Reviewerly/exHarmony huggingface dataset:
Geochemical Survey: A Tool for Subsurface Geological Mapping in Alluvial Terrain

The regional geochemical mapping was carried out in the Indo-Gangetic alluvial plain of the Western and Central India which was previously classified as Varanasi alluvium, a polycyclic sequence of large alluvial fans transported by perennial Himalayan Rivers. The results based on the geochemical dispersion pattern of major and minor oxides as well as trace elements conclude that there are four major factors determining the dispersion patterns of the variables which are: (1) the underlying lithology with a strike of NE-SW, (2) transport of sediments along the general slope of the study area towards south and south-west as reflected in the Digital Elevation Model, (3) formation of Reh and (4) anthropogenic influence. The present study focusing on the effect of the underlying geology on the geochemical signature of the study area infers that the geochemical distribution pattern of certain variables clearly follows the trend of the Delhi fold belt. It can be interpreted that the subsurface lithology belonging to Delhi Supergroup is present at a depth of 500m which is considerably shallower, imprinting its geochemical signature on the sediments. The presence of minor outcrops of quartzite belonging to the Delhi Supergroup with the NE-SW strike is also documented in the study area. Based on this study we propose to reclassify the Varanasi Alluvium sediments as equivalent to Banda Alluvium. The study also demonstrates the utility of geochemical mapping as a tool to map the subsurface geology of an alluvial terrain. The results of this study are also corroborated by the regional scale gravity survey.
Modeling effects of linguistic complexity on L2 processing effort: The case of eye movement in text reading

Abstract This study examined how linguistic complexity features contribute to second language (L2) processing effort by analyzing the Dutch English-L2 learners’ eye movements from GECO and MECO, two eye-tracking corpora. Processing effort was operationalized as reading rate, mean fixation duration, regression rate, skipping rate, and mean saccade amplitude. In Study 1, the lexical, syntactic, and discoursal indices in 272 snippets of a novel in GECO were regressed against these eye-movement measures. The results showed that the one-component partial least square regression (PLS-R) models could explain 11%–37% of the variance in these eye-movement measures and outperformed eight readability formulas (six traditional and two recent cognitively inspired formulas based on the readers’ perception on text difficulty) in predicting L2 processing effort. In Study 2, the eye-tracking data from MECO were used to evaluate whether the findings from Study 1 could be applied more broadly. The results revealed that although the predictability of these PLS-R components decreased, they still performed better than the readability formulas. These findings suggest that the linguistic indices identified can be used to predict L2 text processing effort and provide useful implications for developing systems to assess text difficulty for L2 learners.
MASK CLASSIFICATION SEGMENTATION METHOD BASED ON GROUPED CONVOLUTION AND SPATIAL PYRAMIDAL CONVOLUTION MODEL FOR THYROID CANCER IDENTIFICATION

Ultrasound plays different roles in the whole process of thyroid cancer management. With the advancement of ultrasound imaging technology and diagnostic level, it is gradually becoming an irreplaceable role in the diagnosis and treatment of thyroid cancer. However, the diagnosis of thyroid cancer by means of sonographic features is subjective and highly dependent on the operator’s experience and knowledge. To avoid unnecessary puncture biopsies, alleviate anxiety and improve diagnostic efficiency, in this paper, we utilize a purely convolutional model with a pyramid structure strategy to extract sonographic features at different scales. Combined with a per-pixel classification segmentation method, which is different from the previous mainstream, it is used for the intelligent recognition of thyroid cancer. Finally, the experimental results show that our radiologists achieve better performance than five mainstream segmentation methods in four metrics (Sensitivity, Jarccard, Dice, ASD) on the thyroid cancer dataset. It provides the possibility to help radiologists to overcome diagnostic subjectivity and obtain accurate, reproducible and more objective diagnostic results.
Analysis of /t̪a:/ and /t̪ʊn/ bols of the tabla from a musical standpoint

Tabla, the Indian harmonic musical drum, produces several distinct sounds or bols, which are rhythmically arranged into different taals (beat-cycles) to work as a syllabic musical poem. Prior research on tabla has primarily focused on the tabla membrane’s vibrational attributes and how such attributes get influenced by the tabla’s structure. Thus, there is a need to study the tabla from a musical standpoint and understand the linkages between its vibrations and the acoustical attributes of its musical bols. We fill this gap by analyzing the two most important bols; /t̪a:/ and /t̪ʊn/. Towards this, we studied bols produced by ten experienced tabla players using four different tablas tuned to different notes spanning an octave. To extract the tabla’s vibro-acoustical features, we employed two stereoscopically arranged high-speed cameras and an audio data acquisition system in tandem. Our work brings out several salient features of these bols including correspondences between the method of producing a bol and its acoustical attributes, the presence of nearly degenerate modes, the phenomenon of frequency splitting due to asymmetric loading of tabla membrane and the consequential beats, the musical importance of some inharmonicities, and psychoacoustic implications of certain frequencies missing in a bol’s spectrum. Such understanding will not only help us understand the musical acoustics of the tabla better, but also guide us towards an improved tabla design.
Linguistic Reasoning Petri Nets for Knowledge Representation and Reasoning

This chapter proposes a linguistic reasoning Petri net (LRPN) model and develops an ordered weighted linguistic reasoning (OWLR) algorithm for knowledge representation and reasoning.
Pythagorean Fuzzy Petri Nets for Knowledge Representation and Reasoning

FPNs are a graphical-based knowledge representation and reasoning tool widely used in many fields for intelligent decision making. However, the traditional FPNs could not accurately represent domain experts’ uncertain and ambiguous knowledge with the increasing complexity of knowledge base systems. Additionally, the task of assessing the truth degrees of input places is limited to small-scale expert groups in current practices. In response to these problems, this chapter develops a new type of FPN model, called Pythagorean fuzzy Petri nets (PFPNs), for knowledge representation and reasoning in the large group context. Pythagorean fuzzy sets are introduced to capture imprecise and ambiguous knowledge and a large group truth determination method is proposed to get the truth degrees of input places with massive data. Finally, an application example regarding security risk assessment is given to show the effectiveness and advantages of our proposed PFPNs.
Picture Fuzzy Petri Nets for Knowledge Representation and Acquisition

FPNs have been applied in many fields as a potential modeling tool for knowledge representation and reasoning.
Research on the Optimal Design of Campus Space Based on Artificial Intelligence and its Impact on Education Quality

This study, the application of artificial intelligence optimization in education is investigated, with a particular focus on campus planning, teaching management, and student management. By comparing AI-designed campuses to their traditional counterparts, the research concludes that AI optimization
Methods of constructing superposition measures

The resource theory of quantum superposition is an extension of the quantum coherent theory, in which linear independence relaxes the requirement of orthogonality. It can be used to quantify the nonclassical in superposition of finite number of optical coherent states. Based on convex roof extended, state transformation and weight, we give three methods of constructing superposition measures of quantum states, respectively. We also generalize the superposition resource theory from two perspectives.
Intuitionistic Fuzzy Petri Nets for Knowledge Representation and Reasoning

FPNs are an important modeling tool for knowledge representation and reasoning, which have been extensively used in a lot of fields.
FPNs for Knowledge Representation and Reasoning: A Bibliometric Analysis

FPNs are a powerful modeling tool for the knowledge representation and reasoning of expert systems. During the past decades, a variety of models and methods have been developed to improve the performance of FPNs. However, there is a lack of a comprehensive bibliometric analysis of the literature on the FPN topic. The objective of this chapter is to conduct a bibliometric analysis of the FPN studies to generate a global picture of developments, focus areas, and research trends in this field. To achieve this goal, 287 journal articles extracted from the Web of Science over the period of 2000–2022 were analyzed with respect to cooperation network, co-citation network, and keyword co-occurrence network. According to the current research trends, possible future research directions concerning FPNs are revealed. This chapter provides important reference for scholars and practitioners to grasp the research status, hot topics, and future research agenda of the FPN domain.
Current Situation and Countermeasures of County School Predicament: Taking Yixian Middle School as an Example

This paper presents a case study of Yixian Middle School (YMS) in China, discussing the challenges faced by county high schools (CHS) and how it affects education qual-ity and resources. The reasons for the county school predicament (CSP) are analysed, and the strategies employed by YMS to deal with
Applying Complex SEE Transformation in Cryptography

: In this paper, the capability of complex SEE (Sadiq-Emad-Emad) integral transform to be used in cryptography has been demonstrated, discussed and proven by practical example, in which the complex SEE transform had been used to encrypt a message and then decrypt the resulted ciphertext back into the original text.
How to leverage anthropomorphism for chatbot service interfaces: The interplay of communication style and personification

Although chatbots are oftentimes used in customer service encounters, interactions are oftentimes perceived as not satisfactory. One key aspect for designing chatbots is the use of anthropomorphic design elements. In this experimental study, we examine the two anthropomorphic chatbot design elements of personification, which includes a human-like appearance, and social orientation of communication style, which means a more sensitive and extensive communication. We tested the influence of the two design elements on social presence, satisfaction, trust and empathy towards a chatbot. First, the results show a significant influence of both anthropomorphic design elements on social presence. Second, our findings illustrate that social presence influences trusting beliefs, empathy, and satisfaction. Third, social presence acts as a mediator for both anthropomorphic design elements for satisfaction with a chatbot. Our implications provide a better understanding of anthropomorphic chatbot design elements when designing chatbots for short-term interactions, and we offer actionable implications for practice that enable more effective chatbot implementations.
Smart evaluation of videos for unusual-event identification in automated vehicle monitoring systems

Many methods have been tried to perfect vehicle recognition, which includes quickly locating the same objects in cameras with different sensor types and perhaps propagating trustworthy information across cameras to improve detection. In this study, we look at various important issues and provide a new method for spotting certain abnormalities. We suggest an automated vehicle monitoring system (AVMS) to enhance detection accuracy. If the current reading from a certain local monitor is out of the ordinary, that monitor will issue a notification, and all of those alerts will go toward deciding whether or not an abnormal event has occurred. The effective implementation of any large-scale surveillance system relies on our algorithm meeting several parameters. It simply takes a few minutes to set up and is completely hands-free. The experimental findings demonstrate the effectiveness and simplicity of the suggested approach, with the assembled image quality being on the level with that of state-of-the-art techniques.
Feature Selection for Efficient Local-to-global Bayesian Network Structure Learning

Local-to-global learning approach plays an essential role in Bayesian network (BN) structure learning. Existing local-to-global learning algorithms first construct the skeleton of a DAG (directed acyclic graph) by learning the MB (Markov blanket) or PC (parents and children) of each variable in a dataset, then orient edges in the skeleton. However, existing MB or PC learning methods are often computationally expensive especially with a large-sized BN, resulting in inefficient local-to-global learning algorithms. To tackle the problem, in this article, we link feature selection with local BN structure learning and develop an efficient local-to-global learning approach using filtering feature selection. Specifically, we first analyze the rationale of the well-known Minimum-Redundancy and Maximum-Relevance (MRMR) feature selection approach for learning a PC set of a variable. Based on the analysis, we propose an efficient F2SL (feature selection-based structure learning) approach to local-to-global BN structure learning. The F2SL approach first employs the MRMR approach to learn the skeleton of a DAG, then orients edges in the skeleton. Employing independence tests or score functions for orienting edges, we instantiate the F2SL approach into two new algorithms, F2SL-c (using independence tests) and F2SL-s (using score functions). Compared to the state-of-the-art local-to-global BN learning algorithms, the experiments validated that the proposed algorithms in this article are more efficient and provide competitive structure learning quality than the compared algorithms.
The Use and Usefulness of The Futures Triangle

This paper presents the Futures Triangle as a framework to help guide thinking about the future. It works as a thinking tool or cognitive map, helping us interpret the past, orientate the present in charting our move to the future. This article uses four case studies to demonstrate the use and usefulness of the Futures Triangle for advancing futures studies in that it scaffolds the thinking about possible, probable and plausible futures, eliciting images of the future, interpreting the past and orientating the present, integrating knowledge and values for charting plausible futures, and increasing democratic participation in imaging desired futures. These case studies illustrates the effectiveness and utility of the Futures Triangle in enhancing futures studies and its potential for broadening engagement and understanding in the field.
The Innovation of Labeled Products in an Emerging Economy: Direct Effect on the Financial and Non-Financial Performance of Companies

ABSTRACTIn today’s competitive markets, innovation is widely recognized as a critical driver of growth and sustainable development. It encompasses various forms such as product implementation, process improvement, marketing strategies, and organizational structures. Innovation creates value and ensures adaptability to an increasingly changing environment. Despite a multitude of investigations having been carried out to evaluate the correlation between innovation and performance, the majority of the scholarly attention has been directed toward technological innovation in developed nations. Regrettably, scant attention has been devoted to research endeavors investigating this relationship in emerging economies. This study aims to fill this gap by examining the impact of product, process, marketing, and organizational innovation on the financial and non-financial performances of firms operating in the labeled products subsector in Morocco, an area that remains unexplored. Using data from 70 firms, Partial Least Squares-Structural Equation Modeling was employed to test the hypotheses model. The findings revealed that while there was no significant effect of innovation type on the performance of labeled local products, it did lead to tendencies in this subsector. This study has meaningful implications for managers and policymakers on how to reinforce innovative strategies to generate future performance.KEYWORDS: Innovation typeperformancelabeled productPLS-SEMMorocco Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. Regions (=states) are currently the highest administrative divisions in Morocco.Additional informationFundingThe work was supported by the VLIR-UOS [MA2017TEA1444A103].
P4wC Camps for National Identity and Value-Building Among Students: The Ukrainian Experience

This chapter describes how P4wCPhilosophy for/with Children (P4wC) has been implemented in the program of extracurricular educational short-term campsCamps, and how the methodology has been combined with different non-formal educational approaches. It focuses on events that were organized by the National Center “Junior Academy of Sciences of UkraineUkraine” between 2016 and 2021. Each camp was named after the subject to which it was devoted: “Philosophy,” “Ukrainian Culture,” “Religion and Diplomacy,” and “Cultural Diplomacy.” As educational initiatives these campsCamps proved to be attractive for teenagers and effective in obtaining educational results. They provided knowledge and trained skills which were lacking in the regular school curriculum. The camp’s program was always based on non-formal educationEducation principles and approaches. P4wC was at the core of each individual program, which included thematic interactive lectures and meetings with famous people, art sessions, excursions, museum visits, and workshops on the development of emotional intelligence, critical thinkingCritical thinking, successful communication, creativity, design thinkingThinking, storytelling, and reflectionReflection. Value framework was developed thanks to the chosen camp’sCamps topic on Ukrainian history and cultureCulture of the XVII-XX centuries. This topic features dignity, freedomFreedom and trust as main values. The Ukrainian school curriculum continues to be under Russian colonial rules on Ukrainian history and culture. It had a negative impact on the national identityNational identity formation of the Ukrainian young generation, after Independence of the nation was restored in 1991. The experience of using and adapting P4wCPhilosophy for/with Children (P4wC) itself contributed to enriching this methodology’s world practices.
Stateful Computing

A function produces its output as soon as it receives its input.Function process The function compositions are instant and costless. A process, Process on the other hand, takes time and space to process an input into an output. The space and the time are presented as the states and the state transitions. A state is anything that a process may update: a state as a state of the world, a state of memory, and a state of a living room floor.
Drawing Types and Functions

Whatever else there might be in a computer, there are always lots of wires. In modern computers, most wires are printed on a chip. In higher animals, the wires grow as nerves. In String diagram string diagrams, the wires are drawn as strings.
They’re back! QR codes for marketing the academic library after pandemic lockdowns

AbstractThe Use of QR codes for various functions in society resurged during the pandemic. We reviewed the literature on QR use in Libraries. We carried out an exploratory study to see if students returning to campus in spring 2022 could be persuaded to visit our Library website using QR codes displayed on posters and postcards distributed around campus. We measured relatively few visits to our website from our QR code marketing materials in comparison to visits originating from a search engine or a URL in Blackboard or on the college website.Keywords: QR codesoutreachmarketinglibrary servicesCOVID-19 pandemic AcknowledgmentsWith thanks to Kathy Killoran & Alexander Bolesta in our Office of Undergraduate Studies for providing seat count data.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsKate CauleyKate Cauley is Assistant Professor and the Web and Emerging Technologies Librarian.Maria KiriakovaMaria Kiriakova is Associate Professor and Associate Librarian for Technical Services and Collection Development Librarian.Ellen SextonEllen Sexton is Associate Professor in the Library, responsible for media & budget.
Overcoming the Challenges in Multi-class Context-Based Sentiment Analysis

Sentiment analysis has been extensively utilized during the past ten years in a variety of fields, including business, social networking, education, etc. For the contextual analysis, in which we need to identify and process the sentiments expressed by the writer, is a complicated task because of the large volume of data and multi-valued outcome, so the application of sentiment analysis for multi-valued and multi-class has yet remained challenging. The majority of the sentiment analysis has been done with binary classification, whereas many challenges are involved in the multi-class classification related to context-based sentiment analysis. Machine learning (ML) algorithms are less effective in classification, because of skewed distribution, especially while attempting to predict the minority classes. A major problem is with imbalanced datasets and multiple dependent variables in the outcome. This study aims to give a solution to all these multi-class challenges by developing a combinational machine learning model to analyze students’ sentiments and map it to their behaviour.
Tabular Data Interpretation on COVID-19 Through Explainable Machine Learning

Over the last decade, several AutoML techniques have been introduced for developing fully automated ML pipelines, from preprocessing to final deployment. Researchers have shown that consumers do not have trust in AutoML-generated models because of data gaps and errors. Despite the fact that such autonomously synthesized ML pipelines are capable of achieving a comparable performance. The lack of explanations for the built-in ML pipelines and the transparency of AutoML systems affect the clinical decisions in Healthcare. The paper builds on a substantial body of related work in the fields of trust in artificial intelligence (AI), XAI, AutoML, and explaining the decision-making of AutoML systems.
Food Recommendation System Based on Mood of the User

Living in the twenty-first century we are provided with many services to tackle our hunger cravings, but the abundance of these services in this era has put humankind into a great dilemma of 'What to Eat?' Many of us just keep scrolling within the options and end by choosing nothing. In the food business, machine learning and artificial intelligence applications are increasingly extensive. This paper presents the recommendation system using hybrid filtering approach to recommend dishes and the recipes based on the mood. Existing recommendation system does not recommend food based on the hormonal changes at a particular mood. The proposed system provides the personalization to the user. A website is designed to give suggestions to the users of food according to their mood which will save time. This model is connected to the Django REST framework.