Topic
Surprise
About: Surprise is a research topic. Over the lifetime, 4371 publications have been published within this topic receiving 99386 citations.
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19 Jul 2013
TL;DR: The first Law of Intelligence Failure as mentioned in this paper was broken by Pearl Harbor and the East Africa Embassy Bombings, and the 9/11 attacks by the terrorist group al-Mabrouk et al.
Abstract: Introduction: Breaking the First Law of Intelligence Failure 1. Why Does Intelligence Fail, and How Can It Succeed? Part I: The Problem of Conventional Surprise Attack 2. Pearl Harbor: Challenging the Conventional Wisdom3. The Battle of Midway: Explaining Intelligence Success4. Testing the Argument: Classic Cases of Surprise Attack Part II: The Problem of Terrorist Surprise Attack 5. The East Africa Embassy Bombings: Disaster Despite Warning6. New York City: Preventing a Day of Terror7. The 9/11 Attacks: A New Explanation8. Testing the Argument: Why Do Terrorist Plots Fail? Conclusion: Preventing Surprise Attacks Today Appendix: Unsuccessful Plots and Attacks against American Targets, 1987-2012 Notes Bibliography Index
43 citations
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TL;DR: In this article, the authors consider two cases of Web 2.0 driven micro-mobilization processes and show that new network technology helped the process of issue-expansion on which the emergence of these strategic surprises is dependent.
Abstract: New social network technology (Web 2.0) provides individuals and small groups with powerful resources for rapid political mobilization. This can create strategic surprises to policy-makers. Two cases of Web 2.0 driven micro-mobilization processes are considered. In both cases, new network technology helped the process of issue-expansion on which the emergence of these strategic surprises is dependent. Policy-makers were taken by surprise because their repertoires of action are focused primarily on official arrangements of consultation and on the news coverage by traditional media. Policy-makers' capacities and resources are not attuned to the political use of network technology by citizens.
43 citations
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TL;DR: The approach to emotion classification has future applicability in the field of affective computing, which includes all the methods used for the automatic assessment of emotions and their applications in healthcare, education, marketing, website personalization, recommender systems, video games, and social media.
Abstract: Emotions constitute an indispensable component of our everyday life. They consist of conscious mental reactions towards objects or situations and are associated with various physiological, behavioral, and cognitive changes. In this paper, we propose a comparative analysis between different machine learning and deep learning techniques, with and without feature selection, for binarily classifying the six basic emotions, namely anger, disgust, fear, joy, sadness, and surprise, into two symmetrical categorical classes (emotion and no emotion), using the physiological recordings and subjective ratings of valence, arousal, and dominance from the DEAP (Dataset for Emotion Analysis using EEG, Physiological and Video Signals) database. The results showed that the maximum classification accuracies for each emotion were: anger: 98.02%, joy:100%, surprise: 96%, disgust: 95%, fear: 90.75%, and sadness: 90.08%. In the case of four emotions (anger, disgust, fear, and sadness), the classification accuracies were higher without feature selection. Our approach to emotion classification has future applicability in the field of affective computing, which includes all the methods used for the automatic assessment of emotions and their applications in healthcare, education, marketing, website personalization, recommender systems, video games, and social media.
43 citations
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TL;DR: Suicide is clearly a major public health issue and around the world there are about 1 million deaths from suicide each year, more than in all the various wars and conflicts currently ongoing.
Abstract: [⇓][1]
![Figure][2]
Around the world there are about 1 million deaths from suicide each year. This is more than in all the various wars and conflicts currently ongoing, a fact that would doubtless surprise many, including some policy makers. Suicide is clearly a major public health
42 citations
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TL;DR: The present study evaluated the discrepancy-attention link in a display where novel and familiar stimuli are equated for saliency, and found that novelty captures and binds attention.
Abstract: While the classical distinction between task-driven and stimulus-driven biasing of attention appears to be a dichotomy at first sight, there seems to be a third category that depends on the contrast or discrepancy between active representations and the upcoming stimulus, and may be termed novelty, surprise, or prediction failure. For previous demonstrations of the discrepancy-attention link, stimulus-driven components (saliency) may have played a decisive role. The present study was conducted to evaluate the discrepancy-attention link in a display where novel and familiar stimuli are equated for saliency. Eye tracking was used to determine fixations on novel and familiar stimuli as a proxy for attention. Results show a prioritization of attention by the novel color, and a de-prioritization of the familiar color, which is clearly present at the second fixation, and spans over the next couple of fixations. Saliency, on the other hand, did not prioritize items in the display. The results thus reinforce the notion that novelty captures and binds attention.
42 citations