Earticle

Cloud computing technology relieves the maintenance burden of corporate IT infrastructure, which is the highly expected area for cost saving to be achieved through corporate IT technology innovation. But logically more monitored items occur especially in cloud service using virtualization technology. In reality, monitoring tool is increasingly gaining more attention because of its capability of monitoring service quality, as virtualization-based equipments not seen physically. The system developed in this paper, allows for real-time total monitoring of all elements that configure services by implementing Agentless total computing monitoring system for comprehensive management of IT infrastructure service, as well as for service quality monitoring with Replay function through monitoring log analysis and Big Data analysis on resources traffic and performance data. Furthermore, Android application was developed in order to provide monitoring and alarming services.

This paper presents a method of conversion of color image of histopathological slides to grayscale as a preprocessing step for image segmentation. The method utilizes characteristics of these images to reduce the amount of information lost during the conversion. It is a global mapping scheme and the same color does not map to two different grayscale values. Color information is added to the brightness information to reduce information loss and the weights for the added color are dependent on the distribution of the pixels in a particular image. The method was tested on a large set of histopathological slide images and convincing results were obtained some of which has been published in this paper.

Among creative and innovative problem solving algorithms, TRIZ solves difficult problems by finding contradictions of the problems. In TRIZ, there are two types of contradictions. One is a technical contradiction and the other is physial contradiction. A technical contradiction occurs between two desirable functions of a system. We call it a trade-off contradiction. On the other hand, a physical contradiction appears when two opposite properties are required from the same part of a system. In TRIZ, the ARIZ has been developed by Altshuller for reducing trial-and-error while solving problems. It is known as an inventive problem solving. It transforms difficult technical contradictions of a given problem into the corresponding physical contradiction to solve the problem easily. However, ARIZ-85c, the most recent version of the ARIZ, has inefficient and time-consuming features that cause trial-and-errors. In this paper, we propose the Butterfly algorithm based on the Butterfly diagram to reduce trial-and-error features by giving the right solution strategy based on propositional logic when selecting technical contradictions and physical contradictions for a given problem. The Butterfly algorithm can systematically find the solution strategy for the problem, and thus it helps to solve contradictions efficiently.

Predictive modeling is the process of creating a statistical model from data with the purpose of predicting future behavior. In recent years, the amount of available data has increased exponentially and “Big Data Analysis” is expected to be at the core of most future innovations. Due to the rapid development in the field of data analysis, there is still a lack of consensus on how one should approach predictive modeling problems in general. Another innovation in the field of predictive modeling is the use of data analysis competitions for model selection. This competitive approach is interesting and seems fruitful, but one could ask if the framework provided by for example Gane Project based on big data framework gives a trustworthy resemblance of real-world predictive modeling problems. In this thesis, we will state and test a set of hypotheses about predicative modeling, both in general and in the scope of data analysis competitions. We will then describe a conceptual big data framework for approaching predictive modeling problems. To test the validity and usefulness of this framework, we will participate in a series of predictive modeling competitions on the platform provided by Gane, and describe our approach to these competitions.

This paper proposes a method of distinguishing edible mushrooms from non-edible mushrooms using Principal Component Analysis (PCA) algorithm. This system functions by projecting a mushroom image onto a feature space that spans the significant variations among known set of mushroom images. Mushrooms possess certain significant features such as its stalk size, cap shape etc, and PCA extracts these dominant features and these are the eigenvectors of the set of mushrooms. The projection operation characterizes individual mushroom images by a weighted sum of the eigenvector features and hence to recognize a particular mushroom, so it is necessary only to compare these weights to those individual ones. The performance of the proposed method showed around 85% ~96% success rate that increases with the number of training images, and hence proves to be a reliable algorithm for the recognition of mushrooms.

In this paper, we propose an in-memory distributed processing method that can rapidly process vehicle location and traffic event data using Spark Streaming. The proposed system enables to share information about surrounding vehicles, pedestrians, and traffic events in real time with drivers who use the WEVING service. In the proposed method, vehicle location and traffic event streams are indexed using the grid indexing technique according to time, and the continuous range query method is processed based on the index. Also, traffic events are grouped based on occurrence time, location, content, and road segment of the traffic event transferred in real time in order to avoid duplicated traffic events. Through experiments, we show that the proposed method is able to deduplicate similar traffic events efficiently.

In this paper, we present a compression method of the electrocardiogram (ECG) signals by a discrete cosine transform of principal components in order to reduce the dimensionality of different cardiac records. In first, the principal components analysis is used for high dimensional electrocardiographic data (ECG signal periods) so that we obtain its low dimensional representation by finding orthogonal linear combinations of ECG data with maximal variances. Secondly, we apply a varying discrete cosine transform thresholds of different principal directions of the data to increase the compression rate but preserving a good signal morphology. The results of the simulation on several MIT-BIH database records show that the proposed technique gave good compression ratios without distortions between the original and the reconstructed signal.

Virtual machine technology is one of core technologies for mobile, cloud, and IoT, which is intended to provide environments independent of certain hardware. The scope of utilization of virtual machines is being widened further because virtual machines simplify complicated execution and development environments fragmented by type of devices or platforms and provide consistent program development methods and common execution methods. Smart virtual machine (SVM) is a software processor that can be loaded on various devices such as smart devices and embedded devices and are core modules of smart cross platforms for providing language/platform-independent functions to users in order to improve the reusability of contents. SVM supports programming languages such as C/C++, Java, and Objective C and diverse execution environments such as Android, iOS, Linux, and Windows. In the present paper, a debugger for more effective support for the development of SVM applications was designed and implemented. Through analysis of existing diverse debug file formats, a debug file format that can cover the diverse languages and execution environments supported by SVM was designed, assemblers and compilers were expanded based on the designed debug file format, and debugging processes were shown in which a debugger was implemented using the proposed debug file format.

In this paper, we illustrate the decision making intrinsic that was followed while designing for the users of a music archive. The archive at stake is the Ionian Music Archive, an on-line service at refinement phase that holds records of the characteristic music of the Ionian Islands in Greece. The main challenge was how to best engage user adaptation on a system with certain attributes such as a high variation on users’ music knowledge. We propose a two-folded decision making process for its design. The one side of the process lies on design rationale techniques at earlier stages and the other side is akin to formal multi-criteria approaches and is applied on every high-level decision to further weigh the user adaptation attributes that are included in the system. The initial feedback we collected is very encouraging, setting the basis for future implementations.

At present, information retrieval systems are simply expressed with a combination of keyword search according to the direct keyword matching method to get the information that users need. Because of this, documents retrieval systems serve too many documents due to term ambiguity. This makes the user need extra time and effort to get closer the document. To overcome these problems, this paper proposes the information retrieval system based on the content that connects documents according to the degree of semantic link that expresses a fuzzy value by fuzzy function. This paper also proposes an algorithm that produces a hierarchical structure using the degree of concept and content among documents. As a result, we are able to select and to provide user-interested documents.

The users of academic databases and R&D information often use search services to obtain necessary data for their studies. Most web users create various search queries and request these needed information from the system, and as a final destination of searching selected information lists and obtaining information, users are moved to the detailed page of the corresponding information. Similarly, in cases of academic information services providing journal and patent information, the final destination of the user is the specific metadata information page or the full article page, and in this case, providing other similar types of academic information and the names of researchers in other related fields is essential for satisfying the information requirements of the users. In case of the NTIS (National Science and Technology Information Service), it provides search services on national R&D information (tasks, participating personnel, research products, facilities and equipment, etc.), but lacks provision of similar documents within the same DB or among different DBs. In this article, the authors explain user queries and search items provided by the search engine, serviced by the NTIS for data categorized as research products including journals, patents, research reports, and trend analyses, and search service methods for listing similar documents within the same or different contents, and names of researchers in related fields using boosting technologies. This way, the R&D information desired by the user may be efficiently provided in the final service screen, which can reduce repeated efforts on searching.

In the context of SaaS, where the change requests can be frequent, there is the need for a systematic requirements management process so as to maintain requirements updated and ease the management of changes required to improve the service to provide. Changes to perform need to be prioritized and their impact on the system should be assessed. The extraction and analysis of the use of the services provided through the web and their relationship to the requirements can help identify improvements and help keep the service useful for longer period of time. This paper presents REQAnalytics, a recommender system that collects information on the usage of a web service, relates that information back to the requirements, and generates reports with recommendations and change suggestions that can increase the quality of that service. The proposed approach aims to provide reports of the analysis made in a language closer to the business where, for example, it indicates new workflows and navigation paths, identifies the features that can be removed and presents the relationship between requirements and the proposed changes helping to maintain the software requirements specification updated and useful.

Administrative agency has occurred amount of records and the types also vary during task progressing. Administrative information dataset, one of the types, is defined as produced by administrative information system. Administrative information dataset which is reflected in the present and treat policy and legislation should be important to manage and preserve. However lack of research on this area and systematic transfer process have not been established. The reason why many administrative information dataset has been lost and neglected. In this paper, we suggest administrative information dataset which is detail analysis.

This study seeks to develop an adaptive teaching and learning mode utilizing elementary school education UCC and play-based exploration by extracting the computer science subjects from the ICT basic quality curriculum and based on literature review on problem solving learning model, play and UCC. The developed model is applied to real education practice in an experiment then the results are analyzed to examine what kinds of effects there are in terms of various learning abilities of students and study achievements. By UPBI teaching and learning model, the nature of computer education was fully utilized so both online and offline study were allowed in parallel. In order to efficiently implement the UCC based learning system designed, skilled teachers’ ICT ability is required along with advanced school and home information facilities.

Given that usability is one of the most important aspects of software quality, several methods have been developed in order to establish techniques capable of evaluating this attribute from early phases of the software development process. However, the choice of the most appropriate method for a particular scenario is still a difficult decision, due to the existence of a vast number of approaches that are described in the literature for this purpose. Therefore, a systematic mapping review was conducted in order to identify the most commonly used usability evaluation techniques in software developments. A total of 1169 studies were identified, of which only 215 studies were selected for this review. According to the analysis, most cases studies establish the use of usability questionnaires as assessment tool. In addition, health informatics and Web applications are the software domain and type of application that are frequently reported in these evaluations. This work has allowed to reach promising results in this area. It is intended to be a guide for specialists to support the choice of the most suitable method for a particular scenario.

For the situation inference in the internet of the things environment, this research suggests a method to utilize relations between various events that compose data stream. Connecting various events in respect of mutual correlation and connectivity of various events can provide good clue for situation inference. To divide and connect various events in respect of mutual correlation and connectivity can be solved with the method to add identification characteristic value or position value, etc. on the event value reported from the sensor and adding them. When giving the identification value, in case of providing estimation on correlation and connectivity between the procedure that divides each event based on identification value and the sensors, efficient situation inference is possible. This research showed that various events from various sensors could be divided each other based on correlation and connectivity on the event and practice situation inference by using this.

Web 2.0 technologies have changed the learning process and have affected the unidirectional relationship obtaining between the teacher and the student. Web 2.0 has given rise to new bidirectional learning strategies in which the learner is both an actor and active member in the learning process. Given the wide use of these technologies as educational tools, our objective in this article is to analyze learners’ comments in discussion forums, which are popular platforms amongst students. For this purpose, we use the Latent Semantic Analysis (LSA), a statistical model that can allow us to establish the relationship between the set of messages published by learners and course content that students need to master. We also use sentiment analysis techniques to identify the students’ opinions about educational issues that are problematic. The implementation of both methods has revealed that the data available in educational discussion boards can improve the quality of learning and teaching.

Atomicity races in ARINC 653 applications are a kind of concurrency bugs which causes nondeterministic behaviors by parallel processes. The defects must be detected to ensure the reliability of the applications, because they may lead to unpredictable results to the programmer. This paper presents a tool, called AR653, to dynamically detect atomicity races for an execution of the application. The tool monitors only minimal information, such as processes, semaphores, and read/write accesses to shared resources, and analyzes the relation of synchronizations to report atomicity races throgh a locking decipline of semaphores. We compared the accuracy of AR653 with CodeSonar using synthetic programs on a simulation system for integrated modular avionics. The emperiacal results show that our tool correctly reports atomicity races in cases of using shared pointers as well as in cases of using shared variables, while CodeSonar only locates atomicity races in cases of unsing shared variables.

Nowadays, NAND flash memory has become major storage in building portable information devices because of its non-volatility, shock-resistance, energy-friendly, and robust access time for read operations. One drawback of the flash memory file system is how to provide efficient roll back or crash recovery. This type of observation motivates our research on the investigation of crash recovery of flash-memory file systems. In this paper, we introduced a concept of shadow paging and presented a crash recovery scheme for log-based file systems over flash memory using shadow paging. In this paper a scheme is proposed for the crash recovery for log-based file systems over flash memory using shadow paging. We have also described the initialization and mounting of flash memory and we have given brief background on flash memory management like wear leveling, flash translation layer (FTL) and flash file system such as FAT/FAT32, ExFAT, JFFS2, LogFS and YAFFS. In addition, we have improved the Wear-Levelling of Garbage Collection Algorithms such as CATA, Greedy, and Cost Benefit by improving the flash memory device information.

Due to dynamic changing business and operating environment, software evolution is inevitable. As software evolves, there is a need for a retest of the system in order to ensure its validity and thus regression testing assures that this happens. Regression Testing has several research categories, one of the most popular areas is Test Case Prioritization which orders test cases based on a particular criteria to be tested in accordance to available resources, and thus ensuring that the most critical test cases are tested first. In this study we examine the different types of test case prioritization and a description of the techniques presented alongside the issue raised in each study. This will give an insight into the main problems currently plaguing test case prioritization and thus pave way for further research areas to tap into.

Cloud computing is becoming more and more important in research organization and IT industry. It has various advantages like scalability and availability of computing resources. Virtual cluster systems are also used in scientific computing environment. The system like vcluster, integrates computing resources from heterogeneous clouds. It maintains the information of all clouds which are involved in a federated environment. The virtual machine allocation is based on the available resources and information of utilized resources. In order to do that vcluster has to request the clouds to get the real time information of virtual machines which include huge synchronization overhead. In this paper, we propose a methodology to reduce overhead in synchronization mechanism. An analysis shows that the proposed mechanism minimizes the overhead of synchronization. It also has ability to discover, correct and report the errors.

The purpose of sensor and network operation is to obtain real-world context information. Therefore, it is important to determine a sliding window size on the basis of context. This study proposes a method of determining a window size for data stream flexibly and variably. In the proposed method, a sliding window size is controlled variably depending on the evaluation of the event detected and reported by a sensor. Therefore, it is necessary to make criteria to judge rapid changes in sensing values and critical events, and to come up with a reasonable interoperability scheme in consideration of the lapse of time, and the development and declination of event context. This study tries to find a reasonable sliding window size control method interacting with context and thereby to contribute to the research on flexible and efficient context reasoning and context awareness.

Web data mining is a hot research topic that is a technique used to crawl through various web resources to collect required information. As the importance of such web data mining has been recognized, extensive studies have been conducted actively to analyze the data in a Social Networking Service (SNS). In a SNS, a large amount of data, which has a variety of characteristics, is generated through voluntary participation of users, which is also called “big social data”. Big social data can identify not only content registered on the web but also the relations of the friends of users. In this paper, we introduce Formal Concept Analysis (FCA) as the basis for a practical and well founded methodological approach for web data analysis which identifies conceptual structures among data sets. As well as, we propose a framework for discovering hidden knowledge by using polarity from Twitter contents. Additionally, we show the experiments that demonstrate how our framework can be applied for knowledge discovery.

High efficiency video coding (HEVC) was recently introduced two years ago as the new standard for video coding. This new codec will be the most widely prevalent standard. Because of the industry needs for authentication and copyright protection methodologies the robustness of this standard is required to be developed. This paper presents the first robust digital watermarking method for the HEVC based on Repetition- BCH syndrome code technique without intra-frame distortion drift. The objective of this article is to implement a new technique that can offer high robustness against noise channel errors and increase the error detection rate in the HEVC video sequences transmitted over noisy communication channels. The proposed technique does not significantly affect the video quality, nor does it escalate the bitrate. The results show that the proposed technique offers greater robustness against noise channel while preserving good quality for extracted watermark. The proposed technique significantly contributes to the performance of the error detection and correction. In addition, this technique can recover the watermark when the watermarked frames dropping rate is less than 20%.

Recently the Natural User Interface (NUI) technology which is capable of appreciating the whole human body has come to the fore with the development of digital technology. And this new interface has settled as the competitive contents in the growing experiential learning contents market, by arousing participants’ interests and maximizing their learning effect, through a gesture recognition-based noncontact type interactive education. This study suggests various interpretations on the basis of the education contents production which enables participant to experience the human body in real time. Also it has developed a gesture interface by utilizing the Kinect sensor that can recognize a participant’s skeleton and behavior, and its education contents design has produced the end product by using Unity 3D authoring tool which can combine the real-time 3D model and animation together. Consequently, participant will be able to learn various inner organs such as brain and heart by different gestures, and the developed real-time human body exploring augmented reality system is expected to be widely used in many educational institutions

This paper proposes a new feature vector selection method for voice pattern recognition tasks, especially for speaker or emotion recognition. During the model training phase, robust speaker or emotion models are constructed by using meaningful feature vectors while discarding confusing vectors that may induce recognition error. To select meaningful feature vectors, the proposed method classifies feature vectors into overlapped and non-overlapped sets using log-likelihood ratio. Speaker- and emotion-recognition experiments confirmed that these robust models significantly reduce recognition errors.

We commonly call the modern society as “Convergence”. Especially, mobile, smart, cloud computing can be called core technologies of the large technological direction of convergence. Many people have used an internet at various purposes. Internet have been used the optimal space to share information by information producer or consumer. But, recently, through the development of networks and devices, the internet is expanding beyond PCs and mobile devices into enterprise assets such as field equipment, and consumer items such as cars and televisions. The internet of things will enable forms of collaboration and communication between people and things, and between things themselves, hitherto unknown and unimagined. This paper describes various multiple meanings on internet of things, and describes the electronic and car domain on internet of things.