• Journal of the Ergonomics Society of Korea
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• v.30 no.4
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• pp.563-569
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• 2011

Objective: This study focuses on the effects of pre-cues informing the location of upcoming visual stimulus on finger movement response in the context of control-on-display interfaces. Background: Previous research on pre-cues focus on attention allocation and motion studies were limited to indirect control conditions. The design of this study aimed to collect data on the exact landing point for finger-tap responses to a given visual stimulus. Method: Controlled visual stimuli and tasks were presented on a UI evaluation system built using mobile web standards; response accuracy and response time were measured and collected as appropriate. Among the 16 recruited participants, 11 completed the experiment. Results: Providing pre-cue on the location of stimulus affected response time and response accuracy. The response bias, which is a distance from the center of stimulus to the finger-tap location, was larger when the pre-cue was given during a one-handed operation. Conclusion: Given a pre-cue, response time decreases, but with accuracy penalized. Application: In designing touch-screen UI's - more strictly, visual components also acting as controllers - designers would do well to balance human perceptual and cognitive characteristics strategically.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.107-118
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• 2024

With the seamless growth of the technology, network usage requirements are expanding day by day. The majority of electronic devices are capable of communication, which strongly requires a secure and reliable network. Network-based intrusion detection systems (NIDS) is a new method for preventing and alerting computers and networks from attacks. Machine Learning is an emerging field that provides a variety of ways to implement effective network intrusion detection systems (NIDS). Bagging and Boosting are two ensemble ML techniques, renowned for better performance in the learning and classification process. In this paper, the study provides a detailed literature review of the past work done and proposed a novel ensemble approach to develop a NIDS system based on the voting method using bagging and boosting ensemble techniques. The test results demonstrate that the ensemble of bagging and boosting through voting exhibits the highest classification accuracy of 99.98% and a minimum false positive rate (FPR) on both datasets. Although the model building time is average which can be a tradeoff by processor speed.

• Korean Journal of Cognitive Science
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• v.26 no.3
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• pp.357-375
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• 2015

In our daily life, the probability of target prevalence in visual search varies from very low to high. However, most laboratory studies of visual search used a fixed probability of target prevalence at 50%. The present study examined the properties of information processing during visual search where the probability of target prevalence was manipulated to vary from low (20%), medium (50%), to high (80%). The search items were made of simple shape stimuli, and search accuracy, signal detection measures, and reaction times (RTs) were analyzed for characterizing the effect of target prevalence on the information processing strategies for visual search. The analyses showed that the rates of misses increased whereas those of false alarms decreased in the search condition of low target prevalence, whereas the pattern was reversed in the high prevalence condition. Signal detection measures revealed that the target prevalence shifted response criterion (c) without affecting sensitivity (d'). In addition, RTs for correct rejection responses in the target-absent trials became delayed as the prevalence increased, whereas those for hits in the target-present trials were relatively constant regardless of the prevalence. The RT delay in the target-absent trials indicates that increased target prevalence made the 'quitting threshold' for search termination more conservative. These results support an account that the target prevalence effect in visual search arises from a shift of decision criteria and the subsequent changes in search information processing, while rejecting the account of a speed-accuracy tradeoff.