• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.808-826
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• 2017

Most of conventional DSS's(Digital Signage Systems) have been built based on LAMP framework. Recent researches have shown that MEAN or MERN stack framework is simpler, more flexible, faster and more suitable for web-based application than LAMP stack framework. In this paper, we propose a design and implementation of MEARN (ME(A+R)N) stack-based real-time digital signage system, MR-DSS, which supports handing real-time tasks like urgent/instant messaging, system status monitoring and so on, efficiently in addition to conventional digital signage CMS service tasks. MR-DSCMS, CMS of MR-DSS, is designed to provide most of its normal services by REST APIs and real-time services like urgent/instant messaging by Socket.IO base under MEARN stack environment. In addition to architecture description of components composing MR-DSS, design and implementation issues are clarified in more detail. Through experimental testing, it is shown that 1) MR-DSS works functionally well, 2) the networking load performance of MR-DSCMS's REST APIs is better compared to a well-known open source Xibo CMS, and 3) real-time messaging via Socket.IO works much faster than REST APIs.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.71-80
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• 2013

Due to the increased use of mobile devices, there is a lot of discussion on mobile accessibility. Mobile accessibility means that everyone, who includes the disabled, the elderly people, can easily use the functions of mobile devices. In this paper, we presented and implemented a mobile interface using a speech I/O APIs to improve the accessibility. The proposed interfaces are implemented on Android platforms and they used speech recognition and text-to-speech APIs supported as built-in services. In addition, to facilitate the internet access for visually impaired or blind people, we also implemented the web browsing application (web reader).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.3
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• pp.547-554
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• 2008

In this paper, two-stage pipelined floating-point multiplier (FP-MUL) is designed. The FP-MUL processor supports single precision multiplication for 3D graphic APIs, such as OpenGL and Direct3D and has area-efficient and low-latency architecture via saturated arithmetic, area-efficient sticky-bit generator, and flagged prefix adder. The FP-MUL has about 4-ns delay time under $0.13{\mu}m$ CMOS standard cell library and consists of about 7,500 gates. Because its maximum performance is about 250 MFLOPS, it can be applicable to mobile 3D graphics application.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.199-204
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• 2019

This paper introduces smart mirror that provides the contents needed for an individual's daily life. When a command that is designated as voice recognition is entered, Smart Mirror is produced that outputs desired contents from a display. The contents of the current smart mirror include time, weather, subway information, schedule and photography. Smart mirror sold for commercial private households is difficult to distribute due to high prices, but the smart mirror production presented in this paper can lower the manufacturing cost and can be more easily used by voice recognition.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.588-600
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• 2019

Each year Malaria affects over 200 million people worldwide. Particularly, African continent is highly hit by this disease. According to many researches, this continent is ideal for Anopheles mosquitoes which transmit Malaria parasites to thrive. Rainfall volume is one of the major factor favoring the development of these Anopheles in the tropical Sub-Sahara Africa (SSA). However, the surveillance, monitoring and reporting of this epidemic is still poor and bureaucratic only. In our paper, we proposed a method to fast monitor and report Malaria instances by using Social Network Systems (SNS) and precipitation volume in Nigeria. We used Twitter search Application Programming Interface (API) to live-stream Twitter messages mentioning Malaria, preprocessed those Tweets and classified them into Malaria cases in Nigeria by using Support Vector Machine (SVM) classification algorithm and compared those Malaria cases with average precipitation volume. The comparison yielded a correlation of 0.75 between Malaria cases recorded by using Twitter and average precipitations in Nigeria. To ensure the certainty of our classification algorithm, we used an oversampling technique and eliminated the imbalance in our training Tweets.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.973-983
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• 2019

The importance of information security has been increasingly emphasized at the national, organizational, and individual levels due to the widespread adoption of software applications. High-safety software, which includes embedded software, should run without errors, similar to software used in the airline and nuclear energy sectors. Software development techniques in the above sectors are now being used to improve software security in other fields. Secure coding, in particular, is a concept encompassing defensive programming and is capable of improving software security. In this paper, we propose a software security vulnerability detection method using an improved coding standard searching technique. Public static analysis tools were used to assess software security and to classify the commands that induce vulnerability. Software security can be enhanced by detecting Application Programming Interfaces (APIs) and patterns that can induce vulnerability.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.43-53
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• 2020

There are three standards for FIDO1 authentication technology: Universal Second Factor (U2F), Universal Authentication Framework (UAF), and Client to Authenticator Protocols (CTAP). FIDO2 refers to the WebAuthn standard established by W3C for the creation and use of a certificate in a web application that complements the existing CTAP. In Korea, the FIDO certified market is dominated by UAF, which deals with standards for smartphone (Android, iOS) apps owned by the majority of the people. As the market requires certification through FIDO on PCs, FIDO Alliance and W3C established standards that can be certified on the platform-independent Web and published 『Web Authentication: An API for Accessing Public Key Credentials Level 1』 on March 4, 2019. Most PC do not contain biometrics, so they are not being utilized contrary to expectations. In this paper, we intend to present a model that allows login in PC environment through biometric recognition of smartphone and FIDO UAF authentication. We propose a model in which a user requests login from a PC and performs FIDO authentication on a smartphone, and authentication is completed on the PC without any other user's additional gesture.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.3
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• pp.53-64
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• 2005

In this paper, proposes Kerberos certification mechanism that improve certification service of PKINIT base that announce in IETF CAT Working Croup. Also proposed Authentication Mechanism for reusability of Ticket that after Ticket's Lifetime is ended, message exchange that Local Client receives Remote Server's service. Since my suggestion to regional services are not described in Kerberos, authentication between regions can be performed via PKINIT(Public Key Cryptography for Initial Authentication) presented by IETF(Internet Engineering Task Force) CAT working group. The new protocol is better than the authentication mechanism proposed by IETF CAT Working group in terms of communication complexity and mechanism according to simplified Ticket issue processing.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.37-40
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• 2021

This paper presents a design of a speech to sign language converter for deaf and hard of hearing people. The device is low-cost, low-power consumption, and it can be able to work entirely offline. The speech recognition is implemented using an open-source API, Pocketsphinx library. In this work, we proposed a context-oriented language model, which measures the similarity between the recognized speech and the predefined speech to decide the output. The output speech is selected from the recommended speech stored in the database, which is the best match to the recognized speech. The proposed context-oriented language model can improve the speech recognition rate by 21% for working entirely offline. A decision module based on determining the similarity between the two texts using Levenshtein distance decides the output sign language. The output sign language corresponding to the recognized speech is generated as a set of sequential images. The speech to sign language converter is deployed on a Raspberry Pi Zero board for low-cost deaf assistive devices.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.119-130
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• 2022

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.