• Asia pacific journal of information systems
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• v.21 no.2
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• pp.89-116
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• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.

• Journal of Korea Water Resources Association
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• v.49 no.8
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• pp.685-692
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• 2016

This study aimed to develop the indicator-based approach to assess water use vulnerability in watersheds and applied to the unit watershed within the Han River watershed. Vulnerability indices were comprised of three sub-components (exposure, sensitivity, adaptive capacity) with respect to water use. The indicators were made up of 16 water use indicators. Then we estimated vulnerability indices using the Technique for Order of Preference by Similarity to Ideal Solution approach (TOPSIS). We collected environmental and socio-economic data from national statistics database, and used them for simulated results by the Soil and Water Assessment Tool (SWAT) model. For estimating the weighted values for each indicator, expert surveys for subjective weight and data-based Shannon's entropy method for objective weight were utilized. With comparing the vulnerability ranks and analyzing rank correlation between two methods, we evaluated the vulnerabilities for the Han River watershed. For water use, vulnerable watersheds showed high water use and the water leakage ratio. The indices from both weighting methods showed similar spatial distribution in general. Such results suggests that the approach to consider different weighting methods would be important for reliably assessing the water use vulnerability in watersheds.

• Investigative Magnetic Resonance Imaging
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• v.20 no.2
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• pp.81-87
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• 2016

Purpose: To analyze the feasibility of three-dimensional (3D) diffusion-weighted (DW) PSIF (reversed FISP [fast imaging with steady-state free precession]) sequence in order to evaluate peripheral nerves in the elbow. Materials and Methods: Ten normal, asymptomatic volunteers were enrolled (6 men, 4 women, mean age 27.9 years). The following sequences of magnetic resonance images (MRI) of the elbow were obtained using a 3.0-T machine: 3D DW PSIF, 3D T2 SPACE (sampling perfection with application optimized contrasts using different flip angle evolution) with SPAIR (spectral adiabatic inversion recovery) and 2D T2 TSE (turbo spin echo) with modified Dixon (m-Dixon) sequence. Two observers used a 5-point grading system to analyze the image quality of the ulnar, median, and radial nerves. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of each nerve were measured. We compared 3D DW PSIF images with other sequences using the Wilcoxon-signed rank test and Friedman test. Inter-observer agreement was measured using intraclass correlation coefficient (ICC) analysis. Results: The mean 5-point scores of radial, median, and ulnar nerves in 3D DW PSIF (3.9/4.2/4.5, respectively) were higher than those in 3D T2 SPACE SPAIR (1.9/2.8/2.8) and 2D T2 TSE m-Dixon (1.7/2.8/2.9) sequences (P < 0.05). The mean SNR in 3D DW PSIF was lower than 3D T2 SPACE SPAIR, but there was no difference between 3D DW PSIF and 2D T2 TSE m-Dixon in all of the three nerves. The mean CNR in 3D DW PSIF was lower than 3D T2 SPACE SPAIR and 2D T2 TSE m-Dixon in the median and ulnar nerves, but no difference among the three sequences in the radial nerve. Conclusion: The three-dimensional DW PSIF sequence may be feasible to evaluate the peripheral nerves around the elbow in MR imaging. However, further optimization of the image quality (SNR, CNR) is required.

• Journal of Korean Medicine Rehabilitation
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• v.32 no.1
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• pp.51-62
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• 2022

Objectives Objective of this study is to investigate the role of paraspinal muscles by examining the correlation between slip percentage (SP) of spondylolisthesis and fatty infiltration of lumbar paraspinal muscle. Methods Retrospective analysis was performed on 45 patients diagnosed with spondylolisthesis based on medical records. Using T2-weighted axial magnetic resonance imaging, cross-sectional areas (CSAs) of psoas major (PM), multifidus (MU) and erector spinae (ES) were calculated and divided by CSA of lower level vertebral body (VB). SP was measured using sagittal T2-weighted images. Correlation of SP with muscle relative cross-sectional area (RCSA) and muscle fatty infiltration by Goutallier classification was respectively analyzed using Spearman correlation. Statistic assessment conducted by Wilcoxon signed rank test and paired t-test using program GraphPad prism 5 (GraphPad Software, Inc., San Diego, CA, USA). Results Spondylolisthesis forward slip percentage by Taillard's method was negatively associated with both side MU RCSAs. No significant correlation was found between PM RCSA, ES RCSA and SP. Forward slippage was significantly correlated with fatty infiltration of lumbar paraspinal muscle measured by Goutallier classification. Conclusions This study is to understand the role of paraspinal muscle affecting spinal instability by investigating correlation between statistical deviation of lumbar muscle characters (RCSA, fatty infiltration of lumbar muscle) and SP. We found that spondylolisthesis SP is positively related to fatty infiltration of lumbar paraspinal muscle. and is negatively associated with both side MU RCSAs.

• Korean Journal of Radiology
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• v.22 no.9
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• pp.1514-1524
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• 2021

Objective: To develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for prognosis prediction in patients with glioblastoma. Materials and Methods: One hundred and fifty patients (92 male [61.3%]; mean age ± standard deviation, 60.5 ± 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (K^trans), fractional volume of vascular plasma space (V_p), and fractional volume of extravascular extracellular space (V_e) maps of DCE MRI, wherein the regions of interest were based on both T1-weighted contrast-enhancing areas and non-enhancing T2 hyperintense areas. Using feature selection algorithms, salient radiomic features were selected from the 642 features. Next, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105); the risk score was validated in the validation set (n = 45) by investigating the difference in prognosis between the "radiomics risk score" groups. Finally, multivariable Cox regression analysis for progression-free survival was performed using the radiomics risk score and clinical variables as covariates. Results: 16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both p < 0.001 by the log-rank test). The radiomics risk score and presence of isocitrate dehydrogenase (IDH) mutation showed independent associations with progression-free survival in opposite directions (hazard ratio, 3.56; p = 0.004 and hazard ratio, 0.34; p = 0.022, respectively). Conclusion: We developed and validated the "radiomics risk score" from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.

• Korean Journal of Radiology
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• v.23 no.5
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• pp.539-547
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• 2022

Objective: To investigate the association between functional tumor burden of peritoneal carcinomatosis (PC) derived from diffusion-weighted imaging (DWI) and overall survival in patients with advanced ovarian carcinoma (OC). Materials and Methods: This prospective study was approved by the local research ethics committee, and informed consent was obtained. Fifty patients (mean age ± standard deviation, 57 ± 12 years) with stage III-IV OC scheduled for primary or interval debulking surgery (IDS) were recruited between June 2016 and December 2021. DWI (b values: 0, 400, and 800 s/mm²) was acquired with a 16-channel phased-array torso coil. The functional PC burden on DWI was derived based on K-means clustering to discard fat, air, and normal tissue. A score similar to the surgical peritoneal cancer index was assigned to each abdominopelvic region, with additional scores assigned to the involvement of critical sites, denoted as the functional peritoneal cancer index (fPCI). The apparent diffusion coefficient (ADC) of the largest lesion was calculated. Patients were dichotomized by immediate surgical outcome into high- and low-risk groups (with and without residual disease, respectively) with subsequent survival analysis using the Kaplan-Meier curve and log-rank test. Multivariable Cox proportional hazards regression was used to evaluate the association between DWI-derived results and overall survival. Results: Fifteen (30.0%) patients underwent primary debulking surgery, and 35 (70.0%) patients received neoadjuvant chemotherapy followed by IDS. Complete tumor debulking was achieved in 32 patients. Patients with residual disease after debulking surgery had reduced overall survival (p = 0.043). The fPCI/ADC was negatively associated with overall survival when accounted for clinicopathological information with a hazard ratio of 1.254 for high fPCI/ADC (95% confidence interval, 1.007-1.560; p = 0.043). Conclusion: A high DWI-derived functional tumor burden was associated with decreased overall survival in patients with advanced OC.

• Investigative Magnetic Resonance Imaging
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• v.22 no.1
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• pp.1-9
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• 2018

Purpose: This study was designed to optimize the flip angle (FA) and scan timing of the hepatobiliary phase (HBP) using the 3D T1-weighted, gradient-echo (GRE) imaging with controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) technique on gadoxetic acid-enhanced 3T liver MR imaging. Materials and Methods: Sixty-two patients who underwent gadoxetic acid-enhanced 3T liver MR imaging were included in this study. Four 3D T1-weighted GRE imaging studies using the CAIPIRINHA technique and FAs of $9^{\circ}$ and $13^{\circ}$ were acquired during HBP at 15 and 20 min after intravenous injection of gadoxetic acid. Two abdominal radiologists, who were blinded to the FA and the timing of image acquisition, assessed the sharpness of liver edge, hepatic vessel clarity, lesion conspicuity, artifact severity, and overall image quality using a five-point scale. Quantitative analysis was performed by another radiologist to estimate the relative liver enhancement (RLE) and the signal-to-noise ratio (SNR). Statistical analyses were performed using the Wilcoxon signed rank test and one-way analysis of variance. Results: The scores of the HBP with an FA of $13^{\circ}$ during the same delayed time were significantly higher than those of the HBP with an FA of $9^{\circ}$ in all the assessment items (P < 0.01). In terms of the delay time, images at the same FA obtained with a 20-min-HBP showed better quality than those obtained with a 15-min-HBP. There was no significant difference in qualitative scores between the 20-min-HBP and the 15-min-HBP images in the non-liver cirrhosis (LC) group except for the hepatic vessel clarity score with $9^{\circ}$ FA. In the quantitative analysis, a statistically significant difference was found in the degree of RLE in the four HBP images (P = 0.012). However, in the subgroup analysis, no significant difference in RLE was found in the four HBP images in either the LC or the non-LC groups. The SNR did not differ significantly in the four HBP images. In the subgroup analysis, 20-min-HBP imaging with a $13^{\circ}$ FA showed the highest SNR value in the LC-group, whereas 15-min-HBP imaging with a $13^{\circ}$ FA showed the best value of SNR in the non-LC group. Conclusion: The use of a moderately high FA improves the image quality and lesion conspicuity on 3D, T1-weighted GRE imaging using the CAIPIRINHA technique on gadoxetic acid, 3T liver MR imaging. In patients with normal liver function, the 15-min-HBP with a $13^{\circ}$ FA represents a feasible option without a significant decrease in image quality.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.272-278
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• 2004

Recently, Pepe et al. (2003) employed the receiver operating characteristic (ROC) approach to rank candidate genes from a microarray experiment that can be used for the biomarker development with the ultimate purpose of the population screening of a cancer, In the cancer microarray experiment based on n patients the researcher often wants to compare the tumor tissue with the normal tissue within the same individual using a common reference RNA. This design is referred to as a reference design or an indirect design. Ideally, this experiment produces n pairs of microarray data, where each pair consists of two sets of microarray data resulting from reference versus normal tissue and reference versus tumor tissue hybridizations. However, for certain individuals either normal tissue or tumor tissue is not large enough for the experimenter to extract enough RNA for conducting the microarray experiment, hence there are missing values either in the normal or tumor tissue data. Practically, we have $n_1$ pairs of complete observations, $n_2$ 'normal only' and $n_3$ 'tumor only' data for the microarray experiment with n patients, where n=$n_1$+$n_2$+$n_3$. We refer to this data set as a mixed data set, as it contains a mix of fully observed and partially observed pair data. This mixed data set was actually observed in the microarray experiment based on human tissues, where human tissues were obtained during the surgical operations of cancer patients. Pepe et al. (2003) provide the rationale of using ROC approach based on two independent samples for ranking candidate gene instead of using t or Mann -Whitney statistics. We first modify ROC approach of ranking genes to a paired data set and further extend it to a mixed data set by taking a weighted average of two ROC values obtained by the paired data set and two independent data sets.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.2
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• pp.27-49
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• 2015

The purpose of this study is to suggest an appropriate index for evaluating preferences of Web contents by examining the h-index and its variants. It focuses on how successfully each index represents relative user preference towards topical subjects. Based on data obtained from a popular IT blog (engadget.com), subject values of the h-index and its variants were calculated using 53 subject categories, article counts and the 'Likes' counts aggregated in each category. These values were compared through critical analysis of the indices and Spearman rank correlation analysis. A PFNet (Pathfinder Network) of subjects weighted by $h_T$ values was drawn and cluster analysis was conducted. Based on the four criteria suggested for the evaluation of Web contents, we concluded that the $h_T$-index is a relatively appropriate tool for the Web contents preference evaluation. The $h_T$-index was applied to visually represent the relative weight (topic preference by user 'Likes' count) for each subject category of the real online contents after suggesting the relative appropriateness of the $h_T$-index. Applying scientometric indicators to Web information could provide new insights into, and potential methods for, Web contents evaluation. In addition, information on the focus of users' attention would help online informants to plan more effective content strategies. The study tries to expand the application area of the h-type indices to non-academic online environments. The research procedure enables examination of the appropriateness of the index and highlights considerations for applying the indicators to Web contents.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.6
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• pp.529-536
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• 2004

The main idea of this paper is to combine position information in sentence and query type classification to make the documents ranking to query more accessible. First, the use of conceptual graphs for the representation of document contents In information retrieval is discussed. The method is based on well-known strategies of text comparison, such as Dice Coefficient, with position-based weighted term. Second, we introduce a method for learning query type classification that improves the ability to retrieve answers to questions from Question Answering system. Proposed methods employ naive bayes classification in machine learning fields. And, we used a collection of approximately 30,000 question-answer pairs for training, obtained from Frequently Asked Question(FAQ) files on various subjects. The evaluation on a set of queries from international TREC-9 question answering track shows that the method with machine learning outperforms the underline other systems in TREC-9 (0.29 for mean reciprocal rank and 55.1% for precision).