• Title/Summary/Keyword: Computing Platform

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Comparison of encryption algorithm performance between low-spec IoT devices (저 사양 IoT 장치간의 암호화 알고리즘 성능 비교)

  • Park, Jung Kyu;Kim, Jaeho
    • Journal of Internet of Things and Convergence
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    • v.8 no.1
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    • pp.79-85
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    • 2022
  • Internet of Things (IoT) connects devices with various platforms, computing power, and functions. Due to the diversity of networks and the ubiquity of IoT devices, demands for security and privacy are increasing. Therefore, cryptographic mechanisms must be strong enough to meet these increased requirements, while at the same time effective enough to be implemented in devices with long-range specifications. In this paper, we present the performance and memory limitations of modern cryptographic primitives and schemes for different types of devices that can be used in IoT. In addition, detailed performance evaluation of the performance of the most commonly used encryption algorithms in low-spec devices frequently used in IoT networks is performed. To provide data protection, the binary ring uses encryption asymmetric fully homomorphic encryption and symmetric encryption AES 128-bit. As a result of the experiment, it can be seen that the IoT device had sufficient performance to implement a symmetric encryption, but the performance deteriorated in the asymmetric encryption implementation.

Parallel Implementations of Digital Focus Indices Based on Minimax Search Using Multi-Core Processors

  • HyungTae, Kim;Duk-Yeon, Lee;Dongwoon, Choi;Jaehyeon, Kang;Dong-Wook, Lee
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.2
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    • pp.542-558
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    • 2023
  • A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.

An Efficient Data Collection Method for Deep Learning-based Wireless Signal Identification in Unlicensed Spectrum (딥 러닝 기반의 이기종 무선 신호 구분을 위한 데이터 수집 효율화 기법)

  • Choi, Jaehyuk
    • Journal of IKEEE
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    • v.26 no.1
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    • pp.62-66
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    • 2022
  • Recently, there have been many research efforts based on data-based deep learning technologies to deal with the interference problem between heterogeneous wireless communication devices in unlicensed frequency bands. However, existing approaches are commonly based on the use of complex neural network models, which require high computational power, limiting their efficiency in resource-constrained network interfaces and Internet of Things (IoT) devices. In this study, we address the problem of classifying heterogeneous wireless technologies including Wi-Fi and ZigBee in unlicensed spectrum bands. We focus on a data-driven approach that employs a supervised-learning method that uses received signal strength indicator (RSSI) data to train Deep Convolutional Neural Networks (CNNs). We propose a simple measurement methodology for collecting RSSI training data which preserves temporal and spectral properties of the target signal. Real experimental results using an open-source 2.4 GHz wireless development platform Ubertooth show that the proposed sampling method maintains the same accuracy with only a 10% level of sampling data for the same neural network architecture.

Analysis of the Impact of Host Resource Exhaustion Attacks in a Container Environment (컨테이너 환경에서의 호스트 자원 고갈 공격 영향 분석)

  • Jun-hee Lee;Jae-hyun Nam;Jin-woo Kim
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.33 no.1
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    • pp.87-97
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    • 2023
  • Containers are an emerging virtualization technology that can build an isolated environment more lightweight and faster than existing virtual machines. For that reason, many organizations have recently adopted them for their services. Yet, the container architecture has also exposed many security problems since all containers share the same OS kernel. In this work, we focus on the fact that an attacker can abuse host resources to make them unavailable to benign containers-also known as host resource exhaustion attacks. Then, we analyze the impact of host resource exhaustion attacks through real attack scenarios exhausting critical host resources, such as CPU, memory, disk space, process ID, and sockets in Docker, the most popular container platform. We propose five attack scenarios performed in several different host environments and container images. The result shows that three of them put other containers in denial of service.

A study on improvement of policy of artificial intelligence for national defense considering the US third offset strategy (미국의 제3차 상쇄전략을 고려한 국방 인공지능 정책 발전방안)

  • Se Hoon Lee;Seunghoon Lee
    • Industry Promotion Research
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    • v.8 no.1
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    • pp.35-45
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    • 2023
  • This paper addressed the analysis of the trend and direction of the US defense strategy based on their third offset strategy and presented the practical policy implication of ensuring the security of South Korea appropriately in the future national defense environment. The countermeasures for the development ability of advanced weapon systems and secure core technologies for Korea were presented in consideration of the US third offset strategy for the future national defense environment. First, to carry out the innovation of national defense in Korea based on artificial intelligence(AI), the long-term basis strategy for the operation of the unmanned robot and autonomous weapon system should be suggested. Second, the platform for AI has to be developed to obtain the development of algorithms and computing abilities for securing the collection/storage/management of national defense data. Lastly, advanced components and core technologies are identified, which the Korean government can join to develop with the US on a basis of the Korea-US alliance, and the technical cooperation with the US should be stronger.

Optimizing User Experience While Interacting with IR Systems in Big Data Environments

  • Minsoo Park
    • International journal of advanced smart convergence
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    • v.12 no.4
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    • pp.104-110
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    • 2023
  • In the user-centered design paradigm, information systems are created entirely tailored to the users who will use them. When the functions of a complex system meet a simple user interface, users can use the system conveniently. While web personalization services are emerging as a major trend in portal services, portal companies are competing for a second service, such as introducing 'integrated communication platforms'. Until now, the role of the portal has been content and search, but this time, the goal is to create and provide the personalized services that users want through a single platform. Personalization service is a login-based cloud computing service. It has the characteristic of being able to enjoy the same experience at any time in any space with internet access. Personalized web services like this have the advantage of attracting highly loyal users, making them a new service trend that portal companies are paying attention to. Researchers spend a lot of time collecting research-related information by accessing multiple information sources. There is a need to automatically build interest information profiles for each researcher based on personal presentation materials (papers, research projects, patents). There is a need to provide an advanced customized information service that regularly provides the latest information matched with various information sources. Continuous modification and supplementation of each researcher's information profile of interest is the most important factor in increasing suitability when searching for information. As researchers' interest in unstructured information such as technology markets and research trends is gradually increasing from standardized academic information such as patents, it is necessary to expand information sources such as cutting-edge technology markets and research trends. Through this, it is possible to shorten the time required to search and obtain the latest information for research purposes. The interest information profile for each researcher that has already been established can be used in the future to determine the degree of relationship between researchers and to build a database. If this customized information service continues to be provided, it will be useful for research activities.

Context-Based Prompt Selection Methodology to Enhance Performance in Prompt-Based Learning

  • Lib Kim;Namgyu Kim
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.4
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    • pp.9-21
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    • 2024
  • Deep learning has been developing rapidly in recent years, with many researchers working to utilize large language models in various domains. However, there are practical difficulties that developing and utilizing language models require massive data and high-performance computing resources. Therefore, in-context learning, which utilizes prompts to learn efficiently, has been introduced, but there needs to be clear criteria for effective prompts for learning. In this study, we propose a methodology for enhancing prompt-based learning performance by improving the PET technique, which is one of the contextual learning methods, to select PVPs that are similar to the context of existing data. To evaluate the performance of the proposed methodology, we conducted experiments with 30,100 restaurant review datasets collected from Yelp, an online business review platform. We found that the proposed methodology outperforms traditional PET in all aspects of accuracy, stability, and learning efficiency.

New Tool to Simulate Microbial Contamination of on-Farm Produce: Agent-Based Modeling and Simulation (재배단계 농산물의 안전성 모의실험을 위한 개체기반 프로그램 개발)

  • Han, Sanghyun;Lee, Ki-Hoon;Yang, Seong-Gyu;Kim, Hwang-Yong;Kim, Hyun-Ju;Ryu, Jae-Gee
    • Journal of Food Hygiene and Safety
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    • v.32 no.1
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    • pp.8-13
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    • 2017
  • This study was conducted to develop an agent-based computing platform enabling simulation of on-farm produce contamination by enteric foodborne pathogens, which is herein called PPMCS (Preharvest Produce Microbial Contamination Simulator). Also, fecal contamination of preharvest produce was simulated using PPMCS. Although Agent-based Modeling and Simulation, the tool applied in this study, is rather popular in where socio-economical human behaviors or ecological fate of animals in their niche are to be predicted, the incidence of on-farm produce contamination which are thought to be sporadic has never been simulated using this tool. The agents in PPMCS including crop, animal as a source of fecal contamination, and fly as a vector spreading the fecal contamination are given their intrinsic behaviors that are set to be executed at certain probability. Once all these agents are on-set following the intrinsic behavioral rules, consequences as the sum of all the behaviors in the system can be monitored real-time. When fecal contamination of preharvest produce was simulated in PPMCS as numbers of animals, flies, and initially contaminated plants change, the number of animals intruding cropping area affected most on the number of contaminated plants at harvest. For further application, the behaviors and variables of the agents are adjustable depending on user's own scenario of interest. This feature allows PPMCS to be utilized in where different simulating conditions are tested.

Exploring the 4th Industrial Revolution Technology from the Landscape Industry Perspective (조경산업 관점에서 4차 산업혁명 기술의 탐색)

  • Choi, Ja-Ho;Suh, Joo-Hwan
    • Journal of the Korean Institute of Landscape Architecture
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    • v.47 no.2
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    • pp.59-75
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    • 2019
  • This study was carried out to explore the 4th Industrial Revolution technology from the perspective of the landscape industry to provide the basic data necessary to increase the virtuous circle value. The 4th Industrial Revolution, the characteristics of the landscape industry and urban regeneration were considered and the methodology was established and studied including the technical classification system suitable for systematic research, which was selected as a framework. First, the 4th Industrial Revolution technology based on digital data was selected, which could be utilized to increase the value of the virtuous circle for the landscape industry. From 'Element Technology Level', and 'Core Technology' such as the Internet of Things, Cloud Computing, Big Data, Artificial Intelligence, Robot, 'Peripheral Technology', Virtual or Augmented Reality, Drones, 3D 4D Printing, and 3D Scanning were highlighted as the 4th Industrial Revolution technology. It has been shown that it is possible to increase the value of the virtuous circle when applied at the 'Trend Level', in particular to the landscape industry. The 'System Level' was analyzed as a general-purpose technology, and based on the platform, the level of element technology(computers, and smart devices) was systematically interconnected, and illuminated with the 4th Industrial Revolution technology based on digital data. The application of the 'Trend Level' specific to the landscape industry has been shown to be an effective technology for increasing the virtuous circle values. It is possible to realize all synergistic effects and implementation of the proposed method at the trend level applying the element technology level. Smart gardens, smart parks, etc. have been analyzed to the level they should pursue. It was judged that Smart City, Smart Home, Smart Farm, and Precision Agriculture, Smart Tourism, and Smart Health Care could be highly linked through the collaboration among technologies in adjacent areas at the Trend Level. Additionally, various utilization measures of related technology applied at the Trend Level were highlighted in the process of urban regeneration, public service space creation, maintenance, and public service. In other words, with the realization of ubiquitous computing, Hyper-Connectivity, Hyper-Reality, Hyper-Intelligence, and Hyper-Convergence were proposed, reflecting the basic characteristics of digital technology in the landscape industry can be achieved. It was analyzed that the landscaping industry was effectively accommodating and coordinating with the needs of new characters, education and consulting, as well as existing tasks, even when participating in urban regeneration projects. In particular, it has been shown that the overall landscapig area is effective in increasing the virtuous circle value when it systems the related technology at the trend level by linking maintenance with strategic bridgehead. This is because the industrial structure is effective in distributing data and information produced from various channels. Subsequent research, such as demonstrating the fusion of the 4th Industrial Revolution technology based on the use of digital data in creation, maintenance, and service of actual landscape space is necessary.

A Rule-based JMS Message Routing System for Dynamic Message Communication in based Distributed Systems (분산환경에서 동적 메시지 교환을 위한 룰 기반 JMS 메시지 라우팅 시스템)

  • Cho, Poong-Youn;Choi, Jae-Hyun;Park, Jae-Won;Lee, Nam-Yong
    • The Journal of Society for e-Business Studies
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    • v.13 no.3
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    • pp.1-20
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    • 2008
  • Today's computing environment which is getting distributed to communicate with various systems needs dynamic inter-connectivity of the systems. MOM(Message Oriented Middleware) is popularly used for transmitting XML messages among the distributed systems for the inter-connectivity. But, they do not support event-based message routing functionalities with XML transformation for processing effective message routing, which is essential to inter-connectivity, and there is no integrated platform to cope with these requirements. Although event-based message routing and XML transformation have been studied in a wide range of computer science areas, development of message routing systems is considered as a tough job due to the technological difficulties. In order to address these requirements, we proposed a novel system, named RMRS(Rule-based Message Routing System), which supports event-based message routing as well as XML message transformation. To make the proposed system easy to use, we also redesigned ECA(Event- Condition-Action) rule to fit in our system and developed a tool to map source XML structure into target XML structure.

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