The importance of the convergence education is getting more and more emphasized. But the university convergence education has not yet met today's needs. So this study is focused on showing effective practice methods and finding development directions of the university convergence criticism education. One of a significant trend of the contemporary academic education of liberal arts is the tendency of the convergent and integrative studies. What is essential in the convergent and integrative studies creativity. The zeitgeist of knowledge-based society is change and innovation. In response to its request, college education is being asked to do convergence education, And general education as the basis of convergence education is strengthened.
After almost one century of discontinuity in the archival tradition of Chosun dynasty, Korea entered the new age of records and archival management by legislating and executing the basic laws (The Records and Archives Management of Public Agencies Ad of 1999). Annals of Chosun dynasty recorded major historical facts of the five hundred years of national affairs. The Annals are major accomplishment in human history and rare in the world. It was possible because the Annals were composed of collected, selected and complied records of primary sources written and compiled by generations of historians, As important public records are needed to be preserved in original forms in modern archives, we had to develop and establish a modern archival system to appraise and select important national records for archival preservation. However, the colonialization of Korea deprived us of the opportunity to do the task, and our fine archival tradition was not succeeded. A centralized archival system began to develop since the establishment of GARS under the Ministry of Government Administration in 1969. GARS built a modem repository in Pusan in 1984 succeeding to the tradition of History Archives of Chosun dynasty. In 1998, GARS moved its headquarter to Taejon Government Complex and acquired state-of-the-art audio visual archives preservation facilities. From 1996, GARS introduced an automated archival management system to remedy the manual registration and management system complementing the preservation microfilming. Digitization of the holdings was the key project to provided the digital images of archives to users. To do this, the GARS purchased new computer/server systems and developed application softwares. Parallel to this direction, GARS drastically renovated its manpower composition toward a high level of professionalization by recruiting more archivists with historical and library science backgrounds. Conservators and computer system operators were also recruited. The new archival laws has been in effect from January 1, 2000. The new laws made following new changes in the field of records and archival administration in Korea. First, the laws regulate the records and archives of all public agencies including the Legislature, the Judiciary, the Administration, the constitutional institutions, Army, Navy, Air Force, and National Intelligence Service. A nation-wide unified records and archives management system became available. Second, public archives and records centers are to be established according to the level of the agency; a central archives at national level, special archives for the National Assembly and the Judiciary, local government archives for metropolitan cities and provinces, records center or special records center for administrative agencies. A records manager will be responsible for the records management of each administrative divisions. Third, the records in the public agencies are registered in the computer system as they are produced. Therefore, the records are traceable and will be searched or retrieved easily through internet or computer network. Fourth, qualified records managers and archivists who are professionally trained in the field of records management and archival science will be assigned mandatorily to guarantee the professional management of records and archives. Fifth, the illegal treatment of public records and archives constitutes a punishable crime. In the future, the public records find archival management will develop along with Korean government's 'Electronic Government Project.' Following changes are in prospect. First, public agencies will digitize paper records, audio-visual records, and publications as well as electronic documents, thus promoting administrative efficiency and productivity. Second, the National Assembly already established its Special Archives. The judiciary and the National Intelligence Service will follow it. More archives will be established at city and provincial levels. Third, the more our society develop into a knowledge-based information society, the more the records management function will become one of the important national government functions. As more universities, academic associations, and civil societies participate in promoting archival awareness and in establishing archival science, and more people realize the importance of the records and archives management up to the level of national public campaign, the records and archival management in Korea will develop significantly distinguishable from present practice.
Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.
The conflict between major corporations and small and medium-sized businesses is being aggravated, the trickle down effect is not working properly, and, as the controversy surrounding the effectiveness of the business limiting system continues to swirl, the plan proposed to protect the business domain of small and medium-sized businesses, resolve polarization between these businesses and large corporations, and protect small family run stores is the suitable business type designation system for small and medium-sized businesses. The current status of carrying out this system of selecting suitable business types among small and medium-sized businesses involves receiving applications for 234 items among the suitable business types and items from small and medium-sized businesses in manufacturing, and then selecting the items of the consultative group by analyzing and investigating the actual conditions. Suitable business type designation in the service industry will involve designation with priority on business types that are experiencing social conflict. Three major classifications of the service industry, related to the livelihood of small and medium-sized businesses, will be first designated, and subsequently this will be expanded sequentially. However, there is the concern that when designated as a suitable business type or item, this will hinder the growth motive for small to medium-sized businesses, and designation all cause decrease in consumer welfare. Also it is highly likely that it will operate as a prior regulation, cause side-effects by limiting competition systematically, and also be in violation against the main regulations of the FTA system. Moreover, it is pointed out that the system does not sufficiently reflect reverse discrimination factor against large corporations. Because conflict between small to medium sized businesses and large corporations results from the expansion of corporations to the service industry, which is unrelated to their key industry, it is necessary to introduce an advanced contract method like a master franchise or local franchise system and to develop local small to medium sized businesses through a franchise system to protect these businesses and dealers. However, this method may have an effect that contributes to stronger competitiveness of small to medium sized franchise businesses by advancing their competitiveness and operational methods a step further, but also has many negative aspects. First, as revealed by the Ministry of Knowledge Economy, the franchise industry is contributing to the strengthening of competitiveness through the economy of scale by organizing existing individual proprietors and increasing the success rate of new businesses. It is also revealed to be a response measure by the government to stabilize the economy of ordinary people and is emphasized as a 'useful way' to revitalize the service industry and improve the competitiveness of individual proprietors, and has been involved in contributions to creating jobs and expanding the domestic market by providing various services to consumers. From this viewpoint, franchises fit the purpose of the suitable business type system and is not something that is against it. Second, designation as a suitable business type may decrease investment for overseas expansion, R&D, and food safety, as well negatively affect the expansion of overseas corporations that have entered the domestic market, due to the contraction and low morale of large domestic franchise corporations that have competitiveness internationally. Also because domestic franchise businesses are hard pressed to secure competitiveness with multinational overseas franchise corporations that are operating in Korea, the system may cause difficulty for domestic franchise businesses in securing international competitiveness and also may result in reverse discrimination against these overseas franchise corporations. Third, the designation of suitable business type and item can limit the opportunity of selection for consumers who have up to now used those products and can cause a negative effect that reduces consumer welfare. Also, because there is the possibility that the range of consumer selection may be reduced when a few small to medium size businesses monopolize the market, by causing reverse discrimination between these businesses, the role of determining the utility of products must be left ot the consumer not the government. Lastly, it is desirable that this is carried out with the supplementation of deficient parts in the future, because fair trade is already secured with the enforcement of the franchise trade law and the best trade standard of the Fair Trade Commission. Overlapping regulations by the suitable business type designation is an excessive restriction in the franchise industry. Now, it is necessary to establish in the domestic franchise industry an environment where a global franchise corporation, which spreads Korean culture around the world, is capable of growing, and the active support by the government is needed. Therefore, systems that do not consider the process or background of the growth of franchise businesses and harm these businesses for the sole reason of them being large corporations must be removed. The inhibition of growth to franchise enterprises may decrease the sales of franchise stores, in some cases even bankrupt them, as well as cause other problems. Therefore the suitable business type system should not hinder large corporations, and as both small dealers and small to medium size businesses both aim at improving competitiveness and combined growth, large corporations, small dealers and small to medium sized businesses, based on their mutual cooperation, should not include franchise corporations that continue business relations with them in this system.
Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.