• ETRI Journal
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• v.41 no.2
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• pp.235-241
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• 2019

In this paper, we propose a rank-weighted reconstruction feature to improve the robustness of a feed-forward deep neural network (FFDNN)-based acoustic model. In the FFDNN-based acoustic model, an input feature is constructed by vectorizing a submatrix that is created by slicing the feature vectors of frames within a context window. In this type of feature construction, the appropriate context window size is important because it determines the amount of trivial or discriminative information, such as redundancy, or temporal context of the input features. However, we ascertained whether a single parameter is sufficiently able to control the quantity of information. Therefore, we investigated the input feature construction from the perspectives of rank and nullity, and proposed a rank-weighted reconstruction feature herein, that allows for the retention of speech information components and the reduction in trivial components. The proposed method was evaluated in the TIMIT phone recognition and Wall Street Journal (WSJ) domains. The proposed method reduced the phone error rate of the TIMIT domain from 18.4% to 18.0%, and the word error rate of the WSJ domain from 4.70% to 4.43%.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.91-100
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• 2023

Pervasive computing is characterized by a key characteristic that affects the operating environment of services and users. It places more emphasis on dynamic environments where available resources continuously vary without prior knowledge of their availability, while in static environments the services provided to users are determined in advance. At the same time, Cloud computing paradigm introduced flexibility of use according to the user's profile and needs. In this paper, we aimed to provide Context-Aware Transactional Service applications with solutions so that it can be integrated and invoked like any service in the digital ecosystem. Being able to compose is not enough, each service and application must be able to offer a well-defined behavior. This behavior must be controlled to meet the dynamicity and adaptability necessary for the new user's requirements. The motivation in this paper is to offer design patterns that will provide a maximum of automatism in order to guarantee short reaction times and minimal human intervention. Our proposal includes a cloud service model by developing a PaaS service that allows CATS adaptation. A new specification for the validation of CATS model has been also introduced using the ACTA formalism.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.28-33
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• 2006

The initial research of Task Computing in the ubiquitous computing (UbiComp) environment revealed the need for access control of services. Context-awareness of service requests in ubiquitous computing necessitates a well-designed model to enable effective and adaptive invocation. However, nowadays little work is being undertaken on service access control under the UbiComp environment, which makes the exposed service suffer from the problem of ill-use. One of the research focuses is how to handle the access to the resources over the network. Policy-Based Access Control is an access control method. It adopts a security policy to evaluate requests for resources but has a light-weight combination of the resources. Motivated by the problem above, we propose a universal model and an algorithm to enhance service access control in UbiComp. We detail the architecture of the model and present the access control implementation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1569-1584
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• 2013

Ubiquitous interaction in a pervasive environment is the main attribute of smart spaces. Pervasive systems are weaving themselves in our daily life, making it possible to collect user information invisibly, in an unobtrusive manner by known and even unknown parties. Huge number of interactions between users and pervasive devices necessitate a comprehensive trust model which unifies different trust factors like context, recommendation, and history to calculate the trust level of each party precisely. Trusted computing enables effective solutions to verify the trustworthiness of computing platforms. In this paper, we elaborate Unified Trust Model (UTM) which calculates entity's trustworthiness based on history, recommendation, context and platform integrity measurement, and formally use these factors in trustworthiness calculation. We evaluate UTM behaviour by simulating in different scenario experiments using a Trust and Reputation Models Simulator for Wireless Sensor Networks. We show that UTM offers responsive behaviour and can be used effectively in the low interaction environments.

• Journal of Information Technology Applications and Management
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• v.13 no.2
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• pp.67-86
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• 2006

Based on literature relating to the TAM (technology acceptance model) and TPB (theory of planned behavior), this study extends the TAM in a mobile banking context. The extended model was tested using LISREL analysis on the sample of 222 users who have experience with the banking service. The model was partially supported in a mobile banking context, accounting for 49% of the variance in the usage intention. The results showed that the perceived usefulness, the perceived credibility, and the perceived financial cost play a significant role in influencing the usage intention of the mobile banking service. In addition, instant connectivity and perceived credibility were found to influence the perceived usefulness, and self-efficacy and instant connectivity were found to influence the perceived ease of use. Implications of these findings are discussed for researchers and practitioners.

• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.515-526
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• 2019

DOOH(Digital Out Of Home) advertising targets for reaching consumers through outdoor digital display medias. Traditionally, scheduling of advertisement contents over DOOH medias is usually done by operator's strategy, but an efficient ad scheduling strategy is not easy to find under various advertising contexts. In this paper, we present a context-aware factorization machine-based recommendation model for the scheduling under various advertising contexts, and provide analysis for understanding of the contexts' effects on advertising based on the recommendation model. Through simulation results on the dataset adapted from a real dataset of RecSys challenge 2015, it is shown that the proposed model and analysis based on the model will be effective for better scheduling of ad contents under advertising contexts over DOOH networks.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.347-358
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• 2022

The main concern of this article is to discuss the problem of a two-temperature fiber-reinforced micropolar thermoelastic medium with voids under the effect rotation, mechanical force in the context four different theories with memory-dependent derivative (MDD) and variable thermal conductivity. The three-phase-lag model (3PHL), dual-phase-lag model (DPL), Green-Naghdi theory (G-N II, G-N III), coupled theory, and the Lord-Shulman theory (L-S) are employed to solve the present problem. Analytical expressions of the physical quantities are obtained by using Laplace-Fourier transforms technique. Numerical results are shown graphically and the results obtained are analyzed. The most significant points are highlighted.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.415-422
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• 2024

Recent advances in construction automation have led to increased use of deep learning-based computer vision technology for construction monitoring. However, monitoring systems based on supervised learning struggle with recognizing complex risk factors in construction environments, highlighting the need for adaptable solutions. Large multimodal models, pretrained on extensive image-text datasets, present a promising solution with their capability to recognize diverse objects and extract semantic information. This paper proposes a methodology that generates training data for multimodal models, including safety-centric descriptions using GPT-4V, and fine-tunes the LLaVA model using the LoRA method. Experimental results from seven construction site hazard scenarios show that the fine-tuned model accurately assesses safety status in images. These findings underscore the proposed approach's effectiveness in enhancing construction site safety monitoring and illustrate the potential of large multimodal models to tackle domain-specific challenges.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.99-107
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• 2021

With the recent establishment of a ubiquitous-based medical and healthcare environment, the medical information system for obtaining situation information from various sensors is increasing. In the medical information system environment based on context-awareness, the patient situation can be determined as normal or emergency using situational information. In addition, medical staff can easily access patient information after simple user authentication using ID and Password through applications on smart devices. However, these services of authentication and patient information access are staff-oriented systems and do not fully consider the ubiquitous-based healthcare information system environment. In this paper, we present a authentication service model based context-awareness system for providing situational information-driven authentication services to users who access medical information, and implemented proposed system. The authentication service model based context-awareness system is a service that recognizes patient situations through sensors and the authentication and authorization of medical staff proceed differently according to patient situations. It was implemented using wearables, biometric data measurement modules, camera sensors, etc. to configure various situational information measurement environments. If the patient situation was emergency situation, the medical information server sent an emergency message to the smart device of the medical staff, and the medical staff that received the emergency message tried to authenticate using the application of the smart device to access the patient information. Once all authentication was completed, medical staff will be given access to high-level medical information and can even checked patient medical information that could not be seen under normal situation. The authentication service model based context-awareness system not only fully considered the ubiquitous medical information system environment, but also enhanced patient-centered systematic security and access transparency.

• 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.