Activity Coaching: A Machine Learning Approach (, , , and ), In Sensors, volume 18, .

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      17. Robustness of reconfigurable complex systems by a multi-agent simulation: Application on power distribution systems (, , , and ), In 2018 Annual IEEE International Systems Conference (SysCon), .

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      18. Low-power Appliance Recognition using Recurrent Neural Networks (, , and ), In Applications of Intelligent Systems, .

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      19. A remotely piloted aerial system for a faster processing of traffic collisions towards reducing the resulting road congestion (, and ), In , .

        Abstract

        This paper presents the motivation, design, implementation, and testing of a remotely piloted aerial system, designed to facilitate police officers processing traffic collisions. A drone remotely controlled by the police officer can reach faster the accident scene and act as the police officer's eye, ear, and voice in the sky. A complete system prototype has been constructed and tested to validate the proposed system. The results show that the system performance is promising in terms of system functionality, safety, and cost.


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        doi
      20. Household CO2-efficient energy management ( and ), In Energy Informatics, Springer, .

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      21. Mining Sequential Patterns for Appliance Usage Prediction (, , , and ), In International Conference on Smart Cities and Green ICT Systems, .

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      22. Modeling 911 emergency events in Cuenca-Ecuador using geo-spatial data (, , and ), In International Conference on Technology Trends, .

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      23. LOD-GF: an integral linked open data generation framework (, , , , , and ), In Conference on Information Technologies and Communication of Ecuador, .

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      2017


      1. two bestsellers (), Saccargia Holding BV Publisher, .

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      2. Planning meets activity recognition: Service coordination for intelligent buildings (, , , , and ), In Pervasive and Mobile Computing, Elsevier, volume 38, .

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        url
      3. Automated Generation Algorithm for Synthetic Medium Voltage Radial Distribution Systems (, , and ), In IEEE Journal on Emerging and Selected Topics in Circuits and Systems, volume 7, .

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        url
      4. Sizing and Siting of Large-Scale Batteries in Transmission Grids to Optimize the Use of Renewables (, , , and ), In IEEE Journal on Emerging and Selected Topics in Circuits and Systems, volume 7, .

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        url
      5. MuGKeG: Secure Multi-channel Group Key Generation Algorithm for Wireless Networks (, , and ), In Wireless Personal Communications, Springer, volume 96, .

        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
      6. A Stochastic Model for Transit Latency in OpenFlow SDNs (, , , and ), In Computer Networks, Elsevier, volume 113, .

        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
      7. Indoor self-localization via bluetooth low energy beacons (, , and ), In IDRBT JOURNAL OF IJBT, volume 1, .

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      8. Metrics for Sustainable Data Centers (, , , and ), In IEEE Transactions on Sustainable Computing, .

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      9. Automated compliance verification of business processes in Apromore (, and ), In Proceedings of the BPM Demo Track 2017, CEUR Workshop Proceedings (CEUR-WS.org), .

        Abstract

        This paper presents the integration of two plugins, a declarative process specification generator and a compliance verifier, into the Apromore advanced business process analytics platform. The integrated toolchain has a range of applications of interest to both practitioners and researchers. For example, it can be used in the areas of business process compliance, flexibility and variability. The generator can extract a set of formal specifications that declaratively describe a set of business process variants; whereas the verifier can check whether temporal properties over business process models hold. The verifier can use two different model checker tools: NuSMV2 and NuXMV. These plugins allow business analysts to verify if a newly developed process model adheres to rules and regulations or a specification dictated by existing process model variants.


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        urlpdf
      10. Post Summarization of Microblogs of Sporting Events (, , , , and ), In Proceedings of the 26th International Conference on World Wide Web Companion, .

        Abstract

        Every day 645 million Twitter users generate approximately 58 million tweets. This motivates the question if it is possible to generate a summary of events from this rich set of tweets only. Key challenges in post summarization from microblog posts include circumnavigating spam and conversational posts. In this study, we present a novel technique called lexi-temporal clustering (LTC), which identifies key events. LTC uses k-means clustering and we explore the use of various distance measures for clustering using Euclidean, cosine similarity and Manhattan distance. We collected three original data sets consisting of Twitter microblog posts covering sporting events, consisting of a cricket and two football matches. The match summaries generated by LTC were compared against standard summaries taken from sports sections of various news outlets, which yielded up to 81% precision, 58% recall and 62% F-measure on different data sets. In addition, we also report results of all three variants of the recall-oriented understudy for gisting evaluation (ROUGE) software, a tool which compares and scores automatically generated summaries against standard summaries.


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        urldoi
      11. Prediction of Running Injuries from Training Load: a Machine Learning Approach (, , and ), In International Conference on eHealth, Telemedicine, and Social Medicine, .

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        url
      12. Comparison of Energy Consumption in Wi-Fi and Bluetooth Communication in a Smart Building (, , and ), In IEEE Annual Computing and Communication Workshop and Conference, .

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        url
      13. Power-Based Device Recognition for Occupancy Detection (, , and ), In Service-Oriented Computing - ICSOC 2017 Workshops, volume in press, .

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        doi
      14. Semantic web and augmented reality for searching people, events and points of interest within of a university campus (, , and ), In 2017 XLIII Latin American Computer Conference (CLEI), .

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      15. Tv program recommender using user authentication on middleware ginga (, , , and ), In 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), .

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      16. Runtime Modifications of Spark Data Processing Pipelines (, , , and ), In 2017 International Conference on Cloud and Autonomic Computing, ICCAC, .

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        url
      17. Methodological guidelines for publishing library data as linked data (, , , , and ), In 2017 International Conference on Information Systems and Computer Science (INCISCOS), .

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      18. Cloud Ready Applications Composed via HTN Planning (, , and ), In IEEE International Conference on Service Oriented Computing and Applications, .

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      19. iTrack: A residential energy monitoring system tailored to meet local needs (, , , , and ), In , .

        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
      20. Tracing back the chain: Cognitive pre-requisite analysis for CIS capstone project ( and ), In , volume Part F1346, .

        Abstract

        © 2017 Association for Computing Machinery. When teaching the first part of the capstone project to the senior students at the College of Computer Sciences and Information Technology, some problems have been reported for the past few semesters. This paper aims to identify the cause of the problem by comparing necessary skills for the capstone project to skills acquired in prior courses, tracing back through the pre-requisite dependency chain. The comparison is set between the Course Learning Outcomes identified in the capstone project and the Course Objectives of courses offered in previous years. The analysis revealed two main discrepancies, namely: Several mismatches and missing links that explained the problems initially observed. It also led to identifying a weakness in one of the previous courses and triggered an adjustment in the course content.


        Keywords: Bloom's taxonomy, CIS capstone, Course design, Course evaluation


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        doi
      21. Challenges and trends about smart big geospatial data: A position paper (, and ), In 2017 IEEE International Conference on Big Data (Big Data), .

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