• Journal of KIISE:Computer Systems and Theory
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• v.32 no.5
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• pp.232-242
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• 2005

This paper concerns automatic hardware synthesis from data flow graph (DFG) specification for fast HW/SW cosynthesis. A node in BFG represents a coarse grain block such as FIR and DCT and a port in a block may consume multiple data samples per invocation, which distinguishes our approach from behavioral synthesis and complicates the problem. In the presented design methodology, a dataflow graph with specified algorithm can be mapped to various hardware structures according to the resource allocation and schedule information. This simplifies the management of the area/performance tradeoff in hardware design and widens the design space of hardware implementation of a dataflow graph compared with the previous approaches. Through experiments with some examples, the usefulness of the proposed technique is demonstrated.

• Science of Emotion and Sensibility
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• v.21 no.4
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• pp.77-90
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• 2018

Behavioral studies of emotion recognition in autism spectrum disorders (ASD) have yielded mixed results. Most of the studies focused on emotion recognition abilities with regard to ASD using stimuli with unisensory modality, making it difficult to determine difficulties in real life emotion perception in ASD. Herein, we review the recent behavioral and cognitive neuroscience studies on emotion recognition functions in ASD, including both unisensory and multisensory emotional information, to elucidate the possible difficulties in emotion recognition in ASD. In our study, we discovered that people with ASD have problems in the process of integrating emotional information during emotion recognition tasks. The following four points are discussed: (1) The restrictions of previous studies, (2) deficits in emotion recognition in ASD especially in recognizing multisensory information, (3) possible compensation mechanisms for emotion recognition in ASD, and (4) the possible roles of attention and language functions in emotion recognition in ASD. The compensatory mechanisms proposed herein for ASD with regard to emotion recognition abilities could contribute to a therapeutic approach for improving emotion recognition functions in ASD.

• The Journal of Information Systems
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• v.31 no.4
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• pp.75-100
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• 2022

Purpose Recently, hyper-realistic virtual influencers have received much attention in the field of corporate marketing. However, there is a lack of research that suggests specific processes affecting behavioral intentions through trust and attachment between virtual influencers and consumers. In addition, previous studies have failed to consider the characteristics of hyper-realistic virtual influencers. Therefore, this study investigates the effects of trust and attachment to hyper-realistic virtual influencers on consumers' behavioral intentions based on the trust-building model. Design/methodology/approach Based on the previous research, seven antecedent factors for trust-building were derived: Reality, Attractiveness, Awareness, Interactivity, Professionalism, Social Presence, and Predictability. Next, the survey was conducted on Chinese people who had experienced interacting with hyper-realistic virtual influencers on social network services within the last 3 months at the time of data collection. A total of 326 respondents were used for the final analysis and hypotheses were tested using a structural equation model technique. Findings The results of this study are as follows. First, this study confirmed that reality, attractiveness, awareness, social presence, and predictability as antecedent factors for trust-building of hyper-realistic virtual influencers have a positive effect on trust. Second, this study confirmed that trust in hyper-realistic virtual influencers has a significant positive effect on attachment. Lastly, this study confirmed that trust and attachment to the hyper-realistic virtual influencer significantly and positively affect relationship retention and purchase intentions.

• Journal of Ecology and Environment
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• v.31 no.2
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• pp.89-95
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• 2008

Sensing the approach of a predator is critical to the survival of prey, especially when the prey has no choice but to escape at a precisely timed moment. Escape behavior has been approached from both proximate and ultimate perspectives. On the proximate level, empirical research about electrophysiological mechanisms for detecting predators has focused on vision, an important modality that helps prey to sense approaching danger. Studies of looming-sensitive neurons in locusts are a good example of how the selective sensitivity of nervous systems towards specific targets, especially approaching objects, has been understood and realistically modeled in software and robotic systems. On the ultimate level, general optimality models have provided an evolutionary framework by considering costs and benefits of visually elicited escape responses. A recent paper showed how neural network models can be used to understand the evolution of visually mediated antipredatory behaviors. We discuss this new trend towards integration of these relatively disparate approaches, the proximate and the ultimate perspectives, for understanding of the evolution of behavior of predators and prey. Focusing on one of the best-studied escape pathway models, the Orthopteran LGMD/DCMD pathway, we discuss how ultimate-level optimality modeling can be integrated with proximate-level studies of escape behaviors in animals.

• Journal of Korean Academy of Nursing
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• v.35 no.8
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• pp.1433-1442
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• 2005

Purpose. The purposes of this study were 1) to examine the differences in suicidal ideation and psychological variables by gender, 2) compare the contribution of demographic-behavioral variables and psychosocial variables in explaining the variance in suicidal ideation, and 3) identify the most important predictors of suicidal ideation for male adolescents and female adolescents. Methods. The subjects consisted of 271 male adolescents and 230 female adolescents. Data were collected through self-report questionnaires, which were constructed to include SSI-C, DEP subscale of the SCL-90-R, PACI, and SWLS. The data were analyzed by the SPSS/WIN program. Results. Suicidal ideation differed by gender. Depression and family communication differed by gender. The unique contribution of demographic-behavioral variables and psychosocial variables in explaining the variance in suicidal ideation differed between male adolescents and female adolescents. The significant predictors of suicidal ideation for male adolescents were life satisfaction, depression, and family communication, explaining $28\%$ of the variance in suicidal ideation. The significant predictors of suicidal ideation for female adolescents were depression, smoking, and life satisfaction, explaining $38\%$ of the variance in suicidal ideation. Conclusion. The findings of this study suggest that the approach to effective suicide prevention program for adolescents should consider gender differences.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.711-720
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• 2020

This research aims to analyze the factors affecting the acceptance of social commerce, including performance expectancy, effort expectancy, social support, facilitating conditions, hedonic motivation, habitability, price saving orientation, and privacy concerns using the Unified Theory of Acceptance and Use of Technology (UTAUT2). UTAUT2 has been examined and modified in various contexts. The research model studies the acceptance and use of technology in the context of customers. This study adopts a quantitative method using the partial least squares regression (PLS) approach involving 244 respondents. The respondents are users of social commerce in Indonesia. The result of this research indicates that social influence, facilitating conditions, hedonic motivation, habit, price value orientation, and privacy concerns have a significant effect on behavioral intention. On the other hand, performance expectancy and effort expectancy does not affect behavioral intention. Furthermore, price value has a significant effect on social commerce user behavior. Lastly, facilitating conditions and habits does not affect social commerce user behavior. This research contributes to the development of theory by examining an additional variable, which is privacy concern. This study is significant since social media and social commerce have grown exponentially nowadays. Implications of the results for the development of the theory (UTAUT2) and practice are discussed in the article.

• Journal of the Korea Furniture Society
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• v.28 no.4
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• pp.278-285
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• 2017

The meaning and context of objects has been changed in various ways depending on the times. Functionality of objects has been developed in various directions according to the point of view of the objects. Objects have been used as mediums to realize art in daily life, and to realize the relational forms. Based on this, it is confirmed that furniture can be an appropriate medium to articulate everyday life and to derive relational form. The 'furniture as a behavior' has the following characteristics. First, it functions to make daily life unfamiliar. By doing something unfamiliar in everyday life, users experience similar experiences as when they entered the surreal world of art museum. In this way, the possibility of everyday arts is acquired. Second, the mental and behavioral functions of furniture are continuously changed by interaction with the user. Behavior does not occur unconsciously, but involves changes in consciousness that the user intentionally does. Therefore, as the user's level of consciousness grows, personal experience changes, and the behavioral function of the household changes. The study of 'furniture as a behavior' is suggested as one approach to design furniture.

• Journal of Computing Science and Engineering
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• v.7 no.2
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• pp.112-121
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• 2013

The last years have seen a rise of interest in using electroencephalography-based brain computer interfacing methodology for investigating non-medical questions, beyond the purpose of communication and control. One of these novel applications is to examine how signal quality is being processed neurally, which is of particular interest for industry, besides providing neuroscientific insights. As for most behavioral experiments in the neurosciences, the assessment of a given stimulus by a subject is required. Based on an EEG study on speech quality of phonemes, we will first discuss the information contained in the neural correlate of this judgement. Typically, this is done by analyzing the data along behavioral responses/labels. However, participants in such complex experiments often guess at the threshold of perception. This leads to labels that are only partly correct, and oftentimes random, which is a problematic scenario for using supervised learning. Therefore, we propose a novel supervised-unsupervised learning scheme, which aims to differentiate true labels from random ones in a data-driven way. We show that this approach provides a more crisp view of the brain states that experimenters are looking for, besides discovering additional brain states to which the classical analysis is blind.

• East Asian Journal of Business Economics (EAJBE)
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• v.6 no.2
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• pp.49-57
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• 2018

Purpose - to examine the theories related to the internationalization of multinational corporations as well as theories related to internationalization of small and medium-sized enterprises. Research design, data, and methodology - traditional theories, e.g. eclectic paradigm and behavioral theory and product life cycle etc. were examined and recent advances theories - network theory, entrepreneurship - were also examined to outline the theory of internationalization of firm. Results - the main schools of international researches are divided into two; one is the economics school, the other is the behavioral school. The economics school has considered internationalization as a pattern of investment in foreign markets explained by rational economic analysis of internalization, ownership, and location advantages. Apart from the economics approach, a theory relevant to smaller firms highlights slow and incremental overseas market commitment. Recent research on the network perspective is fast emerging and it can be applied and well explained on the internationalization of smaller firms, focusing on firm behavior in the context of a network of interorganizational and inter-personal relationships Conclusions - Small medium-sized enterprises have been recently rising, however, there has still been little consolidation of literature in internationalization and most of the relevant theories have been still focusing on explaining the globalization of multinational corporations. Little studied on the internationalization in the context of smaller firms which are distinctly differentiated from larger firms including international new ventures, which the motivation to study strongly calls for more information and studied on small medium-sized enterprises.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2236-2257
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• 2020

Ransomware is malicious software that encrypts the user-related files and data and holds them to ransom. Such attacks have become one of the serious threats to cyberspace. The avoidance techniques that ransomware employs such as obfuscation and/or packing makes it difficult to analyze such programs statically. Although many ransomware detection studies have been conducted, they are limited to a small portion of the attack's characteristics. To this end, this paper proposed a framework for the behavioral-based dynamic analysis of high survivable ransomware (HSR) with integrated valuable feature sets. Term Frequency-Inverse document frequency (TF-IDF) was employed to select the most useful features from the analyzed samples. Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized to develop and implement a machine learning-based detection model able to recognize certain behavioral traits of high survivable ransomware attacks. Experimental evaluation indicates that the proposed framework achieved an area under the ROC curve of 0.987 and a few false positive rates 0.007. The experimental results indicate that the proposed framework can detect high survivable ransomware in the early stage accurately.