• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.248-268
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• 2018

The performance of smartphone applications are usually constrained in user interactions due to resource limitation and it promises great opportunities to improve the performance by exploring the smartphone built-in and embedded sensing techniques. However, heterogeneity in techniques, semantic gap between sensor data and usable context, and complexity of contextual situations keep the techniques from seamless integration. Relevant studies mainly focus on feasibility demonstration of emerging sensing techniques, which rarely address both general architectures and comprehensive technical solutions. Based on a proposed functional model, this investigation provides a general architecture to deal with the dynamic context for context-aware automation and decision support. In order to take advantage of the built-in sensors to improve the performance of mobile applications, an ontology-based method is employed for context modelling, linguistic variables are used for heterogeneous context presentation, and semantic distance-based rule matching is employed to customise functions to the contextual situations. A case study on mobile application authentication is conducted with smartphone built-in hardware modules. The results demonstrate the feasibility of the proposed solutions and their effectiveness in improving operational efficiency.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2007년도 학술대회 2부
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• pp.165-170
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• 2007

최근 사용자 중심적 디자인이 제품 설계에 중요한 경영 철학으로 대두되면서, 사용자의 니즈와 요구사항을 제품에 반영하기 위한 다각도의 시도가 이루어지고 있다. 이 중 ethnography 적 조사 방법들은 사용자의 생활 속 경험 데이터에 기반하여 latent 한 니즈를 가시화 하기 위한 도구로써 점차 중요성이 커지고 있는 추세이다. 그리고 이와 관련 조사 방법들은 photo diary, experience sampling method, shadow tracking, contextual inquiry, in-depth interview 등 매우 다양하다. 각각이 조사 방법들은 도출되는 사용자의 생활 데이터 특성, 조사 참가자 조건, 소요 시간, 필요한 조사 참가자의 수 등에 따라 모두 특징이 상이하다. 휴대폰의 시장 cycle을 고려한다면, 휴대폰 도메인에서 이러한 조사 방법을 적용하기 위해서는 조사 목적 및 조사 진행 상황에 맞게 적절히 방법을 선택하여 사용자의 의미 있는 숨은 니즈를 발견하는 것이 매우 중요하다. 그러기 위해서는 각 방법들을 휴대폰의 사용자 조사에 적용하였을 때, 고유의 특징에 의해서 어떤 유용한 정보를 제공할 수 있는지 파악하는 것이 효과적인 조사를 위해 반드시 필요하다. 따라서 본 논문에서는 휴대폰 니즈/신기능 발굴 목적으로 적용된 총 6 가지 조사 방법의 적용 사례를 간략히 소개 하고자 한다. 이를 통하여 기법의 특징을 기술하고, 이에 기반한 기법의 장-단점 비교를 통해 각 방법이 다른 방법 대비 상대적으로 의미있는 데이터를 제공하는 내용은 무엇인지, 어떤 방법들이 함께 쓰였을 때 시너지 효과를 발휘 할 수 있는지, 조사 목적에 따라서 생략될 수 있는 방법과 참가자에게 유용한 데이터를 끌어내기 위해 반드시 수행해야 하는 방법은 무엇인지에 대하여 기술하고자 한다.

• 인터넷정보학회논문지
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• 제25권4호
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• pp.7-21
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• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.2122-2140
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• 2020

Proper and precise embedding of commercial ads within Webpages requires Ad-hoc analysis and understanding of their content. By the successful implementation of this step, both publishers and advertisers gain mutual benefits through increasing their revenues on the one hand, and improving user experience on the other. In this research work, we propose a novel multi-level context-based ads serving approach through which ads will be served at generic publisher websites based on their contextual relevance. In the proposed approach, knowledge encoded in domain-specific and generic semantic repositories is exploited in order to analyze and segment Webpages into sets of contextually-relevant segments. Semantically-enhanced indexes are also constructed to index ads based on their textual descriptions provided by advertisers. A modified cosine similarity matching algorithm is employed to embed each ad from the Ads repository into one or more contextually-relevant segments. In order to validate our proposal, we have implemented a prototype of an ad serving system with two datasets that consist of (11429 ads and 93 documents) and (11000 documents and 15 ads), respectively. To demonstrate the effectiveness of the proposed techniques, we experimentally tested the proposed method and compared the produced results against five baseline metrics that can be used in the context of ad serving systems. In addition, we compared the results produced by our system with other state-of-the-art models. Findings demonstrate that the accuracy of conventional ad matching techniques has improved by exploiting the proposed semantically-enhanced context-based ad serving model.

• ETRI Journal
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• 제34권5호
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• pp.743-752
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• 2012

Semantic interpretation of the relationship between noun compound (NC) elements has been a challenging issue due to the lack of contextual information, the unbounded number of combinations, and the absence of a universally accepted system for the categorization. The current models require a huge corpus of data to extract contextual information, which limits their usage in many situations. In this paper, a new semantic relations interpreter for NCs based on novel lightweight binary features is proposed. Some of the binary features used are novel. In addition, the interpreter uses a new feature selection method. By developing these new features and techniques, the proposed method removes the need for any huge corpuses. Implementing this method using a modular and plugin-based framework, and by training it using the largest and the most current fine-grained data set, shows that the accuracy is better than that of previously reported upon methods that utilize large corpuses. This improvement in accuracy and the provision of superior efficiency is achieved not only by improving the old features with such techniques as semantic scattering and sense collocation, but also by using various novel features and classifier max entropy. That the accuracy of the max entropy classifier is higher compared to that of other classifiers, such as a support vector machine, a Na$\ddot{i}$ve Bayes, and a decision tree, is also shown.

• 대한임베디드공학회논문지
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• 제2권2호
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• pp.95-100
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• 2007

Recently ubiquitous application services are developed plentifully using RFID techniques in the field of distribution and security industries. However, except these field the applications using RFID are not mature yet. In this study, we proposed a real-time context service model of the u-conference based on the real-time contextual information acquired from conference and exposition. With collection of real-time contextual information for u-conference, the model can provide a lot of information services on the state of session attendee, doorway control, affairs, user certification, presentation progress etc. For the verification of proposed real-time context service model of u-conference, we design and implement the conference progress state service included the state of session attendee, user certification and presentation progress etc. This service provides the presentation state information included the current presenter, the paper list, the number of session attendee, the schedule and place of each session using the collecting RFID tag and the related information.

• 한국컴퓨터정보학회논문지
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• 제22권10호
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• pp.121-128
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• 2017

In this paper, we propose a multi-label classification method in which multi-label classification estimation techniques are applied to resolving location prediction problem. Most of previous studies related to location prediction have focused on the use of single-label classification by using contextual information such as user's movement paths, demographic information, etc. However, in this paper, we focused on the case where users are free to visit multiple locations, forcing decision-makers to use multi-labeled dataset. By using 2373 contextual dataset which was compiled from college students, we have obtained the best results with classifiers such as bagging, random subspace, and decision tree with the multi-label classification estimation methods like binary relevance(BR), binary pairwise classification (PW).

• Journal of Multimedia Information System
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• 제7권1호
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• pp.1-10
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• 2020

The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.

• 대한의용생체공학회:의공학회지
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• 제10권3호
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• pp.317-322
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• 1989

In this paper, brain CT images are automatically segmented to reconstruct the 3-D scene from consecutive CT sections. Contextual segmentation technique was applied to overcome the partial volume artifact and statistical fluctuation phenomenon of soft tissue images. Images are hierarchically analyzed by region growing and graph editing techniques. Segmented regions are discriptively decided to the final organs by using the semantic informations.

• 대한인간공학회지
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• 제30권6호
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• pp.765-773
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• 2011

Objective: This study aims towards exploring a model of the HCI evaluation methods to improve the usability of mobile device, based on a case of a Mobile PDA system in a Museum context. Background: Mobile PDA systems in a Museum context is widely utilized for the educational purposes, but it is criticized for low usability that the device only play a role in textbooks on legs without any interactive educational activities. Therefore, the usability improvements on the mobile PDA system should be considered. Method 1: This model was developed with a combination of the expert review and the user testing, and with a combination of the qualitative and quantitative studies. In more details, first of all, a qualitative study was conducted as a combination of three different methods: 1) expert review with heuristics, 2) interviews with persons working in a museum, and 3) contextual enquiry. Results 1: The experts review provided with critical usability issues, and the semi-constructive interview helped to understand the background of the mobile device. Lastly, the contextual enquiry showed user experience problems and directions of improving the device from user's perspective. Method 2: Based on the results of the qualitative study, a questionnaire was designed. Results 2: The analysis of the quantitative study was conducted to generalize the problems, and prioritize the direction of improving the device within the limitation of the cost and time in a museum. Conclusion: This study has implications in developing an example of a HCI evaluation model to improve the user experience of the mobile device as well as finding problems and directions of how to improve the mobile PDA systems in the museum. Application: In fact, most of the studies related to the evaluation of the mobile device have been conducted in a laboratory context due to the cost and time. This paper, however, attempted to apply to various HCI research techniques from different constituents in real context.