Deep learning techniques, which use a massive technology known as convolutional neural networks, have shown excellent results in a variety of areas, including image processing and interpretation. However, as the depth of these networks grows, so does the demand for a large amount of labeled data required to train these networks. In particular, the medical field suffers from a lack of images because the procedure for obtaining labeled medical images in the healthcare field is difficult, expensive, and requires specialized expertise (...) to add labels to images. Moreover, the process may be prone to errors and time-consuming. Current research has revealed transfer learning as a viable solution to this problem. Transfer learning allows us to transfer knowledge gained from a previous process to improve and tackle a new problem. This study aims to conduct a comprehensive survey of recent studies that dealt with solving this problem and the most important metrics used to evaluate these methods. In addition, this study identifies problems in transfer learning techniques and highlights the problems of the medical dataset and potential problems that can be addressed in future research. According to our review, many researchers use pre-trained models on the Imagenet dataset in many applications such as skin cancer, breast cancer, and diabetic retinopathy classification tasks. These techniques require further investigation of these models, due to training them on natural, non-medical images. In addition, many researchers use data augmentation techniques to expand their dataset and avoid overfitting. However, not enough studies have shown the effect of performance with or without data augmentation. Accuracy, recall, precision, F1 score, receiver operator characteristic curve, and area under the curve were the most widely used measures in these studies. Furthermore, we identified problems in the datasets for melanoma and breast cancer and suggested corresponding solutions. (shrink)

'This book is a masterpiece of theoretical thought. It anticipates the actual achievements of much of what we now call sociolinguistics.

Liquidity risk arises from the inability to unwind or hedge trading positions at the prevailing market prices. The risk of liquidity is a wide and complex topic as it depends on several factors and causes. While much has been written on the subject, there exists no clear-cut mathematical description of the phenomena and typical market risk modeling methods fail to identify the effect of illiquidity risk. In this paper, we do not propose a definitive one either, but we attempt to (...) derive novel mathematical algorithms for the dynamic modeling of trading volumes during the closeout period from the perspective of multiple-asset portfolio, as well as for financial entities with different subsidiary firms and multiple agents. The robust modeling techniques are based on the application of initial-value-problem differential equations technique for portfolio selection and risk management purposes. This paper provides some crucial parameters for the assessment of the trading volumes of multiple-asset portfolio during the closeout period, where the mathematical proofs for each theorem and corollary are provided. Based on the new developed econophysics theory, this paper presents for the first time a closed-form solution for key parameters for the estimation of trading volumes and liquidity risk, such as the unwinding constant, half-life, and mean lifetime and discusses how these novel parameters can be estimated and incorporated into the proposed techniques. The developed modeling algorithms are appealing in terms of theory and are promising for practical econophysics applications, particularly in developing dynamic and robust portfolio management algorithms in light of the 2007–2009 global financial crunch. In addition, they can be applied to artificial intelligence and machine learning for the policymaking process, reinforcement machine learning techniques for the Internet of Things data analytics, expert systems in finance, FinTech, and within big data ecosystems. (shrink)

Speech is a primary means of human communication and one of the most basic features of human conduct. Voice is an important part of its subsystems. A speech disorder is a condition that affects the ability of a person to speak normally, which occasionally results in voice impairment with psychological and emotional consequences. Early detection of voice problems is a crucial factor. Computer-based procedures are less costly and easier to administer for such purposes than traditional methods. This study highlights the (...) following issues: recent studies, methods of voice pathology detection, machine learning and deep learning (DL) methods used in data classification, main datasets utilized, and the role of Internet of things (IoT) systems employed in voice pathology diagnosis. Moreover, this study presents different applications, open challenges, and recommendations for future directions of IoT systems and artificial intelligence (AI) approaches in the voice pathology diagnosis. Finally, this study highlights some limitations of voice pathology datasets in comparison with the role of IoT in the healthcare sector, which shows the urgent need to provide efficient approaches and easy and ideal medical diagnostic procedures and treatments of disease identification for doctors and patients. This review covered voice pathology taxonomy, detection techniques, open challenges, limitations, and recommendations for future directions to provide a clear background for doctors and patients. Standard databases, including the Massachusetts Eye and Ear Infirmary, Saarbruecken Voice Database, and the Arabic Voice Pathology Database, were used in most articles reviewed in this article. The classes, features, and main purpose for voice pathology identification are also highlighted. This study focuses on the extraction of voice pathology features, especially speech analysis, extends feature vectors comprising static and dynamic features, and converts these extended feature vectors into solid vectors before passing them to the recognizer. (shrink)

Ocherk 1. Voprosy ontologii -- Ocherk 2. Voprosy ėtiki i ėstetiki -- Ocherk 3. Voprosy gnoseologii i aksiologii.

The article focuses on Hryhorii Skovoroda’s philosophical dialogue dedicated to the nature of human and happiness as a bright example of a harmonious fusion of philosophical ideas and individual style. A comparative analysis based on a hermeneutic approach helped to assess the equivalency in representing the lexical, syntactical, and emotional levels of the reconstructed Ukrainian version of Skovoroda’s dialogue via English translation, and thus contribute to clarifying the reliable strategies of translating a chronologically remote text of philosophical discourse. The research (...) stresses the importance for a translator of philosophical works of Hryhorii Skovoroda to thoroughly consider the contextual meaning, inseparability of the concepts and style, and unique syntactic and emotive patterns of the source text conflating explicit and implicit semantics. In addition to deep understanding by translators of key philosophical terms, charged with contextual connotations, an equivalent translation of philosophic texts also demands sufficient knowledge of the related historical, cultural, and linguistic background to ensure a comprehensive perception of the intended senses and vectors of the mental activity by a reader. (shrink)

In this article, cognitivism is understood as the view that the engine of human (individual and collective) action is the intentional, dispositional, or other mental capacities of the brain or the mind. Cognitivism has been criticized for considering the essence of human action to reside in its alleged source in mental processes at the expense of the social surroundings of the action, criticism that has often been inspired by Ludwig Wittgenstein's later philosophy. This article explores the logical extent of the (...) critique of cognitivism, arguing that by positing collectively shared knowledge of criteria as the engine of human action many such critiques themselves display latent cognitivism. (shrink)

In this article, cognitivism is understood as the view that the engine of human action is the intentional, dispositional, or other mental capacities of the brain or the mind. Cognitivism has been criticized for considering the essence of human action to reside in its alleged source in mental processes at the expense of the social surroundings of the action, criticism that has often been inspired by Ludwig Wittgenstein's later philosophy. This article explores the logical extent of the critique of cognitivism, (...) arguing that by positing collectively shared knowledge of criteria as the engine of human action many such critiques themselves display latent cognitivism. (shrink)

Because the techno-economic paradigm of contemporary conceptualizations of innovation is often taken for granted in the literature, this chapter opens up this self-evident notion. First, the chapter consults the work of Joseph Schumpeter, who can be seen as the founding father of the current conceptualization of innovation as technological and commercial. Second, we open up the concept by reflecting on two aspects of Schumpeter’s conceptualization of innovation, namely its destructive and its constructive aspect, based on findings in the history of (...) innovation. Finally, we synthesize our findings and propose an ontic-ontological conceptualization of innovation as ontogenetic process and outcome with six dimensions—newness, political dimension, economic dimension, temporal dimension, human dimension and risk—that moves beyond its technological and commercial orientation. (shrink)

Research on brain or cognitive/affective processes, culture, social interaction, and structural analysis are overlapping but often independent ways humans have attempted to understand the origins of their evolution, historical, and contemporary development. Each level seeks to employ its own theoretical concepts and methods for depicting human nature and categorizing objects and events in the world, and often relies on different sources of evidence to support theoretical claims. Each level makes reference to different temporal bandwidths (milliseconds, seconds, minutes, hours, days, months, (...) years, decades, and centuries) and focuses on different spatio-temporal activities and controlled and non-controlled stimulus conditions. Biological mechanisms and environmental pressures for survival simultaneously created a gradual intersection and enhancement of cognitive/affective skills, cultural practices, and changes in collaborative social interaction and communicative skills. The evolution of a given level of analysis is assumed to have been incremental and overlapping. These innovative and independent ways humans have learned to characterize their brain or cognitive/affective and social/economic/political life often depend on unexamined, representational re-descriptions or cognitive/affective and socio-cultural devices and forms of communication that facilitate the depiction of practices and beliefs we attribute to respondents or subjects and research colleagues. (shrink)

Russkai︠a︡ t︠erkovʹ i drugie statʹi -- V temnykh religioznykh luchakh.

The "mathematization" of knowledge is a historically inevitable process governed by two circumstances. In the first place there is the need for the further extension of knowledge in all areas of human activity, whether it be the study of natural phenomena or the theory of taking decisions in the economic or social sphere. Marx pointed out long ago that a science reaches its highest levels only when it succeeds in making use of mathematics. The second circumstance rendering the process of (...) deepening and broadening the mathematization of knowledge not only necessary but possible lies in the constant development both of mathematics itself and of the technical means broadening the sphere of its application. (shrink)

On verbal epistemology of the neo-Nyaya school in Indic philosophy; includes Sanskrit text, Apūrvavāda from Nyāyasiddhāntadīpa of Śaśadhara.

T. 1. Opytʺ psikhologicheskoĭ istorīi i kritiki osnovnykhʺ voprosovʺ zhizni. 3. izd. 1905.--T. 2. Metafizika zhizni i khrīstianskoe otkrovenīe. 2. izd. 1906.

В книге очерки Владимира Брагинского в жанре ироничного эссе о любви.