• Title/Summary/Keyword: Distance-based classifications

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Distance-Based Keystroke Dynamics Smartphone Authentication and Threshold Formula Model (거리기반 키스트로크 다이나믹스 스마트폰 인증과 임계값 공식 모델)

  • Lee, Shincheol;Hwang, Jung Yeon;Lee, Hyungu;Kim, Dong In;Lee, Sung-Hoon;Shin, Ji Sun
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.28 no.2
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    • pp.369-383
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    • 2018
  • User authentication using PIN input or lock pattern is widely used as a user authentication method of smartphones. However, it is vulnerable to shoulder surfing attacks and because of low complexity of PIN and lock pattern, it has low security. To complement these problems, keystroke dynamics have been used as an authentication method for complex authentication and researches on this have been in progress. However, many studies have used imposter data in classifier training and validation. When keystroke dynamics authentications are actually applied in reality, it is realistic to use only legitimate user data for training, and using other people's data as imposter training data may result in problems such as leakage of authentication data and invasion of privacy. In response, in this paper, we experiment and obtain the optimal ratio of the thresholds for distance based classification. By suggesting the optimal ratio, we try to contribute to the real applications of keystroke authentications.

SVM-based Utterance Verification Using Various Confidence Measures (다양한 신뢰도 척도를 이용한 SVM 기반 발화검증 연구)

  • Kwon, Suk-Bong;Kim, Hoi-Rin;Kang, Jeom-Ja;Koo, Myong-Wan;Ryu, Chang-Sun
    • MALSORI
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    • no.60
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    • pp.165-180
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    • 2006
  • In this paper, we present several confidence measures (CM) for speech recognition systems to evaluate the reliability of recognition results. We propose heuristic CMs such as mean log-likelihood score, N-best word log-likelihood ratio, likelihood sequence fluctuation and likelihood ratio testing(LRT)-based CMs using several types of anti-models. Furthermore, we propose new algorithms to add weighting terms on phone-level log-likelihood ratio to merge word-level log-likelihood ratios. These weighting terms are computed from the distance between acoustic models and knowledge-based phoneme classifications. LRT-based CMs show better performance than heuristic CMs excessively, and LRT-based CMs using phonetic information show that the relative reduction in equal error rate ranges between $8{\sim}13%$ compared to the baseline LRT-based CMs. We use the support vector machine to fuse several CMs and improve the performance of utterance verification. From our experiments, we know that selection of CMs with low correlation is more effective than CMs with high correlation.

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An Adjustment for a Regional Incongruity in Global land Cover Map: case of Korea

  • Park Youn-Young;Han Kyung-Soo;Yeom Jong-Min;Suh Yong-Cheol
    • Korean Journal of Remote Sensing
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    • v.22 no.3
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    • pp.199-209
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    • 2006
  • The Global Land Cover 2000 (GLC 200) project, as a most recent issue, is to provide for the year 2000 a harmonized land cover database over the whole globe. The classifications were performed according to continental or regional scales by corresponding organization using the data of VEGETATION sensor onboard the SPOT4 Satellite. Even if the global land cover classification for Asia provided by Chiba University showed a good accuracy in whole Asian area, some problems were detected in Korean region. Therefore, the construction of new land cover database over Korea is strongly required using more recent data set. The present study focuses on the development of a new upgraded land cover map at 1 km resolution over Korea considering the widely used K-means clustering, which is one of unsupervised classification technique using distance function for land surface pattern classification, and the principal components transformation. It is based on data sets from the Earth observing system SPOT4/VEGETATION. Newly classified land cover was compared with GLC 2000 for Korean peninsula to access how well classification performed using confusion matrix.

Global Sequence Homology Detection Using Word Conservation Probability

  • Yang, Jae-Seong;Kim, Dae-Kyum;Kim, Jin-Ho;Kim, Sang-Uk
    • Interdisciplinary Bio Central
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    • v.3 no.4
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    • pp.14.1-14.9
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    • 2011
  • Protein homology detection is an important issue in comparative genomics. Because of the exponential growth of sequence databases, fast and efficient homology detection tools are urgently needed. Currently, for homology detection, sequence comparison methods using local alignment such as BLAST are generally used as they give a reasonable measure for sequence similarity. However, these methods have drawbacks in offering overall sequence similarity, especially in dealing with eukaryotic genomes that often contain many insertions and duplications on sequences. Also these methods do not provide the explicit models for speciation, thus it is difficult to interpret their similarity measure into homology detection. Here, we present a novel method based on Word Conservation Score (WCS) to address the current limitations of homology detection. Instead of counting each amino acid, we adopted the concept of 'Word' to compare sequences. WCS measures overall sequence similarity by comparing word contents, which is much faster than BLAST comparisons. Furthermore, evolutionary distance between homologous sequences could be measured by WCS. Therefore, we expect that sequence comparison with WCS is useful for the multiple-species-comparisons of large genomes. In the performance comparisons on protein structural classifications, our method showed a considerable improvement over BLAST. Our method found bigger micro-syntenic blocks which consist of orthologs with conserved gene order. By testing on various datasets, we showed that WCS gives faster and better overall similarity measure compared to BLAST.

Cut-Through versus Cut-Out: No Easy Way to Predict How Single Lag Screw Design Cephalomedullary Nails Used for Intertrochanteric Hip Fractures Will Fail?

  • Garrett W. Esper;Nina D. Fisher;Utkarsh Anil;Abhishek Ganta;Sanjit R. Konda;Kenneth A. Egol
    • Hip & pelvis
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    • v.35 no.3
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    • pp.175-182
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    • 2023
  • Purpose: This study aims to compare patients in whom fixation failure occurred via cut-out (CO) or cut-through (CT) in order to determine patient factors and radiographic parameters that may be predictive of each mechanism. Materials and Methods: This retrospective cohort study includes 18 patients with intertrochanteric (IT) hip fractures (AO/OTA classification 31A1.3) who underwent treatment using a single lag screw design intramedullary nail in whom fixation failure occurred within one year. All patients were reviewed for demographics and radiographic parameters including tip-to-apex distance (TAD), posteromedial calcar continuity, neck-shaft angle, lateral wall thickness, and others. Patients were grouped into cohorts based on the mechanism of failure, either lag screw CO or CT, and a comparison was performed. Results: No differences in demographics, injury details, fracture classifications, or radiographic parameters were observed between CO/CT cohorts. Of note, a similar rate of post-reduction TAD>25 mm (P=0.936) was observed between groups. A higher rate of DEXA (dual energy X-ray absorptiometry) confirmed osteoporosis (25.0% vs. 60.0%) was observed in the CT group, but without significance. Conclusion: The mechanism of CT failure during intramedullary nail fixation of an IT fracture did not show an association with clinical data including patient demographics, reduction accuracy, or radiographic parameters. As reported in previous biomechanical studies, the main predictive factor for patients in whom early failure might occur via the CT effect mechanism may be related to bone quality; however, conduct of larger studies will be required in order to determine whether there is a difference in bone quality.

On Optimizing Dissimilarity-Based Classifications Using a DTW and Fusion Strategies (DTW와 퓨전기법을 이용한 비유사도 기반 분류법의 최적화)

  • Kim, Sang-Woon;Kim, Seung-Hwan
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.47 no.2
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    • pp.21-28
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    • 2010
  • This paper reports an experimental result on optimizing dissimilarity-based classification(DBC) by simultaneously using a dynamic time warping(DTW) and a multiple fusion strategy(MFS). DBC is a way of defining classifiers among classes; they are not based on the feature measurements of individual samples, but rather on a suitable dissimilarity measure among the samples. In DTW, the dissimilarity is measured in two steps: first, we adjust the object samples by finding the best warping path with a correlation coefficient-based DTW technique. We then compute the dissimilarity distance between the adjusted objects with conventional measures. In MFS, fusion strategies are repeatedly used in generating dissimilarity matrices as well as in designing classifiers: we first combine the dissimilarity matrices obtained with the DTW technique to a new matrix. After training some base classifiers in the new matrix, we again combine the results of the base classifiers. Our experimental results for well-known benchmark databases demonstrate that the proposed mechanism achieves further improved results in terms of classification accuracy compared with the previous approaches. From this consideration, the method could also be applied to other high-dimensional tasks, such as multimedia information retrieval.