• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.141-149
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• 2023

Repacked mobile apps constitute about 78% of all malware of Android, and it greatly affects the technical ecosystem of Android. Although many methods exist for repacked app detection, most of them suffer from performance issues. In this manuscript, a novel method using the Constant Key Point Selection and Limited Binary Pattern (CKPS: LBP) Feature extraction-based Hashing is proposed for the identification of repacked android applications through the visual similarity, which is a notable feature of repacked applications. The results from the experiment prove that the proposed method can effectively detect the apps that are similar visually even that are even under the double fold content manipulations. From the experimental analysis, it proved that the proposed CKPS: LBP method has a better efficiency of detecting 1354 similar applications from a repository of 95124 applications and also the computational time was 0.91 seconds within which a user could get the decision of whether the app repacked. The overall efficiency of the proposed algorithm is 41% greater than the average of other methods, and the time complexity is found to have been reduced by 31%. The collision probability of the Hashes was 41% better than the average value of the other state of the art methods.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.30-43
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• 2018

Many studies have been carried out to retrieve images using colors, shapes, and textures which are characteristic of images. In addition, there is also progress in research related to the disease images of the crop. In this paper, to be a help to identify the disease occurred in crops grown in the agricultural field, we propose a similarity-based crop disease search system using the diseases image of horticulture crops. The proposed system improves the similarity retrieval performance compared to existing ones through the combination descriptor without using a single descriptor and applied the weight based calculation method to provide users with highly readable similarity search results. In this paper, a total of 13 Descriptors were used in combination. We used to retrieval of disease of six crops using a combination Descriptor, and a combination Descriptor with the highest average accuracy for each crop was selected as a combination Descriptor for the crop. The retrieved result were expressed as a percentage using the calculation method based on the ratio of disease names, and calculation method based on the weight. The calculation method based on the ratio of disease name has a problem in that number of images used in the query image and similarity search was output in a first order. To solve this problem, we used a calculation method based on weight. We applied the test image of each disease name to each of the two calculation methods to measure the classification performance of the retrieval results. We compared averages of retrieval performance for two calculation method for each crop. In cases of red pepper and apple, the performance of the calculation method based on the ratio of disease names was about 11.89% on average higher than that of the calculation method based on weight, respectively. In cases of chrysanthemum, strawberry, pear, and grape, the performance of the calculation method based on the weight was about 20.34% on average higher than that of the calculation method based on the ratio of disease names, respectively. In addition, the system proposed in this paper, UI/UX was configured conveniently via the feedback of actual users. Each system screen has a title and a description of the screen at the top, and was configured to display a user to conveniently view the information on the disease. The information of the disease searched based on the calculation method proposed above displays images and disease names of similar diseases. The system's environment is implemented for use with a web browser based on a pc environment and a web browser based on a mobile device environment.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.6
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• pp.514-528
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• 2004

An emotion-based video scene retrieval algorithm is proposed in this paper. First, abrupt/gradual shot boundaries are detected in the video clip representing a specific story Then, five video features such as 'average color histogram' 'average brightness', 'average edge histogram', 'average shot duration', and 'gradual change rate' are extracted from each of the videos and mapping between these features and the emotional space that user has in mind is achieved by an interactive genetic algorithm. Once the proposed algorithm has selected videos that contain the corresponding emotion from initial population of videos, feature vectors from the selected videos are regarded as chromosomes and a genetic crossover is applied over them. Next, new chromosomes after crossover and feature vectors in the database videos are compared based on the similarity function to obtain the most similar videos as solutions of the next generation. By iterating above procedures, new population of videos that user has in mind are retrieved. In order to show the validity of the proposed method, six example categories such as 'action', 'excitement', 'suspense', 'quietness', 'relaxation', 'happiness' are used as emotions for experiments. Over 300 commercial videos, retrieval results show 70% effectiveness in average.

• Journal of Information Technology and Architecture
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• v.9 no.1
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• pp.57-66
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• 2012

Recently, with a rapidly growing of the mobile content market, a variety of mobile-based applications are being launched. But mobile devices, compared to the average computer, take a lot of effort and time to get the final contents you want to use due to the restrictions such as screen size and input methods. To solve this inconvenience, a recommender system is required, which provides customized information that users prefer by filtering and forecasting the information.In this study, an tailored multi-information recommendation system utilizing a Personalized information recommendation system on smartphone is proposed. Filtering of information is to predict and recommend the information the individual would prefer to by using the user-based collaborative filtering. At this time, the degree of similarity used for the user-based collaborative filtering process is Euclidean distance method using the Pearson's correlation coefficient as weight value.As a real applying case to evaluate the performance of the recommender system, the scenarios showing the usefulness of recommendation service for the actual restaurant is shown. Through the comparison experiment the augmented reality based multi-recommendation services to the existing single recommendation service, the usefulness of the recommendation services in this study is verified.

• Korean Journal of Environment and Ecology
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• v.2 no.1
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• pp.37-49
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• 1988

This study was conducted to investigate the user's effects on the avifauna in Chiak Mountain National Park. The survey was carried over 5 districts which were 3 main trail (valley) by line transect method from May to October 1988. The observed birds were 44 species and 613 individuals. these consist of 22 species for residents, 13 species for summer visitor, 5 species for winter visitor and 4 species for passage migrant. The average density of birds was 2.96 ea/ha, and main dominant species were Panus minar, Paradoxomis webbiana fulvicanda, Panus palustris hellmayri and Parus ater amurensis in order. The observed frequency of wild-birds was 6.77 times/km in average. No. of species, species diversities and observed frequency were lower in main trail of high user's density than sub trail and closed trail, especially in summer. But similarity indices were affected by inhabitation environments than user's density. Therefore, from now, in Chiak Mountain National Park management, the enjoyment oriented activities will be regulated and the control of trail use on main trail are necessary during breeding periods for wild birds protection.

• Journal of Environmental Health Sciences
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• v.17 no.2
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• pp.54-66
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• 1991

As a result of measuring illumination and making up a question at home visit directly by investigator who trained over twenty days period from October 4 to 24, 1990, in order to render help which illumination problem against house, society against eyes or framing of health instruction potgram by seizing natural lighting actual conditions of house and actual conditions of wearing spectacles and by investigating interrelationship, I can summarize as follows. 1) In property of investigation subject, woman 66.9%, In an age, the twenties was largest of 27.4%, the forties was 20.2%, the fifties was 18.6%, the thirties was 17.4%. In academic career, those of upper secondary school grauates was largest of 28.6%, those who possess university career was 25.9%, those who middle school career was 20.9%, decoding of Korean alphabet was 2%. 2) By a residence area, a big city was 43.3%, farming and fishing villages were 20.3%, the rest was a small town and the administrative office of town, township. In positon of house, the middle area was 43.6%, resident of suburb area was 38.0%. In form of house, a Korean-style house was 40.8%, a western-style house was 34.8%, an apartment house was 11.0%. In the a standard of living, the middle classes 77.2%, the lower classes were 15.3%. In residential house unit of area, from 21 to 30 unit of area was largest of 31.5%, from 10 to 20 unit of area was 19.9%, from 31 to 40 was 18.7%. 3) The wearing spectacles rate of study user was 44.1%. By the area, those who wearing spectacles was more than a half of 50.8% in the resident of big city area. As passing from the farm area to the city, that is being resident of big city was high wearing spectacles rate. In position of house, as being residence in central street showed high wearing spectacles rate. (central street was 51.5%, the middle area was 44.5% and the suburb area was 40.1%.) It seemed similarity difference a variable by position of house from wearing spectacles in standard of 1%. By form of house, wearing spectacles rate those who resident in apartment house was 49.5%, that rate those who resident in a western-style house was high of 49.0%, that rate those who resident in a Korean-style house was the lowest 39.0%. By social position of resident in room, in students case who study showed very high, as university students were very high of 62.3% idn wearing spectacles rate, middle and high school students 'were 50.0%, members of society were 47.6%, workers 20.3%. It seemed similarity difference from academic career in standard of 1%. By an age, the thirties was high of 54.1% in wearing spectacles rate, the twenties was 43.2%, the teenage was the lowest of 11.8%. 4) In illumination of study, over 200Lux was high of 40.1%. but below 99Lux which inappropriate illumination to see the books was 32.4%. Average by area, below 99Lux was 22.7% and over 400Lux was 50.0% in case of wooden floor. As examine by area, below 99Lux was high of 27.0% a case of wooden floor in the big city area, it was not good in illumination passing from the farm area(15.0%) to the city(19.0%). Average illlumination by area of the main living room below 99Lux was high of 37.5%, less than 200Lux was 58.5% of whole. In general, illumination of the main livingroom was inappropriate. By area, the big city was 32.5% below 99Lux, the middle and small city area were 33.8%, town and township area were 45.0%, farming and fishing area were 42.8%. By area, in the big city, illumination of study was 52.5% over 200Lux and 28.9% below 99Lux. In case of the middle and small city, study user of below 99Lux was 38.8% and over 200Lux was 46.9%. In case of the seat of town township, below 99Lux was 34.1% and over 200Lux was 39.7%. In case of farming and fishing area, illumination of study was 33.4% below 99Lux and 48.4% over 200Lux. It tends to high rate of inappropriate illumination. 5) By position of house, in case of wooden floor, less than 100Lux was 24.5% in central street. It was bad illumination than others position of house. In case of the main livingroom, less than 100Lux was 40.4% in the suburb area. It was bad iliumnation than others position of house. In case of study, less than 100Lux was 35.4% in the middle area, it was worse in illumination. In case of the main living room, is seemed similarity difference in standard of 1%. 6) By form of house, in case of wooden floor, illumination of less than 100Lux was 23.8% in a western-style house, it was bad illumination than others form of house. In case of the main livingroom, illumination of less than 100Lux was 47.4% in a Korean-style house, it was remarkably bad illumination than others form of house. In case of study, a Korean-style house was 38.8%, it was very bad illumination than others form of house. In case of the main livingroom and study, it seemed similatrity difference each as P < 0.01 and P < 0.05 in standard of 1%. 7) The wearing spectacles rate of those who use room of illumination over 400Lux was 40.7%, and that of those who use room of illumination less than 100Lux was 28.1%. It seemed similarity differecce in standard of 1%. 8) In period of wearing spectacles, 21.3% of total investigator-highest-was from before five years, 8.6% was from before three years. Among those who use of illumintion less than 99Lux, 34.0% began to wear spectacles from before two years 31.7% was from before five years, 30.3% was from before four years. It seemed similarity difference from period of wearing spectacles by illumination in standard of 1 %. 9) Among cause which sight grow worse, the first was that it was each 33.2% and 27.4% in response rate because watch TV nearly to wearing spectacles person and non-wearing person. The second was that a lot of seeing books was 25.3% in wearing spectacles person and response rate for dark illumination was 7.4% in nonwearing spectacles person. It seemed similarity difference in standard of 1%. (P < 0.01). 10) In experience which take medicine good for eyes, it was 50.1% in wearing spectacles person and 8.5% in non-wearing spectacles person. It seemed similarity difference in standard of 1%(P < 0.01). As we have seen above, inappropriate illumination can be a cause of wearing spectacles. Nevertheless, actually, is realities to indifferent against illumination of house. So it must learn knowledge about health obstacle of illumination through society instruction and school eduction against students as well as general residents. In case that natural lighting is inappropriate structural of house, we must be able to maintain appropriate illumination through artificial illumination. And so eyes which is core of human life have to be protected, related the authorities, related group, and all health medical personnel will organically cooperate with and make efforts.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.714-717
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• 2008

협업여과 추천기법에는 사용자 기반 협업여과와 아이템 기반 협업여과가 있으며, 절차는 유사도 측정, 이웃 선정, 예측값 생성 단계로 이루어진다. 유사도 측정 단계에는 유클리드 거리(Euclidean Distance), 코사인 유사도(Cosine Similarity), 피어슨 상관계수(Pearson Correlation Coefficient) 방법 등이 있고, 이웃 선정 단계에는 상관 한계치(Correlation-Threshold), 근접 N 이웃(Best-N-Neighbors) 방법 등이 있다. 마지막으로 예측값 생성 단계에는 단순평균(Simple Average), 가중합(Weighted Sum), 조정 가중합(Adjusted Weighted Sum) 등이 있다. 이처럼 협업여과 추천기법에는 다양한 기법들이 사용되고 있다. 따라서 본 논문에서는 사용자 기반 협업여과와 아이템 기반 협업여과 추천기법에 사용되는 유사도 측정 기법과 예측값 생성 기법의 최적화된 조합을 알아보기 위해 성능 실험 및 비교 분석을 하였다. 실험은 GroupLens의 MovieLens 데이터 셋을 활용하였고 MAE(Mean Absolute Error)값을 이용하여 추천기법을 비교 하였다. 실험을 통해 유사도 측정 기법과 예측값 생성 기법의 최적화된 조합을 찾을 수 있었고, 사용자 기반 협업여과와 아이템 기반 협업여과의 성능비교를 통해 아이템 기반 협업여과의 성능이 보다 우수했음을 확인 하였다.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.125-141
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• 2014

Nowadays, overflowing data produced every second from the internet make people to be difficult to search for the useful information. That's why people have invented and developed unique tools that they get some relevant information. In this paper, the recommender system, one of the effective tools, is used and it helps us to get the useful information that we want by using demographic information to predict new items of interest. The demographic recommender system in this paper computes users' similarity using demographic information, age and gender. So we performed demographic analysis on movie ratings on Internet Movie Database (IMDB) web site that movies are rated by thousands of people, where users submitted a movie rating after they watched a recent popular film. Meanwhile, we can understand that user's ratings, among various determinants of box office, is very essential factor in the study on recommendation of movie. This paper is aimed at analyzing movie average ratings directly given by film viewers, categorizing them into groups by sex and age, investigating the entire group and finding the representative group by examining it with F-test and T-test. This result is used to promote and recommend for the target group only. Therefore, this study is considerably significant as presenting utilization for movie business as well as showing how to analyze demographic information on movie ratings on the web.