Two experiments are reported that demonstrate that visual search for a signal from a number of potential signal sources in a sustained monitoring task is dependent upon previous visual-load history. It is shown that both temporal and spatial variations in load produce performance decrements, and occasionally increments, that cannot be predicted from static-load experiments. These data are not consistent with previous attempts to explain performance changes associated with workload history. An interpretation is offered in terms of the persistence of information-processing strategies across changing task conditions.
The concept of situation awareness (SA)—applied broadly over the last decade to human factors issues in aviation, nuclear power generation, and military combat systems—has only recently been introduced to the analysis of driver behavior. In a driving context, SA involves spatial, temporal, goal, and system awareness. These aspects of SA have been integrated into a goal-oriented model of driver behavior that encompasses strategic, tactical, and operational goals of driving. Maintenance of appropriate SA for each type of goal is based on three underlying processes: perception, comprehension of disparate information, and projection and prediction. The model can be used as a basis for understanding the possible impact of new generations of intelligent transportation systems (ITSs) on driver performance. The model allows ITSs to be analyzed for how they are likely to enhance or impair a driver’s performance in pursuit of each type of driving goal. The model may provide a way to determine how an ITS supports or interferes with the required SA to meet a driving goal (e.g., an onboard navigation system that assists strategic decisions).
Feature saliency and processing strategy in the recognition of faces are investigated in a "same"-"different" RT paradigm using pairs of faces constructed from Identikit representations. Facial similarity is varied by manipulating individual feature changes in pairs of faces. Although there was a general tendency for RT to increase as a function of facial similarity, analysis of RTs to component feature changes suggests a dual processing strategy whereby subjects give processing priority to hairline, eyes and chin, followed by a slower feature-byfeature analysis of eyebrows, nose, and mouth.A continuing theme in the study of the perception of human faces has evolved around the issue of whether faces are processed in a Gestalt manner in which the importance of configurational relationships between features is stressed or by means of a piecemeal analysis that goes feature by feature. Variations on this central theme include the issues of serial vs. parallel processing strategies and the saliency of different facial features. The initial stimulants that produced investigations in these directions were most probably Yin's (1969) finding that subjects commonly adopted one of two strategies in attempting to recognize upside-down faces, either searching for a distinguishing feature or attempting to obtain an overall impression, and secondly, an increasing body of data from developmental psychologists on patterns of preferential fixation behavior in the scanning of representations of faces by infants.These issues have been brought together by Bradshaw and Wallace (1971), who suggest that Gestalt or configurational encoding would be evidenced by a parallel processing strategy, whereas a serial approach would be more suggestive of separate feature processing. Using a "same"-"different" RT task with lists of pairs of faces constructed from an Identikit in which pair members differed in terms of numbers of features in common, they concluded that a serial self-terminating processing model best characterized subjects' performance and that there was no evidence for faces to be treated as unitary Gestalten. This conclusion is supported by the results of Tversky (1969), who suggested that subjects perRequests for reprints should be sent to
Current state of the art intrusion detection and prevention systems (IDPS) are signature-based systems that detect threats and vulnerabilities by cross-referencing the threat or vulnerability signatures in their databases. These systems are incapable of taking advantage of heterogeneous data sources for analysis of system activities for threat detection. This work presents a situation-aware intrusion detection model that integrates these heterogeneous data sources and build a semantically rich knowledge-base to detect cyber threats/vulnerabilities.
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