Search results for: Key success paths approach

Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: Data mining, hybrid storage system, recurrent neural network, support vector machine.

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Authors: Eisa A. Alsafran, Francis T. Edum-Fotwe, Wayne E. Lord

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The degree to which a public client actively participates in Public Private Partnership (PPP) schemes, is seen as a determinant of the success of the arrangement, and in particular, efficiency in the delivery of the assets of any infrastructure development. The asset delivery is often an early barometer for judging the overall performance of the PPP. Currently, there are no defined descriptors for the degree of such participation. The lack of defined descriptors makes the association between the degree of participation and efficiency of asset delivery, difficult to establish. This is particularly so if an optimum effect is desired. In addition, such an association is important for the strategic decision to embark on any PPP initiative. This paper presents a conceptual model of different levels of participation that characterise PPP schemes. The modelling was achieved by a systematic review of reported sources that address essential aspects and structures of PPP schemes, published from 2001 to 2015. As a precursor to the modelling, the common areas of Public Client Participation (PCP) were investigated. Equity and risk emerged as two dominant factors in the common areas of PCP, and were therefore adopted to form the foundation of the modelling. The resultant conceptual model defines the different states of combined PCP. The defined states provide a more rational basis for establishing how the degree of PCP affects the efficiency of asset delivery in PPP schemes.

Keywords: Asset delivery, infrastructure development, public private partnership, public client participation.

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Authors: Martin Cermak, Stanislav Sysala

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In this paper we present the efficient parallel implementation of elastoplastic problems based on the TFETI (Total Finite Element Tearing and Interconnecting) domain decomposition method. This approach allow us to use parallel solution and compute this nonlinear problem on the supercomputers and decrease the solution time and compute problems with millions of DOFs. In our approach we consider an associated elastoplastic model with the von Mises plastic criterion and the combination of linear isotropic-kinematic hardening law. This model is discretized by the implicit Euler method in time and by the finite element method in space. We consider the system of nonlinear equations with a strongly semismooth and strongly monotone operator. The semismooth Newton method is applied to solve this nonlinear system. Corresponding linearized problems arising in the Newton iterations are solved in parallel by the above mentioned TFETI. The implementation of this problem is realized in our in-house MatSol packages developed in MatLab.

Keywords: Isotropic-kinematic hardening, TFETI, domain decomposition, parallel solution.

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Authors: Kaustav Kundu, Alberto Portioli Staudacher

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Construction industry plays a vital role in the economy of the world. However, due to high uncertainty and variability in the industry, its performance is not as efficient in terms of quality, lead times, productivity and costs as of other industries. Moreover, there are continuous conflicts among the different actors in the construction supply chains in terms of profit sharing. Previous studies suggested partnership as an important approach to promote cooperation among the different actors in the construction supply chains and thereby it improves the overall performance. Construction practitioners tried to focus on partnership which can enhance the performance of construction supply chains but they are not fully aware of different approaches and techniques for improving partnership. In this research, a systematic review on partnership in relation to construction supply chains is carried out to understand different elements influencing the partnership. The research development of this domain is analyzed by reviewing selected articles published from 1996 to 2015. Based on the papers, three major elements influencing partnership in construction supply chains are identified: ‘Lean approach’, ‘Relationship building’ and ‘E-commerce applications’. This study analyses the contributions in the areas within each element and provides suggestions for future developments of partnership in construction supply chains.

Keywords: Partnership, construction, lean, SCM, supply chain management.

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Authors: Muhibul Haque Bhuyan

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In the current context, the engineering program educators need to think about how to develop the concepts and complex engineering problem-solving skills through various complex engineering activities by the undergraduate engineering students in various engineering courses. But most of them are facing challenges to assess and evaluate these skills of their students. In this study, detailed assessment and evaluation methods for the undergraduate Electrical and Electronic Engineering (EEE) program are stated using the Outcome-Based Education (OBE) approach. For this purpose, a final year course titled control systems has been selected. The assessment and evaluation approach, course contents, course objectives, course outcomes (COs), and their mapping to the program outcomes (POs) with complex engineering problems and activities via the knowledge profiles, performance indicators, rubrics of assessment, CO and PO attainment data, and other statistics, are reported for a student-cohort of control systems course registered by the students of BSc in EEE program in Spring 2021 Semester at the EEE Department of Southeast University (SEU). It is found that the target benchmark was achieved by the students of that course. Several recommendations for the continuous quality improvement (CQI) process are also provided.

Keywords: Complex engineering problem, knowledge profiles, OBE, control systems course, COs, PIs, POs, assessment rubrics.

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Authors: K. Krishna Veni, S. Raja Balachandar

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This paper presents a heuristic to solve large size 0-1 Multi constrained Knapsack problem (01MKP) which is NP-hard. Many researchers are used heuristic operator to identify the redundant constraints of Linear Programming Problem before applying the regular procedure to solve it. We use the intercept matrix to identify the zero valued variables of 01MKP which is known as redundant variables. In this heuristic, first the dominance property of the intercept matrix of constraints is exploited to reduce the search space to find the optimal or near optimal solutions of 01MKP, second, we improve the solution by using the pseudo-utility ratio based on surrogate constraint of 01MKP. This heuristic is tested for benchmark problems of sizes upto 2500, taken from literature and the results are compared with optimum solutions. Space and computational complexity of solving 01MKP using this approach are also presented. The encouraging results especially for relatively large size test problems indicate that this heuristic can successfully be used for finding good solutions for highly constrained NP-hard problems.

Keywords: 0-1 Multi constrained Knapsack problem, heuristic, computational complexity, NP-Hard problems.

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Authors: Azadeh Maroufmashat, Sourena Sattari Khavas, Halle Bakhteeyar

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Multi-energy systems will enhance the system reliability and power quality. This paper presents an integrated approach for the design and operation of distributed energy resources (DER) systems, based on energy hub modeling. A multi-objective optimization model is developed by considering an integrated view of electricity and natural gas network to analyze the optimal design and operating condition of DER systems, by considering two conflicting objectives, namely, minimization of total cost and the minimization of environmental impact which is assessed in terms of CO2 emissions. The mathematical model considers energy demands of the site, local climate data, and utility tariff structure, as well as technical and financial characteristics of the candidate DER technologies. To provide energy demands, energy systems including photovoltaic, and co-generation systems, boiler, central power grid are considered. As an illustrative example, a hotel in Iran demonstrates potential applications of the proposed method. The results prove that increasing the satisfaction degree of environmental objective leads to increased total cost.

Keywords: Multi objective optimization, DER systems, Energy hub, Cost, CO2 emission.

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Authors: Tim Nedyalkov

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A business decision to move to the cloud brings fundamental changes in how an organization develops and delivers its Information Technology solutions. The accelerated pace of digital transformation across businesses and government agencies increases the reliance on cloud-based services. Collecting, managing, and retaining large amounts of data in cloud environments make information security and data privacy protection essential. It becomes even more important to understand what key factors drive successful cloud adoption following the commencement of the Privacy Amendment Notifiable Data Breaches (NDB) Act 2017 in Australia as the regulatory changes impact many organizations and industries. This quantitative correlational research investigated the governance, risk management, and compliance factors contributing to cloud security success. The factors influence the adoption of cloud computing within an organizational context after the commencement of the NDB scheme. The results and findings demonstrated that corporate information security policies, data storage location, management understanding of data governance responsibilities, and regular compliance assessments are the factors influencing cloud computing adoption. The research has implications for organizations, future researchers, practitioners, policymakers, and cloud computing providers to meet the rapidly changing regulatory and compliance requirements.

Keywords: Cloud compliance, cloud security, cloud security governance, data governance, privacy protection.

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Authors: Abdelkader Dekdouk

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In this paper we propose a novel approach for searching eCommerce products using a mobile phone, illustrated by a prototype eCoMobile. This approach aims to globalize the mobile search by integrating the concept of user multilinguism into it. To show that, we particularly deal with English and Arabic languages. Indeed the mobile user can formulate his query on a commercial product in either language (English/Arabic). The description of his information need on commercial products relies on the ontology that represents the conceptualization of the product catalogue knowledge domain defined in both English and Arabic languages. A query expressed on a mobile device client defines the concept that corresponds to the name of the product followed by a set of pairs (property, value) specifying the characteristics of the product. Once a query is submitted it is then communicated to the server side which analyses it and in its turn performs an http request to an eCommerce application server (like Amazon). This latter responds by returning an XML file representing a set of elements where each element defines an item of the searched product with its specific characteristics. The XML file is analyzed on the server side and then items are displayed on the mobile device client along with its relevant characteristics in the chosen language.

Keywords: Mobile computing, search engine, multilingualglobal eCommerce, ontology, XML.

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Authors: Qiuyu Dai, Haochong Zhang, Xiangrong Liu

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Efficient matrix-vector multiplication with diagonal sparse matrices is pivotal in a multitude of computational domains, ranging from scientific simulations to machine learning workloads. When encoded in the conventional Diagonal (DIA) format, these matrices often induce computational overheads due to extensive zero-padding and non-linear memory accesses, which can hamper the computational throughput, and elevate the usage of precious compute and memory resources beyond necessity. The ’DIA-Adaptive’ approach, a methodological enhancement introduced in this paper, confronts these challenges head-on by leveraging the advanced parallel instruction sets embedded within Machine Learning Units (MLUs). This research presents a thorough analysis of the DIA-Adaptive scheme’s efficacy in optimizing Sparse Matrix-Vector Multiplication (SpMV) operations. The scope of the evaluation extends to a variety of hardware architectures, examining the repercussions of distinct thread allocation strategies and cluster configurations across multiple storage formats. A dedicated computational kernel, intrinsic to the DIA-Adaptive approach, has been meticulously developed to synchronize with the nuanced performance characteristics of MLUs. Empirical results, derived from rigorous experimentation, reveal that the DIA-Adaptive methodology not only diminishes the performance bottlenecks associated with the DIA format but also exhibits pronounced enhancements in execution speed and resource utilization. The analysis delineates a marked improvement in parallelism, showcasing the DIA-Adaptive scheme’s ability to adeptly manage the interplay between storage formats, hardware capabilities, and algorithmic design. The findings suggest that this approach could set a precedent for accelerating SpMV tasks, thereby contributing significantly to the broader domain of high-performance computing and data-intensive applications.

Keywords: Adaptive method, DIA, diagonal sparse matrices, MLU, sparse matrix-vector multiplication.

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Authors: Nieto Bernal W., Luna Amaya C.

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This paper aims to present a framework for the organizational knowledge management, which seeks to deploy a standardized structure for the integrated management of knowledge is a common language based on domains, processes and global indicators inspired by the COBIT framework 5 (ISACA, 2012), which supports the integration of three technologies, enterprise information architecture (EIA), the business process modeling (BPM) and service-oriented architecture (SOA). The Gomak Framework is a management platform that seeks to integrate the information technology infrastructure, the structure of applications, information infrastructure, and business logic and business model to support a sound strategy of organizational knowledge management, low process-based approach and concurrent engineering. Concurrent engineering (CE) is a systematic approach to integrated product development that respond to customer expectations, involving all perspectives in parallel, from the beginning of the product life cycle. (European Space Agency, 2000).

Keywords: Business Process Modeling, Enterprise Information Architecture, Government and Knowledge Management, Service Oriented Architecture, Process Management.

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Authors: Barry Ardley, Abi Hunt, Nick Taylor

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As a theoretical and practical framework this study uses the student as producer approach to learning in higher education, as adopted by the Lincoln International Business School, University of Lincoln, UK. Student as producer positions learners as skilled and capable agents, able to participate as partners with tutors in live research projects. To illuminate the nature of this approach to learning and to highlight its critical issues, the authors report on two guided student consultancy projects. These were set up with the assistance of two local organisations in the city of Lincoln UK. Using the student as producer model to deliver the projects enabled learners to acquire and develop a range of key skills and knowledge, not easily accessible in more traditional educational settings. This paper presents a systematic case study analysis of the eight organising principles of the student as producer model, as adopted by university tutors. The experience of tutors implementing student as producer suggests that the model can be widely applied to benefit not only the learning and teaching experiences of higher education students, and staff, but additionally, a university’s research programme and its community partners.

Keywords: Experiential learning, consultancy clients, student as producer.

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Authors: S. Sarumathi, P. Ranjetha, C. Saraswathy, M. Vaishnavi, S. Geetha

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Clustering is an unsupervised learning technique for aggregating data objects into meaningful classes so that intra cluster similarity is maximized and inter cluster similarity is minimized in data mining. However, no single clustering algorithm proves to be the most effective in producing the best result. As a result, a new challenging technique known as the cluster ensemble approach has blossomed in order to determine the solution to this problem. For the cluster analysis issue, this new technique is a successful approach. The cluster ensemble's main goal is to combine similar clustering solutions in a way that achieves the precision while also improving the quality of individual data clustering. Because of the massive and rapid creation of new approaches in the field of data mining, the ongoing interest in inventing novel algorithms necessitates a thorough examination of current techniques and future innovation. This paper presents a comparative analysis of various cluster ensemble approaches, including their methodologies, formal working process, and standard accuracy and error rates. As a result, the society of clustering practitioners will benefit from this exploratory and clear research, which will aid in determining the most appropriate solution to the problem at hand.

Keywords: Clustering, cluster ensemble methods, consensus function, data mining, unsupervised learning.

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Authors: M.A. Lazim, M. Suriani

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Knowing consumers' preferences and perceptions of the sensory evaluation of drink products are very significant to manufacturers and retailers alike. With no appropriate sensory analysis, there is a high risk of market disappointment. This paper aims to rank the selected coffee products and also to determine the best of quality attribute through sensory evaluation using fuzzy decision making model. Three products of coffee drinks were used for sensory evaluation. Data were collected from thirty judges at a hypermarket in Kuala Terengganu, Malaysia. The judges were asked to specify their sensory evaluation in linguistic terms of the quality attributes of colour, smell, taste and mouth feel for each product and also the weight of each quality attribute. Five fuzzy linguistic terms represent the quality attributes were introduced prior analysing. The judgment membership function and the weights were compared to rank the products and also to determine the best quality attribute. The product of Indoc was judged as the first in ranking and 'taste' as the best quality attribute. These implicate the importance of sensory evaluation in identifying consumers- preferences and also the competency of fuzzy approach in decision making.

Keywords: fuzzy decision making, fuzzy linguistic, membership function, sensory evaluation,

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Authors: F. Felipe

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Air Defense Systems contain high-value assets that are expected to fulfill their mission for several years - in many cases, even decades - while operating in a fast-changing, technology-driven environment. Thus, it is paramount that decision-makers can assess how effective an Air Defense System is in the face of new developing threats, as well as to identify the bottlenecks that could jeopardize the security of the airspace of a country. Given the broad extent of activities and the great variety of assets necessary to achieve the strategic objectives, a systems approach was taken in order to delineate the core requirements and the physical architecture of an Air Defense System. Then, value-focused thinking helped in the definition of the measures of effectiveness. Furthermore, analytical methods were applied to create a formal structure that preliminarily assesses such measures. To validate the proposed methodology, a powerful simulation was also used to determine the measures of effectiveness, now in more complex environments that incorporate both uncertainty and multiple interactions of the entities. The results regarding the validity of this methodology suggest that the approach can support decisions aimed at enhancing the capabilities of Air Defense Systems. In conclusion, this paper sheds some light on how consolidated approaches of Systems Engineering and Operations Research can be used as valid techniques for solving problems regarding a complex and yet vital matter.

Keywords: Air defense, effectiveness, system, simulation, decision-support.

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Authors: K. A. Weidenmann, M. Grigo, B. Brylka, P. Elsner, T. Böhlke

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In order to reduce fuel consumption, the weight of automobiles has to be reduced. Fiber reinforced polymers offer the potential to reach this aim because of their high stiffness to weight ratio. Additionally, the use of fiber reinforced polymers in automotive applications has to allow for an economic large-scale production. In this regard, long fiber reinforced thermoplastics made by direct processing offer both mechanical performance and processability in injection moulding and compression moulding. The work presented in this contribution deals with long glass fiber reinforced polypropylene directly processed in compression moulding (D-LFT). For the use in automotive applications both the temperature and the time dependency of the materials properties have to be investigated to fulfill performance requirements during crash or the demands of service temperatures ranging from -40 °C to 80 °C. To consider both the influence of temperature and time, quasistatic tensile tests have been carried out at different temperatures. These tests have been complemented by high speed tensile tests at different strain rates. As expected, the increase in strain rate results in an increase of the elastic modulus which correlates to an increase of the stiffness with decreasing service temperature. The results are in good accordance with results determined by dynamic mechanical analysis within the range of 0.1 to 100 Hz. The experimental results from different testing methods were grouped and interpreted by using different time temperature shift approaches. In this regard, Williams-Landel-Ferry and Arrhenius approach based on kinetics have been used. As the theoretical shift factor follows an arctan function, an empirical approach was also taken into consideration. It could be shown that this approach describes best the time and temperature superposition for glass fiber reinforced polypropylene manufactured by D-LFT processing.

Keywords: Composite, long fiber reinforced thermoplastics, mechanical properties, dynamic mechanical analysis, time temperature superposition.

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Authors: M. Almas Anjum, M. Younus Javed, A. Basit

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In this paper a new approach to face recognition is presented that achieves double dimension reduction, making the system computationally efficient with better recognition results and out perform common DCT technique of face recognition. In pattern recognition techniques, discriminative information of image increases with increase in resolution to a certain extent, consequently face recognition results change with change in face image resolution and provide optimal results when arriving at a certain resolution level. In the proposed model of face recognition, initially image decimation algorithm is applied on face image for dimension reduction to a certain resolution level which provides best recognition results. Due to increased computational speed and feature extraction potential of Discrete Cosine Transform (DCT), it is applied on face image. A subset of coefficients of DCT from low to mid frequencies that represent the face adequately and provides best recognition results is retained. A tradeoff between decimation factor, number of DCT coefficients retained and recognition rate with minimum computation is obtained. Preprocessing of the image is carried out to increase its robustness against variations in poses and illumination level. This new model has been tested on different databases which include ORL , Yale and EME color database.

Keywords: Biometrics, DCT, Face Recognition, Illumination, Computation, Feature extraction.

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Authors: M. A. Sedghamiz, S. Raeissi

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This study presents three different approaches to estimate bubble point pressures for the binary system of CO2 and ethyl palmitate fatty acid ethyl ester. The first method involves the Peng-Robinson (PR) Equation of State (EoS) with the conventional mixing rule of Van der Waals. The second approach involves the PR EOS together with the Wong Sandler (WS) mixing rule, coupled with the UNIQUAC GE model. In order to model the bubble point pressures with this approach, the volume and area parameter for ethyl palmitate were estimated by the Hansen group contribution method. The last method involved the Peng-Robinson, combined with the Wong-Sandler method, but using NRTL as the GE model. Results using the Van der Waals mixing rule clearly indicated that this method has the largest errors among all three methods, with errors in the range of 3.96-6.22%. The PR-WS-UNIQUAC method exhibited small errors, with average absolute deviations between 0.95 to 1.97 percent. The PR-WS-NRTL method led to the least errors, where average absolute deviations ranged between 0.65-1.7%.

Keywords: Bubble pressure, Gibbs excess energy model, mixing rule, CO2 solubility, ethyl palmitate.

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Authors: Jens Friedrich, Matthias A. Gebele, Armin Lechler, Alexander Verl

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Chatter vibrations and process instabilities are the most important factors limiting the productivity of the milling process. Chatter can leads to damage of the tool, the part or the machine tool. Therefore, the estimation and prediction of the process stability is very important. The process stability depends on the spindle speed, the depth of cut and the width of cut. In milling, the process conditions are defined in the NC-program. While the spindle speed is directly coded in the NC-program, the depth and width of cut are unknown. This paper presents a new simulation based approach for the prediction of the depth and width of cut of a milling process. The prediction is based on a material removal simulation with an analytically represented tool shape and a multi-dexel approach for the workpiece. The new calculation method allows the direct estimation of the depth and width of cut, which are the influencing parameters of the process stability, instead of the removed volume as existing approaches do. The knowledge can be used to predict the stability of new, unknown parts. Moreover with an additional vibration sensor, the stability lobe diagram of a milling process can be estimated and improved based on the estimated depth and width of cut.

Keywords: Dexel, process stability, material removal, milling.

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Authors: Bokyung Sung, Jungsoo Kim, Jinman Kwun, Junhyung Park, Jihye Ryeo, Ilju Ko

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In this paper, we propose a practical digital music matching system that is robust to variation in sound qualities. The proposed system is subdivided into two parts: client and server. The client part consists of the input, preprocessing and feature extraction modules. The preprocessing module, including the music onset module, revises the value gap occurring on the time axis between identical songs of different formats. The proposed method uses delta-grouped Mel frequency cepstral coefficients (MFCCs) to extract music features that are robust to changes in sound quality. According to the number of sound quality formats (SQFs) used, a music server is constructed with a feature database (FD) that contains different sub feature databases (SFDs). When the proposed system receives a music file, the selection module selects an appropriate SFD from a feature database; the selected SFD is subsequently used by the matching module. In this study, we used 3,000 queries for matching experiments in three cases with different FDs. In each case, we used 1,000 queries constructed by mixing 8 SQFs and 125 songs. The success rate of music matching improved from 88.6% when using single a single SFD to 93.2% when using quadruple SFDs. By this experiment, we proved that the proposed method is robust to various sound qualities.

Keywords: Digital Music, Music Matching, Variation in Sound Qualities, Robust Matching method.

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Authors: Ifeyinwa E. Achumba, Djamel Azzi, Rinat Khusainov

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There are three approaches to complete Bayesian Network (BN) model construction: total expert-centred, total datacentred, and semi data-centred. These three approaches constitute the basis of the empirical investigation undertaken and reported in this paper. The objective is to determine, amongst these three approaches, which is the optimal approach for the construction of a BN-based model for the performance assessment of students- laboratory work in a virtual electronic laboratory environment. BN models were constructed using all three approaches, with respect to the focus domain, and compared using a set of optimality criteria. In addition, the impact of the size and source of the training, on the performance of total data-centred and semi data-centred models was investigated. The results of the investigation provide additional insight for BN model constructors and contribute to literature providing supportive evidence for the conceptual feasibility and efficiency of structure and parameter learning from data. In addition, the results highlight other interesting themes.

Keywords: Bayesian networks, model construction, parameterlearning, structure learning, performance index, model comparison.

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Authors: Rozilawati Binti Dollah, Masaki Aono

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Classifying biomedical literature is a difficult and challenging task, especially when a large number of biomedical articles should be organized into a hierarchical structure. In this paper, we present an approach for classifying a collection of biomedical text abstracts downloaded from Medline database with the help of ontology alignment. To accomplish our goal, we construct two types of hierarchies, the OHSUMED disease hierarchy and the Medline abstract disease hierarchies from the OHSUMED dataset and the Medline abstracts, respectively. Then, we enrich the OHSUMED disease hierarchy before adapting it to ontology alignment process for finding probable concepts or categories. Subsequently, we compute the cosine similarity between the vector in probable concepts (in the “enriched" OHSUMED disease hierarchy) and the vector in Medline abstract disease hierarchies. Finally, we assign category to the new Medline abstracts based on the similarity score. The results obtained from the experiments show the performance of our proposed approach for hierarchical classification is slightly better than the performance of the multi-class flat classification.

Keywords: Biomedical literature, hierarchical text classification, ontology alignment, text mining.

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Authors: Ana-Marija Stjepić, Dalia Suša Vugec

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Today, digital transformation is one of the leading topics that occupy the attention of scientific circles and business experts. Organizational success is most often reflected through the successful managing of business processes. Given the growing market for digital innovations and its ever-increasing impact on business, organizations need to be prepared for organizational changes that come with the digital era. In order to maintain their competitive advantage in the global market, organizations must adapt their processes to new digitalization conditions. The main goal of this study is to point out the link between the digital transformation and the business process management concept. Therefore, in order to contribute to the scientific field that explores the potential relation between business process management concept and digital transformation, a literature review has been conducted. Papers have been searched within the Business Process Management Journal by keywords related to the term digital transformation. Selected papers have been analyzed according to the topic, type of publication, year of publication, keywords, etc. The results reveal a growing number of papers published on the topic of digital transformation to the Business Process Management Journal, but the lack of case studies. This paper contributes to the extension of academic literature in this important, yet insufficiently researched, scientific field that creates the bond between two strong concepts of digital transformation and business process management.

Keywords: Business process management, digital transformation, digitalization, process change.

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Authors: Johann Barbier, Eric Filiol, Kichenakoumar Mayoura

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We present in this paper a new approach for specific JPEG steganalysis and propose studying statistics of the compressed DCT coefficients. Traditionally, steganographic algorithms try to preserve statistics of the DCT and of the spatial domain, but they cannot preserve both and also control the alteration of the compressed data. We have noticed a deviation of the entropy of the compressed data after a first embedding. This deviation is greater when the image is a cover medium than when the image is a stego image. To observe this deviation, we pointed out new statistic features and combined them with the Multiple Embedding Method. This approach is motivated by the Avalanche Criterion of the JPEG lossless compression step. This criterion makes possible the design of detectors whose detection rates are independent of the payload. Finally, we designed a Fisher discriminant based classifier for well known steganographic algorithms, Outguess, F5 and Hide and Seek. The experiemental results we obtained show the efficiency of our classifier for these algorithms. Moreover, it is also designed to work with low embedding rates (< 10-5) and according to the avalanche criterion of RLE and Huffman compression step, its efficiency is independent of the quantity of hidden information.

Keywords: Compressed frequency domain, Fisher discriminant, specific JPEG steganalysis.

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Authors: J. P. Dubois, O. M. Abdul-Latif

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Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.

Keywords: LS-SVM, medical ultrasound imaging, partially developed speckle, multi-look model.

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Authors: Nasser M. Amaitik, Nabil A. Alfagi

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The world-s largest Pre-stressed Concrete Cylinder Pipe (PCCP) water supply project had a series of pipe failures which occurred between 1999 and 2001. This has led the Man-Made River Authority (MMRA), the authority in charge of the implementation and operation of the project, to setup a rehabilitation plan for the conveyance system while maintaining the uninterrupted flow of water to consumers. At the same time, MMRA recognized the need for a long term management tool that would facilitate repair and maintenance decisions and enable taking the appropriate preventive measures through continuous monitoring and estimation of the remaining life of each pipe. This management tool is known as the Pipe Risk Management System (PRMS) and now in operation at MMRA. Both the rehabilitation plan and the PRMS require the availability of complete and accurate pipe construction and manufacturing data This paper describes a systematic approach of data collection, analysis, evaluation and correction for the construction and manufacturing data files of phase I pipes which are the platform for the PRMS database and any other related decision support system.

Keywords: Asbuilt, History, IMD, MMRA, PDBMS & PRMS

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Authors: A. Haddad, H. Kbab

Abstract:

A supersonic expansion cannot be achieved within a convergent-divergent nozzle if the flow velocity does not reach that of the sound at the throat. The computation of the flow field characteristics at the throat is thus essential to the nozzle developed thrust value and therefore to the aircraft or rocket it propels. Several approaches were developed in order to describe the transonic expansion, which takes place through the throat of a De-Laval convergent-divergent nozzle. They all allow reaching good results but showing a major shortcoming represented by their inability to describe the transonic flow field for nozzles having a small throat radius. The approach initially developed by Kliegel & Levine uses the velocity series development in terms of the normalized throat radius added to unity instead of solely the normalized throat radius or the traditional small disturbances theory approach. The present investigation carries out the application of these three approaches for different throat radiuses of curvature. The method using the normalized throat radius added to unity shows better results when applied to geometries integrating small throat radiuses.

Keywords: De-Laval nozzles, transonic calculations, transonic flow, supersonic nozzle.

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Authors: O. F. Elkommos

Abstract:

Teaching English as a Second Language (TESL) has become a very rich area of research. Practitioners or teachers of English as a foreign or a second language are now promoting both collaborative learning and collaborative teaching. Students learning a language collaboratively and cooperatively are learning in a better environment of team work where they learn from each other. Further, teaching English collaboratively also creates an enriching environment that is also very enriching to students’ and teachers’ experiences of learning and teaching. Moreover, action research stems from actual teacher concerns and students’ needs. Reflection in turn, on the experience of the material taught and the delivery of material is becoming an integral part of the teaching and learning experience self- evaluation and self-development. In this case, the concern of the research field in the area of TESL will be the development of teaching delivery, material and quality of learning. In the present research, the TESL module taught to year two students in the Faculty of Arts and Humanities, British University in Egypt (BUE) will be evaluated reflexively by the students and teachers. The module was taught to students in two different specialisms. It was taught and delivered through collaborative teaching and was evaluated by both teachers and students as very successful and enjoyable. The reflections of both teachers and students as well as student results confirm that it was a success.

Keywords: Action research, addressing differentiation, collaborative teaching, reflective teaching and learning, reflexive learning, reflexive teaching, self-development, self-evaluation, TESL.

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Authors: Gürcan D. Baysal, Ali Tanış

Abstract:

In this study, samples of plant types including rose hip (Rosa canina L.), jujube (Ziziphus jujube), sea buckthorn (Hippophae rhamnoides), elderberry (Sambucus nigra), apricot (Prunus armeniaca), scots pine (Pinus sylvestris), and cedar of Lebanon (Cedrus libani) were grown using plant supporting units called Ekobox and drip irrigation systems in the Karapınar, Konya region of Turkey to reveal the efficiency of Ekobox and drip irrigation compared against a control with no irrigation. The plant diameter, height, and survival rates were determined, compared with each other, and statistically analyzed. According to the statistical analysis of the results, Ekobox applications resulted in the highest values for survival rate, diameter, and height measurements whereas the lowest values were determined in the control groups. These results indicate that the cultivation of plants with Ekobox may help protect against the loss of fertile soils as an effective mechanism for combating erosion and desertification. These advantages may also lead to a lasting economic effect on the cultivation of plants by locals of the Karapınar, Konya province who suffer from an ever-decreasing underground water level as a result of agricultural consumption.

Keywords: Drip irrigation, Ekobox, plant diameter, plant height, plant survival rate.

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Authors: Anders Thorsèn, Behrooz Sangchoolie, Peter Folkesson, Ted Strandberg

Abstract:

As more parts of the power grid become connected to the internet, the risk of cyberattacks increases. To identify the cybersecurity threats and subsequently reduce vulnerabilities, the common practice is to carry out a cybersecurity risk assessment. For safety classified systems and products, there is also a need for safety risk assessments in addition to the cybersecurity risk assessment to identify and reduce safety risks. These two risk assessments are usually done separately, but since cybersecurity and functional safety are often related, a more comprehensive method covering both aspects is needed. Some work addressing this has been done for specific domains like the automotive domain, but more general methods suitable for, e.g., Intelligent Distributed Grids, are still missing. One such method from the automotive domain is the Security-Aware Hazard Analysis and Risk Assessment (SAHARA) method that combines safety and cybersecurity risk assessments. This paper presents an approach where the SAHARA method has been modified to be more suitable for larger distributed systems. The adapted SAHARA method has a more general risk assessment approach than the original SAHARA. The proposed method has been successfully applied on two use cases of an intelligent distributed grid.

Keywords: Intelligent distribution grids, threat analysis, risk assessment, safety, cybersecurity.

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