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한국정보처리학회> JIPS(Journal of Information Processing Systems)> The Needs Analysis of Software Safety Education Program for Common Competency Area

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The Needs Analysis of Software Safety Education Program for Common Competency Area

Ji-Woon Kang , Sung-Ryong Do
  • : 한국정보처리학회
  • : JIPS(Journal of Information Processing Systems) 17권5호
  • : 연속간행물
  • : 2021년 10월
  • : 960-971(12pages)
JIPS(Journal of Information Processing Systems)

DOI


목차

1. Introduction
2. Literature Review
3. Research Method
4. Results
5. Discussion and Conclusion
References

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초록 보기

As the era of the 4th Industrial Revolution enters, the importance of software safety is increasing, but related systematic educational curriculum and trained professional engineers are insufficient. The purpose of this research is to propose the high priority elements for the software safety education program through needs analysis. For this purpose, 74 candidate elements of software safety education program were derived through contents analysis of literature and nominal group technique (NGT) process with five software safety professionals from various industries in South Korea. Targeting potential education participants including industrial workers and students, an on-line survey was conducted to measure the current and required level of each element. Using descriptive statistics, t-test, Borich needs assessment and Locus for focus model, 16 high priority elements were derived for software safety education program. Based on the results, suggestions were made to develop a more effective education program for software safety education.

UCI(KEPA)

간행물정보

  • : 공학분야  > 전자공학
  • : KCI등재
  • : SCOPUS
  • : 격월
  • : 1976-913x
  • : 2092-805X
  • : 학술지
  • : 연속간행물
  • : 2005-2021
  • : 966


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1Study on Data Processing of the IOT Sensor Network Based on a Hadoop Cloud Platform and a TWLGA Scheduling Algorithm

저자 : Guoyu Li , Kang Yang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1035-1043 (9 pages)

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An Internet of Things (IOT) sensor network is an effective solution for monitoring environmental conditions. However, IOT sensor networks generate massive data such that the abilities of massive data storage, processing, and query become technical challenges. To solve the problem, a Hadoop cloud platform is proposed. Using the time and workload genetic algorithm (TWLGA), the data processing platform enables the work of one node to be shared with other nodes, which not only raises efficiency of one single node but also provides the compatibility support to reduce the possible risk of software and hardware. In this experiment, a Hadoop cluster platform with TWLGA scheduling algorithm is developed, and the performance of the platform is tested. The results show that the Hadoop cloud platform is suitable for big data processing requirements of IOT sensor networks.

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2The Nexus among Globalization, ICT and Economic Growth: An Empirical Analysis

저자 : Ximei Liu , Zahid Latif , Daoqi Xiong , Mengke Yang , Shahid Latif , Kaif Ul Wara

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1044-1056 (13 pages)

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Globalization has integrated the world through interaction among countries and people with the help of information and telecommunication technology (ICT). The rapid mode of globalization has put a new life in ICT and economic sector. The key focus of this study is to examine the nexus among the globalization, ICT and economic growth. This study uses autoregressive distributed lag model (ARDL), vector error correction model (VECM) and econometric method spanning from 1990 to 2015. The empirical result highlights that the globalization stimulates economic growth of a country. In addition, both the internet penetration and the mobile phone usage contribute to the economic growth. Lastly, this article contributes important policy lessons on strengthening the economy by utilizing ICT with the rapid globalization.

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3Case-Related News Filtering via Topic-Enhanced Positive-Unlabeled Learning

저자 : Guanwen Wang , Zhengtao Yu , Yantuan Xian , Yu Zhang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1057-1070 (14 pages)

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Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

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4Anomaly Detection of Facilities and Non-disruptive Operation of Smart Factory Using Kubernetes

저자 : Guik Jung , Hyunsoo Ha , Sangjun Lee

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1071-1082 (12 pages)

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Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factory providing effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models.

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5Massive MIMO Channel Estimation Algorithm Based on Weighted Compressed Sensing

저자 : Zhiguo Lv , Weijing Wang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1083-1096 (14 pages)

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Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value uop by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and uop. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.

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6Wireless Mobile Sensor Networks with Cognitive Radio Based FPGA for Disaster Management

저자 : G. A. Preethi Ananthachari

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1097-1114 (18 pages)

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The primary objective of this work was to discover a solution for the survival of people in an emergency flood. The geographical information was obtained from remote sensing techniques. Through helpline numbers, people who are in need request support. Although, it cannot be ensured that all the people will acquire the facility. A proper link is required to communicate with people who are at risk in affected areas. Mobile sensor networks with field-programmable gate array (FPGA) self-configurable radios were deployed in damaged areas for communication. Ad-hoc networks do not have a centralized structure. All the mobile nodes deploy a temporary structure and they act as a base station. The mobile nodes are involved in searching the spectrum for channel utilization for better communication. FPGA-based techniques ensure seamless communication for the survivors. Timely help will increase the survival rate. The received signal strength is a vital factor for communication. Cognitive radio ensures channel utilization in an effective manner which results in better signal strength reception. Frequency band selection was carried out with the help of the GRA-MADM method. In this study, an analysis of signal strength for different mobile sensor nodes was performed. FPGA-based implementation showed enhanced outcomes compared to software-based algorithms.

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7Improved Deep Residual Network for Apple Leaf Disease Identification

저자 : Changjian Zhou , Jinge Xing

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1115-1126 (12 pages)

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Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

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8Research on the Impact of Agricultural Mechanization Service on Wheat Planting Cost: A Case Study of Henan Province

저자 : Zhang Cheng

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1127-1137 (11 pages)

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Given the different effects of agricultural mechanization on various stages of wheat planting in Henan, this article selects 78 observation samples from Henan, a major wheat-growing province. It uses different research methods (multiple linear regression, social network analysis model, multi-layer sensory nerves network) to conduct a comparative study, and the calculation results of the model show that the experimental results have a strong convergence and consistency. Agricultural mechanization services have significant effects on the three stages of wheat planting: harvesting, plowing and sowing. A higher degree of mechanized service in several stages can reduce the cost of growing wheat on family farms.

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9A Survey on Image Emotion Recognition

저자 : Guangzhe Zhao , Hanting Yang , Bing Tu , Lei Zhang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1138-1156 (19 pages)

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Emotional semantics are the highest level of semantics that can be extracted from an image. Constructing a system that can automatically recognize the emotional semantics from images will be significant for marketing, smart healthcare, and deep human-computer interaction. To understand the direction of image emotion recognition as well as the general research methods, we summarize the current development trends and shed light on potential future research. The primary contributions of this paper are as follows. We investigate the color, texture, shape and contour features used for emotional semantics extraction. We establish two models that map images into emotional space and introduce in detail the various processes in the image emotional semantic recognition framework. We also discuss important datasets and useful applications in the field such as garment image and image retrieval. We conclude with a brief discussion about future research trends.

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10KAWS: Coordinate Kernel-Aware Warp Scheduling and Warp Sharing Mechanism for Advanced GPUs

저자 : Viet Tan Vo , Cheol Hong Kim

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1157-1169 (13 pages)

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Modern graphics processor unit (GPU) architectures offer significant hardware resource enhancements for parallel computing. However, without software optimization, GPUs continuously exhibit hardware resource underutilization. In this paper, we indicate the need to alter different warp scheduler schemes during different kernel execution periods to improve resource utilization. Existing warp schedulers cannot be aware of the kernel progress to provide an effective scheduling policy. In addition, we identified the potential for improving resource utilization for multiple-warp-scheduler GPUs by sharing stalling warps with selected warp schedulers. To address the efficiency issue of the present GPU, we coordinated the kernel-aware warp scheduler and warp sharing mechanism (KAWS). The proposed warp scheduler acknowledges the execution progress of the running kernel to adapt to a more effective scheduling policy when the kernel progress attains a point of resource underutilization. Meanwhile, the warp-sharing mechanism distributes stalling warps to different warp schedulers wherein the execution pipeline unit is ready. Our design achieves performance that is on an average higher than that of the traditional warp scheduler by 7.97% and employs marginal additional hardware overhead.

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1Multi-channel Long Short-Term Memory with Domain Knowledge for Context Awareness and User Intention

저자 : Dan-Bi Cho , Hyun-young Lee , Seung-Shik Kang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 867-878 (12 pages)

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In context awareness and user intention tasks, dataset construction is expensive because specific domain data are required. Although pretraining with a large corpus can effectively resolve the issue of lack of data, it ignores domain knowledge. Herein, we concentrate on data domain knowledge while addressing data scarcity and accordingly propose a multi-channel long short-term memory (LSTM). Because multi-channel LSTM integrates pretrained vectors such as task and general knowledge, it effectively prevents catastrophic forgetting between vectors of task and general knowledge to represent the context as a set of features. To evaluate the proposed model with reference to the baseline model, which is a single-channel LSTM, we performed two tasks: voice phishing with context awareness and movie review sentiment classification. The results verified that multichannel LSTM outperforms single-channel LSTM in both tasks. We further experimented on different multichannel LSTMs depending on the domain and data size of general knowledge in the model and confirmed that the effect of multi-channel LSTM integrating the two types of knowledge from downstream task data and raw data to overcome the lack of data.

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2Route Optimization Algorithm Based on Game Theory for Tourism Routes at Pseudo-Imperial Palace

저자 : Guangjie Liu , Jinlong Zhu , Qiucheng Sun , Jiaze Hu , Hao Yu

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 879-891 (13 pages)

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With improvements in living conditions, an increasing number of people are choosing to spend their time traveling. Comfortable tour routes are affected by the season, time, and other local factors. In this paper, the influencing factors and principles of scenic spots are analyzed, a model used to find the available routes is built, and a multi-route choice model based on a game theory utilizing a path recommendation weight is developed. A Monte Carlo analysis of a tourist route subjected to fixed access point conditions is applied to account for uncertainties such as the season, start time, end time, stay time, number of scenic spots, destination, and start point. We use the Dijkstra method to obtain multiple path plans and calculate the path evaluation score using the Monte Carlo method. Finally, according to the user preference in the input path, game theory generates path ordering for user choice. The proposed approach achieves a state-of-the-art performance at the pseudo-imperial palace. Compared with other methods, the proposed method can avoid congestion and reduce the time cost.

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3Image Semantic Segmentation Using Improved ENet Network

저자 : Chaoxian Dong

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 892-904 (13 pages)

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An image semantic segmentation model is proposed based on improved ENet network in order to achieve the low accuracy of image semantic segmentation in complex environment. Firstly, this paper performs pruning and convolution optimization operations on the ENet network. That is, the network structure is reasonably adjusted for better results in image segmentation by reducing the convolution operation in the decoder and proposing the bottleneck convolution structure. Squeeze-and-excitation (SE) module is then integrated into the optimized ENet network. Small-scale targets see improvement in segmentation accuracy via automatic learning of the importance of each feature channel. Finally, the experiment was verified on the public dataset. This method outperforms the existing comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU) values. And in a short running time, the accuracy of the segmentation and the efficiency of the operation are guaranteed.

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4Range Segmentation of Dynamic Offloading (RSDO) Algorithm by Correlation for Edge Computing

저자 : Jieun Kang , Svetlana Kim , Jae-ho Kim , Nak-myoung Sung , Yong-Ik Yoon

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 905-917 (13 pages)

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In recent years, edge computing technology consists of several Internet of Things (IoT) devices with embedded sensors that have improved significantly for monitoring, detection, and management in an environment where big data is commercialized. The main focus of edge computing is data optimization or task offloading due to data and task-intensive application development. However, existing offloading approaches do not consider correlations and associations between data and tasks involving edge computing. The extent of collaborative offloading segmented without considering the interaction between data and task can lead to data loss and delays when moving from edge to edge. This article proposes a range segmentation of dynamic offloading (RSDO) algorithm that isolates the offload range and collaborative edge node around the edge node function to address the offloading issue.The RSDO algorithm groups highly correlated data and tasks according to the cause of the overload and dynamically distributes offloading ranges according to the state of cooperating nodes. The segmentation improves the overall performance of edge nodes, balances edge computing, and solves data loss and average latency.

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5Supply Chain Collaboration Degree of Manufacturing Enterprises Using Matter-Element Method

저자 : Qiang Xiao , Shuangshuang Yao , Mengjun Qiang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 918-932 (15 pages)

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Evaluation of the collaboration of the upstream and downstream enterprises in the manufacturing supply chain is important to improve their synergistic effect. From the supply chain perspective, this study establishes the evaluation model of the manufacturing enterprise collaboration on the basis of fuzzy entropy according to synergistic theory. Downstream enterprises carry out coordinated capital, business, and information flows as subsystems and research enterprises as composite systems. From the three subsystems, the collaboration evaluation index is selected as the order parameter. The compound fuzzy matter-element matrix is established by using its improved algorithm. Subordinate membership and standard deviation fuzzy matter-element matrixes are constructed. Index weight is determined using the entropy weight method. The closeness of each matter element is then calculated. Through a representative of the home appliance industry, namely, Gree Electric Appliances Inc. of Zhuhai, empirical analysis of data in 2011-2017 from the company and its upstream and downstream enterprise collaboration shows a good trend, but the coordinated development has not reached stability. Gree Electric Appliances Inc. of Zhuhai need to strengthen the synergy with upstream and downstream enterprises in terms of cash, business, and information flows to enhance competitiveness. Experimental results show that this method can provide precise suggestions for enterprises, improve the degree of collaboration, and accelerate the development and upgrading of the manufacturing industry.

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6Joint Detection Method for Non-orthogonal Multiple Access System Based on Linear Precoding and Serial Interference Cancellation

저자 : Jianpo Li , Qiwei Wang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 933-946 (14 pages)

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In the non-orthogonal multiple access (NOMA) system, multiple user signals on the single carrier are superimposed in a non-orthogonal manner, which results in the interference between non-orthogonal users and noise interference in the channel. To solve this problem, an improved algorithm combining regularized zero-forcing (RZF) precoding with minimum mean square error-serial interference cancellation (MMSE-SIC) detection is proposed. The algorithm uses RZF precoding combined with successive over-relaxation (SOR) method at the base station to preprocess the source signal, which can balance the effects of non-orthogonal inter-user interference and noise interference, and generate a precoded signal suitable for transmission in the channel. At the receiver, the MMSE-SIC detection algorithm is used to further eliminate the interference in the signal for the received superimposed signal, and reduce the calculation complexity through the QR decomposition of the matrix. The simulation results show that the proposed joint detection algorithm has good applicability to eliminate the interference of non-orthogonal users, and it has low complexity and fast convergence speed. Compared with other traditional method, the improved method has lower error rate under different signal-to-interference and noise ratio (SINR).

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7Research on Water Edge Extraction in Islands from GF-2 Remote Sensing Image Based on GA Method

저자 : Yan Bian , Yusheng Gong , Guopeng Ma , Ting Duan

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 947-959 (13 pages)

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Aiming at the problem of low accuracy in the water boundary automatic extraction of islands from GF-2 remote sensing image with high resolution in three bands, new water edges automatic extraction method in island based on GF-2 remote sensing images, genetic algorithm (GA) method, is proposed in this paper. Firstly, the GA-OTSU threshold segmentation algorithm based on the combination of GA and the maximal inter-class variance method (OTSU) was used to segment the island in GF-2 remote sensing image after pre-processing. Then, the morphological closed operation was used to fill in the holes in the segmented binary image, and the boundary was extracted by the Sobel edge detection operator to obtain the water edge. The experimental results showed that the proposed method was better than the contrast methods in both the segmentation performance and the accuracy of water boundary extraction in island from GF-2 remote sensing images.

KCI등재SCOUPUS

8The Needs Analysis of Software Safety Education Program for Common Competency Area

저자 : Ji-Woon Kang , Sung-Ryong Do

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 960-971 (12 pages)

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As the era of the 4th Industrial Revolution enters, the importance of software safety is increasing, but related systematic educational curriculum and trained professional engineers are insufficient. The purpose of this research is to propose the high priority elements for the software safety education program through needs analysis. For this purpose, 74 candidate elements of software safety education program were derived through contents analysis of literature and nominal group technique (NGT) process with five software safety professionals from various industries in South Korea. Targeting potential education participants including industrial workers and students, an on-line survey was conducted to measure the current and required level of each element. Using descriptive statistics, t-test, Borich needs assessment and Locus for focus model, 16 high priority elements were derived for software safety education program. Based on the results, suggestions were made to develop a more effective education program for software safety education.

KCI등재SCOUPUS

9SAT-Analyser Traceability Management Tool Support for DevOps

저자 : Iresha Rubasinghe , Dulani Meedeniya , Indika Perera

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 972-988 (17 pages)

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At present, DevOps environments are getting popular in software organizations due to better collaboration and software productivity over traditional software process models. Software artefacts in DevOps environments are vulnerable to frequent changes at any phase of the software development life cycle that create a continuous integration continuous delivery pipeline. Therefore, software artefact traceability management is challenging in DevOps environments due to the continual artefact changes; often it makes the artefacts to be inconsistent. The existing software traceability related research shows limitations such as being limited to few types of artefacts, lack of automation and inability to cope with continuous integrations. This paper attempts to overcome those challenges by providing traceability support for heterogeneous artefacts in DevOps environments using a prototype named SAT-Analyser. The novel contribution of this work is the proposed traceability process model consists of artefact change detection, change impact analysis, and change propagation. Moreover, this tool provides multi-user accessibility and is integrated with a prominent DevOps tool stack to enable collaborations. The case study analysis has shown high accuracy in SAT-Analyser generated results and have obtained positive feedback from industry DevOps practitioners for its efficacy.

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10Implementation of Search Engine to Minimize Traffic Using Blockchain-Based Web Usage History Management System

저자 : Sunghyun Yu , Cheolmin Yeom , Yoojae Won

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 989-1003 (15 pages)

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With the recent increase in the types of services provided by Internet companies, collection of various types of data has become a necessity. Data collectors corresponding to web services profit by collecting users' data indiscriminately and providing it to the associated services. However, the data provider remains unaware of the manner in which the data are collected and used. Furthermore, the data collector of a web service consumes web resources by generating a large amount of web traffic. This traffic can damage servers by causing service outages. In this study, we propose a website search engine that employs a system that controls user information using blockchains and builds its database based on the recorded information. The system is divided into three parts: a collection section that uses proxy, a management section that uses blockchains, and a search engine that uses a built-in database. This structure allows data sovereigns to manage their data more transparently. Search engines that use blockchains do not use internet bots, and instead use the data generated by user behavior. This avoids generation of traffic from internet bots and can, thereby, contribute to creating a better web ecosystem.

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