• Journal of Korea Spatial Information System Society
• /
• v.7 no.3 s.15
• /
• pp.67-81
• /
• 2005

Recently, the interest and research on the development of new application services such as the Location Based Service and Telemetics providing the emergency service, neighbor information search, and route search according to the development of the Geographic Information System have been increasing. User's search in the spatio-temporal database which is used in the field of Location Based Service or Telemetics usually fixes the current time on the time axis and queries the spatial and aspatial attributes. Thus, if the range of query on the time axis is extensive, it is difficult to efficiently deal with the search operation. For solving this problem, the snapshot, a method to summarize the location data of moving objects, was introduced. However, if the range to store data is wide, more space for storing data is required. And, the snapshot is created even for unnecessary space that is not frequently used for search. Thus, non storage space and memory are generally used in the snapshot method. Therefore, in this paper, we suggests the Hash-based Spatio-Temporal Clustering Algorithm(H-STCA) that extends the two-dimensional spatial hash algorithm used for the spatial clustering in the past to the three-dimensional spatial hash algorithm for overcoming the disadvantages of the snapshot method. And, this paper also suggests the knowledge extraction algorithm to extract the knowledge for the path search of moving objects from the past location data based on the suggested H-STCA algorithm. Moreover, as the results of the performance evaluation, the snapshot clustering method using H-STCA, in the search time, storage structure construction time, optimal path search time, related to the huge amount of moving object data demonstrated the higher performance than the spatio-temporal index methods and the original snapshot method. Especially, for the snapshot clustering method using H-STCA, the more the number of moving objects was increased, the more the performance was improved, as compared to the existing spatio-temporal index methods and the original snapshot method.

• The KIPS Transactions:PartC
• /
• v.10C no.6
• /
• pp.683-694
• /
• 2003

We present X-tree Diff, a change detection algorithm for tree-structured data. Our work is motivated by need to monitor massive volume of web documents and detect suspicious changes, called defacement attack on web sites. From this context, our algorithm should be very efficient in speed and use of memory space. X-tree Diff uses a special ordered labeled tree, X-tree, to represent XML/HTML documents. X-tree nodes have a special field, tMD, which stores a 128-bit hash value representing the structure and data of subtrees, so match identical subtrees form the old and new versions. During this process, X-tree Diff uses the Rule of Delaying Ambiguous Matchings, implying that it perform exact matching where a node in the old version has one-to one corrspondence with the corresponding node in the new, by delaying all the others. It drastically reduces the possibility of wrong matchings. X-tree Diff propagates such exact matchings upwards in Step 2, and obtain more matchings downwsards from roots in Step 3. In step 4, nodes to ve inserted or deleted are decided, We aldo show thst X-tree Diff runs on O(n), woere n is the number of noses in X-trees, in worst case as well as in average case, This result is even better than that of BULD Diff algorithm, which is O(n log(n)) in worst case, We experimented X-tree Diff on reat data, which are about 11,000 home pages from about 20 wev sites, instead of synthetic documets manipulated for experimented for ex[erimentation. Currently, X-treeDiff algorithm is being used in a commeercial hacking detection system, called the WIDS(Web-Document Intrusion Detection System), which is to find changes occured in registered websites, and report suspicious changes to users.

• Korean Journal of Heritage: History & Science
• /
• v.56 no.2
• /
• pp.264-279
• /
• 2023

The 50th anniversary of the UNESCO World Heritage Convention was in 2022. In order to reflect on the present and future of the meaning of World Heritage, this paper examines the development and changes of the UNESCO World Heritage system. After promulgating the convention in 1972, the UNESCO World Heritage system prioritized the protection of heritage sites in the world that were at risk due to armed conflicts and natural disasters to bequeath heritage to the next generation. In addition, the UNESCO World Heritage's emphasis on Outstanding Universal Value represents the particular culture of human beings formed during a certain period of time, and acts as a significant source of soft power in public diplomacy. The UNESCO World Heritage might be perceived as a shared heritage that has not only become a channel to understand various national values, but also an effective medium to convey one of UNESCO's main principles, that is, peacebuilding. However, the UNESCO World Heritage is now at the center of conflicts of heritage interpretation between many stakeholders related to invisible wars, such as cultural wars, memory wars, and history wars as the social, political, and cultural contexts concerning World Heritage have dramatically shifted with the passing of time. Paying attention to such changing contexts, this paper seeks to understand the main developments in UNESCO World Heritage's discourse concerning changes to the World Heritage Operation Guidelines and heritage experts' meetings by dividing its 50-year history into five phases. Next, this paper analyzes the main shifts in keywords related to UNESCO World Heritage through UNESDOC, which is a platform on which all UNESCO publications are available. Finally, this paper discusses three main changes of UNESCO World Heritage: 1) changes in focus in World Heritage inscriptions, 2) changes in perception of World Heritage protection, and 3) changes of view on the role of the stakeholders in World Heritage. It suggests new emerging issues regarding heritage interpretation and ethics, climate change, and human rights.

• Asia pacific journal of information systems
• /
• v.21 no.2
• /
• pp.89-116
• /
• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.29 no.3
• /
• pp.106-115
• /
• 2011

This study, based on (edited in 18th century), analysed the landscape system and cultural landscape elements of Yuhoidang(Hageowon 何去園) Garden in Musu-dong, Daejeon, and the findings are as in the following. YuHoidang(Gwon Yijin 權以鎭) managed Hageowon Garden in Musu-dong, located on the southern branch of Mt. Bomun, to realize his utopia. The completion of Hageowon Garden was only possible due to his installation of a variety of facilities in family gravesite on the hill behind his house: Shimyoso(Samgeunjeongsa 三近精舍, in 1707), Naboji(納汚池, in 1713), Banhwanwon(in 1714) and expended exterior space(in 1727). With regard to the landscape system of the village, the main range of mountains consists of Mt. Daedun, Mt. Odae and Mt. Bomun. The main high mountain of the three is Mt. Bomun, where 'Blue Dragon' hill branches off on the east side(Eungbong), 'White Tiger' in the west(Cheongeun and Sajeong) and Ansan(inner mountain) in the south. The landscape system is featured by 'mountains in back and rivers in front'. The river in the south-west, with its source in Mt. Juryun is called as the 'Stream of outer perfect spot', while the 'Stream of inner perfect spot' rises from Eungbong, passing through the east part of the village into the south-western direction. Banhwanwon Garden(盤桓園) was created with the stream in the east and natural bedrocks, and its landscape elements includes Naboji, Hwalsudam, Gosudae, Sumi Waterfall, Dogyeong(path of peach trees), Odeeokdae(platform with persimmon trees), Maeryong(Japanese apricot tree), springs and observatories. An expanded version of Banhwanwon was Hageowon garden, where a series of 'water-trees-stone' including streams, four ponds, five observation platforms, three bamboo forests and Chukgyeongwon(縮景園) of an artificial hill gives the origin forest a scenic atmosphere. When it comes to semantics landscape elements, there are (1) Yuhoidang to cherish the memory of a deceased parents, (2) Naboji for family unification, (3) Gosudae to keep fidelity, (4) Odeokdae to collect virtue and wisdom, (5) Sumi Waterfall to aspire to be a man of noble character, (6) Yocheondae for auspicious life, (7) Sumanheon and Gigungjae to be in pursuit of hermitic life, (8) Hwalsudam for development of family and study, (9) Mongjeong to repay favor of ancestors, (10) Seokgasan, a symbol of secluded life, (11) Hageowon to enjoy guarding graves in retired life. The spatial composition of Hageowon was realized through (1) Yuhoidang's inside gardens(Naboji, Jucheondang, Odeokdae, Dogyeong, Back yard garden and others) (2) Sumanheon(收漫軒) Byeolup or Yuhoidang's back yard gardens (Seokyeonji, Yocheondae, Sumanheon, Baegyeongdae, Amseokwon and others) (3) Chukgyeongwon of the artificial hill(which is also the east garden of Sumanheon, being composed of Hwalsudam, Sumi Waterfall and Gasan or 12 mountaintops) (4) the scenic spots for unifying Confucianism, Buddhism and Taoism are Cemetry garden in the back hill of the village, the temple of Yeogyeongam, Sansinkak(ancestral ritual place of folk religion) and Geoeopjae(family school). On top of that, Chagyeongwon Garden(借景園) commands a panoramic distant view of nature's changing beauty through the seasons.

• Journal of Intelligence and Information Systems
• /
• v.23 no.1
• /
• pp.95-108
• /
• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

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

• Progress in Medical Physics
• /
• v.20 no.2
• /
• pp.80-87
• /
• 2009

Phase transformation, superelastic characteristics and variation of surface residual stress were studied for Nitinol shape memory alloy through application of UNSM technology, and life extension methods of stent were also studied by using elastic resilience and corrosion resistance. Nitinol wire of ${\phi}1.778$ mm showed similar surface roughness before and after UNSM treatment, but drawing traces and micro defects were all removed by UNSM treatment. It also changed the surface residual stress from tensile to compressive values, and XRD result showed less intensive austenite peak and clear martensite and additional R-phase peaks after UNSM treatment. Fatigue resistance could be greatly improved through removal of surface defects and rearrangement of surface residual stress from tensile to compressive state, and development of surface modification system to improve not only bio-compatability but also resistance to corrosion and wear will make it possible to develop vascular stent which can be used for circulating system diseases which run first cause of death of recent Koreans.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.12
• /
• pp.569-581
• /
• 2016

This experimental study employs a pretest -posttest method concerning the integrated therapy of neurofeedback training, brain gymnastics, and oriental herbal tea effects on seniors living alone. The purpose of the study is to identify effects on brain functions and quality of life. The study participants included 23 seniors living alone (male 10, female 13), ranging in age from 65~90. The experiment lasted 8 weeks, from December 22, 2014, to February 28, 2015. The neurofeedback training utilized the 2 Channel neurofeedback system by Braintech Corporation and was conducted a total of 16 times over 8 weeks, having two sessions per week lasting for 30 minutes focusing on relaxation, concentration and memory. Brain gymnastics, developed by the Korean Science Institute of Psychiatry, ran for 30 minutes, twice a week for a total of 16 sessions administered over 8 weeks. Participants were required to drink 3 cups of oriental herbal tea developed according to Donguibogam for 8 weeks. The results of applying integrated therapy found positive effects on brain function resulting from changes in level of tension and anti-stress quotient. Quality of life, daily life basic measurements, and depression symptoms were significantly influenced by decreases in blood pressure and blood sugar. Results of this study find the integrated therapy of neurofeedback training, brain gymnastics, and oriental herbal tea can significantly enhance brain functions and quality of life within seniors living alone. Therefore integrated therapy involving neurofeedback, brain gymnastics, and oriental herbal tea is a necessary intervention to improve the quality of life of seniors living alone, and will improve the quality of life through healing both mind and body in a more practical and systematical manner.

• Asia pacific journal of information systems
• /
• v.21 no.4
• /
• pp.45-62
• /
• 2011

The notion of information systems (IS) continuance has recently emerged as one of the most important research issues in the field of IS. A great deal of research has been conducted thus far on the basis of theories adapted from various disciplines including consumer behaviors and social psychology, in addition to theories regarding information technology (IT) acceptance. This previous body of knowledge provides a robust research framework that can already account for the determination of IS continuance; however, this research points to other, thus-far-unelucidated determinant factors such as habit, which were not included in traditional IT acceptance frameworks, and also re-emphasizes the importance of emotion-related constructs such as satisfaction in addition to conscious intention with rational beliefs such as usefulness. Experiences should also be considered one of the most important factors determining the characteristics of information system (IS) continuance and the features distinct from those determining IS acceptance, because more experienced users may have more opportunities for IS use, which would allow them more frequent use than would be available to less experienced or non-experienced users. Interestingly, experience has dual features that may contradictorily influence IS use. On one hand, attitudes predicated on direct experience have been shown to predict behavior better than attitudes from indirect experience or without experience; as more information is available, direct experience may render IS use a more salient behavior, and may also make IS use more accessible via memory. Therefore, experience may serve to intensify the relationship between IS use and conscious intention with evaluations, On the other hand, experience may culminate in the formation of habits: greater experience may also imply more frequent performance of the behavior, which may lead to the formation of habits, Hence, like experience, users' activation of an IS may be more dependent on habit-that is, unconscious automatic use without deliberation regarding the IS-and less dependent on conscious intentions, Furthermore, experiences can provide basic information necessary for satisfaction with the use of a specific IS, thus spurring the formation of both conscious intentions and unconscious habits, Whereas IT adoption Is a one-time decision, IS continuance may be a series of users' decisions and evaluations based on satisfaction with IS use. Moreover. habits also cannot be formed without satisfaction, even when a behavior is carried out repeatedly. Thus, experiences also play a critical role in satisfaction, as satisfaction is the consequence of direct experiences of actual behaviors. In particular, emotional experiences such as enjoyment can become as influential on IS use as are utilitarian experiences such as usefulness; this is especially true in light of the modern increase in membership-based hedonic systems - including online games, web-based social network services (SNS), blogs, and portals-all of which attempt to provide users with self-fulfilling value. Therefore, in order to understand more clearly the role of experiences in IS continuance, analysis must be conducted under a research framework that includes intentions, habits, and satisfaction, as experience may not only have duration-based moderating effects on the relationship between both intention and habit and the activation of IS use, but may also have content-based positive effects on satisfaction. This is consistent with the basic assumptions regarding the determining factors in IS continuance as suggested by Oritz de Guinea and Markus: consciousness, emotion, and habit. The principal objective of this study was to explore and assess the effects of experiences in IS continuance, with special consideration given to conscious intentions and unconscious habits, as well as satisfaction. IN service of this goal, along with a review of the relevant literature regarding the effects of experiences and habit on continuous IS use, this study suggested a research model that represents the roles of experience: its moderating role in the relationships of IS continuance with both conscious intention and unconscious habit, and its antecedent role in the development of satisfaction. For the validation of this research model. Korean university student users of 'Cyworld', one of the most influential social network services in South Korea, were surveyed, and the data were analyzed via partial least square (PLS) analysis to assess the implications of this study. In result most hypotheses in our research model were statistically supported with the exception of one. Although one hypothesis was not supported, the study's findings provide us with some important implications. First the role of experience in IS continuance differs from its role in IS acceptance. Second, the use of IS was explained by the dynamic balance between habit and intention. Third, the importance of satisfaction was confirmed from the perspective of IS continuance with experience.