• Journal of KIISE:Computing Practices and Letters
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• v.12 no.3
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• pp.192-207
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• 2006

Today, the SSL protocol has been used as core part in various computing environments or security systems. But, the SSL protocol has several problems, because of the rigidity on operating. First, SSL protocol brings considerable burden to the CPU utilization so that performance of the security service in encryption transaction is lowered because it encrypts all data which is transferred between a server and a client. Second, SSL protocol can be vulnerable for cryptanalysis due to the key in fixed algorithm being used. Third, it is difficult to add and use another new cryptography algorithms. Finally. it is difficult for developers to learn use cryptography API(Application Program Interface) for the SSL protocol. Hence, we need to cover these problems, and, at the same time, we need the secure and comfortable method to operate the SSL protocol and to handle the efficient data. In this paper, we propose the SSL component which is designed and implemented using CBD(Component Based Development) concept to satisfy these requirements. The SSL component provides not only data encryption services like the SSL protocol but also convenient APIs for the developer unfamiliar with security. Further, the SSL component can improve the productivity and give reduce development cost. Because the SSL component can be reused. Also, in case of that new algorithms are added or algorithms are changed, it Is compatible and easy to interlock. SSL Component works the SSL protocol service in application layer. First of all, we take out the requirements, and then, we design and implement the SSL Component, confidentiality and integrity component, which support the SSL component, dependently. These all mentioned components are implemented by EJB, it can provide the efficient data handling when data is encrypted/decrypted by choosing the data. Also, it improves the usability by choosing data and mechanism as user intend. In conclusion, as we test and evaluate these component, SSL component is more usable and efficient than existing SSL protocol, because the increase rate of processing time for SSL component is lower that SSL protocol's.

• Journal of Science of Art and Design
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• v.5
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• pp.87-129
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• 2003

The purpose of this thesis is to propose a new concept of design of the 21st century, on the basis of the study on the general signification of the structures and the signs of industrial product design, by examining the difference between modern and post-modern design, which is expected to lead the users to different design practice and interpretation of it. The starting point of this study is the different styles and patterns of 'Gestalt' in the post-modern design of the late 20th century from modern design - the factor of determination in industrial product design. That is to say, unlike functional and rational styles of modern product design, the late 20th century is based upon the pluralism characterized by complexity, synthetic and decorativeness. So far, most of the previous studies on design seem to have excluded visual aspects and usability, focused only on effective communication of design phenomena. These partial studies on design, blinded by phenomenal aspects, have resulted in failure to discover a principle of fundamental system. However, design varies according to the times; and the transformation of design is reflected in Design Pragnanz to constitute a new text of design. Therefore, it can be argued that Design Pragnanz serves as an essential factor under influence of the significance of text. In this thesis, therefore, I delve into analysis of the 20th century product design, in the light of Gestalt theory and Design Pragnanz, which have been functioning as the principle of the past design. For this study, I attempted to discover the fundamental elements in modern and post-modern designs, and to examine the formal structure of product design, the users' aesthetic preference and its semantics, from the integrative viewpoint. Also, with reference to history and theory of design my emphasis is more on fundamental visual phenomena than on structural analysis or process of visualization in product design, in order to examine the formal properties of modern and post-modern designs. Firstly, In Chapter 1, 'Issues and Background of the Study', I investigated the Gestalt theory and Design Pragnanz, on the premise of formal distinction between modern and post-modern designs. These theories are founded upon the discussion on visual perception of Gestalt in Germany in 1910's, in pursuit of the principle of perception centered around visual perception of human beings. In Chapter 2, I dealt with functionalism of modern design, as an advance preparation for the further study on the product design of the late 20th century. First of all, in Chapter 2-1, I examined the tendency of modern design focused on functionalism, which can be exemplified by the famous statement 'Form follows function'. Excluding all unessential elements in design - for example, decoration, this tendency has attained the position of the international style based on the spirit of Bauhause - universality and regularity - in search of geometric order, standardization and rationalization. In Chapter 2-2, I investigated the anthropological viewpoint that modern design started representing culture in a symbolic way including overall aspects of the society - politics, economics and ethics, and its criticism on functionalist design that aesthetic value is missing in exchange of excessive simplicity in style. Moreover, I examined the pluralist phenomena in post-modern design such as kitsch, eclecticism, reactionism, hi-tech and digital design, breaking away from functionalist purism of modern design. In Chapter 3, I analyzed Gestalt Pragnanz in design in a practical way, against the background of design trends. To begin with, I selected mass product design among those for the 20th century products as a target of analysis, highlighting representative styles in each category of the products. For this analysis, I adopted the theory of J. M Lehnhardt, who gradated in percentage the aesthetic and semantic levels of Pragnantz in design expression, and that of J. K. Grutter, who expressed it in a formula of M = O : C. I also employed eight units of dichotomies, according to the G. D. Birkhoff's aesthetic criteria, for the purpose of scientific classification of the degree of order and complexity in design; and I analyzed phenomenal aspects of design form represented in each unit. For Chapter 4, I executed a questionnaire about semiological phenomena of Design Pragnanz with 28 units of antonymous adjectives, based upon the research in the previous chapter. Then, I analyzed the process of signification of Design Pragnanz, founded on this research. Furthermore, the interpretation of the analysis served as an explanation to preference, through systematic analysis of Gestalt and Design Pragnanz in product design of the late 20th century. In Chapter 5, I determined the position of Design Pragnanz by integrating the analyses of Gestalt and Pragnanz in modern and post-modern designs In this process, 1 revealed the difference of each Design Pragnanz in formal respect, in order to suggest a vision of the future as a result, which will provide systemic and structural stimulation to current design.

• Asia pacific journal of information systems
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• v.19 no.3
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• pp.51-67
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• 2009

Developing an agile recommender system for nomadic users has been regarded as a promising application in mobile and ubiquitous settings. To increase the quality of personalized recommendation in terms of accuracy and elapsed time, estimating future context of the user in a correct way is highly crucial. Traditionally, time series analysis and Makovian process have been adopted for such forecasting. However, these methods are not adequate in predicting context data, only because most of context data are represented as nominal scale. To resolve these limitations, the alignment-prediction algorithm has been suggested for context prediction, especially for future context from the low-level context. Recently, an ontological approach has been proposed for guided context prediction without context history. However, due to variety of context information, acquiring sufficient context prediction knowledge a priori is not easy in most of service domains. Hence, the purpose of this paper is to propose a novel context prediction methodology, which does not require a priori knowledge, and to increase accuracy and decrease elapsed time for service response. To do so, we have newly developed pattern-based context prediction approach. First of ail, a set of individual rules is derived from each context attribute using context history. Then a pattern consisted of results from reasoning individual rules, is developed for pattern learning. If at least one context property matches, say R, then regard the pattern as right. If the pattern is new, add right pattern, set the value of mismatched properties = 0, freq = 1 and w(R, 1). Otherwise, increase the frequency of the matched right pattern by 1 and then set w(R,freq). After finishing training, if the frequency is greater than a threshold value, then save the right pattern in knowledge base. On the other hand, if at least one context property matches, say W, then regard the pattern as wrong. If the pattern is new, modify the result into wrong answer, add right pattern, and set frequency to 1 and w(W, 1). Or, increase the matched wrong pattern's frequency by 1 and then set w(W, freq). After finishing training, if the frequency value is greater than a threshold level, then save the wrong pattern on the knowledge basis. Then, context prediction is performed with combinatorial rules as follows: first, identify current context. Second, find matched patterns from right patterns. If there is no pattern matched, then find a matching pattern from wrong patterns. If a matching pattern is not found, then choose one context property whose predictability is higher than that of any other properties. To show the feasibility of the methodology proposed in this paper, we collected actual context history from the travelers who had visited the largest amusement park in Korea. As a result, 400 context records were collected in 2009. Then we randomly selected 70% of the records as training data. The rest were selected as testing data. To examine the performance of the methodology, prediction accuracy and elapsed time were chosen as measures. We compared the performance with case-based reasoning and voting methods. Through a simulation test, we conclude that our methodology is clearly better than CBR and voting methods in terms of accuracy and elapsed time. This shows that the methodology is relatively valid and scalable. As a second round of the experiment, we compared a full model to a partial model. A full model indicates that right and wrong patterns are used for reasoning the future context. On the other hand, a partial model means that the reasoning is performed only with right patterns, which is generally adopted in the legacy alignment-prediction method. It turned out that a full model is better than a partial model in terms of the accuracy while partial model is better when considering elapsed time. As a last experiment, we took into our consideration potential privacy problems that might arise among the users. To mediate such concern, we excluded such context properties as date of tour and user profiles such as gender and age. The outcome shows that preserving privacy is endurable. Contributions of this paper are as follows: First, academically, we have improved sequential matching methods to predict accuracy and service time by considering individual rules of each context property and learning from wrong patterns. Second, the proposed method is found to be quite effective for privacy preserving applications, which are frequently required by B2C context-aware services; the privacy preserving system applying the proposed method successfully can also decrease elapsed time. Hence, the method is very practical in establishing privacy preserving context-aware services. Our future research issues taking into account some limitations in this paper can be summarized as follows. First, user acceptance or usability will be tested with actual users in order to prove the value of the prototype system. Second, we will apply the proposed method to more general application domains as this paper focused on tourism in amusement park.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.13 no.7
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• pp.667-678
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• 2002

In this paper, a new radiating structure with a multi-layered two-dimensional metallic disk array was proposed for shaping the flat-topped element pattern. It is an infinite periodic planar array structure with metallic disks finitely stacked above the radiating circular waveguide apertures. The theoretical analysis was in detail performed using rigid full-wave analysis, and was based on modal representations for the fields in the partial regions of the array structure and for the currents on the metallic disks. The final system of linear algebraic equations was derived using the orthogonal property of vector wave functions, mode-matching method, boundary conditions and Galerkin's method, and also their unknown modal coefficients needed for calculation of the array characteristics were determined by Gauss elimination method. The application of the algorithm was demonstrated in an array design for shaping the flat-topped element patterns of $\pm$20$^{\circ}$ beam width in Ka-band. The optimal design parameters normalized by a wavelength for general applications are presented, which are obtained through optimization process on the basis of simulation and design experience. A Ka-band experimental breadboard with symmetric nineteen elements was fabricated to compare simulation results with experimental results. The metallic disks array structure stacked above the radiating circular waveguide apertures was realized using ion-beam deposition method on thin polymer films. It was shown that the calculated and measured element patterns of the breadboard were in very close agreement within the beam scanning range. The result analysis for side lobe and grating lobe was done, and also a blindness phenomenon was discussed, which may cause by multi-layered metallic disk structure at the broadside. Input VSWR of the breadboard was less than 1.14, and its gains measured at 29.0 GHz. 29.5 GHz and 30 GHz were 10.2 dB, 10.0 dB and 10.7 dB, respectively. The experimental and simulation results showed that the proposed multi-layered metallic disk array structure could shape the efficient flat-topped element pattern.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.7
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• pp.380-394
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• 2015

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2014. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering have been reviewed as groups of heat and mass transfer, cooling and heating, and air-conditioning, the flow inside building rooms, and smoke control on fire. Research issues dealing with duct and pipe were reduced, but flows inside building rooms, and smoke controls were newly added in thermal and fluid engineering research area. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the results for thermal contact resistance measurement of metal interface, a fan coil with an oval-type heat exchanger, fouling characteristics of plate heat exchangers, effect of rib pitch in a two wall divergent channel, semi-empirical analysis in vertical mesoscale tubes, an integrated drying machine, microscale surface wrinkles, brazed plate heat exchangers, numerical analysis in printed circuit heat exchanger. In the area of pool boiling and condensing, non-uniform air flow, PCM applied thermal storage wall system, a new wavy cylindrical shape capsule, and HFC32/HFC152a mixtures on enhanced tubes, were actively studied. In the area of industrial heat exchangers, researches on solar water storage tank, effective design on the inserting part of refrigerator door gasket, impact of different boundary conditions in generating g-function, various construction of SCW type ground heat exchanger and a heat pump for closed cooling water heat recovery were performed. (3) In the field of refrigeration, various studies were carried out in the categories of refrigeration cycle, alternative refrigeration and modelling and controls including energy recoveries from industrial boilers and vehicles, improvement of dehumidification systems, novel defrost systems, fault diagnosis and optimum controls for heat pump systems. It is particularly notable that a substantial number of studies were dedicated for the development of air-conditioning and power recovery systems for electric vehicles in this year. (4) In building mechanical system research fields, seventeen studies were reported for achieving effective design of the mechanical systems, and also for maximizing the energy efficiency of buildings. The topics of the studies included energy performance, HVAC system, ventilation, and renewable energies, piping in the buildings. Proposed designs, performance performance tests using numerical methods and experiments provide useful information and key data which can improve the energy efficiency of the buildings. (5) The field of architectural environment was mostly focused on indoor environment and building energy. The main researches of indoor environment were related to the evaluation of work noise in tunnel construction and the simulation and development of a light-shelf system. The subjects of building energy were worked on the energy saving of office building applied with window blind and phase change material(PCM), a method of existing building energy simulation using energy audit data, the estimation of thermal consumption unit of apartment building and its case studies, dynamic window performance, a writing method of energy consumption report and energy estimation of apartment building using district heating system. The remained studies were related to the improvement of architectural engineering education system for plant engineering industry, estimating cooling and heating degree days for variable base temperature, a prediction method of underground temperature, the comfort control algorithm of car air conditioner, the smoke control performance evaluation of high-rise building, evaluation of thermal energy systems of bio safety laboratory and a development of measuring device of solar heat gain coefficient of fenestration system.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.4
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• pp.479-488
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• 2002

In recent years the amount of digital video used has risen dramatically to keep pace with the increasing use of the Internet and consequently an automated method is needed for indexing digital video databases. Textual information, both superimposed and embedded scene texts, appearing in a digital video can be a crucial clue for helping the video indexing. In this paper, a new method is presented to extract both superimposed and embedded scene texts in a freeze-frame of news video. The algorithm is summarized in the following three steps. For the first step, a color image is converted into a gray-level image and applies contrast stretching to enhance the contrast of the input image. Then, a modified local adaptive thresholding is applied to the contrast-stretched image. The second step is divided into three processes: eliminating text-like components by applying erosion, dilation, and (OpenClose+CloseOpen)/2 morphological operations, maintaining text components using (OpenClose+CloseOpen)/2 operation with a new Geo-correction method, and subtracting two result images for eliminating false-positive components further. In the third filtering step, the characteristics of each component such as the ratio of the number of pixels in each candidate component to the number of its boundary pixels and the ratio of the minor to the major axis of each bounding box are used. Acceptable results have been obtained using the proposed method on 300 news images with a recognition rate of 93.6%. Also, my method indicates a good performance on all the various kinds of images by adjusting the size of the structuring element.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.147-161
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• 2010

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.658-672
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• 2022

The unique teaching and learning difficulties of speed-related units in elementary school science are mainly due to the student's lack of mathematical thinking ability and procedural knowledge on speed measurement, and curriculums and textbooks must be constructed with these in mind. To identify the implications of composing a new science curriculum and relevant textbooks, this study reviewed the structure and contents of the speed-related units of three curriculums from the 2007 revised curriculum to the 2015 revised curriculum and the resulting textbooks and examined their relevance in light of the literature. Results showed that the current content carries the risk of making students calculate only the speed of an object through a mechanical algorithm by memorization rather than grasp the multifaceted relation between traveled distance, duration time, and speed. Findings also highlighted the need to reorganize the curriculum and textbooks to offer students the opportunity to learn the meaning of speed step-by-step by visualizing materials such as double number lines and dealing with simple numbers that are easy to calculate and understand intuitively. In addition, this paper discussed the urgency of improving inquiry performance such as process skills by observing and measuring an actual object's movement, displaying it as a graph, and interpreting it rather than conducting data interpretation through investigation. Lastly, although the current curriculum and textbooks emphasize the connection with daily life in their application aspects, they also deal with dynamics-related content somewhat differently from kinematics, which is the main learning content of the unit. Hence, it is necessary to reorganize the contents focusing on cases related to speed so that students can grasp the concept of speed and use it in their everyday lives. With regard to the new curriculum and textbooks, this study proposes that students be provided the opportunity to systematically and deeply study core topics rather than exclude content that is difficult to learn and challenging to teach so that students realize the value of science and enjoy learning it.