• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.35-46
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• 2010

Sequential pattern mining is one of the essential data mining tasks, and it is widely used to analyze data generated in various application fields such as web-based applications, E-commerce, bioinformatics, and USN environments. Recently data generated in the application fields has been taking the form of continuous data streams rather than finite stored data sets. Considering the changes in the form of data, many researches have been actively performed to efficiently find sequential patterns over data streams. However, conventional researches focus on reducing processing time and memory usage in mining sequential patterns over a target data stream, so that a research on mining more interesting and useful sequential patterns that efficiently reflect the characteristics of the data stream has been attracting no attention. This paper proposes a mining method of sequential patterns over data streams with a gap constraint, which can help to find more interesting sequential patterns over the data streams. First, meanings of the gap for a sequential pattern and gap-constrained sequential patterns are defined, and subsequently a mining method for finding gap-constrained sequential patterns over a data stream is proposed.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.1-9
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• 2013

Even though the multiple pages TLB are effective in improving the performance, a conventional method with OS support cannot utilize multiple page sizes in user application. Thus, we propose a new multiple-TLB structure supporting multiple page sizes for high performance and low power consumption without any operating system support. The proposed TLB is organised as two parts of a S-TLB(Small TLB) with a small page size and a L-TLB(Large TLB) with a large page size. Both are designed as fully associative bank structures. The S-TLB stores small pages are evicted from the L-TLB, and the L-TLB stores large pages including a small page generated by the CPU. Each one bank module of S-TLB and L-TLB can be selectively accessed base on particular one and two bits of the virtual address generated from CPU, respectively. Energy savings are achieved by reducing the number of entries accessed at a time. Also, this paper proposed the simple 1-bit LRU policy to improve the performance. The proposed LRU policy can present recently referenced block by using an additional one bit of each entry on TLBs. This method can simply select a least recently used page from the L-TLB. According to the simulation results, the proposed TLB can reduce Energy * Delay by about 76%, 57%, and 6% compared with a fully associative TLB, a ARM TLB, and a Dual TLB, respectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.875-888
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• 2021

In this paper, we search integral distinguishers of lightweight block cipher PIPO and propose a key recovery attack on 8-round PIPO-64/128 with the obtained 6-round distinguishers. The lightweight block cipher PIPO proposed in ICISC 2020 is designed to provide the efficient implementation of high-order masking for side-channel attack resistance. In the proposal, various attacks such as differential and linear cryptanalyses were applied to show the sufficient security strength. However, the designers leave integral attack to be conducted and only show that it is unlikely for PIPO to have integral distinguishers longer than 5-round PIPO without further analysis on Division Property. In this paper, we search integral distinguishers of PIPO using a MILP-aided Division Property search method. Our search can show that there exist 6-round integral distinguishers, which is different from what the designers insist. We also consider linear operation on input and output of distinguisher, respectively, and manage to obtain totally 136 6-round integral distinguishers. Finally, we present an 8-round PIPO-64/128 key recovery attack with time complexity 2^124.5849 and memory complexity of 2⁹³ with four 6-round integral distinguishers among the entire obtained distinguishers.

• The Korean Society of Law and Medicine
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• v.21 no.3
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• pp.145-178
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• 2020

This paper examines discussions surrounding cognitive liberty, neuro-privacy, and mental integrity from the perspective of Neuro-rights. The right to control one's neurological data entails self-determination of collection and usage of one's data, and the right to object to any way such data may be employed to negatively impact oneself. As innovations in neurotechnologies bear benefits and downsides, a novel concept of the neuro-rights has been suggested to protect individual liberty and rights. In Oct. 2020, the Chilean Senate presented the 'Proyecto de ley sobre neuroderechos' to promote the recognition and protection of neuro-rights. This new bill defines all data obtained from the brain as neuronal data and outlaws the commerce of this data. Neurotechnology, especially when paired with big data and artificial intelligence, has the potential to turn one's neurological state into data. The possibility of inferring one's intent, preferences, personality, memory, emotions, and so on, poses harm to individual liberty and rights. However, the collection and use of neural data may outpace legislative innovation in the near future. Legal protection of neural data and the rights of its subject must be established in a comprehensive way, to adapt to the evolving data economy and technical environment.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.3
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• pp.351-359
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• 2019

Neurocognitive testing commonly uses the MMSE (Mini-Mental State Examination) to evaluate the overall cognitive function of patients at outpatient clinics, but the MMSE has recently been extensively used in the SNSB II (Seoul Neuropsychological Screening Battery II) for making diagnoses. We retrospectively investigated the results of routine neurocognitive tests and the results of the blood tests of 120 elderly patients who had been referred to a South Central Medical Center from 2017 to 2018 and who had been examined at a public health center. These subjects' space-time capability was high on the sub-region of the global deterioration scale (GDS). GDS showed a significant increase as the Na decreased on the electrolyte analysis. The subjects' concentration, their language-based orientation for space and time, their memory, and their scores for the frontal lobe function on GDS showed statistically significant reductions (P<0.001) For the normal and abnormal groups according to the ALT and creatinine levels, the frontal/execute function areas showed statistically significant differences (P<0.001) as well as negative correlation between GDS and ALT (P<0.01). In conclusion, this study provides basic information to develop test items that are important for patient screening and diagnosis, and several routine blood chemistry factors provide basic information for diagnosing and assessing the status and progress of cognitively impaired patients.

• 기호학연구
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• no.54
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• pp.177-203
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• 2018

On this study, we analyzed the movie With gods's narrative structure. This movie makes most people who has seen it feel deeply moved. We think that people can get deep feeling from a film at last after people who has seen the movie accept a context, story, and a message of the movie gladly. We focused on a special system in this movie With gods that can make give an ordinary message and big touch to people. Also we focused on every episodes of every space in a movie, and someone who moved freely between the spaces. A repetitive form and repetitive contents in a narrative become a special code itself -repetitiveness- for people and make them do auto-communication. Specifically, an information, the movie's repetitiveness, out of people become a special code for them, and then that make people bring memory and new information by themselves. Watching movie, people can look back up on life with every trial in the movie. In short, a repetitiveness and an auto-communication are a special system in the movie, that can make deeply touched.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.5
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• pp.69-79
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• 2010

In the current cloud environments, the applications are executed on the remote cloud server, and they also utilize computing resources of the remote cloud server such as physical memory and CPU. Therefore, if remote server is exposed to security threat, every applications in remote server can be victim by several security-attacks. Especially, despite many advantages, both individuals and businesses often have trouble to start the cloud services according to the malicious administrator of the cloud server. We propose a security-enhanced local process executing scheme resolving vulnerability of current cloud computing environments. Since secret data is stored in the local, we can protect secret data from security threats of the cloud server. By utilizing computing resource of local computer instead of remote server, high-secure processes can be set free from vulnerability of remote server.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.71-80
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• 2021

The selection of an appropriate neural network algorithm is an important step for accurate data prediction in machine learning. Many algorithms based on basic artificial neural networks have been devised to efficiently predict future data. These networks include deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) neural networks. Developers face difficulties when choosing among these networks because sufficient information on their performance is unavailable. To alleviate this difficulty, we evaluated the performance of each algorithm by comparing their errors and processing times. Each neural network model was trained using a tax dataset, and the trained model was used for data prediction to compare accuracies among the various algorithms. Furthermore, the effects of activation functions and various optimizers on the performance of the models were analyzed The experimental results show that the GRU and LSTM algorithms yields the lowest prediction error with an average RMSE of 0.12 and an average R2 score of 0.78 and 0.75 respectively, and the basic DNN model achieves the lowest processing time but highest average RMSE of 0.163. Furthermore, the Adam optimizer yields the best performance (with DNN, GRU, and LSTM) in terms of error and the worst performance in terms of processing time. The findings of this study are thus expected to be useful for scientists and developers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1071-1077
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• 2022

A security SoC that can be used to implement elliptic curve cryptography (ECC) based public-key infrastructures was designed. The security SoC has an architecture in which a hardware accelerator for the elliptic curve digital signature algorithm (ECDSA) is interfaced with the Cortex-A53 CPU using the AXI4-Lite bus. The ECDSA hardware accelerator, which consists of a high-performance ECC processor, a SHA3 hash core, a true random number generator (TRNG), a modular multiplier, BRAM, and control FSM, was designed to perform the high-performance computation of ECDSA signature generation and signature verification with minimal CPU control. The security SoC was implemented in the Zynq UltraScale+ MPSoC device to perform hardware-software co-verification, and it was evaluated that the ECDSA signature generation or signature verification can be achieved about 1,000 times per second at a clock frequency of 150 MHz. The ECDSA hardware accelerator was implemented using hardware resources of 74,630 LUTs, 23,356 flip-flops, 32kb BRAM, and 36 DSP blocks.

• Korean Journal of Acupuncture
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• v.40 no.1
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• pp.1-12
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• 2023

Objectives : This study aims to provide information regarding the status and quality of mobile applications (MAs) using self-acupressure by performing a systematic review. Methods : We conducted comprehensive searching on five international databases and two app markets from inception to July 31, 2022 to identify MAs using self-acupressure. We analyzed the characteristics of each MA regarding the name of the MA, registered app markets, target symptoms, developers, the year and country of development, cost, target age, media function, and expertise. We assessed the quality of each MA using Mobile Application Rating Scale (MARS). Results : We identified a total of 30 MAs using self-acupressure (25 MAs from the app market and 6 MAs from clinical studies, with 1 MA in common). 17 out of 24 MAs from the app market provided self-acupressure regimens for various symptoms and the others provided regimens for specific symptoms such as memory, anxiety, depression, asthma, allergy, low back pain, and headache. 14 developers were reported. 23 MAs were developed after 2013. The largest number of MAs were developed in the United States. The target age group of 12 MAs was above the age of 3, and that of 11 MAs was above the age of 12. 14 MAs provided multimedia functions such as videos. 13 MAs provided information of expertise. From clinical studies, only 3 out of 6 MAs were accessible through the app market. 4 MAs were developed by the researchers of the study. In terms of MARS, the score of MAs from the app market was higher than that of MAs from clinical studies in both objective and subjective evaluation areas. Conclusions : This study summarizes the characteristics of MAs using self-acupressure. More MAs using self-acupressure should be developed and further clinical research for MA on each symptom and disease is warranted for the diversification of MA fields using self-acupressure.