Search results for: Deep learning network

Authors: Amogh Katti, J. Sai Asritha, D. Nivedh, M. Kalyan Srinivas, B. Somnath Chakravarthi

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Today's modern education system uses Learning Management System (LMS) portals for the scoring and grading of student performances, to maintain student records, and teachers are instructed to accept assignments through online submissions of .pdf, .doc, .ppt, etc. There is a risk of data tampering in the traditional portals; we will apply the Blockchain model instead of this traditional model to avoid data tampering and also provide a decentralized mechanism for overall fairness. Blockchain technology is a better and also recommended model because of the following features: consensus mechanism, decentralized system, cryptographic encryption, smart contracts, Ethereum blockchain. The proposed system ensures data integrity and tamper-proof assignment submission and grading, which will be helpful for both students and also educators.

Keywords: Education technology, learning management system, decentralized applications, blockchain.

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Authors: Nasser Mozayani, Maryam Barzegar, Hoda Madani

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Vehicular communications play a substantial role in providing safety in transportation by means of safety message exchange. Researchers have proposed several solutions for securing safety messages. Protocols based on a fixed key infrastructure are more efficient in implementation and maintain stronger security in comparison with dynamic structures. These protocols utilize zone partitioning to establish distinct key infrastructure under Certificate Authority (CA) supervision in different regions. Secure anonymous broadcasting (SAB) is one of these protocols that preserves most of security aspects but it has some deficiencies in practice. A very important issue is region change of a vehicle for its mobility. Changing regions leads to change of CA and necessity of having new key set to resume communication. In this paper, we propose solutions for informing vehicles about region change to obtain new key set before entering next region. This hinders attackers- intrusion, packet loss and lessons time delay. We also make key request messages secure by confirming old CA-s public key to the message, hence stronger security for safety message broadcasting is attained.

Keywords: Secure broadcasting, Certificate authority (CA), Key exchange, Vehicular network.

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Authors: Yaojun Wang, Yaoqing Wang

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Stock selection is an important decision-making problem. Many machine learning and data mining technologies are employed to build automatic stock-selection system. A profitable stock-selection system should consider the stock’s investment value and the market timing. In this paper, we present a hybrid system including both engage for stock selection. This system uses a case-based reasoning (CBR) model to execute the stock classification, uses a decision-tree model to help with market timing and stock selection. The experiments show that the performance of this hybrid system is better than that of other techniques regarding to the classification accuracy, the average return and the Sharpe ratio.

Keywords: Case-based reasoning, decision tree, stock selection, machine learning.

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Authors: Rustam Asnawi, Wan Fatimah Wan Ahmad

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Currently electronic slide (e-slide) is one of the most common styles in educational presentation. Unfortunately, the utilization of e-slide for the visually impaired is uncommon since they are unable to see the content of such e-slides which are usually composed of text, images and animation. This paper proposes a model for presenting e-slide in multimodal presentation i.e. using conventional slide concurrent with voicing, in both languages Malay and English. At the design level, live multimedia presentation concept is used, while at the implementation level several components are used. The text content of each slide is extracted using COM component, Microsoft Speech API for voicing the text in English language and the text in Malay language is voiced using dictionary approach. To support the accessibility, an auditory user interface is provided as an additional feature. A prototype of such model named as VSlide has been developed and introduced.

Keywords: presentation, self-learning, slide, visually impaired

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Authors: Tatjana Eitrich, Bruno Lang

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This work deals with aspects of support vector learning for large-scale data mining tasks. Based on a decomposition algorithm that can be run in serial and parallel mode we introduce a data transformation that allows for the usage of an expensive generalized kernel without additional costs. In order to speed up the decomposition algorithm we analyze the problem of working set selection for large data sets and analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our modifications and settings lead to improvement of support vector learning performance and thus allow using extensive parameter search methods to optimize classification accuracy.

Keywords: Support Vector Machines, Shared Memory Parallel Computing, Large Data

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Authors: S. Arun Rajan, S. Bhavani

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Wireless sensor networks can be tiny, low cost, intelligent sensors connected with advanced communication systems. WSNs have pulled in significant consideration as a matter of fact that, industrial as well as medical solicitations employ these in monitoring targets, conservational observation, obstacle exposure, movement regulator etc. In these applications, sensor hubs are thickly sent in the unattended environment with little non-rechargeable batteries. This constraint requires energy-efficient systems to drag out the system lifetime. There are redundancies in data sent over the network. To overcome this, multiple virtual spine scheduling has been presented. Such networks problems are called Maximum Lifetime Backbone Scheduling (MLBS) problems. Though this sleep wake cycle reduces radio usage, improvement can be made in the path in which the group heads stay selected. Cluster head selection with emphasis on geometrical relation of the system will enhance the load sharing among the nodes. Also the data are analyzed to reduce redundant transmission. Multi-hop communication will facilitate lighter loads on the network.

Keywords: WSN, wireless sensor networks, MLBS, maximum lifetime backbone scheduling.

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Authors: Aurelija Burinskienė, Dalė Dzemydienė, Arūnas Miliauskas

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This research aims at developing the approach for more effective freight delivery and transportation process management. The road congestions and the identification of causes are important, as well as the context information recognition and management. The measure of many parameters during the transportation period and proper control of driver work became the problem. The number of vehicles per time unit passing at a given time and point for drivers can be evaluated in some situations. The collection of data is mainly used to establish new trips. The flow of the data is more complex in urban areas. Herein, the movement of freight is reported in detail, including the information on street level. When traffic density is extremely high in congestion cases, and the traffic speed is incredibly low, data transmission reaches the peak. Different data sets are generated, which depend on the type of freight delivery network. There are three types of networks: long-distance delivery networks, last-mile delivery networks and mode-based delivery networks; the last one includes different modes, in particular, railways and other networks. When freight delivery is switched from one type of the above-stated network to another, more data could be included for reporting purposes and vice versa. In this case, a significant amount of these data is used for control operations, and the problem requires an integrated methodological approach. The paper presents an approach for providing e-services for drivers by including the assessment of the multi-component infrastructure needed for delivery of freights following the network type. The construction of such a methodology is required to evaluate data flow conditions and overloads, and to minimize the time gaps in data reporting. The results obtained show the possibilities of the proposing methodological approach to support the management and decision-making processes with functionality of incorporating networking specifics, by helping to minimize the overloads in data reporting.

Keywords: Transportation networks, freight delivery, data flow, monitoring, e-services.

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Authors: Omnia F. Elkommos

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In the course of teaching stylistics to undergraduate students of the Department of English Language and Literature, Faculty of Arts and Humanities, the linguistic tool kit of theories comes in handy and useful for the better understanding of the different literary genres: Poetry, drama, and short stories. In the present paper, a model of teaching of stylistics is compiled and suggested. It is a collaborative group project technique for use in the undergraduate diverse specialisms (Literature, Linguistics and Translation tracks) class. Students initially are introduced to the different linguistic tools and theories suitable for each literary genre. The second step is to apply these linguistic tools to texts. Students are required to watch videos performing the poems or play, for example, and search the net for interpretations of the texts by other authorities. They should be using a template (prepared by the researcher) that has guided questions leading students along in their analysis. Finally, a practical analysis would be written up using the practical analysis essay template (also prepared by the researcher). As per collaborative learning, all the steps include activities that are student-centered addressing differentiation and considering their three different specialisms. In the process of selecting the proper tools, the actual application and analysis discussion, students are given tasks that request their collaboration. They also work in small groups and the groups collaborate in seminars and group discussions. At the end of the course/module, students present their work also collaboratively and reflect and comment on their learning experience. The module/course uses a drama play that lends itself to the task: ‘The Bond’ by Amy Lowell and Robert Frost. The project results in an interpretation of its theme, characterization and plot. The linguistic tools are drawn from pragmatics, and discourse analysis among others.

Keywords: Applied linguistic theories, collaborative learning, cooperative principle, discourse analysis, drama analysis, group project, online acting performance, pragmatics, speech act theory, stylistics, technology enhanced learning.

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Authors: Roland S. Nattermann, Reiner Anderl

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Existing proceeding-models for the development of mechatronic systems provide a largely parallel action in the detailed development. This parallel approach is to take place also largely independent of one another in the various disciplines involved. An approach for a new proceeding-model provides a further development of existing models to use for the development of Adaptronic Systems. This approach is based on an intermediate integration and an abstract modeling of the adaptronic system. Based on this system-model a simulation of the global system behavior, due to external and internal factors or Forces is developed. For the intermediate integration a special data management system is used. According to the presented approach this data management system has a number of functions that are not part of the "normal" PDM functionality. Therefore a concept for a new data management system for the development of Adaptive system is presented in this paper. This concept divides the functions into six layers. In the first layer a system model is created, which divides the adaptronic system based on its components and the various technical disciplines. Moreover, the parameters and properties of the system are modeled and linked together with the requirements and the system model. The modeled parameters and properties result in a network which is analyzed in the second layer. From this analysis necessary adjustments to individual components for specific manipulation of the system behavior can be determined. The third layer contains an automatic abstract simulation of the system behavior. This simulation is a precursor for network analysis and serves as a filter. By the network analysis and simulation changes to system components are examined and necessary adjustments to other components are calculated. The other layers of the concept treat the automatic calculation of system reliability, the "normal" PDM-functionality and the integration of discipline-specific data into the system model. A prototypical implementation of an appropriate data management with the addition of an automatic system development is being implemented using the data management system ENOVIA SmarTeam V5 and the simulation system MATLAB.

Keywords: Adaptronic, Data-Management, LOEWE-CentreAdRIA

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Authors: Alexandros Leontitsis, Archana P. Sangole

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This article presents a short discussion on optimum neighborhood size selection in a spherical selforganizing feature map (SOFM). A majority of the literature on the SOFMs have addressed the issue of selecting optimal learning parameters in the case of Cartesian topology SOFMs. However, the use of a Spherical SOFM suggested that the learning aspects of Cartesian topology SOFM are not directly translated. This article presents an approach on how to estimate the neighborhood size of a spherical SOFM based on the data. It adopts the L-curve criterion, previously suggested for choosing the regularization parameter on problems of linear equations where their right-hand-side is contaminated with noise. Simulation results are presented on two artificial 4D data sets of the coupled Hénon-Ikeda map.

Keywords: Parameter estimation, self-organizing feature maps, spherical topology.

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Authors: Watcharawalee Lertlum, Borworn Papasratorn

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Conventional WBL is effective for meaningful student, because rote student learn by repeating without thinking or trying to understand. It is impossible to have full benefit from conventional WBL. Understanding of rote student-s intention and what influences it becomes important. Poorly designed user interface will discourage rote student-s cultivation and intention to use WBL. Thus, user interface design is an important factor especially when WBL is used as comprehensive replacement of conventional teaching. This research proposes the influencing factors that can enhance student-s intention to use the system. The enhanced TAM is used for evaluating the proposed factors. The research result points out that factors influencing rote student-s intention are Perceived Usefulness of Homepage Content Structure, Perceived User Friendly Interface, Perceived Hedonic Component, and Perceived (homepage) Visual Attractiveness.

Keywords: E-learning, Web-Based learning, Intention to use, Rote student, Influencing.

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Authors: Gianluigi De Mare, Tony Leopoldo Luigi Lenza, Rino Conte

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The evolution of technology and construction techniques has enabled the upgrading of transport networks. In particular, the high-speed rail networks allow convoys to peak at above 300 km/h. These structures, however, often significantly impact the surrounding environment. Among the effects of greater importance are the ones provoked by the soundwave connected to train transit. The wave propagation affects the quality of life in areas surrounding the tracks, often for several hundred metres. There are substantial damages to properties (buildings and land), in terms of market depreciation. The present study, integrating expertise in acoustics, computering and evaluation fields, outlines a useful model to select project paths so as to minimize the noise impact and reduce the causes of possible litigation. It also facilitates the rational selection of initiatives to contain the environmental damage to the already existing railway tracks. The research is developed with reference to the Italian regulatory framework (usually more stringent than European and international standards) and refers to a case study concerning the high speed network in Italy.

Keywords: Impact, compensation for financial loss, depreciation of property, railway network design, genetic algorithms.

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Authors: U. Bottigli, R.Chiarucci, B. Golosio, G.L. Masala, P. Oliva, S.Stumbo, D.Cascio, F. Fauci, M. Glorioso, M. Iacomi, R. Magro, G. Raso

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Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be presented through the ROC (Receiver Operating Characteristic) curves. In particular the best performances are obtained with the Neural Networks in comparison with the K-Nearest Neighbours and the Support Vector Machine: The Radial Basis Function supply the best results with 0.89 ± 0.01 of area under ROC curve but similar results are obtained with the Probabilistic Neural Network and a Multi Layer Perceptron.

Keywords: Neural Networks, K-Nearest Neighbours, Support Vector Machine, Computer Aided Detection

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Authors: Rachel Baruch

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This article describes the implementation of an experimental model for teaching ICT tools and digital environments in teachers training college. In most educational systems in the Western world, new programs were developed in order to bridge the digital gap between teachers and students. In spite of their achievements, these programs are limited due to several factors: The teachers in the schools implement new methods incorporating technological tools into the curriculum, but meanwhile the technology changes and advances. The interface of tools changes frequently, some tools disappear and new ones are invented. These conditions require an experimental model of training the pre-service teachers. The appropriate method for instruction within the domain of ICT tools should be based on exposing the learners to innovations, helping them to gain experience, teaching them how to deal with challenges and difficulties on their own, and training them. This study suggests some principles for this approach and describes step by step the implementation of this model.

Keywords: ICT tools, e-learning, pre-service teachers.

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Authors: Sanjeev Gupta, Mayank Dave

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The issue of real-time and reliable report delivery is extremely important for taking effective decision in a real world mission critical Wireless Sensor Network (WSN) based application. The sensor data behaves differently in many ways from the data in traditional databases. WSNs need a mechanism to register, process queries, and disseminate data. In this paper we propose an architectural framework for data placement and management. We propose a reliable and real time approach for data placement and achieving data integrity using self organized sensor clusters. Instead of storing information in individual cluster heads as suggested in some protocols, in our architecture we suggest storing of information of all clusters within a cell in the corresponding base station. For data dissemination and action in the wireless sensor network we propose to use Action and Relay Stations (ARS). To reduce average energy dissipation of sensor nodes, the data is sent to the nearest ARS rather than base station. We have designed our architecture in such a way so as to achieve greater energy savings, enhanced availability and reliability.

Keywords: Cluster head, data reliability, real time communication, wireless sensor networks.

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Authors: Guy Leshem, Ya'acov Ritov

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Traffic Management and Information Systems, which rely on a system of sensors, aim to describe in real-time traffic in urban areas using a set of parameters and estimating them. Though the state of the art focuses on data analysis, little is done in the sense of prediction. In this paper, we describe a machine learning system for traffic flow management and control for a prediction of traffic flow problem. This new algorithm is obtained by combining Random Forests algorithm into Adaboost algorithm as a weak learner. We show that our algorithm performs relatively well on real data, and enables, according to the Traffic Flow Evaluation model, to estimate and predict whether there is congestion or not at a given time on road intersections.

Keywords: Machine Learning, Boosting, Classification, TrafficCongestion, Data Collecting, Magnetic Loop Detectors, SignalizedIntersections, Traffic Signal Timing Optimization.

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Authors: Aishwarya Shekhar, Devesh Kumar Srivastava

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Cloud computing has emerged as a promising direction for cost efficient and reliable service delivery across data communication networks. The dynamic location of service facilities and the virtualization of hardware and software elements are stressing the communication networks and protocols, especially when data centres are interconnected through the internet. Although the computing aspects of cloud technologies have been largely investigated, lower attention has been devoted to the networking services without involving IT operating overhead. Cloud computing has enabled elastic and transparent access to infrastructure services without involving IT operating overhead. Virtualization has been a key enabler for cloud computing. While resource virtualization and service abstraction have been widely investigated, networking in cloud remains a difficult puzzle. Even though network has significant role in facilitating hybrid cloud scenarios, it hasn't received much attention in research community until recently. We propose Network as a Service (NaaS), which forms the basis of unifying public and private clouds. In this paper, we identify various challenges in adoption of hybrid cloud. We discuss the design and implementation of a cloud platform.

Keywords: Cloud computing, networking, infrastructure, hybrid cloud, open stack, Naas.

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Authors: Vivek V, Hafeez Syed, Darren W Terrell, Harit Naik, Halliburton

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Applying a rigorous process to optimize the elements of a supply-chain network resulted in reduction of the waiting time for a service provider and customer. Different sources of downtime of hydraulic pressure controller/calibrator (HPC) were causing interruptions in the operations. The process examined all the issues to drive greater efficiencies. The issues included inherent design issues with HPC pump, contamination of the HPC with impurities, and the lead time required for annual calibration in the USA. HPC is used for mandatory testing/verification of formation tester/pressure measurement/logging-while drilling tools by oilfield service providers, including Halliburton. After market study andanalysis, it was concluded that the current HPC model is best suited in the oilfield industry. To use theexisting HPC model effectively, design andcontamination issues were addressed through design and process improvements. An optimum network is proposed after comparing different supply-chain models for calibration lead-time reduction.

Keywords: Hydraulic Pressure Controller/Calibrator, M/LWD, Pressure, FTWD

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Authors: Bruno Trstenjak, Dzenana Donko

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Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: Case based reasoning, classification, expert's knowledge, hybrid model.

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Authors: Varun Singh, Mainak Bandyopadhyay, Maharana Pratap Singh

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The shortest path (SP) problem concerns with finding the shortest path from a specific origin to a specified destination in a given network while minimizing the total cost associated with the path. This problem has widespread applications. Important applications of the SP problem include vehicle routing in transportation systems particularly in the field of in-vehicle Route Guidance System (RGS) and traffic assignment problem (in transportation planning). Well known applications of evolutionary methods like Genetic Algorithms (GA), Ant Colony Optimization, Particle Swarm Optimization (PSO) have come up to solve complex optimization problems to overcome the shortcomings of existing shortest path analysis methods. It has been reported by various researchers that PSO performs better than other evolutionary optimization algorithms in terms of success rate and solution quality. Further Geographic Information Systems (GIS) have emerged as key information systems for geospatial data analysis and visualization. This research paper is focused towards the application of PSO for solving the shortest path problem between multiple points of interest (POI) based on spatial data of Allahabad City and traffic speed data collected using GPS. Geovisualization of results of analysis is carried out in GIS.

Keywords: GIS, Outliers, PSO, Traffic Data.

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Authors: A. Badri

Abstract:

Restructured electricity markets may provide opportunities for producers to exercise market power maintaining prices in excess of competitive levels. In this paper an oligopolistic market is presented that all Generation Companies (GenCos) bid in a Cournot model. Genetic algorithm (GA) is applied to obtain generation scheduling of each GenCo as well as hourly market clearing prices (MCP). In order to consider network constraints a multiperiod framework is presented to simulate market clearing mechanism in which the behaviors of market participants are modelled through piecewise block curves. A mixed integer linear programming (MILP) is employed to solve the problem. Impacts of market clearing process on participants- characteristic and final market prices are presented. Consequently, a novel multi-objective model is addressed for security constrained optimal bidding strategy of GenCos. The capability of price-maker GenCos to alter MCP is evaluated through introducing an effective-supply curve. In addition, the impact of exercising market power on the variation of market characteristics as well as GenCos scheduling is studied.

Keywords: Optimal bidding strategy, Cournot equilibrium, market power, network constraints, market auction mechanism

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Authors: Diganta Kumar Pathak, Manashjyoti Bhuyan, Vaskar Deka

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Vertical Handover(VHO) among different communication technologies ensuring uninterruption and service continuity is one of the most important performance parameter in Heterogenous networks environment. In an integrated Universal Mobile Telecommunicatin System(UMTS) and Wireless Local Area Network(WLAN), WLAN is given an inherent priority over UMTS because of its high data rates with low cost. Therefore mobile users want to be associated with WLAN maximum of the time while roaming, to enjoy best possible services with low cost. That encourages reduction of number of VHO. In this work the reduction of number of VHO with respect to varying number of WLAN Access Points(APs) in an integrated UMTS and WLAN network is investigated through simulation to provide best possible cost effective service to the users. The simulation has been carried out for an area (7800 × 9006)m2 where COST-231 Hata model and 3GPP (TR 101 112 V 3.1.0) specified models are used for WLAN and UMTS path loss models respectively. The handover decision is triggered based on the received signal level as compared to the fade margin. Fade margin gives a probabilistic measure of the reliability of the communication link. A relationship between number of WLAN APs and the number of VHO is also established in this work.

Keywords: VHO, UMTS, WLAN, MT, AP, BS.

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Authors: Refaat M Mohamed, Ayman El-Baz, Aly A. Farag

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This paper introduces a novel approach to estimate the clique potentials of Gibbs Markov random field (GMRF) models using the Support Vector Machines (SVM) algorithm and the Mean Field (MF) theory. The proposed approach is based on modeling the potential function associated with each clique shape of the GMRF model as a Gaussian-shaped kernel. In turn, the energy function of the GMRF will be in the form of a weighted sum of Gaussian kernels. This formulation of the GMRF model urges the use of the SVM with the Mean Field theory applied for its learning for estimating the energy function. The approach has been tested on synthetic texture images and is shown to provide satisfactory results in retrieving the synthesizing parameters.

Keywords: Image Modeling, MRF, Parameters Estimation, SVM Learning.

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Authors: Heide Lukosch, Roy Van Bussel, Sebastiaan Meijer

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Serious games have proven to be a useful instrument to engage learners and increase motivation. Nevertheless, a broadly accepted, practical instructional design approach to serious games does not exist. In this paper, we introduce the use of an instructional design model that has not been applied to serious games yet, and has some advantages compared to other design approaches. We present the case of mechanics mechatronics education to illustrate the close match with timing and role of knowledge and information that the instructional design model prescribes and how this has been translated to a rigidly structured game design. The structured approach answers the learning needs of applicable knowledge within the target group. It combines advantages of simulations with strengths of entertainment games to foster learner-s motivation in the best possible way. A prototype of the game will be evaluated along a well-respected evaluation method within an advanced test setting including test and control group.

Keywords: Serious Gaming, Simulation, Complex Learning.

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Authors: Mohamed S. Negm, Waleed S. Mandour

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The present study aims primarily at investigating words of peace (lexemes of peace) in the formal speeches of the Egyptian president Abdulfattah El-Sisi in a two-year span of time, from 2018 to 2019. This paper attempts to shed light not only on the contextual use of the antonyms, war and peace, but also it underpins quantitative analysis through the current methods of corpus linguistics. As such, the researchers have deployed a corpus-based approach in collecting, encoding, and processing 30 presidential speeches over the stated period (23,411 words and 25,541 tokens in total). Further, semantic fields and collocational networkzs are identified and compared statistically. Results have shown a significant propensity of adopting peace, including its relevant collocation network, textually and therefore, ideationally, at the expense of war concept which in most cases surfaces euphemistically through the noun conflict. The president has not justified the action of war with an honorable cause or a valid reason. Such results, so far, have indicated a positive sociopolitical mindset the Egyptian president possesses and moreover, reveal national and international fair dealing on arising issues.

Keywords: Corpus-assisted discourse studies, critical discourse analysis, collocation network, corpus linguistics.

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Authors: Atanu K Samanta, Asim Ali Khan

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Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.

Keywords: Artificial neural network, ANN, brain tumor, computer-aided diagnostic, CAD system, gray-level co-occurrence matrix, GLCM, level set method, tumor segmentation.

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Authors: C. B. Masera, M. Imprialou, L. Budd, C. Morton

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Even though signalised intersections are necessary for urban road traffic management, they can act as bottlenecks and disrupt traffic operations. Interrupted traffic flow causes congestion, delays, stop-and-go conditions (i.e. excessive acceleration/deceleration) and longer journey times. Vehicle and infrastructure connectivity offers the potential to provide improved new services with additional functions of assisting drivers. This paper focuses on one of the applications of vehicle-to-infrastructure communication namely Green Light Optimal Speed Advisory (GLOSA). To assess the effectiveness of GLOSA in the urban road network, an integrated microscopic traffic simulation framework is built into VISSIM software. Vehicle movements and vehicle-infrastructure communications are simulated through the interface of External Driver Model. A control algorithm is developed for recommending an optimal speed that is continuously updated in every time step for all vehicles approaching a signal-controlled point. This algorithm allows vehicles to pass a traffic signal without stopping or to minimise stopping times at a red phase. This study is performed with all connected vehicles at 100% penetration rate. Conventional vehicles are also simulated in the same network as a reference. A straight road segment composed of two opposite directions with two traffic lights per lane is studied. The simulation is implemented under 150 vehicles per hour and 200 per hour traffic volume conditions to identify how different traffic densities influence the benefits of GLOSA. The results indicate that traffic flow is improved by the application of GLOSA. According to this study, vehicles passed through the traffic lights more smoothly, and waiting times were reduced by up to 28 seconds. Average delays decreased for the entire network by 86.46% and 83.84% under traffic densities of 150 vehicles per hour per lane and 200 vehicles per hour per lane, respectively.

Keywords: Connected vehicles, GLOSA, intelligent transportation systems, infrastructure-to-vehicle communication.

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Authors: Jeong Hoon Lee, Jin Hwan Yu

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The number of people using SNS with their mobile devices is soaring. This research focuses on the Twitter service that has the most third-party applications and delved into the fact that there were not sufficient studies on the UX design aspects of Twitter applications. Among social network services which have emerged as a major social topic lately, this research try to analyze the UX design of the Twitter application which is also called micro-blogging service. Therefore this research sets its goal to draw components of the UX design aspect of the Tweeter application on which there are not enough analysis yet. Moreover, this research suggests improvement of mobile application which will assure better users- experience. In order to analyze the UX design aspect of the mobile twitter application, with relevant document and user research, evaluating factors of the UX Design which would affect users- experience were organized. The subjects for cases were selected among six paid and free social networking applications that had been consistently ranked from 1st to 100th in the Korean application store during May, 2012 after closely monitoring the rank. From May 15th to May 11th in 2012, in accordance with the evaluating standard, surveys were conducted in a form of interviews with 20 subjects who have used the Twitter application to find out problems and solutions for the UX design of the mobile Twitter application.

Keywords: Social network service, twitter, user experience design, interface design.

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Authors: Senda Trabelsi, Sébastien Poncet, Michel Poirier

Abstract:

Ice slurries are considered as a promising phase-changing secondary fluids for air-conditioning, packaging or cooling industrial processes. An experimental study has been here carried out to measure the rheological characteristics of ice slurries. Ice slurries consist in a solid phase (flake ice crystals) and a liquid phase. The later is composed of a mixture of liquid water and an additive being here either (1) Propylene-Glycol (PG) or (2) Ethylene-Glycol (EG) used to lower the freezing point of water. Concentrations of 5%, 14% and 24% of both additives are investigated with ice mass fractions ranging from 5% to 85%. The rheological measurements are carried out using a Discovery HR-2 vane-concentric cylinder with four full-length blades. The experimental results show that the behavior of ice slurries is generally non-Newtonian with shear-thinning or shear-thickening behaviors depending on the experimental conditions. In order to determine the consistency and the flow index, the Herschel-Bulkley model is used to describe the behavior of ice slurries. The present results are finally validated against an experimental database found in the literature and the predictions of an Artificial Neural Network model.

Keywords: Ice slurry, propylene-glycol, ethylene-glycol, rheology, artificial neural network.

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Authors: Ghada A. Alfattni

Abstract:

Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: Imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour.

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