• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.2
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• pp.21-34
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• 2002

In this paper we propos a new hardware architecture of modular exponentiation using a division chain method which has been proposed in (2). Modular exponentiation using the division chain is performed by receding an exponent E as a mixed form of multiplication and addition with divisors d=2 or $d=2^I +1$ and respective remainders r. This calculates the modular exponentiation in about $1.4log_2$E multiplications on average which is much less iterations than $2log_2$E of conventional Binary Method. We designed a linear systolic array multiplier with pipelining and used a horizontal projection on its data dependence graph. So, for k-bit key, two k-bit data frames can be inputted simultaneously and two modular multipliers, each consisting of k/2+3 PE(Processing Element)s, can operate in parallel to accomplish 100% throughput. We propose a new encoding scheme to represent divisors and remainders of the division chain to keep regularity of the data path. When it is synthesized to ASIC using Samsung 0.5 um CMOS standard cell library, the critical path delay is 4.24ns, and resulting performance is estimated to be abort 140 Kbps for a 1024-bit data frame at 200Mhz clock In decryption process, the speed can be enhanced to 560kbps by using CRT(Chinese Remainder Theorem). Futhermore, to satisfy real time requirements we can choose small public exponent E, such as 3,17 or $2^{16} +1$, in encryption and verification process. in which case the performance can reach 7.3Mbps.

• Journal of Gastric Cancer
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• v.15 no.4
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• pp.256-261
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• 2015

Purpose: Gastric cancer is the third leading cause of cancer-related mortality, with the incidence and mortality being higher in men than in women. Various studies have shown that eating carrots may play a major role in the prevention of gastric cancer. We conducted a meta-analysis to determine the relationship between carrot consumption and gastric cancer. Materials and Methods: We searched multiple databases including PubMed, Cochrane Library, Scopus, ScienceDirect, and Persian databases like Scientific Information Database (SID) and IranMedx. The following search terms were used: stomach or gastric, neoplasm or cancer, carcinoma or tumor, and carrot. Statistical analyses were performed using Comprehensive Meta Analysis/2.0 software. Results: We retrieved 81 articles by searching the databases. After considering the inclusion and exclusion criteria, 5 articles were included in this study. The odds ratio (OR) obtained by fixed effects model showed that a 26% reduction in the risk of gastric cancer has been associated with the consumption of carrots) OR=0.74; 95% confidence interval=0.68~0.81; P<0.0001). According to funnel graph, the results showed that the possibility of a publication bias does not exist in this study. Conclusions: The findings of this study showed an inverse relationship between the consumption of carrots and the risk of gastric cancer.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.117-124
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• 2021

Among growth disorders, 'Short Stature' can be improved through rapid diagnosis and treatment, and for that, it is important to detect early'Short Stature'. It is recommended to measure the height steadily for early detection of 'Short Stature' and checking the kid's growth process, but existing height measurement methods have problems such as time and space limitations, cost occurrence, and difficulty in keeping records. So in this paper, we proposed an 'Development of Kid Height Measurement Application based on Image using computer vision' method using smart phones, a medium that is highly accessible to people. In images taken through a smartphone camera, the kid's height is measured using algorithms from OpenCV, a computer vision library, and the measured heights were printed on the screen through 'a comparison graph with the standard height by gender and age' and 'list by date', made possible to check the kid's growth process. It is expected to measure height anytime, anywhere without time and space limitations and costs through this proposed method, and it is expected to help early detection of 'Short Stature' and other disorder through steady height measurement and confirmation of growth process.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.10
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• pp.17-28
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• 1999

In this paper, an ASIC emulation system called ACE (ASIC Emulator) is proposed. It can produce the prototype of target ASIC in a short time and verify the function of ASIC circuit immediately The ACE is consist of emulation software in which there are EDIF reader, library translator, technology mapper, circuit partitioner and LDF generator and emulation hardware including emulation board and logic analyzer. Technology mapping is consist of three steps such as circuit partitioning and extraction of logic function, minimization of logic function and grouping of logic function. During those procedures, the number of basic logic blocks and maximum levels are minimized by making the output to be assigned in a same block sharing product-terms and input variables as much as possible. Circuit partitioner obtain chip-level netlists satisfying some constraints on routing structure of emulation board as well as the architecture of FPGA chip. A new partitioning algorithm whose objective function is the minimization of the number of interconnections among FPGA chips and among group of FPGA chips is proposed. The routing structure of emulation board take the advantage of complete graph and partial crossbar structure in order to minimize the interconnection delay between FPGA chips regardless of circuit size. logic analyzer display the waveform of probing signal on PC monitor that is designated by user. In order to evaluate the performance of the proposed emulation system, video Quad-splitter, one of the commercial ASIC, is implemented on the emulation board. Experimental results show that it is operated in the real time of 14.3MHz and functioned perfectly.

• Journal of the Korean earth science society
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• v.29 no.4
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• pp.341-351
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• 2008

The purpose of this study was to investigate eighth graders' scientific method of inquiry used in their reports. We developed a framework, 'Analysis of Scientific Methods and Information Sources', with a perspective of the Nature of Science to analyze students' planning method, data analysis, and information sources. We then compared results with levels of questions to find out whether they affected students' 'Scientific Method'. In addition, we analyzed students' responses of the survey questionnaire, e.g.. how they liked Scientific Method. Results are as follows: First, 'planning method' consisted of 'consultant' and 'activities'. The 'activities' were 'experiment', 'correlational study', and 'observation' Students planned by utilizing 'consultant' more than the other. In case of planning 'activities'. most of them were 'experiment' Second, 'data analysis' consisted of 'summary', 'table', 'chart', 'graph' and so on. Students analyzed their data by using 'summary' frequently. The types of 'summary' were divided into 'simple summary' and 'relational statement' Third, 'information sources' consisted of 'computer', 'library'. and 'professional consultant' Most of the students gathered information from 'computer' Fourth, the types of 'planning method' and 'summary' were affected by the levels of questions. Fifth, some of the students reported their difficulty in 'planning method' because the collected information was less reliable, lacking, and having difficult technical terms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.10 no.4
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• pp.81-93
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• 2000

In a methodological approach to improve the processing performance of modulo exponentiation which is the primary arithmetic in RSA crypto algorithm, we present a new RSA hardware architecture based on high-radix modulo multiplication and CRT(Chinese Remainder Theorem). By implementing the modulo multiplier using radix-16 arithmetic, we reduced the number of PE(Processing Element)s by quarter comparing to the binary arithmetic scheme. This leads to having the number of clock cycles and the delay of pipelining flip-flops be reduced by quarter respectively. Because the receiver knows p and q, factors of N, it is possible to apply the CRT to the decryption process. To use CRT, we made two s/2-bit multipliers operating in parallel at decryption, which accomplished 4 times faster performance than when not using the CRT. In encryption phase, the two s/2-bit multipliers can be connected to make a s-bit linear multiplier for the s-bit arithmetic operation. We limited the encryption exponent size up to 17-bit to maintain high speed, We implemented a linear array modulo multiplier by projecting horizontally the DG of Montgomery algorithm. The H/W proposed here performs encryption with 15Mbps bit-rate and decryption with 1.22Mbps, when estimated with reference to Samsung 0.5um CMOS Standard Cell Library, which is the fastest among the publications at present.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.