Nielsen's model presents a new isomorphic brain-mind viewpoint, according to which the sole dream generator is found in a REM-on (explicit or covert REM) mechanism. Such a model cannot explain the dreamlike activity during SWS (slow wave sleep), SO (sleep onset) and in the last period of sleep. Moreover the hypothesis contrasts with Solms's data, which show that dreaming is present also in case of destruction of the REM generator. [Nielsen; Solms].

Two aspects of consciousness are first considered: consciousness as awareness (phenomenological meaning) and consciousness as strategic control (functional meaning). As to awareness, three types can be distinguished: first, awareness as the phenomenal experiences of objects and events; second, awareness as meta-awareness, i.e., the awareness of mental life itself; third, awareness as self-awareness, i.e., the awareness of being oneself. While phenomenal experience and self-awareness are usually present during dreaming (even if many modifications are possible), meta-awareness is usually absent (apart from some (...) particular experiences of self-reflectiveness) with the major exception of lucid dreaming. Consciousness as strategic control may also be present in dreams. The functioning of consciousness is then analyzed, following a cognitive model of dream production. In such a model, the dream is supposed to be the product of the interaction of three components: (a) the bottom-up activation of mnemonic elements coming from LTM systems, (b) interpretative and elaborative top-down processes, and (c) monitoring of phenomenal experience. A feedback circulation is activated among the components, where the top-down interpretative organization and the conscious monitoring of the oneiric scene elicitates other mnemonic contents, according to the requirements of the dream plot. This dream productive activity is submitted to unconscious and conscious processes. (shrink)

Leibniz's theory of infinity contains many theoretically difficult aspects. Until today only some of them have been systematically studied by mathematicians and philosophers. For instance Leibniz didn't only discover some of the paradoxical properties of an infinite set: he also discovered that a classification of four kinds of infinity is possible and that their properties can be investigated. This article reconstructs this classification and these properties. The metaphysical implications in Leibniz's system and some of the possible developments of these theories (...) in the modern philosophy of mathematics are also investigated. (shrink)

The traditional recognition method of whitewash behavior of accounting statements needs to analyze a large number of special data samples. The learning rate of the algorithm is low, resulting in low recognition accuracy. To solve the aforementioned problems, this article proposes a method to identify the whitewash behavior of university accounting statements based on the FCM clustering algorithm. This article analyzes the motivation of university accounting statement whitewashing behavior, studies the common means of statement whitewashing, and establishes a fuzzy set (...) for the identification of university accounting statement whitewashing behavior. By calculating the fuzzy partition coefficient, the membership matrix of whitewash behavior recognition is established, and the whitewash behavior is classified through the iteration of the FCM algorithm. The comparative experimental results show that the recognition method has good recognition performance, low recognition error rate, and recognition accuracy of 82%. (shrink)

The fuzzy clustering algorithm has been widely used in the research area and production and life. However, the conventional fuzzy algorithms have a disadvantage of high computational complexity. This article proposes an improved fuzzy C-means algorithm based on K-means and principle of granularity. This algorithm is aiming at solving the problems of optimal number of clusters and sensitivity to the data initialization in the conventional FCM methods. The initialization stage of the K-medoid cluster, which is different from others, has a (...) strong representation and is capable of detecting data with different sizes. Meanwhile, through the combination of the granular computing and FCM, the optimal number of clusters is obtained by choosing accurate validity functions. Finally, the detailed clustering process of the proposed algorithm is presented, and its performance is validated by simulation tests. The test results show that the proposed improved FCM algorithm has enhanced clustering performance in the computational complexity, running time, cluster effectiveness compared with the existing FCM algorithms. (shrink)

Although machine intelligence is increasingly employed in healthcare, the realm of decision-making in medical ethics remains largely unexplored from a technical perspective. We propose an approach based on fuzzy cognitive maps (FCMs), which builds on Beauchamp and Childress’ prima-facie principles. The FCM’s weights are optimized using a genetic algorithm to provide recommendations regarding the initiation, continuation, or withdrawal of medical treatment. The resulting model approximates the answers provided by our team of medical ethicists fairly well and offers a high degree (...) of interpretability. Possible applications of such a system include informal guidance on medical ethics dilemmas as well as educational purposes. (shrink)

Boredom—that inescapable accoutrement of human existence—is more than a common affective encounter. It is an experience of key phenomenological significance. Boredom gives rise to perceptions of meaninglessness, difficulties in effective agency, lapses in attention, an altered perception of the passage of time, and to an impressively diverse array of behavioral outcomes. Above all, it shapes our world and lives. Boredom’s presence demarcates what is engaging, interesting, or meaningful from what is not; it alerts us when we find ourselves in situations (...) that either are lacking in personal significance or cannot properly engage us; and it spurs us into action and thought. Within the phenomenological tradition, the importance of boredom has been highlighted by Martin Heidegger, who in his 1929-30 lecture course, The Fundamental Concepts of Metaphysics: World, Finitude, Solitude (FCM) (Heidegger 1995), makes a case for its ontological significance. Notwithstanding Heidegger’s detailed engagement with it, boredom has not received sustained attention within the phenomenological tradition, even though phenomenology is particularly well suited to explicate the phenomenon of boredom. The aim of this entry is to present three different phenomenological perspectives on boredom. In doing so, it hopes to showcase not just what boredom is, according to phenomenology, but also what a phenomenology of boredom can reveal about human existence. (shrink)

Financial accountants falsify financial statements by means of financial techniques such as financial practices and financial standards, and when compared with conventional financial data, it is found that the falsified financial data often lack correlation or even contradict each other in terms of financial data indicators. At the same time, there are also inherent differences in reporting patterns from conventional financial data, but these differences are difficult to test manually. In this paper, the fuzzy C-means clustering method is used to (...) amplify these differences and thus identify false financial statements. In the proposed algorithm, firstly, the normalization constraint of the FCM clustering algorithm on the sum of affiliation of individual samples is relaxed to the constraint on the sum of affiliation of all samples, thus reducing the sensitivity of the algorithm to noise and isolated points; secondly, a new affiliation correction method is proposed to address the problem that the difference in affiliation is too large after the relaxation of the constraint. In the discussion of this paper, most of the information comes from the annual reports of companies, administrative penalty decisions of the Securities Regulatory Commission, and some information comes from research reports made by securities institutions, which are limited sources of information. The proposed method can correct the affiliation to a reasonable range, effectively avoiding the problem that some samples have too much affiliation and become a class of their own and also avoiding the problem that it is difficult to choose the termination threshold of the algorithm iteration due to too little affiliation, and can ensure that the constraint on the sum of affiliation of all samples is always satisfied during the iteration of the algorithm. The method has the characteristics of high recognition accuracy and has the significance of theoretical method innovation. (shrink)

Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land (...) cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method. (shrink)

In this book authors study neutrosophic super matrices. The concept of neutrosophy or indeterminacy happens to be one the powerful tools used in applications like FCMs and NCMs where the expert seeks for a neutral solution. Thus this concept has lots of applications in fuzzy neutrosophic models like NRE, NAM etc. These concepts will also find applications in image processing where the expert seeks for a neutral solution.

In economic researches, much effort was devoted to the problem of how to increase the economics of countries. However, the development of a country may fluctuate a lot due to international and domestic problems. Thus, we should also evaluate the robustness of countries against unexpected economic recessions. In this paper, we use perturbation to quantify the robustness of countries using two renowned algorithms: method of reflections and fitness-complexity method. The robustness characterizes the stability of countries’ competitiveness against economic recessions. The (...) experiments in the international trade networks show that FCM could characterize the robustness better than MR. High fitness countries of FCM have strong robustness against economic crises, which enlarges the application fields of FCM. Additionally, we simulate the trade conflict between USA and China. The simulation results show that China suffers much in the trade conflict, while USA loses very little and has strong robustness in this conflict. (shrink)