Search results for: Bayesian network; structure learning

Authors: Ondřej Nowak

Abstract:

This paper presents an exploration into the structure of the corporate governance network and interlocking directorates in the Czech Republic. First a literature overview and a basic terminology of the network theory is presented. Further in the text, statistics and other calculations relevant to corporate governance networks are presented. For this purpose an empirical data set consisting of 2 906 joint stock companies in the Czech Republic was examined. Industries with the highest average number of interlocks per company were healthcare, and energy and utilities. There is no observable link between the financial performance of the company and the number of its interlocks. Also interlocks with financial companies are very rare.

Keywords: Corporate Governance, Interlocking Directorates, Network Theory, Czech Republic.

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Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient, but not the magnitude. A neural network with two hidden layers was then used to learn the coefficient magnitudes, along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: Quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction.

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Authors: Keivan Abdollahi, Alireza Mortezaei

Abstract:

The main objective of seismic rehabilitation in the foundations is decreasing the range of horizontal and vertical vibrations and omitting high frequencies contents under the seismic loading. In this regard, the advantages of micropiles network is utilized. Reduction in vibration range of foundation can be achieved by using high dynamic rigidness module such as deep foundations. In addition, natural frequency of pile and soil system increases in regard to rising of system rigidness. Accordingly, the main strategy is decreasing of horizontal and vertical seismic vibrations of the structure. In this case, considering the impact of foundation, pile and improved soil foundation is a primary concern. Therefore, in this paper, effective factors are studied on the seismic rehabilitation of foundations applying network micropiles in sandy soils with nonlinear reaction.

Keywords: Micropile network, rehabilitation, vibration, seismic load.

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Authors: Bahloul Amel

Abstract:

The present study debates students’ perceptions of the use of technology in learning English as a Foreign Language. Its aim is to explore and understand students’ preparation and presentation of Posters, PowerPoint and Animated Videos by drawing attention to visual and oral elements. The data is collected through observations and semi-structured interviews and analyzed through phenomenological data analysis steps. The themes emerged from the data, visual learning satisfaction in using information and communication technology, providing structure to oral presentation, learning from peers’ presentations, draw attention to using Posters, PowerPoint and Animated Videos as each supports visual learning and organization of thoughts in oral presentations.

Keywords: Animated Videos, EFL, Posters, PowerPoint presentations, Visual Learning.

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Authors: Haiyan Wang

Abstract:

Constuctivism learning theory lays emphasis on the learners' active learning, such as learning initiative, sociality and context. By analyzing the relationship between constructivism learning theory and learner autonomy, this paper explores how to cultivate learners' learner autonomy under the guidance of constructivism learning theory.

Keywords: Constructivism learning theory, learner autonomy, relationship, cultivation.

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Authors: Gunasekaran Raja, Ramkumar Jayaraman, Rajakumar Arul, Kottilingam Kottursamy

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Broadband Wireless Network (BWN) is the promising technology nowadays due to the increased number of smartphones. Buffering scheme using network coding considers the reliability and proper degree distribution in Worldwide interoperability for Microwave Access (WiMAX) multi-hop network. Using network coding, a secure way of transmission is performed which helps in improving throughput and reduces the packet loss in the multicast network. At the outset, improved network coding is proposed in multicast wireless mesh network. Considering the problem of performance overhead, degree distribution makes a decision while performing buffer in the encoding / decoding process. Consequently, BuS (Buffer Scheme) based on network coding is proposed in the multi-hop network. Here the encoding process introduces buffer for temporary storage to transmit packets with proper degree distribution. The simulation results depend on the number of packets received in the encoding/decoding with proper degree distribution using buffering scheme.

Keywords: Encoding and decoding, buffer, network coding, degree distribution, broadband wireless networks, multicast.

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Authors: Zohreh Sheikh Khozani, Wan Hanna Melini Wan Mohtar, Mojtaba Porhemmat

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Prediction of wall shear stress in a rectangular channel, with non-homogeneous roughness distribution, was studied. Estimation of shear stress is an important subject in hydraulic engineering, since it affects the flow structure directly. In this study, the Genetic Algorithm Artificial (GAA) neural network is introduced as a hybrid methodology of the Artificial Neural Network (ANN) and modified Genetic Algorithm (GA) combination. This GAA method was employed to predict the wall shear stress. Various input combinations and transfer functions were considered to find the most appropriate GAA model. The results show that the proposed GAA method could predict the wall shear stress of open channels with high accuracy, by Root Mean Square Error (RMSE) of 0.064 in the test dataset. Thus, using GAA provides an accurate and practical simple-to-use equation.

Keywords: Artificial neural network, genetic algorithm, genetic programming, rectangular channel, shear stress.

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Authors: Babiga Birregah, Prosper K. Doh, Kondo H. Adjallah

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In this work we present some matrix operators named circulant operators and their action on square matrices. This study on square matrices provides new insights into the structure of the space of square matrices. Moreover it can be useful in various fields as in agents networking on Grid or large-scale distributed self-organizing grid systems.

Keywords: Pascal matrices, Binomial Recursion, Circulant Operators, Square Matrix Bipartition, Local Network, Parallel networks of agents.

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Authors: Inam Shamsher, Zaheer Ahmad, Jehanzeb Khan Orakzai, Awais Adnan

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This paper deals with an Optical Character Recognition system for printed Urdu, a popular Pakistani/Indian script and is the third largest understandable language in the world, especially in the subcontinent but fewer efforts are made to make it understandable to computers. Lot of work has been done in the field of literature and Islamic studies in Urdu, which has to be computerized. In the proposed system individual characters are recognized using our own proposed method/ algorithms. The feature detection methods are simple and robust. Supervised learning is used to train the feed forward neural network. A prototype of the system has been tested on printed Urdu characters and currently achieves 98.3% character level accuracy on average .Although the system is script/ language independent but we have designed it for Urdu characters only.

Keywords: Algorithm, Feed Forward Neural Networks, Supervised learning, Pattern Matching.

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Authors: Guruprasad Khataniar, Hitesh Tahbildar, Prakriti Prava Das

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There are various overlay structures that provide efficient and scalable solutions for point and range query in a peer-topeer network. Overlay structure based on m-Binary Search Tree (BST) is one such popular technique. It deals with the division of the tree into different key intervals and then assigning the key intervals to a BST. The popularity of the BST makes this overlay structure vulnerable to different kinds of attacks. Here we present four such possible attacks namely index poisoning attack, eclipse attack, pollution attack and syn flooding attack. The functionality of BST is affected by these attacks. We also provide different security techniques that can be applied against these attacks.

Keywords: BST, eclipse attack, index poisoning attack, pollution attack, syn flooding attack.

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Authors: Ines Hamdi, Mohamed Ben Ahmed

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The understanding of the system level of biological behavior and phenomenon variously needs some elements such as gene sequence, protein structure, gene functions and metabolic pathways. Challenging problems are representing, learning and reasoning about these biochemical reactions, gene and protein structure, genotype and relation between the phenotype, and expression system on those interactions. The goal of our work is to understand the behaviors of the interactions networks and to model their evolution in time and in space. We propose in this study an ontological meta-model for the knowledge representation of the genetic regulatory networks. Ontology in artificial intelligence means the fundamental categories and relations that provide a framework for knowledge models. Domain ontology's are now commonly used to enable heterogeneous information resources, such as knowledge-based systems, to communicate with each other. The interest of our model is to represent the spatial, temporal and spatio-temporal knowledge. We validated our propositions in the genetic regulatory network of the Aarbidosis thaliana flower

Keywords: Ontological model, spatio-temporal modeling, Genetic Regulatory Networks (GRNs), knowledge representation.

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: Activities of daily living, classification, internet of things, machine learning, smart home.

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Authors: Jin Myoung Kim, Tae Ho Cho

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Wireless Sensor networks have a wide spectrum of civil and military applications that call for secure communication such as the terrorist tracking, target surveillance in hostile environments. For the secure communication in these application areas, we propose a method for generating a hierarchical key structure for the efficient group key management. In this paper, we apply A* algorithm in generating a hierarchical key structure by considering the history data of the ratio of addition and eviction of sensor nodes in a location where sensor nodes are deployed. Thus generated key tree structure provides an efficient way of managing the group key in terms of energy consumption when addition and eviction event occurs. A* algorithm tries to minimize the number of messages needed for group key management by the history data. The experimentation with the tree shows efficiency of the proposed method.

Keywords: Heuristic search, key management, security, sensor network.

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Authors: Abdelhadi Lotfi, Abdelkader Benyettou

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In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

Keywords: Classification, probabilistic neural networks, network optimization, pattern recognition.

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Authors: Wei Hu, Tianzhou Chen, Qingsong Shi

Abstract:

In recent years, the development of e-learning is very rapid. E-learning is an attractive and efficient way for computer education. Student interaction and collaboration also plays an important role in e-learning. In this paper, a collaborative web-based e-learning environment is presented. A wide range of interactive and collaborative methods are integrated into a web-based environment. This e-learning environment is designed for information security curriculum.

Keywords: E-learning, information Security, curriculum, web-based environment.

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Authors: Alireza Keshmiri, Shahriar Bagheri, Nan Wu

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This research presents an analytical model for the development of an energy harvester with piezoelectric rings stacked at the boundary of the structure based on the Adomian decomposition method. The model is applied to geometrically non-uniform beams to derive the steady-state dynamic response of the structure subjected to base motion excitation and efficiently harvest the subsequent vibrational energy. The in-plane polarization of the piezoelectric rings is employed to enhance the electrical power output. A parametric study for the proposed energy harvester with various design parameters is done to prepare the dataset required for optimization. Finally, simulation-based optimization technique helps to find the optimum structural design with maximum efficiency. To solve the optimization problem, an artificial neural network is first trained to replace the simulation model, and then, a genetic algorithm is employed to find the optimized design variables. Higher geometrical non-uniformity and length of the beam lowers the structure natural frequency and generates a larger power output.

Keywords: Piezoelectricity, energy harvesting, simulation-based optimization, artificial neural network, genetic algorithm.

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Authors: Penghui Zhang, Hua Zhang, Jun-Bo Wang, Cheng Zeng, Zijian Cao

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With the development of software-defined networks and programmable data planes, in-band network telemetry (INT) has become an emerging technology in communications because it can get accurate and real-time network information. However, due to the expansion of the network scale, existing telemetry systems, to the best of the authors’ knowledge, have difficulty in meeting the common requirements of low overhead, low latency and full coverage for traffic measurement. This paper proposes a network-wide telemetry system with a low-latency low-overhead path planning (INT-LLPP). This paper builds a mathematical model to analyze the telemetry overhead and latency of INT systems. Then, we adopt a greedy-based path planning algorithm to reduce the overhead and latency of the network telemetry with the full network coverage. The simulation results show that network-wide telemetry is achieved and the telemetry overhead can be reduced significantly compared with existing INT systems. INT-LLPP can control the system latency to get real-time network information.

Keywords: Network telemetry, network monitoring, path planning, low latency.

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Authors: Do-Woo Kim, Geon Woo Kim, Jun-Ho Lee, Jong-Wook Han

Abstract:

The home in these days has not one computer connected to the Internet but rather a network of many devices within the home, and that network might be connected to the Internet. In such an environment, the potential for attacks is greatly increased. The general security technology can not apply because of the use of various wired and wireless network, middleware and protocol in digital home environment and a restricted system resource of home information appliances. To offer secure home services home network environments have need of access control for various home devices and information when users want to access. Therefore home network access control for user authorization is a very important issue. In this paper we propose access control model using RBAC in home network environments to provide home users with secure home services.

Keywords: Home network, access control, RBAC, security.

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Authors: B. S. Yew, B. L. Ong, R. B. Ahmad

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A study on the performance of TCP Vegas versus different TCP variants in homogeneous and heterogeneous wired networks are performed via simulation experiment using network simulator (ns-2). This performance evaluation prepared a comparison medium for the performance evaluation of enhanced-TCP Vegas in wired network and for wireless network. In homogeneous network, the performance of TCP Tahoe, TCP Reno, TCP NewReno, TCP Vegas and TCP SACK are analyzed. In heterogeneous network, the performances of TCP Vegas against TCP variants are analyzed. TCP Vegas outperforms other TCP variants in homogeneous wired network. However, TCP Vegas achieves unfair throughput in heterogeneous wired network.

Keywords: TCP Vegas, Homogeneous, Heterogeneous, WiredNetwork.

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Authors: Teerada Apibunyopas, Nithinant Thammakoranonta

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Many organizations bring e-Learning to use as a tool in their training and human development department. It is getting more popular because it is easy to access to get knowledge all the time and also it provides a rich content, which can develop the employees’ skill efficiently. This study is focused on the factors that affect using e-Learning efficiently, so it will make job satisfaction increasing. The questionnaires were sent to employees in large commercial banks, which use e-Learning located in Bangkok, the results from multiple linear regression analysis showed that employee’s characteristics, characteristics of e-Learning, learning and growth have influence on job satisfaction.

Keywords: e-Learning, Job Satisfaction, Learning and growth.

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence/pattern recognition/classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: Hybrid systems, Hidden Markov Models, Recurrent neural networks, Deterministic finite state automata.

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Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: Deep-learning, image classification, image identification, industrial engineering.

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Authors: Danilo López, Nelson Vera, Luis Pedraza

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This paper analyzes fundamental ideas and concepts related to neural networks, which provide the reader a theoretical explanation of Long Short-Term Memory (LSTM) networks operation classified as Deep Learning Systems, and to explicitly present the mathematical development of Backward Pass equations of the LSTM network model. This mathematical modeling associated with software development will provide the necessary tools to develop an intelligent system capable of predicting the behavior of licensed users in wireless cognitive radio networks.

Keywords: Neural networks, multilayer perceptron, long short-term memory, recurrent neuronal network, mathematical analysis.

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Authors: N. Feroze, M. Aslam

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The article is concerned with analysis of failure rate (shape parameter) under the Topp Leone distribution using a Bayesian framework. Different loss functions and a couple of noninformative priors have been assumed for posterior estimation. The posterior predictive distributions have also been derived. A simulation study has been carried to compare the performance of different estimators. A real life example has been used to illustrate the applicability of the results obtained. The findings of the study suggest  that the precautionary loss function based on Jeffreys prior and singly type II censored samples can effectively be employed to obtain the Bayes estimate of the failure rate under Topp Leone distribution.

Keywords: loss functions, type II censoring, posterior distribution, Bayes estimators.

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Authors: Conradie, P.W., Lombard, A., Moller, M.

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The dramatic effect of information technology on society is undeniable. In education, it is evident in the use of terms like active learning, blended learning, electronic learning and mobile learning (ubiquitous learning). This study explores the perceptions of 54 learners in a higher education institution regarding the use of mobile devices in a third year module. Using semi-structured interviews, it was found that mobile devices had a positive impact on learner motivation, engagement and enjoyment. It also improved the consistency of learning material, and the convenience and flexibility (anywhere, anytime) of learning. User-interfacelimitation, bandwidth and cognitive overload, however, were of concern. The use of cloud based resources like Youtube and Google Docs, through mobile devices, positively influenced learner perceptions, making them prosumers (both consumers and producers) of education content.

Keywords: Active learning, education, mobile learning, pedagogy.

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Authors: J. Seke Mboungou Mouyabi

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This paper is an exploration of the conceptual confusion between E-learning and M-learning particularly in Africa. Section I provides a background to the development of E-learning and M-learning. Section II focuses on the conceptual analysis as it applies to Africa. It is with an investigative and expansive mind that this paper is elaborated to respond to a profound question of the suitability of the concepts in a particular era in Africa. The aim of this paper is therefore to shed light on which concept best suits the unique situation of Africa in the era of cloud computing.

Keywords: African Concept, Cloud computing, E-learning, Mlearning

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Authors: Seongchan Kang, Hongsik Yun, Hyukgil Kim, Minwoo Park

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For this study, this researcher conducted a precision network adjustment with QOCA, the precision network adjustment software developed by Jet Propulsion Laboratory, to perform an integrated network adjustment on the Unified Control Points managed by the National Geographic Information Institute. Towards this end, 275 Unified Control Points observed in 2008 were selected before a network adjustment is performed on those 275 Unified Control Points. The RMSE on the discrepancies of coordinates as compared to the results of GLOBK was ±6.07mm along the N axis, ±2.68mm along the E axis and ±6.49mm along the U axis.

Keywords: Network adjustment, QOCA, unified control point.

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Authors: Huan Xiong, Ming Chen

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In order to research Internet quantificationally and better model the performance of network, this paper proposes a novel AS level network performance model (MNPM), it takes autonomous system (AS) as basic modeling unit, measures E2E performance between any two outdegrees of an AS and organizes measurement results into matrix form which called performance matrix (PM). Inter-AS performance calculation is defined according to performance information stored in PM. Simulation has been implemented to verify the correctness of MNPM and a practical application of MNPM (network congestion detection) is given.

Keywords: AS, network performance, model, metric, congestion.

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Authors: Joachim Kimmerle, Johannes Moskaliuk, Ulrike Cress

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The development of Internet technology in recent years has led to a more active role of users in creating Web content. This has significant effects both on individual learning and collaborative knowledge building. This paper will present an integrative framework model to describe and explain learning and knowledge building with shared digital artifacts on the basis of Luhmann-s systems theory and Piaget-s model of equilibration. In this model, knowledge progress is based on cognitive conflicts resulting from incongruities between an individual-s prior knowledge and the information which is contained in a digital artifact. Empirical support for the model will be provided by 1) applying it descriptively to texts from Wikipedia, 2) examining knowledge-building processes using a social network analysis, and 3) presenting a survey of a series of experimental laboratory studies.

Keywords: Individual learning, collaborative knowledge building, systems theory, equilibration.

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Authors: K. Kumpas

Abstract:

Bologna process has influenced enhancing studentcentered learning in Estonian higher education since 2009, but there is no information about what helps or hinders students to achieve learning outcomes and how quality of student-centered learning might be improved. The purpose of this study is to analyze two questions from outcome-based course evaluation questionnaire which is used in Estonian Entrepreneurship University of Applied Sciences. In this qualitative research, 384 students from 22 different courses described what helped and hindered them to achieve learning outcomes. The analysis showed that the aspects that hinder students to achieve learning outcomes are mostly personal: time management, family and personal matters, motivation and non-academic activities. The results indicate that students- learning is commonly supported by school, where teacher, teaching and characteristics of teaching methods help mostly to achieve learning outcomes, also learning material, practical assignments and independent study was brought up as one of the key elements.

Keywords: Learning outcomes, learning quality, student-centered learning

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