• The Korean Journal of Air & Space Law and Policy
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• v.24 no.1
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• pp.35-58
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• 2009

A treaty that governs the compensation on damage caused by aircraft to the third parties on surface was first adopted in Rome in 1933, but without support from the international aviation community it was replaced by another convention adopted again in Rome in 1952. Despite the increase of the compensation amount and some improvements to the old version, the Rome Convention 1952 with 49 State parties as of today is not considered universally accepted. Neither is the Montreal Protocol 1978 amending the Rome Convention 1952, with only 12 State parties excluding major aviation powers like USA, Japan, UK, and Germany. Consequently, it is mostly the local laws that apply to the compensation case of surface damage caused by the aircraft, contrary to the intention of those countries and people who involved themselves in the drafting of the early conventions on surface damage. The terrorist attacks 9/11 proved that even the strongest power in the world like the USA cannot with ease bear all the damages done to the third parties by the terrorist acts involving aircraft. Accordingly as a matter of urgency, the International Civil Aviation Organization(ICAO) picked up the matter and have it considered among member States for a few years through its Legal Committee before proposing for adoption as a new treaty in the Diplomatic Conference held in Montreal, Canada 20 April to 2 May 2009. Accordingly, two treaties based on the drafts of the Legal Committee were adopted in Montreal by consensus, one on the compensation for general risk damage caused by aircraft, the other one on compensation for damage from acts of unlawful interference involving aircraft. Both Conventions improved the old Convention/Protocol in many aspects. Deleting 'surface' in defining the damage to the third parties in the title and contents of the Conventions is the first improvement because the third party damage is not necessarily limited to surface on the soil and sea of the Earth. Thus Mid-air collision is now the new scope of application. Increasing compensation limit in big gallop is another improvement, so is the inclusion of the mental injury accompanied by bodily injury as the damage to be compensated. In fact, jurisprudence in recent years for cases of passengers in aircraft accident holds aircraft operators to be liable to such mental injuries. However, "Terror Convention" involving unlawful interference of aircraft has some unique provisions of innovation and others. While establishing the International Civil Aviation Compensation Fund to supplement, when necessary, the damages that exceed the limit to be covered by aircraft operators through insurance taking is an innovation, leaving the fate of the Convention to a State Party, implying in fact the USA, is harming its universality. Furthermore, taking into account the fact that the damage incurred by the terrorist acts, where ever it takes place targeting whichever sector or industry, are the domain of the State responsibility, imposing the burden of compensation resulting from terrorist acts in the air industry on the aircraft operators and passengers/shippers is a source of serious concern for the prospect of the Convention. This is more so when the risks of terrorist acts normally aimed at a few countries because of current international political situation are spread out to many innocent countries without quid pro quo.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.171-191
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• 2020

This study focused on digital music, which is the most valuable cultural asset in the modern society and occupies a particularly important position in the flow of the Korean Wave. Digital music was collected based on the "Gaon Chart," a well-established music chart in Korea. Through this, the changes in the ranking of the music that entered the chart for 73 weeks were collected. Afterwards, patterns with similar characteristics were derived through time series cluster analysis. Then, a descriptive analysis was performed on the notable features of each pattern. The research process suggested by this study is as follows. First, in the data collection process, time series data was collected to check the ranking change of digital music. Subsequently, in the data processing stage, the collected data was matched with the rankings over time, and the music title and artist name were processed. Each analysis is then sequentially performed in two stages consisting of exploratory analysis and explanatory analysis. First, the data collection period was limited to the period before 'the music bulk buying phenomenon', a reliability issue related to music ranking in Korea. Specifically, it is 73 weeks starting from December 31, 2017 to January 06, 2018 as the first week, and from May 19, 2019 to May 25, 2019. And the analysis targets were limited to digital music released in Korea. In particular, digital music was collected based on the "Gaon Chart", a well-known music chart in Korea. Unlike private music charts that are being serviced in Korea, Gaon Charts are charts approved by government agencies and have basic reliability. Therefore, it can be considered that it has more public confidence than the ranking information provided by other services. The contents of the collected data are as follows. Data on the period and ranking, the name of the music, the name of the artist, the name of the album, the Gaon index, the production company, and the distribution company were collected for the music that entered the top 100 on the music chart within the collection period. Through data collection, 7,300 music, which were included in the top 100 on the music chart, were identified for a total of 73 weeks. On the other hand, in the case of digital music, since the cases included in the music chart for more than two weeks are frequent, the duplication of music is removed through the pre-processing process. For duplicate music, the number and location of the duplicated music were checked through the duplicate check function, and then deleted to form data for analysis. Through this, a list of 742 unique music for analysis among the 7,300-music data in advance was secured. A total of 742 songs were secured through previous data collection and pre-processing. In addition, a total of 16 patterns were derived through time series cluster analysis on the ranking change. Based on the patterns derived after that, two representative patterns were identified: 'Steady Seller' and 'One-Hit Wonder'. Furthermore, the two patterns were subdivided into five patterns in consideration of the survival period of the music and the music ranking. The important characteristics of each pattern are as follows. First, the artist's superstar effect and bandwagon effect were strong in the one-hit wonder-type pattern. Therefore, when consumers choose a digital music, they are strongly influenced by the superstar effect and the bandwagon effect. Second, through the Steady Seller pattern, we confirmed the music that have been chosen by consumers for a very long time. In addition, we checked the patterns of the most selected music through consumer needs. Contrary to popular belief, the steady seller: mid-term pattern, not the one-hit wonder pattern, received the most choices from consumers. Particularly noteworthy is that the 'Climbing the Chart' phenomenon, which is contrary to the existing pattern, was confirmed through the steady-seller pattern. This study focuses on the change in the ranking of music over time, a field that has been relatively alienated centering on digital music. In addition, a new approach to music research was attempted by subdividing the pattern of ranking change rather than predicting the success and ranking of music.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.91-108
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• 2020

Korea's famous soccer players are steadily performing well in international leagues, which led to higher interests of Korean fans in the international leagues. Reflecting the growing social phenomenon of rising interests on international leagues by Korean fans, the study examined the overall consumer perception in the consumption of uniform by domestic soccer fans and compared the changes in perception following the transfers of the players. Among others, the paper examined the consumer perception and purchase factors of soccer fans shown in social media, focusing on periods before and after the recruitment of Heung-Min Son to English Premier League's Tottenham Football Club. To this end, the EPL uniform is the collection keyword the paper utilized and collected consumer postings from domestic website and social media via Python 3.7, and analyzed them using Ucinet 6, NodeXL 1.0.1, and SPSS 25.0 programs. The results of this study can be summarized as follows. First, the uniform of the club that consistently topped the league, has been gaining attention as a popular uniform, and the players' performance, and the players' position have been identified as key factors in the purchase and search of professional football uniforms. In the case of the club, the actual ranking and whether the league won are shown to be important factors in the purchase and search of professional soccer uniforms. The club's emblem and the sponsor logo that will be attached to the uniform are also factors of interest to consumers. In addition, in the decision making process of purchase of a uniform by professional soccer fan, uniform's form, marking, authenticity, and sponsors are found to be more important than price, design, size, and logo. The official online store has emerged as a major purchasing channel, followed by gifts for friends or requests from acquaintances when someone travels to the United Kingdom. Second, a classification of key control categories through the convergence of iteration correlation analysis and Clauset-Newman-Moore clustering algorithm shows differences in the classification of individual groups, but groups that include the EPL's club and player keywords are identified as the key topics in relation to professional football uniforms. Third, between 2002 and 2006, the central theme for professional football uniforms was World Cup and English Premier League, but from 2012 to 2015, the focus has shifted to more interest of domestic and international players in the English Premier League. The subject has changed to the uniform itself from this time on. In this context, the paper can confirm that the major issues regarding the uniforms of professional soccer players have changed since Ji-Sung Park's transfer to Manchester United, and Sung-Yong Ki, Chung-Yong Lee, and Heung-Min Son's good performances in these leagues. The paper also identified that the uniforms of the clubs to which the players have transferred to are of interest. Fourth, both male and female consumers are showing increasing interest in Son's league, the English Premier League, which Tottenham FC belongs to. In particular, the increasing interest in Son has shown a tendency to increase interest in football uniforms for female consumers. This study presents a variety of researches on sports consumption and has value as a consumer study by identifying unique consumption patterns. It is meaningful in that the accuracy of the interpretation has been enhanced by using a cluster analysis via convergence of iteration correlation analysis and Clauset-Newman-Moore clustering algorithm to identify the main topics. Based on the results of this study, the clubs will be able to maximize its profits and maintain good relationships with fans by identifying key drivers of consumer awareness and purchasing for professional soccer fans and establishing an effective marketing strategy.

• Restorative Dentistry and Endodontics
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• v.32 no.3
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• pp.248-259
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• 2007

The aim of this research was to study the effect of intermittent polymerization on marginal adaptation by comparing the marginal adaptation of intermittently polymerized composite to that of continuously polymerized composite. The materials used for this study were Pyramid (Bisco Inc., Schaumburg, U.S.A.) and Heliomolar (Ivoclar Vivadent, Liechtenstein) . The experiment was carried out in class II MOD cavities prepared in 48 extracted human maxillary premolars. The samples were divided into 4 groups by light curing method: group 1- continuous curing (60s light on with no light off), group 2-intermittent curing (cycles of 3s with 2s light on & 1s light off for 90s); group 3- intermittent curing (cycles of 2s with 1s light on & 1s light off for 120s); group 4- intermittent curing (cycles of 3s with 1s light on & 2s light off for 180s). Consequently the total amount of light energy radiated was same in all the groups. Each specimen went through thermo-mechanical loading (TML) which consisted of mechanical loading (720,000 cycles, 5.0 kg) with a speed of 120 rpm for 100hours and thermocycling (6000 thermocycles of alternating water of $50^{\circ}C$ and $55^{\circ}C$). The continuous margin (CM) (%) of the total margin and regional margins, occlusal enamel (OE), vertical enamel (VE), and cervical enamel (CE) was measured before and after TML under a $\times200$ digital light microscope. Three-way ANOVA and Duncan's Multiple Range Test was performed at 95% level of confidence to test the effect of 3 variables on CM (%) of the total margin: light curing conditions, composite materials and effect of TML. In each group, One-way ANOVA and Duncan's Multiple Range Test was additionally performed to compare CM (%) of regions (OE, VE CE). The results indicated that all the three variables were statistically significant (p < 0.05). Before TML, in groups using Pyramid, groups 3 and 4 showed higher CM (%) than groups 1 and 2, and in groups using Heliomolar. groups 3 and 4 showed higher CM (%) than group 1 (p < 0.05). After TML, in both Pyramid and Heliomo)ar groups, group 3 showed higher CM (%) than group 1 (p < 0.05) CM (%) of the regions are significantly different in each group (p < 0.05). Before TML, no statistical difference was found between groups within the VE and CE region. In the OE region, group 4 of Pyramid showed higher CM (%) than group 2, and groups 2 and 4 of Heliomolar showed higher CM (%) than group 1 (p < 0.05). After TML, no statistical difference was found among groups within the VE and CE region. In the OE region, group 3 of Pyramid showed higher CM (%) than groups 1 and 2, and groups 2,3 and 4 of Heliomolar showed higher CM (%) than group 1 (p < 0.05). It was concluded that intermittent polymerization may be effective in reducing marginal gap formation.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.311-322
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• 2019

In order to establish an optimal double cropping system to obtain the maximum annual quantity, we investigated the annual productivity of whole-crop silage (WCS) rice, Jowoo (Jw), Yeongwoo (Yw), and Mogwoo (Mw), and winter feed crops (WFC), Italian ryegrass (IRG), Greenfarm (GF), rye Gogu (GU), and triticale Joseong (JS), in paddy fields of the central plains of Korea. From 2016 to 2019, each crop was subjected to two standard cultivation methods: WCS rice and WFC optimal. Using the WCS optimal mode, the average dry matter yield (DMY) of WCS rice, early flowering Jw, was 15.8 tons/ha and 21.0 for the mid-late heading Yw; there was no significant difference compared to the 19.2 tons/ha late-flowering Mw (p<0.01). The WFC were not significantly different between GF (3.2 tons/ha) and GU (4.5) sown on September 23rd, while JS was the highest at 12.6 tons/ha (p<0.001). There was a significant difference in the order of JS (16.6 tons/ha) > GF (10.5) > GU (4.7)(p<0.001) sown on October 11th. For JS sown on October 31st, the DMY was 11.8 tons/ha, which was significantly higher than that of the other two crops (p<0.05). Except for rye GU, DMY was the highest when sown on October 11th. For WFC optimal mode, the average DMY of JS was the highest at 18.3 tons/ha, which was significantly different from that of GF (10.9) and GU (9.6) (p<0.001). The DMY of WCS rice transplanted on May 10th was the highest at 23.0 tons/ha in Mw, which was not significantly different from that of Yw (21.4) but significantly different from that of Jw (15.9) (p<0.05). On transplanting on May 25th, the DMY of Mw was the highest at 24.2 tons/ha; this was not significantly different from that of Yw (20.7), but it was significantly different from that of Jw (18.6) (p<0.05). When transplanted on June 11th, the DMY was 21.3 tons/ha in Yw, which was significantly higher than the DMY of other two cultivars, Jw and Mw (p<0.05). For the WCS rice-WFC double cropping, the total annual DMY was 33.6 tons/ha with the combination of the WCS rice, Yw, and the triticale JS for WCS optimal mode. Meanwhile, the total annual DMY was 39.6 tons/ha with the combination of the triticale JS and the WCS rice, Yw, for WFC optimal mode. In conclusion, the strategies for obtaining the maximum yield of high-quality forage for WCS rice-WFC, WFC-WCS rice double cropping are as follows: 1) cultivation centered on the optimal mode of WFC, and 2) sowing the WFC, triticale JS in mid-October, harvesting the crops around the end of May and transplanting the WCS rice, Yw, in early June to obtain the maximum DMY of 39.6 tons/ha.

• Journal of Animal Science and Technology
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• v.49 no.4
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• pp.459-470
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• 2007

Domestically, finishing pigs are marketed at 110 kg on an average. However, it is thought to be feasible to increase the market weight to 120kg or greater without decreasing the carcass quality, because most domestic pigs for pork production have descended from lean-type lineages. The present study was undertaken to investigate the growth efficiency and profitability of ‘high’-market wt pigs and the physicochemical characteristics and consumers' acceptability of the high-wt carcass. A total of 96 (Yorkshire × Landrace) × Duroc-crossbred gilts and barrows were fed a finisher diet ad laibtum in 16 pens beginning from 90-kg BW, after which the animals were slaughtered at 110kg (control) or ‘high’ market wt (135 and 125kg in gilts & barrows, respectively) and their carcasses were analyzed. Average daily gain and gain:feed did not differ between the two sex or market wt groups, whereas average daily feed intake was greater in the barrow and high market wt groups than in the gilt and 110-kg market wt groups, respectively(P<0.01). Backfat thickness of the high-market wt gilts and barrows corrected for 135 and 125-kg live wt, which were 23.7 and 22.5 mm, respectively, were greater (P<0.01) than their corresponding 110-kg counterparts(19.7 & 21.1 mm). Percentages of the trimmed primal cuts per total trimmed lean (w/w), except for that of loin, differed statistically (P<0.05) between two sex or market wt groups, but their numerical differences were rather small. Crude protein content of the loin was greater in the high vs. 110-kg market group (P<0.01), but crude fat and moisture contents and other physicochemical characteristics including the color of this primal cut were not different between the two sexes or market weights. Aroma, marbling and overall acceptability scores were greater in the high vs. 110-kg market wt group in sensory evaluation for fresh loin (P<0.01); however, overall acceptabilities for cooked loin, belly and ham were not different between the two market wt groups. Marginal profits of the 135- and 125-kg high-market wt gilt and barrow relative to their corresponding 110-kg ones were approximately -35,000 and 3,500 wons per head under the current carcass grading standard and price. However, if it had not been for the upper wt limits for the A- and B-grade carcasses, marginal profits of the high market wt gilt and barrow would have amounted to 22,000 and 11,000 wons per head, respectively. In summary, 120～125-kg market pigs are likely to meet the consumers' preference better than the 110-kg ones and also bring a profit equal to or slightly greater than that of the latter even under the current carcass grading standard. Moreover, if only the upper wt limits of the A- & B-grade carcasses were removed or increased to accommodate the high-wt carcass, the optimum market weights for the gilt and barrow would fall upon their target weights of the present study, i.e. 135 and 125 kg, respectively.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.157-175
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• 2013

Since Korean government announced 'Smartwork promotion strategy' in 2010, Korean firms and government organizations have started to adopt smartwork. However, the smartwork has been implemented only in a few of large enterprises and government organizations rather than SMEs (small and medium enterprises). In USA, both Yahoo! and Best Buy have stopped their flexible work because of its reported low productivity and job loafing problems. In addition, according to the literature on smartwork, we could draw obstacles of smartwork adoption and categorize them into the three types: institutional, organizational, and technological. The first category of smartwork adoption obstacles, institutional, include the difficulties of smartwork performance evaluation metrics, the lack of readiness of organizational processes, limitation of smartwork types and models, lack of employee participation in smartwork adoption procedure, high cost of building smartwork system, and insufficiency of government support. The second category, organizational, includes limitation of the organization hierarchy, wrong perception of employees and employers, a difficulty in close collaboration, low productivity with remote coworkers, insufficient understanding on remote working, and lack of training about smartwork. The third category, technological, obstacles include security concern of mobile work, lack of specialized solution, and lack of adoption and operation know-how. To overcome the current problems of smartwork in reality and the reported obstacles in literature, we suggest a novel smartwork service model based on NFC(Near Field Communication). This paper suggests NFC-based Smartwork Service Model composed of NFC-based Smartworker networking service and NFC-based Smartwork space management service. NFC-based smartworker networking service is comprised of NFC-based communication/SNS service and NFC-based recruiting/job seeking service. NFC-based communication/SNS Service Model supplements the key shortcomings that existing smartwork service model has. By connecting to existing legacy system of a company through NFC tags and systems, the low productivity and the difficulty of collaboration and attendance management can be overcome since managers can get work processing information, work time information and work space information of employees and employees can do real-time communication with coworkers and get location information of coworkers. Shortly, this service model has features such as affordable system cost, provision of location-based information, and possibility of knowledge accumulation. NFC-based recruiting/job-seeking service provides new value by linking NFC tag service and sharing economy sites. This service model has features such as easiness of service attachment and removal, efficient space-based work provision, easy search of location-based recruiting/job-seeking information, and system flexibility. This service model combines advantages of sharing economy sites with the advantages of NFC. By cooperation with sharing economy sites, the model can provide recruiters with human resource who finds not only long-term works but also short-term works. Additionally, SMEs (Small Medium-sized Enterprises) can easily find job seeker by attaching NFC tags to any spaces at which human resource with qualification may be located. In short, this service model helps efficient human resource distribution by providing location of job hunters and job applicants. NFC-based smartwork space management service can promote smartwork by linking NFC tags attached to the work space and existing smartwork system. This service has features such as low cost, provision of indoor and outdoor location information, and customized service. In particular, this model can help small company adopt smartwork system because it is light-weight system and cost-effective compared to existing smartwork system. This paper proposes the scenarios of the service models, the roles and incentives of the participants, and the comparative analysis. The superiority of NFC-based smartwork service model is shown by comparing and analyzing the new service models and the existing service models. The service model can expand scope of enterprises and organizations that adopt smartwork and expand the scope of employees that take advantages of smartwork.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.