Reviewerly/exHarmony huggingface dataset:
Breast Cancer Prediction using Machine Learning

With the increase in occurrence of human diseases all over the world, it has created a wide number of opportunities for innovating such kind of disease prediction mechanisms. Out of which, Breast Cancer has grown in tremendous number with an increased amount in the past decade and this has gone increasing till now, &amp; would continue to grow. Now moving further, there is a tremendous need for efficient text analytics tools and feature extraction tools to assist the work related in classifying, sharing and retrieving the information on human diseases in general and in the field of Breast Cancer also. In addition to that, the present study has been made with the objective to provide a complete analysis of different classifiers on Breast Cancer dataset, and to generate a new ensemble training method/model of Machine Learning Classification. Here, machine learning algorithms (such as Decision Tree , Logistic Regression ,Support Vector Machine, Random Forest). An Ensemble Learning model for Prediction is proposed to classify the results among different classifiers as mentioned in the above statement. At the end, the Voting Ensemble technique is implemented in order to find out the optimal classifier that predicts the Breast Cancer. The results have been computed on the basis of evaluation parameters such as Accuracy, Precision, Recall and Specificity. On the other hand, the confusion matrix is measured on the basis of evaluation parameters that generally provides more emphasised, predicted and actual instance of data. Whereas, Performance Evaluation for various machine learning algorithms is then computed. Results of this investigation says that, the Voting Ensemble superpowers other machine learning algorithms.
Geochemical Characterization Of the U1 Member, Sahabi formation In Northeast Sirt Basin, Libya

This study is based on three cropped out clay profiles belonging to U1 Member of the Sahabi Formation at As Sahabi area in northeast Sirt Basin. This member horizon is the most productive layer in terms of fish, reptiles and mammals remains. Some of the collected samples were subjected to geochemical analysis using Perkin Elmer the ICP-OES to define the major oxides and Perkin Elmer SciexElan 9000 ICP-MS for trace elements. The statistical relationships between relevant elemental percentages have been used to determine the provenance, depositional environment, paleo-weathering, paleo-climate and tectonic setting.
A qualitative perspective of the role of Malawi Library Association in developing librarianship in Malawi: opportunities and challenges

Purpose The study aims to unpack the role of Malawi Library Association (MALA) in developing librarianship in Malawi. It also explores an array of opportunities and challenges that are present for MALA. Design/methodology/approach The study applies the interpretivist paradigm for the research design. Qualitative data were collected from a purposeful sample totaling 24 practicing librarians and paraprofessionals in different work environments to inform the study phenomenon. Findings The study gathers that the role of MALA has been in its infancy stage for a long time. However, the apparent developments of MALA manifest in its pro-educational initiatives. Overall, MALA is impeded by a litany of obstacles such as financial constraints and a lack of advocacy strategy. Originality/value The study theorizes the role of MALA by triangulating the advocacy coalition framework, institutional theory and professionalization theory in the library and information practice.
The 5G revolution: Tackling challenges in smart cities and intelligent transportation systems

&lt;div&gt;&lt;p&gt;The use of cutting-edge technology and data systems to increase the effectiveness, safety, and environmental responsibility of transport networks is known as intelligent transportation systems (ITS). In order to improve many elements of transportation, ITS incorporates a wide range of technologies, including sensors, communication networks, data analytics, and robots. By addressing the shortcomings of the current 4G network, the next mobile technology, 5G, challenges the electronic communication environment as it stands. By allowing a large number of concurrent connections and networks ubiquitous, even in high mobility scenarios or densely inhabited places, such Smart trains and smart cities (SC) are made possible by cutting-edge technology a new approach of becoming fully integrated. According to this strategy; 5G will enable the true Internet of Things (IoT) and its related automobiles on the World Wide Web (WWW). This conversation attempts to thoroughly explain how 5G wireless networks will impact urban smart transport networks, including semi-autonomous or self-driving cars, and automotive communication over the coming years, as well as any technical, economical, and regulatory issues.&lt;/p&gt;&lt;/div&gt;
Determinants of agriculture information utilization among farmers: Contextualization of Wilson's information behavior model

In modern agriculture, information plays a crucial role in increasing productivity and development. Thus, active utilization of agriculture information can be instrumental for the agriculture development and the quality of life of farmers. Despite Pakistan's agriculture-based economy, literature reports that Pakistani farmers do not utilize the information effectively. Therefore, a study was conducted to explore the determinants of agriculture information utilization among farmers. Data was collected quantitatively through a survey design, and the population included all farmers of District Okara Punjab, Pakistan. The sample was recruited based on convenient sampling technique. Since the questionnaire was developed, therefore its content validity was confirmed through expert scrutiny and the internal consistency and reliability of measures were determined through Cronbach's alpha. The collected data was analyzed using SPSS-22 and path analysis was calculated through the Analysis of Moment Structure (AMOS). Data analysis confirmed that Pakistani farmers were paying average level of active attention towards seeking agricultural information. Whereas path analysis established that the realization of agricultural information needs leads to utilization of agriculture information, and information seeking behavior plays a significant mediating role in this regard.
Syvällinen sosiosemioottinen näkökulma kieleen

Arvioitu teos: Michael Silverstein: Language in culture. Lectures on the social semiotics of language. Cambridge: Cambridge University Press 2023. 353 s. isbn 978-1-009-19884-4.
“You-Don’t-Wanna-Mess”: A Case Study of Nonverbal Behaviors in Chinese Freestyle Rap Battles

Different channels of nonverbal behavior can serve important functions in live music performances. In this paper, we look at cross-channel nonverbal behavior in a Chinese freestyle rap battle, investigating what categories of nonverbal behavior are used and what functions these behaviors are serve. Using the theoretical frameworks of dyadic power theory and the coordination of verbal and nonverbal behaviors, we analyzed four video clips of two Chinese rappers in a freestyle rap battle. We found that nonverbal behaviors were crucial to helping them articulate the verbal content of their raps, displaying power and exerting dominance. Theoretical implications were discussed.
Semi-supervised Learning for Automatic Modulation Recognition Using Haar Time–Frequency Mask and Positional–Spatial Attention

Automatic modulation recognition plays an important role in many military and civilian applications, including cognitive radio, spectrum sensing, signal surveillance, and interference identification. Due to the powerful ability of deep learning to extract hidden features and perform classification, it can extract highly separative features from massive signal samples. Considering the condition of limited training samples, we propose a semi-supervised learning framework based on Haar time–frequency (HTF) mask data augmentation and the positional–spatial attention (PSA) mechanism. Specifically, the HTF mask is designed to increase data diversity, and the PSA is designed to address the limited receptive field of the convolutional layer and enhance the feature extraction capability of the constructed network. Extensive experimental results obtained on the public RML2016.10a dataset show that the proposed semi-supervised framework utilizes 1% of the given labeled data and reaches a recognition accuracy of 92.09% under 6 dB signals.
Silver isotope analysis and systematics of native gold from the Rajapalot Co-enriched gold deposit, Finnish Lapland

Abstract Silver is probably the closest isotopic proxy to track monoisotopic gold and has been shown to have great potential to yield new information on the origin and enrichment processes of gold. This study describes the development of a tailored analytical protocol for accurate analysis of Ag isotopes and provides the first Ag isotope data for the Paleoproterozoic Rajapalot Au-Co deposit, Finnish Lapland. Six native Au samples yield ε 109 Ag values (relative to NIST SRM 978a) from −6.8 to +2.1 and are within the range of Ag isotopic compositions reported for native Au samples. The mean of the analyzed Au samples is ε 109 Ag = −3.8 ± 1.7 (2SD) with most of the samples with negative ε 109 Ag values (−6.7 to −2.0); one sample has a positive ε 109 Ag value of +2.1 ± 0.5. Silver isotope fractionation in the Rajapalot Au deposit was likely associated with physicochemical processes related to deposition and/or re-mobilization of the ore rather than with source region inheritance. It is suggested that redox reactions involving Ag + ↔ Ag 0 phase change primarily account for the isotopic differences within the deposit. Our results also suggest that the Rajapalot Au-Co deposit was formed via multistage ore-forming processes and/or that the primary ore was re-mobilized, which caused isotope fractionation along fluid pathways. Silver isotope variation within a deposit may mark a fractional crystallization trend with the lightest isotopic composition representing the earliest precipitate. Hence, Ag isotopes show potential as an isotopic vectoring tool in search of Au-enriched domains.
The influence of ethical leadership and climate on employee work engagement

Orientation: Ethical leadership is crucial in fostering work engagement among employees in organisations.Research purpose: This study aimed to empirically validate a theoretical model explicating the structural relationships between ethical leadership, ethical climate and work engagement.Motivation for the study: The central role of work engagement in sustaining competitive advantage calls for an analysis of the role of ethical leadership and climate in enhancing an engaged workforce.Research approach/design and method: A non-probability convenience sample consisting of 304 participants was drawn from 10 selected private security companies.Main findings: High levels of reliability were found, and the unidimensionality of the subscales was confirmed through exploratory factor analyses. A good fit with the data was found for the measurement models through confirmatory factor analysis. Structural equation modelling showed a reasonable fit for the structural model. Positive relationships were found between ethical leadership, climate and work engagement.Practical/managerial implications: The findings emphasise the role of ethical leadership and climate in work engagement. Ethical leadership fosters work engagement if employees perceive their leaders as ethical and if there is provision of favourable, transparent policies and procedures.Contribution/value-add: Ethical leadership alone may not be a sufficient condition for employee work engagement; other variables such as a positive ethical climate also play a role. The study suggests specific variables that may also combine with leadership to positively influence work engagement.
Off‐policy correction algorithm for double Q network based on deep reinforcement learning

Abstract A deep reinforcement learning (DRL) method based on the deep deterministic policy gradient (DDPG) algorithm is proposed to address the problems of a mismatch between the needed training samples and the actual training samples during the training of intelligence, the overestimation and underestimation of the existence of Q‐values, and the insufficient dynamism of the intelligence policy exploration. This method introduces the Actor‐Critic Off‐Policy Correction (AC‐Off‐POC) reinforcement learning framework and an improved double Q‐value learning method, which enables the value function network in the target task to provide a more accurate evaluation of the policy network and converge to the optimal policy more quickly and stably to obtain higher value returns. The method is applied to multiple MuJoCo tasks on the Open AI Gym simulation platform. The experimental results show that it is better than the DDPG algorithm based solely on the different policy correction framework (AC‐Off‐POC) and the conventional DRL algorithm. The value of returns and stability of the double‐Q‐network off‐policy correction algorithm for the deep deterministic policy gradient (DCAOP‐DDPG) proposed by the authors are significantly higher than those of other DRL algorithms.
Taraldsen’s Exact Correlation P-Values

This short paper serves as a vignette for the R package “practicalSigni” describing the usage of pvTarald() and qTarald(), respectively, for computing the p-values and quantiles of Taraldsen’s new exact sampling distribution of Pearson correlation coefficient and its generalized versions. A new table allows practitioners to see onesided critical values from Taraldsen’s (2023) exact sampling distribution, which generalizes Fisher’s z-transform. It computes p-values and quantiles for the arbitrary hypothesized value of the population correlation coefficient, ρ ∈ [−1, 1].
The Anthropological Turn: Cultural Practices for the Development of Human Complexity

Introduction. In the worldwide situation of collision accompanied with systemic crises, it is proposed to raise a reflexive question – how institutions and knowledge-systems can be arranged to support complexity, diversity, and subjectivity of Human action. Authors propose an approach to overcoming systemic gaps through reframing and re-actualization of the field of socio-humanitarian practices. It is necessary to “build up” the environment of integration of sciences and cultural practices of human development. The article presents the key provisions of the “Anthropological Turn” approach, provides an overview of the foundations and basic ideas, as well as specific practical models of activity. Materials and Methods. The “Anthropological Turn” sets a fundamentally different framework and actually highlights the objects and mechanisms of interpretation of the results for future humanitarian research. “Anthropological Turn” research program overcomes the naturalistic approach in the study of humanitarian processes. Results. Authors propose and describe four basic practical and organizational models. These models are acting as the activity infrastructure for the implementation of the ideas of the Anthropological Turn: the historical and evolutionary methodology for designing universities of the future; productive activities for the socio-cultural development of territories and technological entrepreneurship in childrenʼs and adult communities; search, development and replication of humanitarian (cultural, educational, etc.) practices; the family as an institution for the development of subjectivity and human identity. Discussion and Conclusion. The described approaches and implemented projects made an ecosystem of educational solutions. On the one hand, they provide support for the development of the professional and educational space and innovative activities of the subjects of Russia, the creation and dissemination of promising educational practices, training of high-level professionals for the social sphere, business; and, on the other hand, it contributes to the formation of a dynamic system for the implementation of continuous professional development of personnel.
Changing Vectors of International Cooperation of Russian Universities in the New Geopolitical Situation

Introduction. With the implementation of state policy aimed at integrating the Russian higher education into the world educational system, universities internationalization has been intensively developing in recent decades. However, the unprecedented sanction pressure of unfriendly countries (starting in 2022) affected universities international activity prompting them to search for new foreign partners. The purpose of the research was to study the vectors of Russian universities’ international activity under transformation in the current geopolitical situation. The analysis of new geography and forms of international cooperation in the educational, scientific, cultural and humanitarian spheres was of specific interest. Materials and Methods. The method of content analysis was employed to study the news on international activity, posted on the websites of leading Russian universities, government authorities and specialized organizations. The results of this analysis were mapped (the mapping method was used). The method of substantive analysis of news reports was used to obtain a characterization of the forms of international cooperation. Statistical data were analyzed in the article by means of quantitative methods. The methods of substantive analysis, systematization and classification of information contained in theoretical sources, normative-legal acts, information and analytical materials were used. Results. A set of sanction restrictions on the internationalization of Russian universities is characterized. Diagrammatic maps of the new geography of their international cooperation are presented. They indicate the growth of international contacts with universities in the CIS countries, Asia, Africa and Latin America against the backdrop of the decreased contacts with Western countries. Discussion and Conclusion. The expansion of international cooperation between Russian universities geography can help minimize risks of sanction pressure, as well as open up new opportunities for the long-term international partnership development and the formation of new world scientific and educational superiority centers. The article can be useful for universities in Russia for forming their international strategy in the new geopolitical conditions.
Fine-Grained Independent Approach for Workout Classification Using Integrated Metric Transfer Learning

Physical activity helps manage weight and stay healthy. It becomes more critical during a pandemic since outside activities are restricted. Using tiny wearable sensors and cutting-edge machine intelligence to track physical activity can help fight obesity. This study introduces machine learning and wearable sensor methods to track physical activity. Daily physical activities are typically unstructured and unplanned, and sitting or standing may be more common than others (walking stairs upstairs down). No activity categorization system has examined how class imbalance affects machine learning classifier performance. Fitness can boost cardiovascular capacity, focus, obesity prevention, and life expectancy. Dumbbells, yoga mats, and horizontal bars are used for home fitness. Home gym-goers utilise social media to learn fitness, but its effectiveness is limited.
Textual Alchemy

This chapter explores the transformative potential of generative models for advanced text generation, focusing on leveraging structural equation modeling techniques. With the rapid advancements in deep learning and natural language processing, generative models have emerged as powerful tools for creative writing, semantic coherence, and contextual understanding. This chapter provides a comprehensive overview of the foundations, methodologies, and applications of generative models in text generation. The chapter begins with an introduction to the evolution of generative models and highlights their significance in various domains. It lays the groundwork by explaining language modeling techniques and the architectures employed in text generation using deep learning algorithms. The subsequent sections delve into the core aspects of generative models for text generation.
Promoting Students' Writing by Using Essay Writing GPT

The purpose of this study is to determine how students might enhance their essay-writing abilities and how they respond to the essay writing GPT. Explanatory sequential research was used in this mixed method study to analyze both the qualitative and quantitative data in distinct steps. In order to conduct the study quantitatively, the researcher used 60 students who were enrolled in an undergraduate English study program at IAIN Curup, Indonesia, that included an essay writing course. With 30 students of experimental and control group. The test is validated by three experts from UIN Raden Fatah Palembang, University of Bengkulu, and UIN Fatmawati Sukarno Bengkulu. The results of this research are that GPT has no significant impact on students' essay writing skill. Experimental and control groups have almost the same score in essay writing tests. GPT has made students simpler to build the idea because they do not need to think harder like traditionally writing. Instead, the students who are in control group easier to arrange the structure of the essay as what they wanted.
Enhancing User Privacy in Natural Language Processing (NLP) Systems

NLP has witnessed a remarkable improvement in applications, from voice assistants to sentiment analysis and language translations. However, in this process, a huge amount of personal data flows through the NLP system. Over time, a variety of techniques and frameworks have been developed to ensure that NLP systems do not ignore user privacy. This chapter highlights the significance of privacy-enhancing technologies (differential privacy, secure multi-party computation, homomorphic encryption, federated learning, secure data aggregation, tokenization and anonymization) in protecting user privacy within NLP systems. Differential privacy introduces noise to query responses or statistical results to protect individual user privacy. Homomorphic encryption allows computations on encrypted data to maintain privacy. Federated learning facilitates collaborative model training without sharing data. Tokenization and anonymization preserve anonymity by replacing personal information with non-identifiable data. This chapter explores these methodologies and techniques for user privacy in NLP systems.
Development of Leadership Competencies: From the Experience of the Yugra International 2023 Forum

The article provides an overview of the most interesting practices when working with foreign students as part of the development of their leadership competencies. The authors pay special attention to the problem of intercultural dialogue in the foreign language environment of students in Russia and technological solutions for establishing interaction between students. The published materials are the result of the Forum of Student Leaders of the Khanty-Mansiysk Autonomous Area-Yugra “Yugra International 2023” and are of a recommendatory nature both for various forum campaigns and for direct work with foreign students.
Enhancing Twitter Tweet Topic Understanding through Ensemble Learning

Since Twitter’s introduction to social media of the hashtag as a content grouping label in 2007, the symbol and its associated usage as a classification la- bel have seen widespread adoption throughout social media and other platforms. While the content of a post can be conveniently classified using said post’s hash- tags, classifying posts that do not contain hashtags proves to be a much more challenging problem. In this paper, we propose a system for identifying a post’s hashtags using only the non-hashtag terms of the post and, by extension, address the issue of classifying the contents of posts that do not contain hashtags.