• Journal of Information Science Theory and Practice
• /
• 제12권2호
• /
• pp.79-95
• /
• 2024

The new AI based conversational chatbot, ChatGPT, launched in November 2022, is causing a stir. There are many opinions about this being a 'threat or a promise,' and thus it is important to understand what has been said about this tool and, based on the growing literature that has emerged on the subject, demystify its effective impact on society. To analyse this impact, a systematic literature review with the support of the preferred reporting items for systematic reviews and meta-analysis protocol was used. The data, scientific documents, were collected using the main scientific databases - SCOPUS and Web of Science - and the results were presented based on a bibliometric and thematic exploration of content. The main findings indicate that people are increasingly using this chatbot in more diverse areas. Therefore, this study contributes at the practical level, aiming to enlighten people in general - both in professional and personal life - about this tool and its impacts. Also, it contributes at the theoretical level, which involves expanding understanding and elucidation of the impacts of ChatGPT in different areas of study.

• Asian Pacific Journal of Cancer Prevention
• /
• 제17권11호
• /
• pp.5025-5030
• /
• 2016

The objective of this study was to clarify how distance from place of residence to the nearest specialist cancer hospital affects the survival of lung cancer patients and the treatment received. For all patients diagnosed with lung cancer in the Aomori cancer registry database for the period from 2009 to 2011 (n=3,986). The distance to the treating hospital was measured as the straight line from a person's place of residence, and compared with findings from the Ederer II method for calculating relative survival. Information on treatments given was obtained by data extraction. We defined a hospital having respiratory medicine as specialist, while all private hospitals and clinics were included in the general category. Patients attending specialist hospitals numbered 2,548 (67.0%), and those treated at general institutions were 1,255 (33.0%). The patients who had the lowest relative survival with localized lesions lived <20 km from general hospitals and clinics. With more advanced stages, relative survival of those living <20 km from a specialist hospital was the lowest. Although the survival rate was not affected by the distance between place of residence and hospital, even when patients are diagnosed at a localized stage at a general hospital or clinic within 20 km from their home, they did survive longer in comparison with patients diagnosed at a specialist hospital.

• Genomics & Informatics
• /
• 제14권1호
• /
• pp.20-28
• /
• 2016

Internet addiction (IA) has become a widespread and problematic phenomenon as smart devices pervade society. Moreover, internet gaming disorder leads to increases in social expenditures for both individuals and nations alike. Although the prevention and treatment of IA are getting more important, the diagnosis of IA remains problematic. Understanding the neurobiological mechanism of behavioral addictions is essential for the development of specific and effective treatments. Although there are many databases related to other addictions, a database for IA has not been developed yet. In addition, bioinformatics databases, especially genetic databases, require a high level of security and should be designed based on medical information standards. In this respect, our study proposes the OAuth standard protocol for database access authorization. The proposed IA Bioinformatics (IABio) database system is based on internet user authentication, which is a guideline for medical information standards, and uses OAuth 2.0 for access control technology. This study designed and developed the system requirements and configuration. The OAuth 2.0 protocol is expected to establish the security of personal medical information and be applied to genomic research on IA.

• Asian Pacific Journal of Cancer Prevention
• /
• 제15권14호
• /
• pp.5545-5550
• /
• 2014

Background: Human biological materials from cancer patients are linked directly with public health issues in medical science research as foundational resources so securing "human biological material" is truly important in bio-industry. However, because South Korea's national R and D project lacks a proper managing system for establishing a national standard for the outputs of certain processes, high-value added human biological material produced by the national R and D project could be lost or neglected. As a result, it is necessary to develop a managing process, which can be started by establishing operating guidelines to handle the output of human biological materials. Materials and Methods: The current law and regulations related to submitting research outcome resources was reviewed, and the process of data 'acquisition' and data 'distribution' from the point of view of big data and health 2.0 was examined in order to arrive at a method for switching paradigms to better utilize human biological materials. Results: For the deposit of biological research resources, the original process was modified and a standard process with relative forms was developed. With deposit forms, research information, researchers, and deposit type are submitted. The checklist's 26 items are provided for publishing. This is a checklist of items that should be addressed in deposit reports. Lastly, XML-based deposit procedure forms were designed and developed to collect data in a structured form, to help researchers distribute their data in an electronic way. Conclusions: Through guidelines included with the plan for profit sharing between depositor and user it is possible to manage the material effectively and safely, so high-quality human biological material can be supplied and utilized by researchers from universities, industry and institutes. Furthermore, this will improve national competitiveness by leading to development in the national bio-science industry.

• International Journal of Computer Science & Network Security
• /
• 제22권2호
• /
• pp.406-412
• /
• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• Genomics & Informatics
• /
• 제19권3호
• /
• pp.21.1-21.5
• /
• 2021

The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the coronavirus disease 2019 (COVID-19) pandemic, researchers have turned to more non-traditional sources of clinical data to characterize the disease in near-real time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present. However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations don't generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best-practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.

• The Journal of Asian Finance, Economics and Business
• /
• 제8권9호
• /
• pp.273-285
• /
• 2021

This study estimates the financial value of return on investment (ROI) of research funds. Four simulation estimations are employed to measure ROI finance value that considers the outputs, outcomes, impacts and total ROI from the allocation input received. Research outputs, outcomes, and impacts can be quantitatively measured based on improvements to existing systems. In terms of input, the Malaysian government has allocated MYR301,350,000 for fundamental research in the 2021 budget compared with 2019, up 9.5 percent from 2019. It brings up the question: To what extent does the input of research funds allocated by the government yield a good return in outputs, outcomes, and impacts to the academic community, society, and country? The result of total ROI shows around MYR7 return is generated by researchers for each Malaysian ringgit channeled by the funder. More specifically, for a research project, it is more difficult to produce impacts and outcomes compared to research outputs. The positive return is evidence that all the allocated funds are beneficial to the stakeholders. The government can apply this approach in calculating ROI for evaluation and fund allocation to universities. Furthermore, the positive financial value of research output, outcome, and impact automatically contribute to a positive innovation environment in Malaysia.

• 한국도서관정보학회지
• /
• 제51권4호
• /
• pp.313-332
• /
• 2020

본 연구에서는 학술 소셜 네트워킹 서비스에서의 연구자 셀프 아카이빙 동기를 학문 분야별로 비교하였다. 대표적인 학술 소셜 네트워킹 서비스인 ResearchGate 이용자를 대상으로 선행연구에서는 온라인 설문조사 결과를 실시하여 연구자의 18가지 셀프 아카이빙 동기 요인(흥미, 개인적/직업적 이익, 평판, 학습, 자기효능감, 이타심, 호혜성, 신용, 공동체 이익, 사회 참여, 홍보, 접근성, 문화, 외부적 요인, 신뢰, 시스템 안정성, 저작권 문제, 부가적인 시간 및 노력)을 도출하였다. 후속 연구인 본 연구에서는 Biglan의 학문 분류 기준을 적용하여 연구자의 학문 분야를 구분하고, 이들 분야별 셀프 아카이빙 동기를 비교하였다. 먼저 연구자들의 학문 분야를 경성-순수, 경성-응용, 연성-순수, 연성-응용으로 구분하여 동기를 분석하였으며, 그 다음 단계에서는 경성-연성과 순수-응용으로 구분하여 비교하였다. 나아가 연구자의 인구통계학적 특성과 ResearchGate 이용 현황에 따른 동기의 차이도 살펴보았다. 연구 결과, 학문 분야에 따라 흥미, 접근성, 외부적 요인, 부가적인 시간 및 노력에 대한 동기에 차이가 있는 것으로 밝혀졌다. 예를 들어 경성-순수 분야의 이용자들은 다른 분야의 이용자들에 비해 흥미에 대한 높은 동기를 가지고 있었으며, 연성-순수 분야의 이용자들은 다른 분야 이용자들과 비교하여 개인적/직업적 이익에 대해 높은 동기를 가지고 있었다. 이러한 다양한 학문분야의 연구자들의 동기에 대해 살펴본 연구 결과는 학술 소셜 네트워킹 서비스에서의 연구 데이터와 결과물 공유 활성화를 위한 전략 개발에 도움이 될 것으로 기대한다.

• 보건의료생명과학 논문지
• /
• 제9권1호
• /
• pp.169-178
• /
• 2021

본 연구는 전문대학생 183명을 대상으로 진로성숙도에 영향을 미치는 외적변인인 사회적지지가 내적변인인 진로결정자기효능감을 매개로 하는가를 검증하기 위해 수행하였다. 연구 결과 사회적지지가 진로성숙도에 영향을 미치는 것으로 나타났으며, 이를 통해 전문대학생의 진로성숙도를 높이기 위해서는 학생 환경에 대한 정보와 평가, 유효한 지원, 정서적 관심을 포함하는 대인적 관계를 이해해야 하며, 사회적지지를 높게 지각할 수 있는 방안이 모색되어야 함을 확인하였다. 사회적지지가 진로성숙도에 미치는 영향에서 진로결정자기효능감의 부분매개 효과을 검증함으로써 그 중요성을 입증하였다. 따라서 진로 및 취업상담자와 교수자는 자신의 불확실한 미래에 대한 불안감을 가지고 있는 학생들에게 자신감을 가지고, 자존감과 자기효능감을 높일 수 있도록 지지해주어 본인 스스로 진로성숙도를 고양시킬 수 있도록 해야 한다.

• 지능정보연구
• /
• 제24권4호
• /
• pp.51-66
• /
• 2018

인공지능 기술의 비약적인 발전으로 인하여, 사용자의 편의성 증대를 목적으로 다양한 분야에서 관련된 제품과 서비스들의 개발이 이루어지고 있다. 이러한 기술의 발전에는 긍정적인 파급 효과에 대한 기대감이 존재하나, 향후 발생 가능한 부정적인 측면에 대한 논의도 활발히 이루어지고 있다. 예를 들어, 인공지능 기술 기반의 자율주행 자동차의 경우 안정성의 향상이라는 측면에서 많은 관심을 받고 있으나, 트롤리 딜레마, 시스템 보안 문제 등의 사회적 이슈 또한 활발히 논의되고 있다. 이에 따라, 인공지능 관련 기술의 발전과 사회적 수용을 위해서는 사회적으로 논의되는 주요 관련 이슈들에 대한 확인과 효과적인 분석이 요구된다. 이를 위해, 본 연구에서는 '이세돌 vs 알파고' 시점인 2016년 3월을 포함하여 2016년 1월부터 2017년 12월까지 2년 동안의 인공지능과 관련된 사회적인 이슈들을 파악하고 온라인상에서 발생되는 사회적 여론에 대하여 다 범주 감성을 분석하고자 한다. 이를 위하여 국내 대표적인 포털 사이트에서 인공지능 관련 뉴스의 수와 관련된 뉴스 제목, 뉴스의 댓글을 웹 크롤링(Web Crawling) 하였다. 사회적 여론에 대한 다 범주 감성 분석은 논의되는 이슈들의 중요성을 고려하여 단순 긍정 또는 부정이 아닌, 분노, 혐오, 두려움, 행복, 중립, 슬픔, 놀라움의 7가지 다 범주 감성으로 분석하였다. 분석 결과, 대부분의 이벤트 기간에 대하여 1위 감성은 '행복'으로 나타났지만 각 키워드에 대하여 나오는 감성이 상이함을 볼 수 있었다. 또한 2016년 상반기, 하반기, 2017년 상반기, 하반기로 나누어 보았을 때 시간이 지남에 따라 '분노'의 감성이 낮아짐을 확인하였다. 이러한 분석 결과를 바탕으로 인공지능과 관련하여 현재 논의되고 있는 다양한 이슈와 동향 파악이 가능하며, 이에 대한 대응 방안 마련에 활용이 가능할 것이다. 향후 감성 분석기의 성능 향상과 댓글에 대한 공감 및 비공감도의 가중치를 추가하여 분석한다면 사회적 여론을 보다 세밀하게 파악 할 수 있을 것이다.