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Banks recorded minus returns due to the subprime mortgage crisis in 2008 and regulations on financial instruments. To overcome this, the financial industry has replaced its system, which originally has been done by human, to computer system while joining hands with the IT industry. As time goes by, consumers tend to go online instead of visiting banks as the costs of financial services have been reduced and their speed gets fast. The purpose of this study is to find out what forms psychological resistance regarding internet banks and empirically verify what actually influences the intent to use internet banks. Thus, this study tries to reveal factors that form psychological resistance inhibiting the proliferation of internet banks through study models comprised of the variables suggested in the Innovation Resistance Model by Ram.

The growth of websites and the Internet has opened up new research, social, entertainment, education and business opportunities. With the fast growth of the Internet, the digital data generated by the websites is becoming so massive that the traditional text software and relational database technology faces a bottleneck while processing such massive data and the results generated by these technologies are not satisfactory. Cloud computing offers a good solution for this problem. Cloud computing is not only capable of storing such massive data but also capable of processing and analyzing such voluminous data faster, by making use of distributed storage and distributed computing technology. A weblog is a group of connected web pages that consists of a log or daily record of information, particular fields or views which is altered, every now and then, by owner of site, other websites or by website users. An enterprise weblog analysis system based on Hadoop architecture with Hadoop Distributed File System (HDFS), Hadoop MapReduce Software Framework and Pig Latin Language aids the business decision-making process of the system administrators and helps them to collect and identify the potential value which is hidden within such huge data generated by the websites. Such a weblog analysis includes the analysis of an Internet site’s entry log as well as provides information about the amount of visitors, days of week and rush hours, views, hits, very often accessed pages, application server traffic trends, performance reports at varying intervals and statistical reports which indicate the performance of program.

Team creativity is supposed to determine corporate performance. However, few studies exist about exploring changes in team creativity according to team affect and team leader's network structures in the team. Especially, in the ICT (Information & Communication Technology) industry where hyper-competition exists among companies, such research issue emerges critical in the case of IS (information systems) maintenance teams and IS development teams. Those two kinds of IS teams possess quite a different team characteristics- IS development team is limited by tight schedule and clear performance measure, while IS maintenance team is quite stable in its operation with relatively flexible time schedule. We collected relevant dataset from 50 teams. Results revealed that in the IS development teams, negative group affective tone is positively related to team creativity. In contrast, in the IS maintenance teams, negative group affective tone is negatively related to team creativity. In addition, we found that leaders’ centrality has a negative effect on team creativity, and that leaders’ structural holes have a positive influence on team creativity.

Contributing to open-source is not simply modifying and correcting the source code. All the acts that help open-source SW developer are contributors to open-source. Therefore, the method that open-source developer can contribute to open-source through community is classified in various ways. This includes reporting of errors, writing of comments, and participating in documentation. Those who give helps to open-source are called ‘Contributor’, and those who are authorized for modifying and adjusting the source cord are called ‘Committer’. In popular projects, 2~3 years of working is required to become a contributor. This experience in open-source community gives much help in accumulating the career of developer. However, in case of open-source community, it‘s hard to find if developers like a contributor or a committer, contributed to the project because they do not grasp at a glance how much you have contributed to the project. Hence, this thesis calculated the scores in accordance with the evaluation formula of open source developers after collecting the data thereof from GitHub that was one of the open source websites. Through this course they will contribute to providing of objective data that shows career of open-source developers at a glance.

Dynamic business process management incorporates the capabilities needed to deal with the fast-paced change inherent in today's business processes. While process-aware information systems (PAIS) face an increasing challenge to dynamically adapt running processes to context changes, support for such dynamic adaptation during process enactment remains limited. Furthermore, while the use of web service APIs for enterprise application integration (EAI) and cloud accessibility has garnered broad community support, current PAIS lack a standardized API and often require use of their proprietary clients or APIs. In this paper, we explore the use of hypermedia as the engine of application state (HATEOAS) for process enactment and process adaptation. Our approach Adapting Processes via Processes using hypermedia (AProProh) situates a HATEOAS-based middleware between RESTful clients and heterogeneous PAIS that can dynamically guide a PAIS-agnostic process client in the navigation, enactment, and adaptation of process instances. Dynamically generated hypermedia enables clients to dynamically apply valid adaptations and adjust their process navigation dynamically to a changed process model while supporting long-running operational requests. A case study based on a prototype realization shows the viability of the approach for supporting dynamic process navigation and process adaptation and characterizes its performance.

As more concerns by the public about stock markets grow bigger, the more the people’s attention is drawn to a systematic method to predict stock prices that fluctuate. More importantly, as the modern stock markets react very sensitively to information for their stock prices, it is very important to predict the prices for investors. For that, this study shall utilize opinion mining and mechanical learning, which are widely used to analyze the meaning of information in systematic ways on analyzing data from news and Twitter to suggest a system that predicts stock prices. The stock price prediction system consists of a data collector, vocabulary analyzer, sentiment analyzer and stock price predictor. The stock price predicting steps consist of collecting contents of news and Twitter, extracting vocabularies by using morpheme analysis, executing sentiment analysis then predicting stock prices via mechanical learning. In order to evaluate the usefulness of the suggested method, we used the stock data for the last whole year on 7 companies in the bio industry that are most sensitive to information for the tests, and the accuracy of the results showed above 80%. The results of this study can be regarded as one of the methods to effectively predict stock prices of companies from various backgrounds in this modern information era that changes dramatically every moment.

In this particular work, we have done power analysis of DES algorithm implemented on 28nm FPGA using HTML (H-HSUL, T-TTL, M-MOBILE_DDR, L-LVCMOS) technology. In this research, we have used high performance software Xilinx ISE where we have selected four different IO Standards i.e. MOBILE_DDR, HSUL_12, LVTTL and LVCMOS (LVCMOS_15, LVCMOS_18, LVCMOS_25 and LVCMOS_33). We have done power analysis of on-chip power like clock power, signals power, IO power, leakage power and supply power. We notified our analysis at five different voltages like 0.5V, 0.8V, 1.0V, 1.2V and 1.5V.

Working with data involves two major factors, storing the data and performing computations by accessing the data. MySQL is the first Database Management Software that provided an effective and efficient method for data storage and computations. However, with the huge amount of data that is getting generated every day from various fields, need for the advanced methods for managing and analyzing the big data is very much obvious. One of such platforms, which were developed exclusively for Big Data Analytics, is Apache Spark. Though MySQL is preferred for small amount of Data and Spark is meant for big data, many of the functionalities are found similar in both and they can be considered for a comparative study. In this work we have executed a set of queries with common functionalities for a dataset on both the frameworks. The obtained results are analyzed by visualizing aids to arrive at appropriate conclusion.

Today, communication and interaction with audiences, beyond a simple exhibition, is considered as important. Therefore, an emotional approach to draw emotional participation is required in exhibition methodology. This study is going to utilize one of such methods focusing on storytelling. This study deducts exhibition storytelling model based on structure of storytelling. This can be applied to planning and analysis of exhibition contents. That is, this study is a research about the methodology that converses an exhibition theme’s content aspect such as historicity, the lessons, the popularity, and the playfulness into its form, technical aspect and shows them and makes audience sympathize them. Through the storytelling structure on the basis of Greimas’ generative model of significance theory, this study draws out a storytelling model for exhibition. The storytelling model can be applied in planning and analyzing contents for an exhibition. As a case, this study proposes an exhibition storytelling for establishing Moon Ik-Jeom’s memorial hall.

The most important step for successful video processing in computer vision is its stabilization. Often, it is required to process high resolution video in a real-time. In this paper, a new method for a real-time digital image stabilization in a video stream is presented. This method preserves the intended camera motion and exploits computing power of GPGPU by utilizing CUDA programing interface. In order to reduce required computation power, local search windows are used for the correspondence search of consecutive video frames. These windows are further processed using Local Binary Patterns, which enables fast correlation using bitwise XOR. The experiments on video sequences from both car-mounted and hand-held camera have demonstrated the effectiveness of this method. The speed of stabilization designates this method for video preprocessing in real-time applications.

Along with the boom of Mobile services and the thriving Model Driven Architecture (MDA), we must consider the growing significance and utility of modeling in the development of software and solutions. The main advantages of MDA are the ability to transform one PIM into several PSMs, one for each platform or technology in which the final system will be deployed, and the automatic code generation that implements the system for those platforms from the corresponding PSMs. Service-oriented architectures (SOA) are also touted as the key to business agility, especially when combined with a model-driven approach. Model-Driven Architecture (MDA) is a well-developed concept that fits well with SOA, but until now it has been a specialized technique that is beyond practical application scope of most enterprises. Model driven and Domain engineering is the foundation for emerging “product line” software development approaches and affects the maintainability, understandability, usability, and reusability characteristics of family of similar systems. However, the existing domain engineering methods do not elicit information necessary for the Component-based Software Development process, such as, selecting and configuring appropriate components. In this paper, we suggest a method that systematically defines, analyzes and designs a crop monitoring frameworks domain to enhance reusability effectively in Model Driven Engineering and Component-based Software Development. For this, we extract information objectively that can be reused in a crop monitoring domain from the requirement analysis phase. We sustain and refine crop monitoring information, and match them to artifacts of each phase in domain engineering. Through this method, reusable domain components and malleable domain architecture can be produced. In addition, we show the practical applicability and features of our approach via a new clouding and IOT technology.

Errors in requirements elicitation cause much more expensive errors in later phases of software engineering. A major source of errors is the ambiguity of the natural languages initially used to write the user requirements. Most of the research studies in the literature have focused on ambiguity detection and correction after the user requirements are written. A better approach would be avoiding the introduction of ambiguities while the user requirements are being written to reduce time and cost. Unfortunately, this problem has been overlooked in the literature except for few research studies concerned with the English language. This paper is concerned with the Arabic language due to its complex structure and because the ambiguity of Arabic user requirements is a serious problem hindering the production of quality software in the middle east. The paper studies main sources of ambiguities in the Arabic language in addition to best practices in writing Arabic user requirements to avoid the introduction of ambiguities as suggested by interviewed Arabic software engineers or as suggested in the literature for English user requirements. Accordingly, the paper suggests a set of best practices suitable for Arabic user requirements based on both practical experience and theoretical background. Examples are provided for justification. Human subject testing and empirical evaluation have shown the effectiveness of the proposed practices.

Mine Blast Algorithm (MBA) is newly developed metaheuristic technique. It has outperformed Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and their variants when solving various engineering optimization problems. MBA has been improved by IMBA, which is modified in this paper to accelerate its convergence speed furthermore. The proposed variant, so called Accelerated MBA (AMBA), replaces the previous best solution with the available candidate solution in IMBA. ANFIS accuracy depends on the parameters it is trained with. Keeping in view the drawbacks of gradients based learning of ANFIS using gradient descent and least square methods in two-pass learning algorithm, many have trained ANFIS using metaheuristic algorithms. In this paper, for getting high performance, the parameters of ANFIS are trained by the proposed AMBA. The experimental results of real-world benchmark problems reveal that AMBA can be used as an efficient optimization technique. Moreover, the results also indicate that AMBA converges earlier than its other counterparts MBA and IMBA.

At the present time, computerized tests are one of the most critical means to evaluate learning. Choosing tailored questions for each learner is a important part of such tests. Since, wide and varied learners with different abilities are involved, even randomized test cannot serve the purpose of assessment. Some form of personalized and intelligent testing is needed in E-Learning. One of the main components in composing intelligent testing is selecting the items from a huge Item Bank as the accuracy of the test depends on the quality of the assessment which in turn depends on the items selected for assessment. Furthermore, pickingappropriate items is critical in developing as assessment sheet that satisfies multiple criteria. It includes the number of test items, the definitedissemination of course concepts to be assessed, and the expected degree of difficultness and discrimination and exposure frequency. These tests, must effectively select questions from a large item bank, and to manage this problem an optimized assessment sheet composition system using the modified form of nature inspired Intelligent Water Drops optimization algorithm is proposed by embedding a local heuristic as evolutionary operator. This system is designed to choosepersonalizedtest items for each and every learner. Furthermore, the proposed approach is able to effectively generate near optimal items from large item bank that satisfy multiple constraints. The results show that the Evolutionary Intelligent Water Drops approach is suitable for the selection of nearoptimal items from large-scale item bank.