Vol. 42, No. 4,
Apr. 2015
Digital Library
A Fingerprint Verification System Based on Fuzzy Vault and Steganography for Smartphone
Han-Sol Nam, Ae-Young Kim, Sang-Ho Lee
This paper proposes a fingerprint verification system on a fuzzy vault with steganography for a smartphone. While biometric-based authentication can provide strong security, the biometric data must be handled carefully as it cannot be re-enrolled when it is revealed to other people. When the transformed data is used for authentication, the original biometric data can be protected. In this paper, we combine a fingerprint verification system with a fuzzy vault scheme to protect the fingerprint data of a smartphone user. In addition, the transformed data using a fuzzy vault scheme increases the security as it is concealed by the steganography scheme. The result of the experiment using fingerprint databases shows that the proposed scheme provides a high level of convenience and security for authentication of a smartphone having with a fingerprint sensor.
I/O Translation Layer Technology for High-performance and Compatibility Using New Memory
Hyunsub Song, Young Je Moon, Sam H. Noh
The rapid advancement of computing technology has triggered the need for fast data I/O processing and high-performance storage technology. Next generation memory technology, which we refer to as new memory, is anticipated to be used for high-performance storage as they have excellent characteristics as a storage device with non-volatility and latency close to DRAM. This research proposes NTL (New memory Translation layer) as a technology to make use of new memory as storage. With the addition of NTL, conventional I/O is served with existing mature disk-based file systems providing compatibility, while new memory I/O is serviced through the NTL to take advantage of the byte-addressability feature of new memory. In this paper, we describe the design of NTL and provide experiment measurement results that show that our design will bring performance benefits.
A Study of a Fast Booting Technique for a New memory+DRAM Hybrid Memory System
Hyeon Ho Song, Young Je Moon, Jae Hyeong Park, Sam H. Noh
Next generation memory technologies, which we denote as ‘new memory’, have both non-volatile and byte addressable properties. These characteristics are expected to bring changes to the conventional computer system structure. In this paper, we propose a fast boot technique for hybrid main memory architectures that have both new memory and DRAM. The key technique used for fast booting is write-tracking. Write-tracking is used to detect and manage modified data detection and involves setting the kernel region to read-only. This setting is used to trigger intentional faults upon modification requests. As the fault handler can detect the faulting address, write-tracking makes use of the address to manage the modified data. In particular, in our case, we make use of the MMU (Memory Management Unit) translation table. When a write occurs to the boot completed state, write-tracking preserves the original state of the modified address of the kernel region to a particular location, and execution continues. Upon booting, the fast booting process restores the preserved data to the original kernel region allowing rapid system boot-up. We develop the fast booting technique in an actual embedded board equipped with new memory. The boot time is reduced to less than half a second compared to around 15 seconds that is required for the original system.
Using Cache Access History for Reducing False Conflicts in Signature-Based Eager Hardware Transactional Memory
This paper proposes a method for reducing false conflicts in signature-based eager hardware transactional memory (HTM). The method tracks the information on all cache blocks that are accessed by a transaction. If the information provides evidence that there are no conflicts for a given transactional request from another core, the method prevents the occurrence of a false conflict by forcing the HTM to ignore the decision based on the signature. The method is very effective in reducing false conflicts and the associated unnecessary transaction stalls and aborts, and can be used to improve the performance of the multicore processor that implements the signature-based eager HTM. When running the STAMP benchmark on a 16-core processor that implements the LogTM-SE, the increased speed (decrease in execution time) achieved with the use of the method is 20.6% on average.
Character-based Subtitle Generation by Learning of Multimodal Concept Hierarchy from Cartoon Videos
Kyung-Min Kim, Jung-Woo Ha, Beom-Jin Lee, Byoung-Tak Zhang
Previous multimodal learning methods focus on problem-solving aspects, such as image and video search and tagging, rather than on knowledge acquisition via content modeling. In this paper, we propose the Multimodal Concept Hierarchy (MuCH), which is a content modeling method that uses a cartoon video dataset and a character-based subtitle generation method from the learned model. The MuCH model has a multimodal hypernetwork layer, in which the patterns of the words and image patches are represented, and a concept layer, in which each concept variable is represented by a probability distribution of the words and the image patches. The model can learn the characteristics of the characters as concepts from the video subtitles and scene images by using a Bayesian learning method and can also generate character-based subtitles from the learned model if text queries are provided. As an experiment, the MuCH model learned concepts from ‘Pororo’ cartoon videos with a total of 268 minutes in length and generated character-based subtitles. Finally, we compare the results with those of other multimodal learning models. The Experimental results indicate that given the same text query, our model generates more accurate and more character-specific subtitles than other models.
ISO/IEC 9126 Quality Model-based Assessment Criteria for Measuring the Quality of Big Data Analysis Platform
The analysis platform of remote-sensing big data is a system that downloads data from satellites, transforms it to a data type of L3, and then analyzes it and produces its analysis results. The objective of this paper is to develop ISO/IEC 9126-1 software quality model-based assessment criteria, in order to evaluate the quality of remote-sensing big data analysis platform. Its detailed research contents are as follows. First, the ISO/IEC 9216 standards and previous software evaluation models will be reviewed. Second, this paper will define evaluation areas, evaluation elements, and evaluation items for measuring the quality of big data analysis platform. Third, the validity of the assessment criteria will be verified by statistical experiments through content validity, reliability validity, and construct validity, by using SPSS 20.0 and Amos 20.0 software. The construct validity will also be conducted by performing the confirmatory factor analysis and path analysis. Lastly, it is significant that our research result demonstrates the first evaluation criteria in measuring the quality of big data analysis platform. It is also expected that our assessment criteria could be used as the basis information for evaluation criteria in the platforms that will be developed in the future.
Project Failure Main Factors Analysis using Text Mining in Audit Evaluation
Kyoungae Jang, Seong Yong Jang, Woo-Je Kim
Corporations should make efforts to recognize the importance of projects, identify their failure factors, prevent risks in advance, and raise the success rates, because the corporations need to make quick responses to rapid external changes. There are some previous studies on success and failure factors of projects, however, most of them have limitations in terms of objectivity and quantitative analysis based on data gathering through surveys, statistical sampling and analysis. This study analyzes the failure factors of projects based on data mining to find problems with projects in an audit report, which is an objective project evaluation report. To do this, we identified the texts in the paragraph of suggestions about improvement. We made use of the superior classification algorithms in this study, which were NaiveBayes, SMO and J48. They were evaluated in terms of data of Recall and Precision after performing 10-fold-cross validation. In the identified texts, the failure factors of projects were analyzed so that they could be utilized in project implementation.
Korean Semantic Role Labeling Using Domain Adaptation Technique
Soojong Lim, Yongjin Bae, Hyunki Kim, Dongyul Ra
Developing a high-performance Semantic Role Labeling (SRL) system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Performances of Korean SRL are degraded by almost 15% or more, when it is directly applied to another domain with relatively small training data. This paper proposes two techniques to minimize performance degradation in the domain transfer. First, a domain adaptation algorithm for Korean SRL is proposed which is based on the prior model that is one of domain adaptation paradigms. Secondly, we proposed to use simplified features related to morphological and syntactic tags, when using small-sized target domain data to suppress the problem of data sparseness. Other domain adaptation techniques were experimentally compared to our techniques in this paper, where news and Wikipedia were used as the sources and target domains, respectively. It was observed that the highest performance is achieved when our two techniques were applied together. In our system"s performance, F1 score of 64.3% was considered to be 2.4~3.1% higher than the methods from other research.
Video Based Face Spoofing Detection Using Fourier Transform and Dense-SIFT
Security systems that use face recognition are vulnerable to spoofing attacks where unauthorized individuals use a photo or video of authorized users. In this work, we propose a method to detect a face spoofing attack with a video of an authorized person. The proposed method uses three sequential frames in the video to extract features by using Fourier Transform and Dense-SIFT filter. Then, classification is completed with a Support Vector Machine (SVM). Experimental results with a database of 200 valid and 200 spoof video clips showed 99% detection accuracy. The proposed method uses simplified features that require fewer memory and computational overhead while showing a high spoofing detection accuracy.
A Design of a Korean Programming Language Ensuring Run-Time Safety through Categorizing C Secure Coding Rules
Yeoneo Kim, Jiwon Song, Gyun Woo
Since most of information is computerized nowadays, it is extremely important to promote the security of the computerized information. However, the software itself can threaten the safety of information through many abusive methods enabled by coding mistakes. Even though the Secure Coding Guide has been proposed to promote the safety of information by fundamentally blocking the hacking methods, it is still hard to apply the techniques on other programming languages because the proposed coding guide is mainly written for C and Java programmers. In this paper, we reclassified the coding rules of the Secure Coding Guide to extend its applicability to programming languages in general. The specific coding guide adopted in this paper is the C Secure Coding Guide, announced by the Ministry of Government Administration and Home Affairs of Korea. According to the classification, we applied the rules of programming in Sprout, which is a newly proposed Korean programming language. The number of vulnerability rules that should be checked was decreased in Sprout by 52% compared to C.
A Concise Korean Programming Language “Sprout”
Junseok Cheon, Dohun Kang, Gunwoo Kim, Gyun Woo
Most programming languages are designed based on English. It becomes another barrier in learning programming languages in non-English speaking country. If a programming language is presented using a native language, the education cost of programming will be much cheaper and the programming itself can be much more fun. However, designing the programming languages based on native languages has not been much focused or published up to now. It is partly because the evolution of popular programming languages is so fast, and partly because the efficiency of programs is much stressed than the source code. But, the designing of programming languages based on native language is not a small issue, especially if we reflect on the education of programming. In fact, there have been significant efforts reported in the Korean programming languages so far, but it has not practically been used in the education. This paper introduces yet another Korean programming language, namely Sprout, which is concise and can be easily learned by beginners. To demonstrate the conciseness of Sprout, we have performed two experiments on Sprout. Firstly, we compared the sizes of the programs in Sprout with those in former Korean programming languages. Secondly, we compared the size of Sprout, the language itself, with those of popular programming languages such as C and Python. According to the experiments, Sprout programs are more concise to 10% on average than those in former Korean languages. Furthermore, Sprout itself is more compact to 24% on average than other popular programming languages.
A MapReduce-based kNN Join Query Processing Algorithm for Analyzing Large-scale Data
HyunJo Lee, TaeHoon Kim, JaeWoo Chang
Recently, the amount of data is rapidly increasing with the popularity of the SNS and the development of mobile technology. So, it has been actively studied for the effective data analysis schemes of the large amounts of data. One of the typical schemes is a Voronoi diagram based on kNN join algorithm (VkNN-join) using MapReduce. For two datasets R and S, VkNN-join can reduce the time of the join query processing involving big data because it selects the corresponding subset Sj for each Ri and processes the query with them. However, VkNN-join requires a high computational cost for constructing the Voronoi diagram. Moreover, the computational overhead of the VkNN-join is high because the number of the candidate cells increases as the value of the k increases. In order to solve these problems, we propose a MapReduce-based kNN-join query processing algorithm for analyzing the large amounts of data. Using the seed-based dynamic partitioning, our algorithm can reduce the overhead for constructing the index structure. Also, it can reduce the computational overhead to find the candidate partitions by selecting corresponding partitions with the average distance between two seeds. We show that our algorithm has better performance than the existing scheme in terms of the query processing time.
Automatic Construction of a Negative/positive Corpus and Emotional Classification using the Internet Emotional Sign
Kyoungae Jang, Sanghyun Park, Woo-Je Kim
Internet users purchase goods on the Internet and express their positive or negative emotions of the goods in product reviews. Analysis of the product reviews become critical data to both potential consumers and to the decision making of enterprises. Therefore, the importance of opinion mining techniques which derive opinions by analyzing meaningful data from large numbers of Internet reviews. Existing studies were mostly based on comments written in English, yet analysis in Korean has not actively been done. Unlike English, Korean has characteristics of complex adjectives and suffixes. Existing studies did not consider the characteristics of the Internet language. This study proposes an emotional classification method which increases the accuracy of emotional classification by analyzing the characteristics of the Internet language connoting feelings. We can classify positive and negative comments about products automatically using the Internet emoticon. Also we can check the validity of the proposed algorithm through the result of high precision, recall and coverage for the evaluation of this method.
Parallel Range Query Processing with R-tree on Multi-GPUs
Hongsu Ryu, Mincheol Kim, Wonik Choi
Ever since the R-tree was proposed to index multi-dimensional data, many efforts have been made to improve its query performances. One common trend to improve query performance is to parallelize query processing with the use of multi-core architectures. To this end, a GPU-base R-tree has been recently proposed. However, even though a GPU-based R-tree can exhibit an improvement in query performance, it is limited in its ability to handle large volumes of data because GPUs have limited physical memory. To address this problem, we propose MGR-tree (Multi-GPU R-tree), which can manage large volumes of data by dividing nodes into multiple GPUs. Our experiments show that MGR-tree is up to 9.1 times faster than a sequential search on a GPU and up to 1.6 times faster than a conventional GPU-based R-tree.
An Improved Depth-Based TDMA Scheduling Algorithm for Industrial WSNs to Reduce End-to-end Delay
Hwakyung Lee, Sang-Hwa Chung, Ik-Joo Jung
Industrial WSNs need great performance and reliable communication. In industrial WSNs, cluster structure reduces the cost to form a network, and the reservation-based MAC is a more powerful and reliable protocol than the contention-based MAC. Depth-based TDMA assigns time slots to each sensor node in a cluster-based network and it works in a distributed manner. DB-TDMA is a type of depth-based TDMA and guarantees scalability and energy efficiency. However, it cannot allocate time slots in parallel and cannot perfectly avoid a collision because each node does not know the total network information. In this paper, we suggest an improved distributed algorithm to reduce the end-to-end delay of DB-TDMA, and the proposed algorithm is compared with DRAND and DB-TDMA.
Yet Another BGP Archive Forensic Analysis Tool Using Hadoop and Hive
A large volume of continuously growing BGP data files can raise two technical challenges regarding scalability and manageability. Due to the recent development of the open-source distributed computing infrastructure, Hadoop, it becomes feasible to handle a large amount of data in a scalable manner. In this paper, we present a new Hadoop-based BGP tool (BGPdoop) that provides the scaleout performance as well as the extensible and agile analysis capability. In particular, BGPdoop realizes a query-based BGP record exploration function using Hive on the partitioned BGP data structure, which enables flexible and versatile analytics of BGP archive files. From the experiments for the scalability with a Hadoop cluster of 20 nodes, we demonstrate that BGPdoop achieves 5 times higher performance and the user-defined analysis capability by expressing diverse BGP routing analytics in Hive queries.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr