Search results for: Semantic Web Usage Mining

Authors: Aree Thongpukdee, Chockpisit Thepsithar, Chuthalak Thammaso

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Use of plants grown in local area for edible has a long tradition in different culture. The indigenous knowledge such as usage of plants as vegetables by local people is risk to disappear when no records are done. In order to conserve and transfer this valuable heritage to the new generation, ethnobotanical study should be investigated and documented. The survey of vegetable plants traditionally used was carried out in the year 2012. Information was accumulated via questionnaires and oral interviewing from 100 people living in 36 villages of 9 districts in Amphoe Huai Mek, Kalasin, Thailand. Local plant names, utilized parts and preparation methods of the plants were recorded. Each mentioned plant species were collected and voucher specimens were prepared. A total of 55 vegetable plant species belonging to 34 families and 54 genera were identified. The plant habits were tree, shrub, herb, climber, and shrubby fern at 21.82%, 18.18%, 38.18%, 20.00% and 1.82% respectively. The most encountered vegetable plant families were Leguminosae (20%), Cucurbitaceae (7.27%), Apiaceae (5.45%), whereas families with 3.64% uses were Araceae, Bignoniaceae, Lamiaceae, Passifloraceae, Piperaceae and Solanaceae. The most common consumptions were fresh or brief boiled young shoot or young leaf as side dishes of ‘jaeo, laab, namprik, pon’ or curries. Most locally known vegetables included 45% of the studied plants which grow along road side, backyard garden, hedgerow, open forest and rice field.

Keywords: Ethnobotanical survey, Thailand, vegetable plants.

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Authors: Kayun Chantarasathaporn, Chonawat Srisa-an

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Design Patterns have gained more and more acceptances since their emerging in software development world last decade and become another de facto standard of essential knowledge for Object-Oriented Programming developers nowadays. Their target usage, from the beginning, was for regular computers, so, minimizing power consumption had never been a concern. However, in this decade, demands of more complicated software for running on mobile devices has grown rapidly as the much higher performance portable gadgets have been supplied to the market continuously. To get along with time to market that is business reason, the section of software development for power conscious, battery, devices has shifted itself from using specific low-level languages to higher level ones. Currently, complicated software running on mobile devices are often developed by high level languages those support OOP concepts. These cause the trend of embracing Design Patterns to mobile world. However, using Design Patterns directly in software development for power conscious systems is not recommended because they were not originally designed for such environment. This paper demonstrates the adapted Design Pattern for power limitation system. Because there are numerous original design patterns, it is not possible to mention the whole at once. So, this paper focuses only in creating Energy Conscious version of existing regular "Builder Pattern" to be appropriated for developing low power consumption software.

Keywords: Design Patterns, Builder Pattern, Low Power Consumption, Object Oriented Programming, Power Conscious System, Software.

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Authors: Carlos H. Cuadra

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Seismic isolation can be used as a retrofit method for historical buildings with the advantage that minimum intervention on super-structure is required. However, selection of isolation devices depends on weight and stiffness of upper structure. In this study, two buildings are considered for analyses to evaluate the applicability of this retrofitting methodology. Both buildings are located at Akita prefecture in the north part of Japan. One building is a wooden structure that corresponds to the old council meeting hall of Noshiro city. The second building is a brick masonry structure that was used as house of a foreign mining engineer and it is located at Ani town. Ambient vibration measurements were performed on both buildings to estimate their dynamic characteristics. Then, target period of vibration of isolated systems is selected as 3 seconds is selected to estimate required stiffness of isolation devices. For wooden structure, which is a light construction, it was found that natural rubber isolators in combination with friction bearings are suitable for seismic isolation. In case of masonry building elastomeric isolator can be used for its seismic isolation. Lumped mass systems are used for seismic response analysis and it is verified in both cases that seismic isolation can be used as retrofitting method of historical construction. However, in the case of the light building, most of the weight corresponds to the reinforced concrete slab that is required to install isolation devices.

Keywords: Historical building, finite element method, masonry structure, seismic isolation, wooden structure.

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Authors: Mpai Mokoena, Nsenda Lukumwena

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South Africa is one of the driest countries in the world and is facing a water crisis. In addition to inadequate infrastructure and poor planning, the country is experiencing high rates of water wastage due to pipe leaks. This study outlines the level of water wastage and develops a smart solution to efficiently manage and reduce the effects of pipe leaks, while monitoring the situation before and after fixing the pipe leaks. To understand the issue in depth, a literature review of journal papers and government reports was conducted. A questionnaire was designed and distributed to the general public. Additionally, the municipality office was contacted from a managerial perspective. The analysis from the study indicated that the majority of the citizens are aware of the water crisis and are willing to participate positively to decrease the level of water wasted. Furthermore, the response from the municipality acknowledged that more practical solutions are needed to reduce water wastage, and resources to attend to pipe leaks swiftly. Therefore, this paper proposes a specific solution for municipalities, local plumbers and citizens to minimize the effects of pipe leaks. The solution provides web and mobile application platforms to report and manage leaks swiftly. The solution is beneficial to the country in achieving water security and would promote a culture of responsibility toward water usage.

Keywords: Urban Distribution Networks, leak management, mobile application, responsible citizens, water crisis, water security.

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Authors: Matthias Dehmer, Frank Emmert Streib, Alexander Mehler, Jürgen Kilian

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Most known methods for measuring the structural similarity of document structures are based on, e.g., tag measures, path metrics and tree measures in terms of their DOM-Trees. Other methods measures the similarity in the framework of the well known vector space model. In contrast to these we present a new approach to measuring the structural similarity of web-based documents represented by so called generalized trees which are more general than DOM-Trees which represent only directed rooted trees.We will design a new similarity measure for graphs representing web-based hypertext structures. Our similarity measure is mainly based on a novel representation of a graph as strings of linear integers, whose components represent structural properties of the graph. The similarity of two graphs is then defined as the optimal alignment of the underlying property strings. In this paper we apply the well known technique of sequence alignments to solve a novel and challenging problem: Measuring the structural similarity of generalized trees. More precisely, we first transform our graphs considered as high dimensional objects in linear structures. Then we derive similarity values from the alignments of the property strings in order to measure the structural similarity of generalized trees. Hence, we transform a graph similarity problem to a string similarity problem. We demonstrate that our similarity measure captures important structural information by applying it to two different test sets consisting of graphs representing web-based documents.

Keywords: Graph similarity, hierarchical and directed graphs, hypertext, generalized trees, web structure mining.

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Authors: Corné J. Coetzee, Etienne Horn

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One of the main challenges in using the Discrete Element Method (DEM) is to specify the correct input parameter values. In general, the models are sensitive to the input parameter values and accurate results can only be achieved if the correct values are specified. For the linear contact model, micro-parameters such as the particle density, stiffness, coefficient of friction, as well as the particle size and shape distributions are required. There is a need for a procedure to accurately calibrate these parameters before any attempt can be made to accurately model a complete bulk materials handling system. Since DEM is often used to model applications in the mining and quarrying industries, a calibration procedure was developed for materials that consist of relatively large (up to 40 mm in size) particles. A coarse crushed aggregate was used as the test material. Using a specially designed large shear box with a diameter of 590 mm, the confined Young’s modulus (bulk stiffness) and internal friction angle of the material were measured by means of the confined compression test and the direct shear test respectively. DEM models of the experimental setup were developed and the input parameter values were varied iteratively until a close correlation between the experimental and numerical results was achieved. The calibration process was validated by modelling the pull-out of an anchor from a bed of material. The model results compared well with experimental measurement.

Keywords: Discrete Element Method (DEM), calibration, shear box, anchor pull-out.

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Authors: Y. H. Wang, C. C. Hsieh

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Good service design can increase organization revenue and consumer satisfaction while reducing labor and time costs. The problems facing consumers in the original serve model for eyewear and optical industry includes the following issues: 1. Insufficient information on eyewear products 2. Passively dependent on recommendations, insufficient selection 3. Incomplete records on progression of vision conditions 4. Lack of complete customer records. This study investigates the case of Kobayashi Optical, applying the Theory of Inventive Problem Solving (TRIZ) to develop innovative solutions for eyewear and optical industry. Analysis results raise the following conclusions and management implications: In order to provide customers with improved professional information and recommendations, Kobayashi Optical is suggested to establish customer purchasing records. Overall service efficiency can be enhanced by applying data mining techniques to analyze past consumer preferences and purchase histories. Furthermore, Kobayashi Optical should continue to develop a 3D virtual trial service which can allow customers for easy browsing of different frame styles and colors. This 3D virtual trial service will save customer waiting times in during peak service times at stores.

Keywords: Theory of inventive problem solving, service design, augmented reality, eyewear and optical industry.

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Authors: Salako Emmanuel Adekunle

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With numerous advantages of ICT in teaching such as using images to improve the retentive memory of students, academic staff is yet to deliver instructions adequately and effectively due to no power supply, lack of technical supports and non-availability of functional ICT tools. This study was conducted to investigate the attitudes of academic staff towards the use of information communication technology as a pedagogical tool for effective teaching in FCT College of Education, Zuba-Abuja, Nigeria. A sample of 200 academic staff from five schools/faculties was involved in the study. The respondents were selected by using simple random sampling technique (SRST). A questionnaire was developed and validated by the experts in Measurement and Evaluation, and reliability co-efficient of 0.85 was obtained. It was used to gather relevant data from the respondents. This study revealed that the respondents had positive attitudes towards the use of ICT as a pedagogical tool for effective teaching. Also, the uses of ICT by the academic staff included: to encourage closer relationship for attainment of higher academic, and to deliver instructions effectively. The study also revealed that there is a significant relationship between the attitudes and the uses of ICT by the academic staff. Based on these findings, some recommendations were made which include: power supply should be provided to operate ICT facilities for effective teaching, and technical assistance on ICT usage for effective delivery of instructions should be provided among other recommendations.

Keywords: Academic staff, attitudes, information communication technology, pedagogical tool, teaching and use.

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Authors: Aya Salama

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Digital Twin has emerged as a compelling research area, capturing the attention of scholars over the past decade. It finds applications across diverse fields, including smart manufacturing and healthcare, offering significant time and cost savings. Notably, it often intersects with other cutting-edge technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, the concept of a Human Digital Twin (HDT) is still in its infancy and requires further demonstration of its practicality. HDT takes the notion of Digital Twin a step further by extending it to living entities, notably humans, who are vastly different from inanimate physical objects. The primary objective of this research was to create an HDT capable of automating real-time human responses by simulating human behavior. To achieve this, the study delved into various areas, including clustering, supervised classification, topic extraction, and sentiment analysis. The paper successfully demonstrated the feasibility of HDT for generating personalized responses in social messaging applications. Notably, the proposed approach achieved an overall accuracy of 63%, a highly promising result that could pave the way for further exploration of the HDT concept. The methodology employed Random Forest for clustering the question database and matching new questions, while K-nearest neighbor was utilized for sentiment analysis.

Keywords: Human Digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification and clustering.

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Authors: DaeWon Lee, HeonChang Yu

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Next generation wireless/mobile networks will be IP based cellular networks integrating the internet with cellular networks. In this paper, we propose a new architecture for a high speed transport system and a mobile management protocol for mobile internet users in a transport system. Existing mobility management protocols (MIPv6, HMIPv6) do not consider real world fast moving wireless hosts (e.g. passengers in a train). For this reason, we define a virtual organization (VO) and proposed the VO architecture for the transport system. We also classify mobility as VO mobility (intra VO) and macro mobility (inter VO). Handoffs in VO are locally managed and transparent to the CH while macro mobility is managed with Mobile IPv6. And, from the features of the transport system, such as fixed route and steady speed, we deduce the movement route and the handoff disruption time of each handoff. To reduce packet loss during handoff disruption time, we propose pre-registration scheme using pre-registration. Moreover, the proposed protocol can eliminate unnecessary binding updates resulting from sequence movement at high speed. The performance evaluations demonstrate our proposed protocol has a good performance at transport system environment. Our proposed protocol can be applied to the usage of wireless internet on the train, subway, and high speed train.

Keywords: Binding update, HMIPv6, packet loss, transport system, virtual organization.

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Authors: Angelina A. Tzacheva, Jaishree Ranganathan

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Interest in (STEM) Science Technology Engineering Mathematics education especially Computer Science education has seen a drastic increase across the country. This fuels effort towards recruiting and admitting a diverse population of students. Thus the changing conditions in terms of the student population, diversity and the expected teaching and learning outcomes give the platform for use of Innovative Teaching models and technologies. It is necessary that these methods adapted should also concentrate on raising quality of such innovations and have positive impact on student learning. Light-Weight Team is an Active Learning Pedagogy, which is considered to be low-stake activity and has very little or no direct impact on student grades. Emotion plays a major role in student’s motivation to learning. In this work we use the student feedback data with emotion classification using surveys at a public research institution in the United States. We use Actionable Pattern Discovery method for this purpose. Actionable patterns are patterns that provide suggestions in the form of rules to help the user achieve better outcomes. The proposed method provides meaningful insight in terms of changes that can be incorporated in the Light-Weight team activities, resources utilized in the course. The results suggest how to enhance student emotions to a more positive state, in particular focuses on the emotions ‘Trust’ and ‘Joy’.

Keywords: Actionable pattern discovery, education, emotion, data mining.

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Authors: Yuan-Jye Tseng, Shin-Han Lin

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In this research, a mathematical model for integrated evaluation of green design and green manufacturing processes is presented. To design a product, there can be alternative options to design the detailed components to fulfill the same product requirement. In the design alternative cases, the components of the product can be designed with different materials and detailed specifications. If several design alternative cases are proposed, the different materials and specifications can affect the manufacturing processes. In this paper, a new concept for integrating green design and green manufacturing processes is presented. A green design can be determined based the manufacturing processes of the designed product by evaluating the green criteria including energy usage and environmental impact, in addition to the traditional criteria of manufacturing cost. With this concept, a mathematical model is developed to find the green design and the associated green manufacturing processes. In the mathematical model, the cost items include material cost, manufacturing cost, and green related cost. The green related cost items include energy cost and environmental cost. The objective is to find the decisions of green design and green manufacturing processes to achieve the minimized total cost. In practical applications, the decision-making can be made to select a good green design case and its green manufacturing processes. In this presentation, an example product is illustrated. It shows that the model is practical and useful for integrated evaluation of green design and green manufacturing processes.

Keywords: Supply chain management, green supply chain, green design, green manufacturing, mathematical model.

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Authors: Robert Koch, Mario Golling, Gabi Dreo

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During the last couple of years, the degree of dependence on IT systems has reached a dimension nobody imagined to be possible 10 years ago. The increased usage of mobile devices (e.g., smart phones), wireless sensor networks and embedded devices (Internet of Things) are only some examples of the dependency of modern societies on cyber space. At the same time, the complexity of IT applications, e.g., because of the increasing use of cloud computing, is rising continuously. Along with this, the threats to IT security have increased both quantitatively and qualitatively, as recent examples like STUXNET or the supposed cyber attack on Illinois water system are proofing impressively. Once isolated control systems are nowadays often publicly available - a fact that has never been intended by the developers. Threats to IT systems don’t care about areas of responsibility. Especially with regard to Cyber Warfare, IT threats are no longer limited to company or industry boundaries, administrative jurisdictions or state boundaries. One of the important countermeasures is increased cooperation among the participants especially in the field of Cyber Defence. Besides political and legal challenges, there are technical ones as well. A better, at least partially automated exchange of information is essential to (i) enable sophisticated situational awareness and to (ii) counter the attacker in a coordinated way. Therefore, this publication performs an evaluation of state of the art Intrusion Detection Message Exchange protocols in order to guarantee a secure information exchange between different entities.

Keywords: Cyber Defence, Cyber Warfare, Intrusion Detection Information Exchange, Early Warning Systems, Joint Intrusion Detection, Cyber Conflict

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Authors: Seda Sahin, Emin Akata

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Obturator Foramen is a specific structure in Pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as Obturator Foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template as a preprocessing step for computation of Pelvic bone rotation on hip radiographs. This method consists of integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor and it is used to detect Obturator Foramen accurately. Marker-controlled Watershed segmentation is applied to separate Obturator Foramen from the background effectively. Then, Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for final extraction of Obturator Foramens. Finally, Pelvic bone rotation rate calculation for each hip radiograph is performed automatically to select and eliminate hip radiographs for further studies which depend on Pelvic bone angle measurements. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results demonstrated that the proposed method is able to segment Obturator Foramen with 96% accuracy.

Keywords: Medical image analysis, marker-controlled watershed segmentation, segmentation of bone structures on hip radiographs, pelvic bone rotation rate, zernike moment feature descriptor.

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Authors: Kamal K. Bharadwaj, Basheer M. Al-Maqaleh

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In the recent past, there has been an increasing interest in applying evolutionary methods to Knowledge Discovery in Databases (KDD) and a number of successful applications of Genetic Algorithms (GA) and Genetic Programming (GP) to KDD have been demonstrated. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski & Winston that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to the rule. Such rules are employed in situations, in which the conditional statement 'If P Then D' holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence are tight or there is simply no information available as to whether it holds or not. Thus, the 'If P Then D' part of the CPR expresses important information, while the Unless C part acts only as a switch and changes the polarity of D to ~D. This paper presents a classification algorithm based on evolutionary approach that discovers comprehensible rules with exceptions in the form of CPRs. The proposed approach has flexible chromosome encoding, where each chromosome corresponds to a CPR. Appropriate genetic operators are suggested and a fitness function is proposed that incorporates the basic constraints on CPRs. Experimental results are presented to demonstrate the performance of the proposed algorithm.

Keywords: Censored Production Rule, Data Mining, MachineLearning, Evolutionary Algorithms.

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Authors: R. Suresh, Pavan Kumar Nimmagadda, Ming Zo Tan, Shane Hart, Sharp Ugwuocha

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Polycrystalline diamond compact (PDC) drill bits are extensively used in the oil and gas industry as well as the mining industry. Industry engineers continually improve upon PDC drill bit designs and hydraulic conditions. Optimized injection nozzles play a key role in improving the drilling performance and efficiency of these ever changing PDC drill bits. In the first part of this study, computational fluid dynamics (CFD) modelling is performed to investigate the hydrodynamic characteristics of drilling fluid flow around the PDC drill bit. An Open-source CFD software – OpenFOAM simulates the flow around the drill bit, based on the field input data. A specifically developed console application integrates the entire CFD process including, domain extraction, meshing, and solving governing equations and post-processing. The results from the OpenFOAM solver are then compared with that of the ANSYS Fluent software. The data from both software programs agree. The second part of the paper describes the parametric study of the PDC drill bit nozzle to determine the effect of parameters such as number of nozzles, nozzle velocity, nozzle radial position and orientations on the flow field characteristics and bit washing patterns. After analyzing a series of nozzle configurations, the best configuration is identified and recommendations are made for modifying the PDC bit design.

Keywords: ANSYS Fluent, computational fluid dynamics, nozzle configuration, OpenFOAM, PDC dill bit.

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Authors: Robert S. Giachetti, Emanuele Grossi

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Loosening of bolted joints in rotating machines can adversely affect their performance, cause mechanical damage, and lead to injuries. In this paper, two potential loosening phenomena in rotating applications are discussed. First, ‘precession,’ is governed by thread/nut contact forces, while the second is based on inertial effects of the fastened assembly. These mechanisms are reviewed within the context of historical usage of left-handed fasteners in rotating machines which appears absent in the literature and common machine design texts. Historically, to prevent loosening of wheel nuts, vehicle manufacturers have used right-handed and left-handed threads on different sides of the vehicle, but most modern vehicles have abandoned this custom and only use right-handed, tapered lug nuts on all sides of the vehicle. Other classical machines such as the bicycle continue to use different handed threads on each side while other machines such as, bench grinders, circular saws and brush cutters still use left-handed threads to fasten rotating components. Despite the continued use of left-handed fasteners, the rationale and analysis of left-handed threads to mitigate self-loosening of fasteners in rotating applications is not commonly, if at all, discussed in the literature or design textbooks. Without scientific literature to support these design selections, these implementations may be the result of experimental findings or aged institutional knowledge. Based on a review of rotating applications, historical documents and mechanical design references, a formal study of the paradoxical nature of left-handed threads in various applications is merited.

Keywords: Rotating machinery, self-loosening fasteners, wheel fastening, vibration loosening.

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Authors: Shaza Melies, Abeer Refky, Nihad Mansoor

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Multimodality involves the communication between verbal and visual components in various discourses. A painting represents a form of communication between the artist and the viewer in terms of colors, shades, objects, and the title. This paper aims to present how multimodality can be used to decode the verbal and visual dimensions a painting holds. For that purpose, this study uses Kress and van Leeuwen’s theoretical framework of visual grammar for the analysis of the multimodal semiotic resources of selected paintings of Salvador Dali. This study investigates the visual decoding of the selected paintings of Salvador Dali and analyzing their social and political meanings using Kress and van Leeuwen’s framework of visual grammar. The paper attempts to answer the following questions: 1. How far can multimodality decode the verbal and non-verbal meanings of surrealistic art? 2. How can Kress and van Leeuwen’s theoretical framework of visual grammar be applied to analyze Dali’s paintings? 3. To what extent is Kress and van Leeuwen’s theoretical framework of visual grammar apt to deliver political and social messages of Dali? The paper reached the following findings: the framework’s descriptive tools (representational, interactive, and compositional meanings) can be used to analyze the paintings’ title and their visual elements. Social and political messages were delivered by appropriate usage of color, gesture, vectors, modality, and the way social actors were represented.

Keywords: Multimodality, multimodal analysis, paintings analysis, Salvador Dali, visual grammar.

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Authors: Parvinder S. Sandhu, Sandeep Khimta, Kiranpreet Kaur

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The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.

Keywords: Growing Neural Gas, data clustering, fault prediction.

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Authors: Junjie Chen, Deguang Xu

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Transient simulation of the hydrogen-assisted self-ignition of propane-air mixtures were carried out in platinum-coated micro-channels from ambient cold-start conditions, using a two-dimensional model with reduced-order reaction schemes, heat conduction in the solid walls, convection and surface radiation heat transfer. The self-ignition behavior of hydrogen-propane mixed fuel is analyzed and compared with the heated feed case. Simulations indicate that hydrogen can successfully cause self-ignition of propane-air mixtures in catalytic micro-channels with a 0.2 mm gap size, eliminating the need for startup devices. The minimum hydrogen composition for propane self-ignition is found to be in the range of 0.8-2.8% (on a molar basis), and increases with increasing wall thermal conductivity, and decreasing inlet velocity or propane composition. Higher propane-air ratio results in earlier ignition. The ignition characteristics of hydrogen-assisted propane qualitatively resemble the selectively inlet feed preheating mode. Transient response of the mixed hydrogen- propane fuel reveals sequential ignition of propane followed by hydrogen. Front-end propane ignition is observed in all cases. Low wall thermal conductivities cause earlier ignition of the mixed hydrogen-propane fuel, subsequently resulting in low exit temperatures. The transient-state behavior of this micro-scale system is described, and the startup time and minimization of hydrogen usage are discussed.

Keywords: Micro-combustion, Self-ignition, Hydrogen addition, Heat transfer, Catalytic combustion, Transient simulation.

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Authors: Manuela Nayantara Jeyaraj

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Having moved into the 21^st century, it is way past being arguable that innovative technology needs to be incorporated into conventional classroom teaching. Though the Western world has found presumable success in achieving this, it is still a concept under battle in developing countries such as Sri Lanka. Reaching the acme of implementing interactive virtual learning within classrooms is a struggling idealistic fascination within the island. In order to overcome this problem, this study is set to reveal facts that limit the implementation of virtual, interactive learning within the school classrooms and provide hacks that could prove the augmented use of the Virtual World to enhance teaching and learning experiences. As each classroom moves along with the usage of technology to fulfill its functionalities, a few intense hacks provided will build the administrative onuses on a virtual system. These hacks may divulge barriers based on social conventions, financial boundaries, digital literacy, intellectual capacity of the staff, and highlight the impediments in introducing students to an interactive virtual learning environment and thereby provide the necessary actions or changes to be made to succeed and march along in creating an intellectual society built on virtual learning and lifestyle. This digital learning environment will be composed of multimedia presentations, trivia and pop quizzes conducted on a GUI, assessments conducted via a virtual system, records maintained on a database, etc. The ultimate objective of this study could enhance every child's basic learning environment; hence, diminishing the digital divide that exists in certain communities.

Keywords: Digital divide, digital learning, digitization, Sri Lanka, teaching methodologies.

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Authors: Mohammad A. Bani-Khaled

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In this work we use the Discrete Proper Orthogonal Decomposition transform to characterize the properties of coupled dynamics in thin-walled beams by exploiting numerical simulations obtained from finite element simulations. The outcomes of the will improve our understanding of the linear and nonlinear coupled behavior of thin-walled beams structures. Thin-walled beams have widespread usage in modern engineering application in both large scale structures (aeronautical structures), as well as in nano-structures (nano-tubes). Therefore, detailed knowledge in regard to the properties of coupled vibrations and buckling in these structures are of great interest in the research community. Due to the geometric complexity in the overall structure and in particular in the cross-sections it is necessary to involve computational mechanics to numerically simulate the dynamics. In using numerical computational techniques, it is not necessary to over simplify a model in order to solve the equations of motions. Computational dynamics methods produce databases of controlled resolution in time and space. These numerical databases contain information on the properties of the coupled dynamics. In order to extract the system dynamic properties and strength of coupling among the various fields of the motion, processing techniques are required. Time- Proper Orthogonal Decomposition transform is a powerful tool for processing databases for the dynamics. It will be used to study the coupled dynamics of thin-walled basic structures. These structures are ideal to form a basis for a systematic study of coupled dynamics in structures of complex geometry.

Keywords: Coupled dynamics, geometric complexity, Proper Orthogonal Decomposition (POD), thin walled beams.

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Authors: Mui Joo Tang, Eang Teng Chan

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Advertising is commonly used to foster sales and reputation of an institution. It is at first the growth of print advertising that has increased the population and number of periodicals of newspaper and its circulation. The rise of Internet and online media has somehow blurred the role of media and advertising though the intention is still to reach out to audience and to increase sales. The relationship between advertising and audience on a product purchase through persuasion has been developing from print media to online media. From the changing media environment and audience, it is the concern of this research to study the impact of online advertising to such a relationship cycle. The content of online advertisements is much of text, multimedia, photo, audio and video. The messages of such content format may indeed bring impacts to its audience and its credibility. This study is therefore reflecting the effectiveness of online advertisement and its influences on generation Y in their purchasing behavior. This study uses Media Dependency Theory to analyze the relationship between the impact of online advertisement and media usage pattern of generation Y. Hierarchy of Effectiveness Model is used as a marketing communication model to study the effectiveness of advertising and further to determine the impact of online advertisement on generation Y in their purchasing decision making. This research uses online survey to reach out the sample of generation Y. The results have shown that online advertisements do not affect much on purchase decision making even though generation Y relies much on the media content including online advertisement for its information and believing in its credibility. There are few other external factors that may interrupt the effectiveness of online advertising. The very obvious influence of purchasing behavior is actually derived from the peers.

Keywords: Generation Y, online advertising, online media, persuasion, print media, purchase decision.

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies  the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: Retail stores, Faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition.

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Authors: Afnan T. Bagasi, Donia M. Bettaieb, Abeer Alsobahi

Abstract:

The wayfinding system affects the course of a museum journey for visitors, both directly and indirectly. The design aspects of this system play an important role, making it an effective communication system within the museum space. However, translating the concepts that pertain to its design, and which are based on integration and connectivity in museum space design, such as intelligibility, lacks customization in the form of specific design considerations with reference to the most important approaches. These approaches link the organizational and practical aspects to the semiotic and semantic aspects related to the space syntax by targeting the visual and perceived consistency of visitors. In this context, the present study aims to identify how to apply the concept of intelligibility by employing integration and connectivity to design a wayfinding system in museums as a kind of composite interior space. Using the available plans and images to extrapolate the considerations used to design the wayfinding system in the Saudi National Museum as a case study, a descriptive analytical method was used to understand the basic organizational and Morphological principles of the museum space through the main aspects of space design (the Morphological and the pragmatic). The study’s methodology is based on the description and analysis of the basic organizational and Morphological principles of the museum space at the level of the major Morphological and Pragmatic design layers (based on available pictures and diagrams) and inductive method about applied level of intelligibility in spatial layout in the Hall of Islam and Arabia at the National Museum Saudi Arabia within the framework of a case study through the levels of verification of the properties of the concepts of connectivity and integration. The results indicated that the application of the characteristics of intelligibility is weak on both Pragmatic and Morphological levels. Based on the concept of connective and integration, we conclude the following: (1) High level of reflection of the properties of connectivity on the pragmatic level, (2) Weak level of reflection of the properties of Connectivity at the morphological level (3) Weakness in the level of reflection of the properties of integration in the space sample as a result of a weakness in the application at the morphological and pragmatic level. The study’s findings will assist designers, professionals, and researchers in the field of museum design in understanding the significance of the wayfinding system by delving into it through museum spaces by highlighting the most essential aspects using a clear analytical method.

Keywords: wayfinding system, museum journey, intelligibility, integration, connectivity, interior design

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Authors: Srinath Ramakkrushnan, Aswathaman Vijayan

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In conventional seedling production, the seedlings are being grown in the open field under natural conditions. Here they are susceptible to sudden changes in climate were their quality and yield is affected. Quality seedlings are essential for good growth and performance of crops in main field; they serve as a foundation for the economic returns to the farmer. Producing quality seedling demands usage of hybrid seeds as they have the ability to result in better yield, greater uniformity, improved color, disease resistance, and so forth. Hybrid seed production poses major operational challenge and its seed use efficiency plays an important role. Thus in order to overcome the difficulties currently present in conventional seedling production and to efficiently use hybrid seeds, ITC Limited Agri Business Divisions - Sustainability Cell as conceptualized a novel method of seedling production unit for farmers in West Godavari District of Andhra Pradesh. The “Green House based Float Seedling" methodology aims at a protected cultivation technique wherein the micro climate surrounding the plant/seedling body is controlled partially or fully as per the requirement of the species. This paper reports on the techno economic evaluation of green house for cultivation of float based seedling production with experimental results that was attained from the pilot implementation in West Godavari District, Rajahmundry region of India.

Keywords: Economic Assessment, Float Seedling, Green House, ITC Limited, Payback period.

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Authors: I. V. Pinto, M. R. Sooriyarachchi

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It can be frequently observed that the data arising in our environment have a hierarchical or a nested structure attached with the data. Multilevel modelling is a modern approach to handle this kind of data. When multilevel modelling is combined with a binary response, the estimation methods get complex in nature and the usual techniques are derived from quasi-likelihood method. The estimation methods which are compared in this study are, marginal quasi-likelihood (order 1 & order 2) (MQL1, MQL2) and penalized quasi-likelihood (order 1 & order 2) (PQL1, PQL2). A statistical model is of no use if it does not reflect the given dataset. Therefore, checking the adequacy of the fitted model through a goodness-of-fit (GOF) test is an essential stage in any modelling procedure. However, prior to usage, it is also equally important to confirm that the GOF test performs well and is suitable for the given model. This study assesses the suitability of the GOF test developed for binary response multilevel models with respect to the method used in model estimation. An extensive set of simulations was conducted using MLwiN (v 2.19) with varying number of clusters, cluster sizes and intra cluster correlations. The test maintained the desirable Type-I error for models estimated using PQL2 and it failed for almost all the combinations of MQL. Power of the test was adequate for most of the combinations in all estimation methods except MQL1. Moreover, models were fitted using the four methods to a real-life dataset and performance of the test was compared for each model.

Keywords: Goodness-of-fit test, marginal quasi-likelihood, multilevel modelling, type-I error, penalized quasi-likelihood, power, quasi-likelihood.

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Authors: Sarabjeet Kaur Kochhar

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With a surge of stream processing applications novel techniques are required for generation and analysis of association rules in streams. The traditional rule mining solutions cannot handle streams because they generally require multiple passes over the data and do not guarantee the results in a predictable, small time. Though researchers have been proposing algorithms for generation of rules from streams, there has not been much focus on their analysis. We propose Association rule profiling, a user centric process for analyzing association rules and attaching suitable profiles to them depending on their changing frequency behavior over a previous snapshot of time in a data stream. Association rule profiles provide insights into the changing nature of associations and can be used to characterize the associations. We discuss importance of characteristics such as predictability of linkages present in the data and propose metric to quantify it. We also show how association rule profiles can aid in generation of user specific, more understandable and actionable rules. The framework is implemented as SUPAR: System for Usercentric Profiling of Association Rules in streaming data. The proposed system offers following capabilities: i) Continuous monitoring of frequency of streaming item-sets and detection of significant changes therein for association rule profiling. ii) Computation of metrics for quantifying predictability of associations present in the data. iii) User-centric control of the characterization process: user can control the framework through a) constraint specification and b) non-interesting rule elimination.

Keywords: Data Streams, User subjectivity, Change detection, Association rule profiles, Predictability.

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Authors: Abimbola M. Enitan, Josiah Adeyemo, Sheena Kumari, Feroz M. Swalaha, Faizal Bux

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Industries produce millions of cubic meters of effluent every year and the wastewater produced may be released into the surrounding water bodies, treated on-site or at municipal treatment plants. The determination of organic matter in the wastewater generated is very important to avoid any negative effect on the aquatic ecosystem. The scope of the present work is to assess the physicochemical composition of the wastewater produced from one of the brewery industry in South Africa. This is to estimate the environmental impact of its discharge into the receiving water bodies or the municipal treatment plant. The parameters monitored for the quantitative analysis of brewery wastewater include biological oxygen demand (BOD5), chemical oxygen demand (COD), total suspended solids, volatile suspended solids, ammonia, total oxidized nitrogen, nitrate, nitrite, phosphorus and alkalinity content. In average, the COD concentration of the brewery effluent was 5340.97 mg/l with average pH values of 4.0 to 6.7. The BOD5 and the solids content of the wastewater from the brewery industry were high. This means that the effluent is very rich in organic content and its discharge into the water bodies or the municipal treatment plant could cause environmental pollution or damage the treatment plant. In addition, there were variations in the wastewater composition throughout the monitoring period. This might be as a result of different activities that take place during the production process, as well as the effects of peak period of beer production on the water usage.

Keywords: Brewery wastewater, environmental pollution, industrial effluents, physicochemical composition.

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Authors: P. Ashok, G. M. Kadhar Nawaz

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Rough set theory is used to handle uncertainty and incomplete information by applying two accurate sets, Lower approximation and Upper approximation. In this paper, the rough clustering algorithms are improved by adopting the Similarity, Dissimilarity–Similarity and Entropy based initial centroids selection method on three different clustering algorithms namely Entropy based Rough K-Means (ERKM), Similarity based Rough K-Means (SRKM) and Dissimilarity-Similarity based Rough K-Means (DSRKM) were developed and executed by yeast dataset. The rough clustering algorithms are validated by cluster validity indexes namely Rand and Adjusted Rand indexes. An experimental result shows that the ERKM clustering algorithm perform effectively and delivers better results than other clustering methods. Outlier detection is an important task in data mining and very much different from the rest of the objects in the clusters. Entropy based Rough Outlier Factor (EROF) method is seemly to detect outlier effectively for yeast dataset. In rough K-Means method, by tuning the epsilon (ᶓ) value from 0.8 to 1.08 can detect outliers on boundary region and the RKM algorithm delivers better results, when choosing the value of epsilon (ᶓ) in the specified range. An experimental result shows that the EROF method on clustering algorithm performed very well and suitable for detecting outlier effectively for all datasets. Further, experimental readings show that the ERKM clustering method outperformed the other methods.

Keywords: Clustering, Entropy, Outlier, Rough K-Means, validity index.

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