• The Korean Journal of Archival Studies
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• no.70
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• pp.189-243
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• 2021

COVID-19 Archives are some of the Disaster Archives. It is necessary to collect disaster records produced in real-time at the disaster scene rather than start collecting records after the disaster recovery. Therefore, this study summarized the definition and purpose of disaster archives to understand the current status of domestic and foreign COVID-19 archives and examined overseas disaster archive collection policies that can be referenced in establishing a COVID-19 archive collection policy. In addition, surveys and interviews were conducted on institutions that establish and operate related archives at home and abroad. As a result, record collection Improvement plans for the COVID-19 archive were proposed: Firstly, in terms of collection policy improvements, the essential elements identified in the survey were selected as additional collection policy elements. Secondly, diversification of participants' groups requires the introduction of clear definitions of collection targets, diversification of promotional methods such as recording record contents through collaboration with related departments, and improving copyright issues that limit record donation. Thirdly, participatory record collection methods with efficient questionnaires in participatory forms and privacy issues are proposed as improvement plans.

• 한국사회정책
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• v.25 no.2
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• pp.429-446
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• 2018

The $4^{th}$ Industrial Revolution, symbolizing the explosive innovation of digital technologies, is expected to have a great impact on labor relations and produce a lot of contested issues. The labor-management issues, created by the $4^{th}$ Industrial Revolution, are as follows: (1) employment restructuring, job re-allocation, and skill-reformation, driven by the technological displacement, resetting of worker-machine relationship, and negotiation on labor intensity and autonomy, (2) the legislation of institutional protection for the digital dependent self-employed, derived from the proliferation of platform-mediated labor, and the statutory recognition of their 'workerness', (3) unemployment safety net, income guarantee, and skill formation assistance for precarious workeforce, (4) the protection of worker privacy from workplace surveillance, (5) protecting labor rights of the digital dependent self-employed and prcarious workers and guaranteeing their unionization and collective bargaining. In comparing how labor unions in Western countries have responded to the $4^{th}$ Industrial Revolution, German unions have showed a strategic approach of policy formation toward digital technological innovations by effectively building and utilizing diverse channel of social dialogue and collective bargaining, while those in the US and UK have adopted the traditional approach of organizing and protesting in attempting to protect the interest of platform-mediated workers (i.e. Uber drivers). In light of the best practice demonstrated by German unions, it is necessary to build the process of productive policy consultation among three parties- the government, employers, and labor unions - at multi levels (i.e. workplace, sectoral and national levels), in order to prevent the destructive damage as well as labor-management confrotation, caused by digital technological innovations. In such policy consultation procesess, moreover, the inclusive and integrated approach is required to tackle with diverse problems, derived from the $4^{th}$ Industrial Revolution, in a holistic manner.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.57-84
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• 2023

With the development of information and communications technology (ICT) and big data technology, anyone can easily obtain and utilize vast amounts of data through the Internet. Therefore, the capability of selecting high-quality data from a large amount of information is becoming more important than the capability of just collecting them. This trend continues in academia; literature reviews, such as systematic and non-systematic reviews, have been conducted in various research fields to construct a healthy knowledge structure by selecting high-quality research from accumulated research materials. Meanwhile, after the COVID-19 pandemic, remote healthcare services, which have not been agreed upon, are allowed to a limited extent, and new healthcare services such as health recommender systems (HRS) equipped with artificial intelligence (AI) and big data technologies are in the spotlight. Although, in practice, HRS are considered one of the most important technologies to lead the future healthcare industry, literature review on HRS is relatively rare compared to other fields. In addition, although HRS are fields of convergence with a strong interdisciplinary nature, prior literature review studies have mainly applied either systematic or non-systematic review methods; hence, there are limitations in analyzing interactions or dynamic relationships with other research fields. Therefore, in this study, the overall network structure of HRS and surrounding research fields were identified using citation network analysis (CNA). Additionally, in this process, in order to address the problem that the latest papers are underestimated in their citation relationships, the GraphSAGE algorithm was applied. As a result, this study identified 'recommender system', 'wireless & IoT', 'computer vision', and 'text mining' as increasingly important research fields related to HRS research, and confirmed that 'personalization' and 'privacy' are emerging issues in HRS research. The study findings would provide both academic and practical insights into identifying the structure of the HRS research community, examining related research trends, and designing future HRS research directions.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.147-163
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• 2016

Gender information of a smart device user is essential to provide personalized services, and multi-modal data obtained from the device is useful for predicting the gender of the user. However, the method for utilizing each of the multi-modal data for gender prediction differs according to the characteristics of the data. Therefore, in this study, an ensemble method for predicting the gender of a smart device user by using three classifiers that have text, application, and acceleration data as inputs, respectively, is proposed. To alleviate privacy issues that occur when text data generated in a smart device are sent outside, a classification method which scans smart device text data only on the device and classifies the gender of the user by matching text data with predefined sets of word. An application based classifier assigns gender labels to executed applications and predicts gender of the user by comparing the label ratio. Acceleration data is used with Support Vector Machine to classify user gender. The proposed method was evaluated by using the actual smart device log data collected from an Android application. The experimental results showed that the proposed method outperformed the compared methods.

• Journal of Korean Society of Archives and Records Management
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• v.17 no.1
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• pp.193-216
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• 2017

The purpose of this study is to understand issues related to the Personal Information Act recently emerging in the field of oral history, and to prepare countermeasures for oral history academics and archives. The Personal Information Act is intended to protect the confidentiality and freedom of the constitutional privacy, and to assure the right to self-determination of information, thereby realizing the dignity and value of the individual. Oral history is intended for living persons; therefore, strict ethical standards are needed to protect the morality of the person behind the sound recordings and appears as the subject of oral history. However, if the uniform application of the Personal Information Act is made, it is a requirement to make the process of consenting and notifying excessively complex and almost impossible to realize, making collection and service of oral history resource improbable. The mechanical and strict application of the Personal Information Act does not come into being because it has the aspect of undermining the inherent intrinsic value of oral history resources and making it difficult to maintain the authenticity of the records. To solve these problems, it is necessary to revise Article 58 (4) of the Personal Information Act of Korea. In addition, it is necessary to establish a guideline for the establishment of independent ethical standards of oral history itself, especially for the protection of the moral rights of third parties.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.5-14
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• 2017

Many web services which use OAuth Protocol offer users to log in using their personal profile information given by resource servers. This method reduces the inconvenience of the users to register for new membership. However, at the time a user finishes using OAuth client web service, even if he logs out of the client web service, the resource server remained in the login state may cause the problem of leaking personal information. In this paper, we propose a solution to mitigate the threat by providing an additional security behavior check: when a user requests to log out of the Web Client service, he or she can make decision whether or not to log out of the resource server via confirmation notification regarding the state of the resource server. By utilizing the proposed method, users who log in through the OAuth Protocol in the public PC environment like department stores, libraries, printing companies, etc. can prevent the leakage of personal information issues that may arise from forgetting to check the other OAuth related services. To verify our study, we implement a Client Web Service that uses OAuth 2.0 protocol and integrate it with our security behavior check. The result shows that with this additional function, users will have a better security when dealing with resource authorization in OAuth 2.0 implementation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2176-2183
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• 2017

The value of Big-Data which is a main factor of the fourth Industrial Revolution enhances industrial productivity in private sector and provides administrative services for nations and corporates in public sector. ICT-developed countries are coming up with Big-Data application in public sector rapidly. Especially, when it comes to social crisis management, they are equipped with pre-forcasting system. Korean Government also emphasizes Big-Data application in public sector for the social crisis management. But the reality where the overall infrastructure vulnerability reveals requires preparation and operation of measurement for social problems. Accordingly, we need to analyze Big-Data application problem and benchmark the precedented cases, thereby, direct policy diversity. Hence, this paper proposes the roles and rules of E-government analyzing problems from Big-Data application. The following policy proposes open Information and legal&institutional improvement, Big-Data service considerations threatening privacy issues in Big-Data ecosystem, necessity of operational and analytical technology for Big-Data and related technology in technical implication of Big-Data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.374-380
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• 2017

The Internet of Things (IoT) refers to intelligent technologies and services that connect all things to the internet so they can interactively communicate with people, other things, and other systems. The development of the IoT environment accompanies advances in network protocols applicable to more lightweight and intelligent sensors, and lightweight and diverse environments. The development of those elemental technologies is promoting the rapid progress in smart car environments that provide safety features and user convenience. These developments in smart car services will bring a positive effect, but can also lead to a catastrophe for a person's life if security issues with the services are not resolved. Although smart cars have various features with different types of communications functions to control the vehicles under the existing platforms, insecure features and functions may bring various security threats, such as bypassing authentication, malfunctions through illegitimate control of the vehicle via data forgery, and leaking of private information. In this paper, we look at types of smart car services in the IoT, deriving the security threats from smart car services based on various scenarios, suggesting countermeasures against them, and we finally propose a safe smart car application plan.

• Korean journal of communication and information
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• v.76
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• pp.151-182
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• 2016

The right to be forgotten (RTBF) has been a population notion to address privacy issues associated with the digitalization of information and the dissemination of such information over the global digital network. In May 2014, the European Court of Justice (ECJ) laid down a landmark RTBF decision to grant individuals the right to be de-listed from search results. ECJ's RTBF decision sparked an increased interest in RTBF in South Korea. Academic and non-academic commentators have provided a mistaken or outstretched interpretation of RTBF in claiming that removal of news articles should be read into RTBF in Korean law. Moreover, the Press Arbitration Commission of Korea (PAC) has proposed revising the Press Arbitration Act (PAA) to allow the alleged victims of news reporting to request the deletion of news stories. This article examines the notion of RTBF from its origin to the latest development abroad and also critically explores Korean laws regulation freedom of expression to evaluate if Korea needs the proposed PAA revision.