Meaning and structural dynamics in poetry: a computational perspective

This work addresses Sri Aurobindo’s mantric poem, Savitri, with a computational linguistics approach. This is one of the longest poems ever written in English. We build the connectivity matrix between all main word pairs and analyse its structure. Concepts emerge as directions that better explain the variance of the data in the hyperspace of words. When projected to the low dimensional space of concepts, the vector of attention as the reader moves through the text shows a large correlation across sections of the poem, thus acting the future and the past over again. These findings suggest that the mathematical structure of Savitri is and reflects a substrate for the author’s main ideas, facilitating the reader’s understanding of the poem’s meaning via its long-range dynamical correlations. Acknowledging an irreducible essence to poetry, future studies on the relationship between words and sounds, and sounds and ideas may provide invaluable hints of the origin of language and its intimate relationship with the evolution of human consciousness.

Reversing Jensen’s Inequality for Information-Theoretic Analyses

In this work, we propose both an improvement and extensions of a reverse Jensen inequality due to Wunder et al. (2021). The new proposed inequalities are fairly tight and reasonably easy to use in a wide variety of situations, as demonstrated in several application examples that are relevant to information theory. Moreover, the main ideas behind the derivations turn out to be applicable to generate bounds to expectations of multivariate convex/concave functions, as well as functions that are not necessarily convex or concave.

Effect of cognitive & metacognitive Strategy developing reading comprehension emphasizing students’ linguality

Reading comprehension ability is potency of students to comprehend meaning of written texts, text details and main ideas. Furthermore, ability of reading comprehension activated learners to communicate with writers. To understand main ideas of written texts, help learners to be aware and to get particular messages from texts. Cognitive and metacognitive knowledges help readers to analyze, to summarize, to judge, and to distinguish main idea of reading texts and also more details about writer viewpoints to predicate and decision making to monitor text contents too. Monolingual students are those groups which must be aware about impacts of metacognitive strategy upon reading development and comprehension through to prepare and emanate bio feedbacks with teachers. Hence, monolingual groups have to be taught more than bilingual ones due to their low – proficiency levels and also their weak knowledge capacities about reading development strategies. Indeed, today understanding the effective strategies which help to learn language skills for all of scholars in TESOL domains is very significant, so every teacher that is aware about efficacy of those psychological strategies like cognitive and metacognitive or both; he or she is able to teach language skills particularly reading comprehension very conveniently and more productive language learning results. Without understanding reading strategy text comprehension to learn language skills is impossible.

Mental health of business academics within the COVID-19 era: can meaningful work help? A qualitative study

PurposeThrough addressing academics in four public business schools in Egypt, the authors of this paper aim to uncover how meaningful work might shape the mental health of the addressed academics post COVID-19.Design/methodology/approachThe author employed a qualitative research method through semi-structured interviews with 44 academics from four business schools selected from among 25 public institutions of higher education in Egypt. The author subsequently used thematic analysis to determine the main ideas in the transcripts.FindingsThe authors’ findings show that business academics usually consider meaningful work as playing a major role in shaping their mental health, especially after a crisis. This indicates that the more they perceive their jobs as valuable and worthwhile, the more they can deal with limitations and mental health issues (e.g. anxiety, stress, inadequate sleep, etc.) that accompany crisis. The findings also show that during the time of the COVID-19 crisis, employees (business academics in this case) have not placed so much importance to their autonomy (ability to choose and/or participate in decision-making processes) in the workplace. Instead, they care more about their relatedness (sense of belongingness) and their level of competence (sense of capability). Accordingly, the authors show that having academics that develop a sense of purpose for their academic duties in a time of crisis has less mental health disorders. Subsequently, post crisis, business academics can feel a continuous sense of relatedness and find ongoing opportunities to work and learn.Originality/valueThis paper contributes by filling a gap in HR management, in which empirical studies on the relationship between mental health and meaningful work have been limited so far.

Scheduling by NSGA-II: Review and Bibliometric Analysis

NSGA-II is an evolutionary multi-objective optimization algorithm that has been applied to a wide variety of search and optimization problems since its publication in 2000. This study presents a review and bibliometric analysis of numerous NSGA-II adaptations in addressing scheduling problems. This paper is divided into two parts. The first part discusses the main ideas of scheduling and different evolutionary computation methods for scheduling and provides a review of different scheduling problems, such as production and personnel scheduling. Moreover, a brief comparison of different evolutionary multi-objective optimization algorithms is provided, followed by a summary of state-of-the-art works on the application of NSGA-II in scheduling. The next part presents a detailed bibliometric analysis focusing on NSGA-II for scheduling applications obtained from the Scopus and Web of Science (WoS) databases based on keyword and network analyses that were conducted to identify the most interesting subject fields. Additionally, several criteria are recognized which may advise scholars to find key gaps in the field and develop new approaches in future works. The final sections present a summary and aims for future studies, along with conclusions and a discussion.

Connectivity Estimation Approaches for Internet of Things-Enabled Wireless Sensor Networks

Internet of things (IoT) envisions a network of billions of devices having various hardware and software capabilities communicating through internet infrastructure to achieve common goals. Wireless sensor networks (WSNs) having hundreds or even thousands of sensor nodes are positioned at the communication layer of IoT. In this study, the authors work on the connectivity estimation approaches for IoT-enabled WSNs. They describe the main ideas and explain the operations of connectivity estimation algorithms in this chapter. They categorize the studied algorithms into two divisions as 1-connectivity estimation algorithms (special case for k=1) and k-connectivity estimation algorithms (the generalized version of the connectivity estimation problem). Within the scope of 1-connectivity estimation algorithms, they dissect the exact algorithms for bridge and cut vertex detection. They investigate various algorithmic ideas for k connectivity estimation approaches by illustrating their operations on sample networks. They also discuss possible future studies related to the connectivity estimation problem in IoT.

Understanding Objectivity among Brazilian Journalists

ABSTRACT – This paper presents the results from an online survey of 234 journalists from all regions of Brazil and their perceptions of journalistic objectivity. The survey questions presented different theoretical possibilities concerning objectivity and were organized around three main ideas: 1) ontological, which measures how journalists understand what the facts are; 2) epistemological, which asks about how accessible these facts are; and 3) methodological, which characterizes the understanding of what would be the most appropriate method for describing the facts. The data were obtained using Google Forms and analyzed using the SPSS software. Our findings, based on non-probability sampling, showed that journalists understand that the facts have a determination prior to the report, the meaning of which can be defined by approximation through an intersubjective method of verification. RESUMO – O objetivo deste artigo é apresentar os resultados de um questionário online que contou com a participação de 234 jornalistas, de todas as regiões do Brasil, e que buscou identificar a percepção da objetividade jornalística por esses profissionais. As perguntas apresentavam possibilidades teóricas distintas frente ao problema da objetividade e foram organizadas em torno de três eixos: 1) ontológico, que procurou medir como os jornalistas entendem o que são os fatos; 2) eixo epistemológico, que perguntou sobre a possibilidade de acesso a esses fatos; e 3) metodológico, que buscou caracterizar o entendimento sobre qual seria o método mais adequado para descrever os fatos. Os dados foram obtidos por meio de Google Forms, sendo sistematizados com o software SPSS. Conclui-se que, nessa amostra não probabilística, os jornalistas compreendem que os fatos possuem uma determinação anterior ao relato, cujo sentido pode ser definido por aproximação, por meio de um método intersubjetivo de verificação. RESUMEN – El propósito de este artículo es presentar los resultados de una encuesta que contó con la participación de 234 periodistas, de todas las regiones de Brasil, y que buscó identificar la percepción de la objetividad periodística por parte de estos profesionales. Las preguntas presentaban distintas posibilidades teóricas en relación al problema de la objetividad y se organizaban en torno a tres ejes: 1) ontológico, que buscaba medir cómo los periodistas entienden cuáles son los hechos; 2) eje epistemológico, que preguntó sobre la posibilidad de acceder a estos hechos; y 3) metodológico, que buscaba caracterizar la comprensión de cuál sería el método más adecuado para describir los hechos. Los datos se obtuvieron a través de un cuestionario en línea, siendo sistematizados con el software SPSS. Se concluye que, en esta muestra no probabilística, los periodistas entienden que los hechos tienen una determinación previa al informe, cuyo significado puede definirse por aproximación, mediante un método intersubjetivo de verificación.

Ontology-Driven Cultural Heritage Conservation: A Case of The Analects of Confucius

Confucianism, recognized as the belief system of Chinese, is one of the most important intangible cultural heritages of China. The main ideas of its founder, Confucius, are written in The Analects of Confucius. However, its scattered chapters and the obscurity of ancient Chinese have prevented many people from understanding it. In order to overcome this difficulty, it needs some modern ways to reveal the vague connotation of Confucianism. This paper aims to describe how to construct the Lunyu ontology in which all concepts are abstract within the core scope, i.e., morality of Confucianism. The key task of this project lies in identifying essential characteristics, a notion that is compliant with the ISO principles on Terminology (ISO 1087 and 704), according to which a concept is defined as a combination of essential characteristics. This paper proposed an approach in the practice of identifying essential characteristics of abstract concepts from different meanings of its Chinese terms in The Analects of Confucius. With this work, Lunyu ontology established a semantic, formal, and explicit representation system for concepts of Confucianism, and the new proposed approach provides a useful reference for other researchers.

Alzheimer’s Disease Classification Based on Image Transformation and Features Fusion

It has become an inevitable trend for medical personnel to analyze and diagnose Alzheimer’s disease (AD) in different stages by combining functional magnetic resonance imaging (fMRI) and artificial intelligence technologies such as deep learning in the future. In this paper, a classification method was proposed for AD based on two different transformation images of fMRI and improved the 3DPCANet model and canonical correlation analysis (CCA). The main ideas include that, firstly, fMRI images were preprocessed, and subsequently, mean regional homogeneity (mReHo) and mean amplitude of low-frequency amplitude (mALFF) transformation were performed for the preprocessed images. Then, mReHo and mALFF images were extracted features using the improved 3DPCANet, and these two kinds of the extracted features were fused by CCA. Finally, the support vector machine (SVM) was used to classify AD patients with different stages. Experimental results showed that the proposed approach was robust and effective. Classification accuracy for significant memory concern (SMC) vs. mild cognitive impairment (MCI), normal control (NC) vs. AD, and NC vs. SMC, respectively, reached 95.00%, 92.00%, and 91.30%, which adequately proved the feasibility and effectiveness of the proposed method.