• Title/Summary/Keyword: Service Feature

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Implementation of a Digital Convergence Platform for Future Home Multimedia Appliances (미래 홈 멀티미디어 가전을 위한 디지털 컨버젼스 플랫폼 구현)

  • Oh, Hwa-Yong;Kim, Dong-Hwan;Lee, Eun-Seo;Chang, Tae-Guy
    • Proceedings of the IEEK Conference
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    • 2005.11a
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    • pp.983-986
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    • 2005
  • This paper describes a digital convergence platform(DCP) whice is implemented based on the MPEG-21 multimedia framework. The DCP is a newly proposed solution in this research for the convergence service of future home multimedia environment. The DCP is a common platform designed to have the feature of configurability, via means of S/W, which is needed for the convergence service of diverse digital media. A distributed peer to peer service and transaction model is also a new feature realized in the DCP using the MPEG-21 multimedia framework. A prototype DCP is implemented to verify its functions of multimedia service and transactions. The developed DCPs are networked with IP clustering storage systems for the distributed service of multimedia. Successful streaming services of the MPEG-2/4 video and audio are verified with the implemented test-bed system of the DCP.

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A Mechanism for Conflict Detection and Resolution for Service Interaction : Toward IP-based Network Services (IP 기반 융합서비스를 위한 서비스 충돌 감지 및 해결에 대한 연구)

  • Oh, Joseph;Shin, Dong-Min
    • IE interfaces
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    • v.23 no.1
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    • pp.24-34
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    • 2010
  • In the telecommunication system which is based on the existing PSTN(public switched telephone network), feature interaction has been an important research issue in order to provide seamless services to users. Recently, rapid proliferation of IP-based network and the various types of IP media supply services, the feature interaction from the perspective of application services has become a significant aspect. This paper presents conflict detection and resolution algorithms for designing and operating a variety of services that are provided through IP-based network. The algorithms use explicit service interactions to detect conflicts between a new service and registered services. They then apply various rules to reduce search space in resolving conflicts. The algorithms are applied to a wide range of realistic service provision scenarios to validate that it can detect conflicts between services and resolve in accordance with different rule sets. By applying the algorithms to various scenarios, it is observed that the proposed algorithms can be effectively used in operating an IP-based services network.

Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

  • Yeom, Ha-Neul;Hwang, Myunggwon;Hwang, Mi-Nyeong;Jung, Hanmin
    • Journal of Information Science Theory and Practice
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    • v.2 no.3
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    • pp.29-39
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    • 2014
  • In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

A TINA-Based Component Modeling for Static Service Composition

  • Shin, Young-Seok;Lim, Sun-Hwan
    • Journal of information and communication convergence engineering
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    • v.2 no.1
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    • pp.40-45
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    • 2004
  • This paper describes a modeling of service composition manager based on TINA (Telecommunication Information Networking Architecture). The Service composition function is mainly motivated by the desire to easily generate new service using existing services from retailers or $3^{rd}$-party service providers. The TINA-C specification for the service composition does not include the detailed composition procedure and its object models. In this paper, we propose a model of components for the service composition, which adapts a static composition feature in a single provider domain. To validate the proposed modeling, we implemented prototype service composition function, which combines two multimedia services; a VOD service and a VCS service. As a result, we obtain the specification of the detailed composition architecture between a retailer domain and a $3^{rd}$-party service provider domain.

Spam Image Detection Model based on Deep Learning for Improving Spam Filter

  • Seong-Guk Nam;Dong-Gun Lee;Yeong-Seok Seo
    • Journal of Information Processing Systems
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    • v.19 no.3
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    • pp.289-301
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    • 2023
  • Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.

Cloud Attack Detection with Intelligent Rules

  • Pradeepthi, K.V;Kannan, A
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.10
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    • pp.4204-4222
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    • 2015
  • Cloud is the latest buzz word in the internet community among developers, consumers and security researchers. There have been many attacks on the cloud in the recent past where the services got interrupted and consumer privacy has been compromised. Denial of Service (DoS) attacks effect the service availability to the genuine user. Customers are paying to use the cloud, so enhancing the availability of services is a paramount task for the service provider. In the presence of DoS attacks, the availability is reduced drastically. Such attacks must be detected and prevented as early as possible and the power of computational approaches can be used to do so. In the literature, machine learning techniques have been used to detect the presence of attacks. In this paper, a novel approach is proposed, where intelligent rule based feature selection and classification are performed for DoS attack detection in the cloud. The performance of the proposed system has been evaluated on an experimental cloud set up with real time DoS tools. It was observed that the proposed system achieved an accuracy of 98.46% on the experimental data for 10,000 instances with 10 fold cross-validation. By using this methodology, the service providers will be able to provide a more secure cloud environment to the customers.

Categorizing Quality Features of Franchisees: In the case of Korean Food Service Industry (프랜차이즈 매장 품질요인의 속성분류: 국내 외식업을 중심으로)

  • Byun, Sook-Eun;Cho, Eun-Seong
    • Journal of Distribution Research
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    • v.16 no.1
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    • pp.95-115
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    • 2011
  • Food service is the major part of franchise business in Korea, accounting for 69.9% of the brands in the market. As the food service industry becomes mature, many franchisees have struggled to survive in the market. In general, consumers have higher levels of expectation toward service quality of franchised outlets compared that of (non-franchised) independent ones. They also tend to believe that franchisees deliver standardized service at the uniform food price, regardless of their locations. Such beliefs seem to be important reasons that consumers prefer franchised outlets to independent ones. Nevertheless, few studies examined the impact of qualify features of franchisees on customer satisfaction so far. To this end, this study examined the characteristics of various quality features of franchisees in the food service industry, regarding their relationship with customer satisfaction and dissatisfaction. The quality perception of heavy-users was also compared with that of light-users in order to find insights for developing differentiated marketing strategy for the two segments. Customer satisfaction has been understood as a one-dimensional construct while there are recent studies that insist two-dimensional nature of the construct. In this regard, Kano et al. (1984) suggested to categorize quality features of a product or service into five types, based on their relation to customer satisfaction and dissatisfaction: Must-be quality, Attractive quality, One-dimensional quality, Indifferent quality, and Reverse quality. According to the Kano model, customers are more dissatisfied when Must-be quality(M) are not fulfilled, but their satisfaction does not arise above neutral no matter how fully the quality fulfilled. In comparison, customers are more satisfied with a full provision of Attactive quality(A) but manage to accept its dysfunction. One-dimensional quality(O) results in satisfaction when fulfilled and dissatisfaction when not fulfilled. For Indifferent quality(I), its presence or absence influences neither customer satisfaction nor dissatisfaction. Lastly, Reverse quality(R) refers to the features whose high degree of achievement results in customer dissatisfaction rather than satisfaction. Meanwhile, the basic guidelines of the Kano model have a limitation in that the quality type of each feature is simply determined by calculating the mode statistics. In order to overcome such limitation, the relative importance of each feature on customer satisfaction (Better value; b) and dissatisfaction (Worse value; w) were calculated following the formulas below (Timko, 1993). The Better value indicates how much customer satisfaction is increased by providing the quality feature in question. In contrast, the Worse value indicates how much customer dissatisfaction is decreased by providing the quality feature. Better = (A + O)/(A+O+M+I) Worse = (O+M)/(A+O+M+I)(-1) An on-line survey was performed in order to understand the nature of quality features of franchisees in the food service industry by applying the Kano Model. A total of twenty quality features (refer to the Table 2) were identified as the result of literature review in franchise business and a pre-test with fifty college students in Seoul. The potential respondents of our main survey was limited to the customers who have visited more than two restaurants/stores of the same franchise brand. Survey invitation e-mails were sent out to the panels of a market research company and a total of 257 responses were used for analysis. Following the guidelines of Kano model, each of the twenty quality features was classified into one of the five types based on customers' responses to a set of questions: "(1) how do you feel if the following quality feature is fulfilled in the franchise restaurant that you visit," and "(2) how do you feel if the following quality feature is not fulfilled in the franchise restaurant that you visit." The analyses revealed that customers' dissatisfaction with franchisees is commonly associated with the poor level of cleanliness of the store (w=-0.872), kindness of the staffs(w=-0.890), conveniences such as parking lot and restroom(w=-0.669), and expertise of the staffs(w=-0.492). Such quality features were categorized as Must-be quality in this study. While standardization or uniformity across franchisees has been emphasized in franchise business, this study found that consumers are interested only in uniformity of price across franchisees(w=-0.608), but not interested in standardizations of menu items, interior designs, customer service procedures, and food tastes. Customers appeared to be more satisfied when the franchise brand has promotional events such as giveaways(b=0.767), good accessibility(b=0.699), customer loyalty programs(b=0.659), award winning history(b=0.641), and outlets in the overseas market(b=0.506). The results are summarized in a matrix form in Table 1. Better(b) and Worse(w) index indicate relative importance of each quality feature on customer satisfaction and dissatisfaction, respectively. Meanwhile, there were differences in perceiving the quality features between light users and heavy users of any specific franchise brand in the food service industry. Expertise of the staffs was labeled as Must-be quality for heavy users but Indifferent quality for light users. Light users seemed indifferent to overseas expansion of the brand and offering new menu items on a regular basis, while heavy users appeared to perceive them as Attractive quality. Such difference may come from their different levels of involvement when they eat out. The results are shown in Table 2. The findings of this study help practitioners understand the quality features they need to focus on to strengthen the competitive power in the food service market. Above all, removing the factors that cause customer dissatisfaction seems to be the most critical for franchisees. To retain loyal customers of the franchise brand, it is also recommended for franchisor to invest resources in the development of new menu items as well as training programs for the staffs. Lastly, if resources allow, promotional events, loyalty programs, overseas expansion, award-winning history can be considered as tools for attracting more customers to the business.

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A Study on the Software Service Model Evaluation Methodology for Industry Convergence (산업융합을 위한 소프트웨어 기반 서비스모델 평가방법론에 관한 연구)

  • Kwon, Hyeog-In
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.12 no.3
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    • pp.1136-1144
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    • 2011
  • SW-based service model is considered strategic industry in the advancement of national economic development. Also SW-based service model has Public Interest and Profitability, it should be conducted assessment considering both sides of private and public reflection. To obtain the goal of this research, firstly, based on broadly reviewing previous literature and logical reasoning in business model evaluation and feasibility of public business. Secondly, derived SW-based service model's feature through a group of experts to analyze. And AHP(Analytic Hierarchy Process) is adopted in developing the influential factors (indexes) for the profitability of each SW-based service model and the weight score of each factor. In the result, We suggested 5 evaluation areas, and 15 evaluation items reflecting private business model, public business evaluation and SW-based service model feature. The findings of this study are thought to be useful as a practical guideline in performing evaluating the SW-based service model for private and public sector.

A Development Method of Web System Combining Service Oriented Architecture with Multi-Software Product Line (서비스지향 아키텍처와 멀티소프트웨어 프로덕트라인을 결합한 웹 시스템 개발 방법)

  • Jung, IlKwon
    • The Journal of Society for e-Business Studies
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    • v.24 no.3
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    • pp.53-71
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    • 2019
  • As software systems become more complex and larger, software systems require a way to reuse software components or modules to provide new functionality. This paper designed a development method of web system combining SOA(Service Oriented Architecture) with MPSL(Multi-Software Product Line). According to provides SOA and MPSL, this paper suggested to service providers and service users to provide and reuse variable services. From the viewpoint of service provider, the suggested method identifies and implements reusable variable services as features by syntax-based, functional-based, and behavior-based methods applying feature identification guidelines and manages them as reuse assets. From the user's point of view, it is possible to develop a web system by constructing a service by workflow model as a method of structure and reconfigure services. As a result of measuring the reuse of the web system constructed in this paper by the function point, the cost reduction effect was verified by applying it to the similar project with the increase of reuse.

Hybrid Feature Selection Method Based on Genetic Algorithm for the Diagnosis of Coronary Heart Disease

  • Wiharto, Wiharto;Suryani, Esti;Setyawan, Sigit;Putra, Bintang PE
    • Journal of information and communication convergence engineering
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    • v.20 no.1
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    • pp.31-40
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    • 2022
  • Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.