• ETRI Journal
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• v.30 no.3
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• pp.394-402
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• 2008

Domain analysis is the process of analyzing related software systems in a domain to find their common and variable parts. In the case of device drivers, they are highly suitable for domain analysis because device drivers of the same domain are implemented similarly for each device and each system that they support. Considering this characteristic, this paper introduces a new approach to the domain analysis of device drivers. Our method uses a code clone detection technique to extract similarity among device drivers of the same domain. To examine the applicability of our method, we investigated whole device drivers of a Linux source. Results showed that many reusable similar codes can be discerned by the code clone detection method. We also investigated if our method is applicable to other kernel sources. However, the results show that the code clone detection method is not useful for the domain analysis of all kernel sources. That is, the applicability of the code clone detection method to domain analysis is a peculiar feature of device drivers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.1932-1950
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• 2018

The paper proposes a code-clone detection method that gives the highest possible precision and recall, without giving much attention to efficiency and scalability. The goal is to automatically create a reliable reference corpus that can be used as a basis for evaluating the precision and recall of clone detection tools. The algorithm takes an abstract-syntax-tree representation of source code and thoroughly examines every possible pair of all duplicate tree patterns in the tree, while avoiding unnecessary and duplicated comparisons wherever possible. The largest possible duplicate patterns are then collected in the set of pattern clusters that are used to identify code clones. The method is implemented and evaluated for a standard set of open-source Java applications. The experimental result shows very high precision and recall. False-negative clones missed by our method are all non-contiguous clones. Finally, the concept of neighbor patterns, which can be used to improve recall by detecting non-contiguous clones and intertwined clones, is proposed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.98-106
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• 2012

CSD(Container Security Device) is a security device with sensors that can detect the abnormal behavior such as illegal opening of a container door. Since the CSD provides security and safety of the container, CSD should not only provide security services such as confidentiality and integrity but also cloning detection. If we can not detect the cloned CSD, an adversary can use the cloned CSD for many illegal purposes. In this paper, we propose a policy based cloned CSD detection mechanism. To evaluate proposed clone detection mechanism, we have implemented the proposed scheme and evaluated the results.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.43-53
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• 2019

BigCloneBench has recently been used for performance evaluation of code clone detection tool using machine learning. However, since BigCloneBench is not a benchmark that is optimized for machine learning, incorrect learning data can be created. In this paper, we have shown through experiments using machine learning that the set of Type-4 clone methods provided by BigCloneBench can additionally be found. Experimental results using Tree-Based Convolutional Neural Network show that our proposed method is effective in improving BigCloneBench's dataset.

• Journal of Software Assessment and Valuation
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• v.7 no.1
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• pp.29-39
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• 2011

To evaluate the quality of clone detection tools, we should know how many clones the tool misses. Hence we need to have the standard code-clone reference corpus for a carefully chosen set of sample source codes. The reference corpus available so far has been built by manually collecting clones from the results of various existing tools. This paper presents a tree-pattern-based clone detection tool that can be used for automatic generation of reference corpus. Our tool is compared with CloneDR for precision and Bellon's reference corpus for recall. Our tool finds no false positives and 2 to 3 times more clones than CloneDR. Compared to Bellon's reference corpus, our tools shows the 93%-to-100% recall rate and detects far more clones.

• Journal of Software Assessment and Valuation
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• v.14 no.2
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• pp.35-44
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• 2018

Tools for detecting cross-language clones usually compare abstract-syntax-tree representations of source code, which lacks scalability. In order to compare large source code to a practical level, we need a similarity checking technique that works on a token level basis. In this paper, we define common tokens that represent all tokens commonly used in programming languages of different paradigms. Each source code of different language is then transformed into the list of common tokens that are compared. Experimental results using exEyes show that our proposed method using common tokens is effective in detecting cross-language clones.

• Microbiology and Biotechnology Letters
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• v.16 no.5
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• pp.363-368
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• 1988

Molecular cloning of gene for $\alpha$-acylamino-$\beta$-lactam acylhydrolase (ALAH) III from Acetobacter turbidans has been attempted by immunochemical detection method, in which polyclonal antibody from mouse Balb/c against this enzyme was employed as a probe. As a cloning vector, λ gtll was chosen for this purpose. Two positive clones has been selected from genomic libraries of A. turbidans, which had somewhat different binding affinities on anti-ALAH III umm and anti-$\beta$-galactosidase. By restriction analysis, both clones has been turned out to lose one of EeoRI sites. From these results, it concluded that deletion of DNA between lacZ gene and inserted DNA has occurred during replication of these clones in host cells.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1224-1248
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• 2022

In various sensor network applications, such as climate observation organizations, sensor nodes need to collect information from time to time and pass it on to the recipient of information through multiple bounces. According to field tests, this information corresponds to most of the energy use of the sensor hub. Decreasing the measurement of information transmission in sensor networks becomes an important issue.Compression sensing (CS) can reduce the amount of information delivered to the network and reduce traffic load. However, the total number of classification of information delivered using pure CS is still enormous. The hybrid technique for utilizing CS was proposed to diminish the quantity of transmissions in sensor networks.Further the energy productivity is a test task for the sensor nodes. However, in previous studies, a clustering approach using hybrid CS for a sensor network and an explanatory model was used to investigate the relationship between beam size and number of transmissions of hybrid CS technology. It uses efficient data integration techniques for large networks, but leads to clone attacks or attacks. Here, a new algorithm called SBEA (Snowball Endurance Algorithm) was proposed and tested with a bow. Thus, you can extend the battery life of your WSN by running effective copy detection. Often, multiple nodes, called observers, are selected to verify the reliability of the nodes within the network. Personal data from the source centre (e.g. personality and geographical data) is provided to the observer at the optional witness stage. The trust and reputation system is used to find the reliability of data aggregation across the cluster head and cluster nodes. It is also possible to obtain a mechanism to perform sleep and standby procedures to improve the life of the sensor node. The sniffers have been implemented to monitor the energy of the sensor nodes periodically in the sink. The proposed algorithm SBEA (Snowball Endurance Algorithm) is a combination of ERCD protocol and a combined mobility and routing algorithm that can identify the cluster head and adjacent cluster head nodes.This algorithm is used to yield the network life time and the performance of the sensor nodes can be increased.

• Journal of Veterinary Clinics
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• v.32 no.4
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• pp.352-355
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• 2015

This study was conducted to ascertain whether the scent detection ability of a donor dog having extraordinary talent in cancer detection can be conserved through cloning. A specially trained dog for colorectal cancer detection was cloned, and she was trained and tested to detect breast cancers using breath samples collected from patients and healthy volunteers. Scent detection sensitivity of the clone was 93.3% and specificity was 99.5%, similar with those of donor (91% and 99%). Furthermore, the clone successfully detected early stage of breast cancers. Therefore, superior canine scent detection ability for cancer screening could be preserved through cloning.

• Journal of Microbiology and Biotechnology
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• v.23 no.12
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• pp.1717-1725
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• 2013

Traditional soybean paste from Shandong Liangshan and Tianyuan Jiangyuan commercial soybean paste were chosen for analysis and comparison of their bacterial and fungal dynamics using denaturing gel gradient electrophoresis and 16S rRNA gene clone libraries. The bacterial diversity results showed that more than 20 types of bacteria were present in traditional Shandong soybean paste during its fermentation process, whereas only six types of bacteria were present in the commercial soybean paste. The predominant bacteria in the Shandong soybean paste were most closely related to Leuconostoc spp., an uncultured bacterium, Lactococcus lactis, Bacillus licheniformis, Bacillus spp., and Citrobacter freundii. The predominant bacteria in the Tianyuan Jiangyuan soybean paste were most closely related to an uncultured bacterium, Bacillus licheniformis, and an uncultured Leuconostoc spp. The fungal diversity results showed that 10 types of fungi were present in the Shandong soybean paste during the fermentation process, with the predominant fungi being most closely related to Geotrichum spp., an uncultured fungal clone, Aspergillus oryzae, and yeast species. The predominant fungus in the commercial soybean paste was Aspergillus oryzae.