• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• ETRI Journal
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• v.39 no.4
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• pp.455-466
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• 2017

This study focuses on a method for sequential data augmentation in order to alleviate data sparseness problems. Specifically, we present corpus expansion techniques for enhancing the coverage of a language model. Recent recurrent neural network studies show that a seq2seq model can be applied for addressing language generation issues; it has the ability to generate new sentences from given input sentences. We present a method of corpus expansion using a sentence-chain based seq2seq model. For training the seq2seq model, sentence chains are used as triples. The first two sentences in a triple are used for the encoder of the seq2seq model, while the last sentence becomes a target sequence for the decoder. Using only internal resources, evaluation results show an improvement of approximately 7.6% relative perplexity over a baseline language model of Korean text. Additionally, from a comparison with a previous study, the sentence chain approach reduces the size of the training data by 38.4% while generating 1.4-times the number of n-grams with superior performance for English text.

• Journal of Information Technology Applications and Management
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• v.28 no.4
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• pp.33-43
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• 2021

While youth unemployment has recorded the lowest level since the global COVID-19 pandemic, SMEs(small and medium sized enterprises) are still struggling to fill vacancies. It is difficult for SMEs to find good candidates as well as for job seekers to find appropriate job offers due to information mismatch. To overcome information mismatch, this study proposes the fine-turning model for bidirectional HR matching based on a pre-learning language model called BERT(Bidirectional Encoder Representations from Transformers). The proposed model is capable to recommend job openings suitable for the applicant, or applicants appropriate for the job through sufficient pre-learning of terms including technical jargons. The results of the experiment demonstrate the superior performance of our model in terms of precision, recall, and f1-score compared to the existing content-based metric learning model. This study provides insights for developing practical models for job recommendations and offers suggestions for future research.

• Journal of KIISE
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• v.44 no.8
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• pp.774-781
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• 2017

Mention detection systems use nouns or noun phrases as a head and construct a chunk of text that defines any meaning, including a modifier. The term "mention detection" relates to the extraction of mentions in a document. In the mentions, a coreference resolution pertains to finding out if various mentions have the same meaning to each other. A pointer network is a model based on a recurrent neural network (RNN) encoder-decoder, and outputs a list of elements that correspond to input sequence. In this paper, we propose the use of mention detection using pointer networks. Our proposed model can solve the problem of overlapped mention detection, an issue that could not be solved by sequence labeling when applying the pointer network to the mention detection. As a result of this experiment, performance of the proposed mention detection model showed an F1 of 80.07%, a 7.65%p higher than rule-based mention detection; a co-reference resolution performance using this mention detection model showed a CoNLL F1 of 52.67% (mention boundary), and a CoNLL F1 of 60.11% (head boundary) that is high, 7.68%p, or 1.5%p more than coreference resolution using rule-based mention detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1396-1412
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• 2023

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.349-356
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• 2016

In this paper, we propose an efficient assignment method that assigns quantization parameter (QP) in accordance with group of picture (GOP) structure given in HEVC encoder. Each video frames can have difference QP values based on given GOP configuration for HEVC encoding. Particularly, for important frames we can assign low QP values, and vice versa. However, there has not been thorough investigation on efficient QP assignment method by far. Even in HEVC reference software encoder, only monotonic QP assignment method is employed. Thus, the proposed method assign adaptive QP values to each GOP so that temporal dynamic activity between GOPs can be exploited. Through the experiment, the proposed method showed a 7.3% gain of compression performance in terms of BD-rate compared to HEVC test model (HM) in low-delay configuration, and outperformed the existing QP assignment study on average.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.12
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• pp.66-72
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• 2009

This paper proposes an efficient binary arithmetic encoder for CABAC which is used one of the entropy coding methods for H.264/AVC. The present binary arithmetic encoding algorithm requires huge complexity of operation and data dependency of each step, which is difficult to be operated in fast. Therefore, renormalization exploits 2-stage pipeline architecture for efficient process of operation, which reduces huge complexity of operation and data dependency. Context model updater is implemented by using a simple expression instead of transIdxMPS table and merging transIdxLPS and rangeTabLPS tables, which decreases hardware size. Arithmetic calculator consists of regular mode, bypass mode and termination mode for appearance probability of binary value. It can operate in maximum speed. The proposed binary arithmetic encoder has 7282 gate counts in 0.18um standard cell library. And input symbol per cycle is about 1.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.167-173
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• 2015

This paper presents a study on the Full-HD HEVC(High Efficiency Video Coding) encoder IP(Intellectual Property) design. The designed IP is for HEVC main profile 4.1, and performs encoding with a speed of 60 fps of full high definition. Before hardware and software design, overall reference model was developed with C language, and we proposed a parallel processing architecture for low-power consumption. And also we coded firmware and driver programs relating IP. The platform for verification of developed IP was developed, and we verified function and performance for various pictures under several encoding conditions by implementing designed IP to FPGA board. Compared to HM-13.0, about 35% decrease in bit-rate under same PSNR was achieved, and about 25% decrease in power consumption under low-power mode was performed.

• Journal of Advanced Navigation Technology
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• v.18 no.6
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• pp.600-608
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• 2014

In this paper, CABAC H/W module is developed using co-design method. After entire H.264/AVC encoder was developed with C using reference SW(JM), CABAC H/W IP is developed as a block in H.264/AVC encoder. Context modeller of CABAC is included on the hardware to update the changed value during binary encoding, which enables the efficient usage of memory and the efficient design of I/O stream. Hardware IP is co-operated with the reference software JM of H.264/AVC, and executed on Virtex-4 FX60 FPGA on ML410 board. Functional simulation is done using Modelsim. Compared with existing H/W module of CABAC with register-level design, the development time is reduced greatly and software engineer can design H/W module more easily. As a result, the used amount of slice in CABAC is less than 1/3 of that of CAVLC module. The proposed co-design method is useful to provide hardware accelerator in need of speed-up of high efficient video encoder in embedded system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.174-176
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• 2022

In this paper, we present a mobile application for 'personalized customized learning' for children with articulation disorders using an artificial intelligence (AI) algorithm. A dataset (Data Set) to analyze, judge, and predict the learner's articulation situation and degree. In particular, we designed a prototype model by looking at how AI can be improved and advanced compared to existing applications from the UX/UI (GUI) aspect. So far, the focus has been on visual experience, but now it is an important time to process data and provide a UX/UI (GUI) experience to users. The UX/UI (GUI) of the proposed mobile application was to be provided according to the learner's articulation level and situation by using CRNN (Convolution Recurrent Neural Network) of DeepLearning and Auto Encoder GPT-3 (Generative Pretrained Transformer). The use of artificial intelligence algorithms will provide a learning environment with a high degree of perfection to children with articulation disorders, thereby enhancing the learning effect. I hope that you do not have any fear or discomfort in conversation by improving the perfection of articulation with 'personalized and customized learning'.