• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1275-1292
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• 2021

As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context. To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects. Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered. In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed. In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context. Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements. Finally, sufficient experiments were conducted on three benchmark datasets in different domains. Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.482-485
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• 2000

A modified watershed transform is proposed which is context-free marker-controlled and minima imposition-free to reduce the over-segmentation and to speedup the transform. In contrast to the conventional methods in which a priori knowledge, such as flat zones, zones of homogeneous texture, and morphological distance, is required for marker extraction, context-free marker extraction is proposed by using the attention operator based on the GST (generalized symmetry transform). By using the context-free marker, the proposed watershed transform exploit marker-constrained labeling to speedup the computation and to reduce the over-segmentation by eliminating the unnecessary geodesic reconstruction such as the minima imposition and thereby eliminating the necessity of the post-processing of region merging. The simulation results show that the proposed method can extract context-free markers inside the objects from the complex background that includes multiple objects and efficiently reduces over-segmentation and computation time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1263-1279
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• 2020

Activating appropriate roles for a session in the role-based access control (RBAC) model has become challenging because of the so-called role explosion. In this paper, factors and issues related to user-driven role management are analysed, and a session role activation (SRA) problem based on reasonable assumptions is proposed to describe the problem of such role management. To solve the SRA problem, we propose an extended RBAC model with context-based role filtering. When a session is created, context conditions are used to filter roles that do not need to be activated for the session. This significantly reduces the candidate roles that need to be reviewed by the user, and aids the user in rapidly activating the appropriate roles. Simulations are carried out, and the results show that the extended RBAC model is effective in filtering the roles that are unnecessary for a session by using predefined context conditions. The extended RBAC model is also implemented in the Apache Shiro framework, and the modifications to Shiro are described in detail.

• Journal of The Korean Association For Science Education
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• v.23 no.1
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• pp.47-56
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• 2003

The present study was carried out to analyse the frequency(%) for five kinds of inquiry context elements for six kinds of the high school science textbooks in the 7th curriculum. All the elements was classified into three process achievement level such as the basic, supplementary and further level introduced firstly in the 7th curriculum. Five elements of the inquiry context categories appeared as pure scientific context(61.8%), everyday context(22.2%), natural environmental context(7.7%), techno-industrial context(5.9%), and social context(2.4%) in the basic, supplementary and further level. Social context wasn't appeared in the supplementary level. In five elements of inquiry context, total elements appeared 7,139(85.5%) kfrequencies in the basic level and 691(8.1%) frequencies in the further level. However total elements appeared 529(6.4%) frequencies in the supplementary level. The kinds and frequencies of the elements for the inquiry context suggested in the basic level were more than those in the supplementary and in the further level. The social context was not appeared in the supplementary level. However five inquiry context elements were all appeared in the basic and further level.

• Journal of the Korea Society of Computer and Information
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• v.21 no.2
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• pp.25-32
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• 2016

In this paper, we design a middleware for context-awareness which provides compound contexts from diverse sensors on a mobile device. Until now, most of context-aware application developers have taken responsibility for context processing from sensing data. Such application-level context processing causes heavily redundant data processing and leads to significant resource waste in energy as well as computing. In the proposed scheme, we define primitive and compound context map which consists of relavant sensors and features. Based on the context definition, each application demands a context of interest to the middleware, and thus similar context-aware applications inherently share context information and procesing within the middleware. We show that the proposed scheme significantly reduces the resource amounts of cpu, memory, and battery, and that the performance gain gets much more when multiple applications which need similar contexts are running.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.300-305
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• 2005

Context-aware intelligent services are essential in ubiquitous computing and intelligent robots environments, which make decisions on which services to start with the consideration of surrounding contexts. In the ubiquitous and intelligent environments, context-aware service engines should be light-weighted due to the resource restrictions on the devices. This paper presents a context-aware service engine which is designed for light-weighted devices. The context-aware service engine has been designed with special attention to improve the execution speed and to minimize the memory requirement.

• Journal of IKEEE
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• v.19 no.2
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• pp.254-259
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• 2015

CABAC is a context-based adaptive binary arithmetic coding method. It increases the encoding efficiency by updating the probability based on the information of the previously coded symbols. Context modeler is a core block of CABAC, which designs a probability model according to the symbol considering statistical correlations. In this paper, an efficient hardware architecture of CABAC context modeler is proposed. The proposed context modeler was designed in Verilog HDL and it was implemented in 0.18 um technology. Its gate count is 29,832 gates including memory. Its operating speed and throughput are 200 MHz and 200 Mbin/s, respectively.

• Convergence Security Journal
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• v.9 no.1
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• pp.11-17
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• 2009

The object of this paper is to design a school-zone safety management system based on context-aware, integrated with computing technology. When it occurs to kidnap of elementary school students, the monitoring device creates context information through a combination object extraction and context-aware technology and alarm administrator about an emergency situation. In addition, the proposed system that requires a human perspective, a railroad crossing, statistics research of traffic, and a variety of applications such as factory automation systems can be used to be the best choice.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.5
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• pp.71-81
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• 2008

In the paper, we designed a context aware task-role based access control system(CAT-RACS) which can control access and prevent illegal access efficiently for various information systems in ubiquitous computing environment. CAT-RACS applied CA-TRBAC, which adds context-role concept for achieve policy composition by context information and security level attribute to be kept confidentiality of information. CA-TRBAC doesn't permit access when context isn't coincident with access control conditions, or role and task's security level aren't accord with object's security level or their level is a lower level, even if user's role and task are coincident with access control conditions. It provides security services of user authentication and access control, etc. by a context-aware security manager, and provides context-aware security services and manages context information needed in security policy configuration by a context information fusion manager. Also, it manages CA-TRBAC policy, user authentication policy, and security domain management policy by a security policy manager.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.447-459
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• 2020

The Internet of Things(IoT) paradigm is flourishing strenuously for the last two decades. Researchers around the globe have their dreams to transmute every real-world object to the virtual object. Consequently, IoT devices are escalating exponentially. The abrupt evolution of these IoT devices has caused a major challenge i.e. object classification. In order to classify devices comprehensively and accurately, this paper proposes a context-aware based multi-classification model for devices, which classifies the smart devices according to people's contexts. However, the classification features of contextual data of different contexts are difficult to extract. The deep learning algorithm has the capability to solve this problem. This paper proposes a context-aware based multi-classification model of devices, which classifies the smart devices according to people's contexts.