• Journal of Information Science Theory and Practice
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• v.10 no.1
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• pp.18-29
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• 2022

This study aimed at overviewing the situation of information literacy education and research in the Association of Southeast Asian Nations (ASEAN) region, and developing an ASEAN network model on information literacy. This research used documentary and qualitative research methods. Key resources consisted of twenty bibliometric studies and related documents and two groups of key persons. The first group consisted of twenty-seven purposive key persons from eight countries, and the second group consisted of seven key persons from five countries. The research instruments comprised a data collection form and focus group/ interviewing forms. Data was collected by focus group discussion and online interviews, and qualitative content analysis was used in data analysis and presented descriptively. Research findings showed that: 1) information literacy education and research in the ASEAN region varied across countries and placed importance on the educational context. Singapore was found to be the most leading and productive country in ASEAN in information literacy with the highest number of journal articles on the international scale, and was among the most contributing groups at the regional and global level; 2) the ASEAN Network on Information Literacy (ASEAN-NIL) has been developed as a model with its principles, objectives, management system, activities, and promotion strategies. Its strengths are an integrated scope, multidimensional orientation, and interdisciplinary and collaborative partnerships at the national, regional, and international level, suitable for the ASEAN context, the online environment, and the digital educational ecosystem.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.373-380
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• 2022

The field of land surveying is changing dramatically due to the way data is processed, analyzed and presented. Also, there is a growing demand for digital spatial information, coming primarily from the GIS (Geographical Information System) user community. Such a demand has created a strong development potential for a new land surveying software. An overview of the development and capabilities of a land surveying software platform based on the Windows system, SurveyingMap, is presented. Among its many features, SurveyingMap provides a lot of adaptability for networks adjustment, geodetic and plane coordinates transformation, contouring, sectioning, DTM (Digital Terrain Model) generation, and large scale mapping applications. The system output is compatible with well known computer aided drafting (CAD) /GIS packages to expand its scope of applications. SurveyingMap is also suitable for non-technical users due to the user-friendly graphic user interface. The system could be used in engineering, architecture, GIS, and academic teaching and research, among other fields. Two applications of SurveyingMap, extension of field control and large scale mapping, for the case study area are established. The results demonstrate that the system is adaptable and reasonably priced for use by college and university students.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.349-350
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• 2022

As research on IT convergence continues, the scope of IoT (Internet of Things) services continues to expand. The IoT service uses a device suitable for the purpose. These IoT devices require an authentication function. In addition, in IoT services that handle important information such as personal information, security of transmission data is required. In this study, we implement a crypto key-based IoT network security system that can authenticate devices for IoT services and securely transmit data between devices. Through this study, IoT service can authenticate the device itself and maintain the confidentiality of transmitted data. However, since it is an IoT service, additional research on the application efficiency of the encryption algorithm is required.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.2
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• pp.147-160
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• 2023

Unlike other currencies, cryptocurrency is not a currency used for general transactions, but is currently applied to various investment assets and its scope is expanding. The purpose of this study is to the effect of personal characteristics on investment intention. As a theoretical background, it was verified by applying the Extended Technical Acceptance Model (ETAM). self-confidence propensity, bandwagon propensity, risk tolerance propensity, network externality, attitude, and Investment intention were composed of variables. The research method collected data from 871 people who had experience in cryptocurrency investment through a survey and analyzed it after excluding the data of 71 people who were judged to be inappropriate. The structural equation modeling method using AMOS was used. As a result of this paper, five hypotheses were accepted as statistically significant. This study concluded that self-confidence propensity, bandwagon propensity, risk tolerance propensity, network externality, and attitude had statistically significant effects on Investment intention. In this respect, this study will be able to provide useful information for cryptocurrency research.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.21-32
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• 2024

This study analyzes the global literature on the black open-access phenomenon from 2011 to 2023. A bibliometric analysis was conducted using the Scopus database. The search strategy employed advanced queries with multiple synonymous terms to ensure exhaustive retrieval of relevant documents. The VOSviewer software was employed to visualize the co-occurrence networks. The findings reported 90 papers published during the study period. An evolving scholarly landscape was revealed, with heightened attention from 2016 onwards, peaking in 2017, 2021, and 2023. Articles constitute 83.3% of the total published documents. Singh and Srichandan are prolific authors, with 11.2% of the total publications. The United States contributes 18.9% of the papers, followed by India and Spain. Information Development and Scientometrics are pivotal journals in scholarly discussions about this scope, contributing 4.4% of publications. Co-occurrence network visualization revealed "Sci-Hub" and "open access" as the most used keywords in the global literature. The findings underscore the need for additional research to discover innovative business models to safeguard intellectual property rights while meeting researchers' evolving needs. The importance of this paper comes from being the first bibliometric study analyzing international literature related to this phenomenon, which provides a basis for future research efforts and policymaking.

• The KIPS Transactions:PartC
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• v.12C no.6 s.102
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• pp.933-940
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• 2005

As the appearance of very high speed telecommunication network, volume of network, is enlarged and complicated, management of the various network equipments and hosting systems become more complicated and significant. Recently, there have been various researches on network management system that is capable of managing and operating the network environment based on SNMP (Simple Network Management Protocol). SNMP has many advantages, which is easy to implement and has a simple structure. However, as the network structure has become more complicated, it has caused a number of problems like the increase of network load and limit of the network management scope in terms of the network expansion and efficiency. Especially, it needs expensive cost and time for developing a network because many network developers are almost depended manually for developing it till now. In this paper, we propose a framework for network management program that automates the generation of information for network management. The Proposed framework is able to automatically generate a network management program by using information related with equipments which were provided along with the network equipments and SNMP library Thus, we ill make not only the SNMP network structure expansion become easier but also errors maintaining and development time of the network management program were dramatically reduced by using generated network program through our proposed framework.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.6
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• pp.1087-1101
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• 2013

Most people are connected to Social Network Services (SNS) through smart devices. Social Network Services are tools that transport information fast and easily. It does not care where he or she comes from. A lot of information circulates and is shared on Social Network Services. but Social Network Services faults are magnified and becoming a serious issue. For instance, malicious users generate multiple IDs easily on Facebook and he can use personal information of others on purpose, because most people tend to undoubtedly accept friend requests. In this paper, we have specified research scope to Facebook, which is one of most popular Social Network Services in the world. We propose a way of minimizing the number of malicious actions on Facebook from malignant users and malicious bots by setting criteria and applying reputation system.

• Journal of KIISE:Software and Applications
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• v.36 no.1
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• pp.30-44
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• 2009

Devices of home network system have different control data formats according to each product company. Therefore, types or protocols of digital devices are various. Also, interaction operating environments are different among various devices. These characteristics of home network system don't support sufficiently functionalities such as data comparability, concurrent control, dynamic plug-in, and so on. That is, the degree of reusability of home network system is very poor. This paper proposes a framework which can be coverable to the scope of reusability widely and a design technique based on framework in order to improve reusability. That is, we extract various parts of home network systems as variation points, classify and define these as variation types, propose a framework which can be reusable those, and proposes a design technique of variability to improve reusability. Finally, proposed technique can be reusable to various domains by applying proposed reusability framework into real home network system's design.

• Journal of Communications and Networks
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• v.18 no.1
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• pp.65-74
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• 2016

Ship ad-hoc network (SANET) extends the coverage of the maritime communication among ships with the reduced cost. To fulfill the growing demands of real-time services, the SANET requires an efficient clock time synchronization algorithm which has not been carefully investigated under the ad-hoc maritime environment. This is mainly because the conventional algorithms only suggest to decrease the beacon collision probability that diminishes the clock drift among the units. However, the SANET is a very large-scale network in terms of geographic scope, e.g., with 100 km coverage. The key factor to affect the synchronization performance is the signal propagation delay, which has not being carefully considered in the existing algorithms. Therefore, it requires a robust multi-hop synchronization algorithm to support the communication among hundreds of the ships under the maritime environment. The proposed algorithm has to face and overcome several challenges, i.e., physical clock, e.g., coordinated universal time (UTC)/global positioning system (GPS) unavailable due to the atrocious weather, network link stability, and large propagation delay in the SANET. In this paper, we propose a logical clock synchronization algorithm with multi-hop function for the SANET, namely multi-hop clock synchronization for SANET (MCSS). It works in an ad-hoc manner in case of no UTC/GPS being available, and the multi-hop function makes sure the link stability of the network. For the proposed MCSS, the synchronization time reference nodes (STRNs) are efficiently selected by considering the propagation delay, and the beacon collision can be decreased by the combination of adaptive timing synchronization procedure (ATSP) with the proposed STRN selection procedure. Based on the simulation results, we finalize the multi-hop frame structure of the SANET by considering the clock synchronization, where the physical layer parameters are contrived to meet the requirements of target applications.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.