Part 1. Yi Saek ŭi segyegwan kwa yŏksa ŭisik -- part 2. Kwŏn Kŭn ŭi segyegwan kwa yŏksa ŭisik.

The capacities required for both peace and war predate 100,000 years ago in the genus Homo are deeply entangled in the modes by which humans physically and perceptually construct their worlds and communities, and may not be sufficiently captured by economic models.

We agree with the authors that an adequate account of the perception-action interface is needed. However, we believe that the answer will not be found in a common representational structure encoding distal events, as the authors propose, but in Gibson's notion of ecological information, which, as we demonstrate, specifies not only perspective but also prospective and retrospective states of affairs.

For Gibson, sensory stimulation is neither the cause nor a component of perception, but merely incidental. Perception is based on the pickup of information, which occurs when purposeful observers actively seek information. I present a case in which only with the active sampling of the ambient optical flow field can observers extract the requisite information for the control of locomotion.

The principle of fairness holds that individuals (beneficiaries) who benefit from a cooperative scheme of others (cooperators) have an obligation to do their share in return for their benefit. The original proponent of this principle, H. L. A. Hart suggests ‘mutuality of restrictions’ as a moral basis because it is fair to mutually restrict the freedom of both beneficiaries and cooperators; so called the fairness obligation. This paper explores ‘mutuality of restrictions’, which is interpreted as a right-based and an equality-based (...) justification of the fairness obligation. It is not argued whether both ways of justifying the obligation makes a success, but that they are in need of presupposing that there is a duty for beneficiaries to do their share in return for their benefit. This suggests turning to a duty-based justification of the fairness obligation. (shrink)

We used functional magnetic resonance imaging to examine the interactive effects of perceived competence and task interest on the cognitive and affective responses to negative feedback. Twenty-four undergraduates performed both interesting and uninteresting tasks and received failure feedback. The participants’ perceived competence in the task was manipulated between subjects prior to scanning with bogus feedback. The results showed that negative feedback processing was contingent upon both perceived competence and task interest. The most adaptive coping mechanism, indicated by activation in the (...) cognitive control network and attenuation in the negative affect region, was identified for the high-competence and high-interest combination. When either competence or interest was low, signals in the cognitive control network were weaker. The most detrimental activation patterns were observed for the combination of low-competence and high interest. Our results reveal the combination of task and learner characteristics that best harnesses the potential benefits of negative feedback and illustrate the neuroscientific mechanisms underlying this observation. (shrink)

이 글의 목적은 정치철학을 ‘정치철학적’으로 이해하고 가르쳐야 한다고 주장하는 것이다. 정치철학의 ‘정치철학적’ 이해는 역사적, 철학적, 그리고 정치적 이해를 포함하지만 거기에만 국한되지 않는다. 그것은 중요한 개념과 이론들을 정치적인 문제를 중심으로 이해하는 것이다. 이 글은 ‘정치철학적’으로 정치철학을 논의해야 하는 필요성을 검토한 후 역사적, 철학적, 그리고 정치적 정치철학의 이해가 보여주는 한계를 설명함으로써 ‘정치철학적’ 정치철학을 주장한다. 나아가 ‘정치철학적’으로 정치철학을 이해한다는 것은 무엇인지 논의한다. 결론적으로 말해, 정치적인 문제들을 중심으로 정치철학을 이해하고 가르칠 때 정치철학이 여전히 유용하며 지속될 수 있다고 주장한다.

In understanding Peirce's unique system of pragmatism following three theories are helpful: first, Herakleitos' saying that 'one can never step into a same river twice', second, Protagoras' saying that 'men are the measure of the world', and third, Darwin's theory of evolution. It is always the case that epistemology takes place on top the metaphysical and cosmological bases. Peirce's metaphysics and cosmology is considered to line up with above three theories. It is interesting fact that Peirce who started up his (...) work in science ended up with such somehow unscientific cosmology. Peirce believes that the world is constantly changing. Inknowing the world Peirce says that the subject of knowing takes a part doing a certain role. Peirce says that 'to believe A', 'to think of A' and 'to think of A as true' are all same sort of mental act. Therefore, to Peirce, truth is only a kind of 'opinion'. This means that the subject of knowing has something to do with truthfulness. In this kind of knowing process 'knowing truth' can only be acquired when possibility of 'inaccuracy' and 'onesideness' are opened. Thus, Peirce's definition of knowing or truth is quite indefinite definition. According to Peirce, we only know the world partially, and moreover, even we get the total sum data about the world,the world will move on forward changing. On this ground, it might be helpful that we look in to current matters in philosophy of mind in different angle. (shrink)

Data from archaeology and paleoanthropology directly challenge the validity of the basic assumptions of the CLASH model. By not incorporating a “deep time” perspective, the hypothesis lacks the evolutionary baseline the authors seek to infer in validating the model.

In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the (...) above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset. (shrink)