Reviewerly/exHarmony huggingface dataset:
Deciphering differential exhumation in the Gangdese orogen in southern Tibet using exposed porphyry alteration systems and geomorphic analysis

Topography and exhumation vary strongly in the Tibetan Plateau, reflecting different geodynamics and surface processes. The Gangdese orogen in southern Tibet is characterized by west-to-east variations in topography and occurrences of the Miocene porphyry copper deposits. We integrated remote sensing based hydrothermal alteration mapping and geomorphological analysis to detect patterns of differential exhumation since the Miocene. High abundances of propylitic and potassic alteration of the eastern Miocene granitoids are identified, indicating &amp;gt;2 km of exhumation since the Miocene. The western Gangdese has intensive phyllic and argillic alteration representing ∼1 km of exhumation. Geomorphic analyses also show E-W changes that most likely resulted from differential rock uplift histories. Channel steepness index values indicate the eastern Gangdese has channel gradients between 211 m1.02 and 457 m1.02, compared to values between 30 m1.02 and 160 m1.02 in the west. Linear inversion of tributaries of the Yarlung river shows a pulse of rock uplift between 25 Ma and 7 Ma with a maximum rate of 110 m/ m.y. in the east. In contrast, the western Gangdese exhibits slower and persistent rock uplift rates between 20 m/m.y. and 40 m/m.y. since 20 Ma. The integrated results record differential exhumation and rock uplift since the Miocene. We invoke the geometric variations of the subducted Indian lithosphere to interpret the E-W differential exhumation since the Miocene. Steeper subduction of the Indian lithosphere beneath the eastern Gangdese induced mantle upwelling, triggering rapid rock uplift and deep exhumation. Conversely, the shallower subduction of the Indian slab in the west generated slow and prolonged rock uplift with less exhumation. Our study refines the exhumation process of the Gangdese since the Miocene.
An Elixir for Blockchain Scalability with Channel based Clustered Sharding

Blockchain refers to distributed ledger technology which stores records without the help of a central authority. Born with bitcoin, this brainstorming technology finds its applications in healthcare, land registry, education, pharmaceutical industry, digital records, manufacturing companies and so on. The properties of blockchain such as immutability, distributed nature, tamper-resistant made it a disruptive technology in many applications. The highlighting feature of this pioneering technology is the distributed storage of ledger on all the nodes of the network. This helps to achieve decentralization without the trust for third party. The transactions are proposed, executed, validated and are then added as blocks to the blockchain. The problems with all the blockchain framework is scalability with respect to storage space and throughput. Scalability is the most significant factor to be considered in this big data era. This article proposes a solution called Channel Based Clustered Sharding (CBCS) approach for Hyperledger fabric blockchain framework. In this work, a lookup table is maintained which helps in forwarding the transactions to the clustered shards for validation. The CBCS approach helps in parallel transaction processing which in turn improves scalability and throughput of the system. The performance of the proposed work is measured with the help of Hyperledger caliper, a benchmarking tool for the performance analysis of Hyperledger fabric. The results show that the performance of the proposed system is increased from 3000 tps to 30,000 tps.
Extracting Explicit and Implicit Aspects Using Deep Learning

The proliferation of user-generated content on social networks and websites has heightened the significance of sentiment analysis, also known as opinion mining, as a critical tool for comprehending people’s attitudes toward various topics. Aspect-level sentiment analysis, which considers specific aspects or features of texts, provides a more comprehensive view of sentiment analysis. The aspect-level approach encompasses both explicit and implicit aspects, where explicit aspects are readily mentioned in texts while implicit aspects are implied or inferred from contextual clues. Despite the significance of implicit aspects in the overall review, previous research has predominantly focused on explicit aspect extraction. Limited attention has been given to the extraction of implicit aspects, despite their potential impact on capturing the complete sentiment picture of texts. Therefore, this study aims to find an aspect extraction solution capable of identifying and extracting both explicit and implicit aspects from texts. This study compares various machine and deep learning models on the SemEval-2014 and SemEval-2016 restaurant datasets. The experimental analysis demonstrates that the proposed Aspect-BiLSTM model emerged as the best-performing model, achieving high accuracy in classifying both explicit and implicit aspects, with 92.9% accuracy for the 2014 and 90.7% accuracy for the 2016 datasets. Notably, the proposed solution was able to capture multiple aspects of texts, making it more robust and versatile. This study highlights the efficacy of the Aspect-BiLSTM model for aspect extraction, which will give valuable insights into the advancement of aspect-level sentiment analysis. Doi: 10.28991/ESJ-2024-08-01-05 Full Text: PDF
Feature Selection Techniques for Enhancing Credit Card Fraud Detection Performance: A Hybrid Metaheuristic Approach Using Nature-Inspired Algorithms

Abstract: The surge in credit card usage has led to a parallel increase in fraudulent transactions, necessitating advanced detection systems. Traditional methods encounter challenges with high-dimensional and imbalanced datasets. This paper proposes a comprehensive approach integrating metaheuristic algorithms and deep learning techniques to enhance fraud detection accuracy. The first contribution, the Rock Hyrax Swarm Optimization Feature Selection (RHSOFS) algorithm, draws inspiration from the collective behaviors of rock hyrax swarms to effectively select relevant features from high-dimensional datasets. RHSOFS identifies an optimal subset of features critical for fraud detection through supervised machine learning. Complementing this, the second contribution leverages a hybrid deep learning model. Beginning by organizing transactional data and constructing a Logical Graph of Behavior Profile (LGBP) to abstract transaction details, the Modified Butterfly Optimization Algorithm (MBOA) selects important features from the dataset. The hybrid model, integrating Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), establishes rational connections between transactional characteristics, enhancing detection performance. To evaluate the approach, comparative efficiency analyses against existing methods, including Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS), are conducted. Results demonstrate the superiority of the approach in terms of both reliability and recognition rate, validated through rigorous statistical testing. The proposed hybrid approach signifies a significant advancement in credit card fraud detection systems, offering enhanced accuracy and efficiency in combating fraudulent activities amidst the growing complexity of transactional datasets.
Exploring the Satisfaction and Continuance Intention to Use E-Learning Systems

In view of the global crisis that has increased the use of online learning, it is imperative to comprehend the factors that affect users' perceptions and behaviors when utilizing e-learning systems. In order to examine the impact of quality factors on user satisfaction and continuance intention using e-learning systems, this study integrates the Information Systems Success Model (ISSM) with the Technology Acceptance Model (TAM). The aim of this research is to shed light on the relationships between the e-learning systems' quality, perceived usefulness, perceived ease of use, user satisfaction, and intention to continue using them. This research employed partial least squares structural equation modeling (PLS-SEM) to assess the research model. The analysis was grounded in survey data collected from a randomly selected sample of 372 students at Arab Open University in Saudi Arabia. The study's results confirm that information quality for platforms and courses positively influences perceived usefulness, system quality, and perceived ease of use. Additionally, perceived usefulness and ease of use are significantly linked to user satisfaction, supporting the notion that enhancing information quality contributes to higher user satisfaction and encourages continued engagement. The developers of e-learning systems and educational institutions may use these findings to enhance the design, content, and usability of their platforms.
CHSH Bell Tests For Optical Hybrid Entanglement

Abstract Optical hybrid entanglement can be created between two qubits, one encoded in a single photon and another one in coherent states with opposite phases. It opens the path to a variety of quantum technologies, such as heterogeneous quantum networks, merging continuous- and discrete-variable encoding, and enabling the transport and interconversion of information. However, reliable characterization of the non-local nature of this quantum state is limited so far to full quantum state tomography. Here, we perform a thorough study of Clauser-Horne-Shimony-Holt (CHSH) Bell inequality tests, enabling practical verification of quantum nonlocality for optical hybrid entanglement. We show that a practical violation of this inequality is possible with simple photon number on/off measurements if detection efficiencies stay above 82%. Another approach, based on photon-number parity measurements, requires 94% efficiency but works well in the limit of higher photon populations. Both tests use no postselection of the measurement outcomes and they are free of the fair-sampling hypothesis. Our proposal paves the way to performing loophole-free tests using feasible experimental tasks such as coherent state interference and photon counting.
Study on Market Effect of Agricultural Product Incentive Policy for Quality and Safety

Economic Management and Big Data Application, pp. 69-82 (2024) No AccessStudy on Market Effect of Agricultural Product Incentive Policy for Quality and SafetyXiaohua Fan, Rui Li, Jialin Luo, Kun Wei, and Xinyue ZhangXiaohua FanLaw School, Central South University, Changsha, 410000, Chinawww.csu.edu.cn, Rui LiSchool of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 611731, Chinawww.uestc.edu.cn, Jialin LuoBusiness and Tourism School, Sichuan Agricultural University, Chengdu, 611800, Chinawww.sicau.edu.cn, Kun WeiBusiness and Tourism School, Sichuan Agricultural University, Chengdu, 611800, Chinawww.sicau.edu.cn, and Xinyue ZhangBusiness and Tourism School, Sichuan Agricultural University, Chengdu, 611800, Chinawww.sicau.edu.cnhttps://doi.org/10.1142/9789811270277_0007Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: The agricultural product incentive policy for quality and safety aims to promote the overall continuous improvement of the agricultural products market’s quality and safety level. But its market effect in the case of information asymmetry determines its actual role. Based on the lemon market theory, this study designed a two-stage single-group post-test experiment. The first stage simulated the consumer’s agricultural product selection behavior in the case of information asymmetry, and the second stage verified the market effect of the agricultural product incentive policy for quality and safety. The results show that under the condition of information asymmetry, the agricultural product incentive policy for quality and safety is indeed conducive to improving the consumers’ perceived quality. Only when the improved perceived quality exceeds the overall average quality of the market, are consumers willing to pay and buy. This study verifies the market effect of agricultural product incentive policy for quality and safety, and reveals its boundary conditions when it works, which has great guiding significance for practice. Keywords: Agricultural productsIncentive policyMarket effectExperimental research FiguresReferencesRelatedDetails Recommended Economic Management and Big Data ApplicationMetrics History KeywordsAgricultural productsIncentive policyMarket effectExperimental researchPDF download
Towards a New Conceptual Model of AI-Enhanced Learning for College Students: The Roles of Artificial Intelligence Capabilities, General Self-Efficacy, Learning Motivation, and Critical Thinking Awareness

In the aftermath of the COVID-19 pandemic, college students have faced various challenges that could negatively impact their critical thinking abilities due to disruptions to education, increased stress and anxiety, less social interaction, and the advancement of distance learning relying more heavily on digital tools. With the increasing integration of AI technology across sectors, higher education institutions have deployed various AI capabilities for intelligent campuses and modernized teaching. However, how to fully utilize AI capabilities to promote students’ thinking awareness on learning effectiveness is still not clear, as critical thinking is an essential skill set holding significant implications for college students’ development. This research adopts the resource-based theory (RBT) to conceptualize the university as a unified entity of artificial intelligence (AI) resources. It aims to investigate whether AI capabilities can foster critical thinking awareness among students by enhancing general self-efficacy and learning motivation. In particular, it examines the causal relationships between AI capabilities, general self-efficacy, motivation and critical thinking awareness. Primary data was collected through a questionnaire administered to 637 college students. Structural equation modeling was employed to test hypotheses pertaining to causality. The results showed that AI capabilities could indirectly enhance students’ critical thinking awareness by strengthening general self-efficacy and learning motivation, but the effect on critical thinking awareness was not significant. Meanwhile, general self-efficacy significantly affected the formation of learning motivation and critical thinking awareness. This indicates that AI capabilities are able to reshape the cognitive learning process, but its direct influence on thinking awareness needs to be viewed with caution. This study explored the role of AI capabilities in education from the perspective of organizational capabilities. It not only proves how AI facilitates cognition, but also discovered the important mediating role of general self-efficacy and motivation in this process. This finding explains the inherent connections between the mechanism links. Furthermore, the study expands research on AI capabilities research from the technical level to the educational field. It provides a comprehensive and in-depth theoretical explanation theoretically, guiding the practice and application of AI in education. The study is of positive significance for understanding the need for the future development of the cultivation of critical thinking awareness talents needed for future development through AI capabilities in education.
Head Movement Invariant Eye Tracking System

The human visual framework, consisting of the eyes, optic nerve, and brain, gives us the sense of sight to capture and interact with our surroundings. The eye, which provides stimulus to generate sight, is a complex organ that collects light and effectively converts it to an electro-chemical impulse inside neurons.Eye trackers are an important input modality for human-computer interfaces with vision-based applications. Eye-tracking systems have been proven to be valuable assistive devices for people with motor disabilities. Despite significant improvements in eye-tracking technology over time, current eye trackers in the market are complex and require intricate calibration procedures. Hence, there is still room for further research to develop an eye-tracking system with simpler calibration procedures.This paper presents the development of a head movement invariant real-time eye-tracking system with simpler calibration procedures that can operate non-intrusively. The complexity of setting up the system is also significantly reduced. Experiments conducted with a sample set of users showed an eye gaze accuracy improvement of over 11% compared to the existing setups commonly available in the market.
Teacher’s Agency as an Indicator of Leadership Potential Development

Nonlinearity, multi-contextuality, new normality, constant variability of the modern world are reflected in politics, economics, education and other spheres of human life. Professional and personal self-determination and orientation of a person is difficult in a situation of diversity of various social environments and spaces, functional and semantic uncertainty of educational contexts. In this regard, the importance in the field of education is acquired by the teacher’s transprofessionalism, his ability to continuously develop, independently build interaction with the multifunctional environment, showing his agency as one of the indicators of leadership potential development. The purpose of the study is to operationalize the content of the concept of the teacher’s agency and its manifestation as an indicator of the development of leadership potential, taking into account the ongoing changes in the modern world. Methodology. The understanding of the essence of the terms “agency” and “leadership potential” of the teacher was carried out using the method of semantic analysis of their interpretations available in domestic and foreign sources. The use of activity-based and acmeological approaches is due to the need to determine the key mechanism for implementing the teacher’s agency in the comparison of significant categories (agency, leadership and management). General scientific research methods were used: theoretical analysis and synthesis, systematization and generalization of scientific literature on the research topic. Results. Connectivism (social polyvalence) is proposed as the main mechanism for the manifestation of the teacher’s agency as a teacher’s ability to find and maintain conditions, forms, ways and means of communication with representatives of different professions, social groups and statuses, beliefs, ages for organizing joint actions, events, productive activities. This made it possible to establish the key role of agency in the development of a teacher’s leadership potential – the creation of an atmosphere of cooperation within and outside the educational organization. Conclusion. The conclusion is made that the teacher, as a carrier and subject of public relations, needs to develop his agency and leadership potential, respectively, for orientation and adaptation in a changing world in the interests of sustainable development of regions and the country as a whole.
Research on Strategies for Improving the Quality of English Blended Teaching in Vocational Colleges through Network Informatization Resources

&lt;p&gt;The blended teaching mode of &amp;ldquo;online+offline&amp;rdquo; has gradually become a widely used teaching mode due to its flexible teaching form and rich and vivid course resources. In the blended teaching mode, this article mainly focuses on how to improve the teaching quality of vocational English courses. Firstly, an analysis was conducted on the learning situation of English courses among vocational college students, including their source structure, learning characteristics, and current development status of English courses. In order to facilitate a structured analysis of students&amp;rsquo; learning ability in English courses, a learning ability analysis model for vocational college students was constructed, and a survey questionnaire was designed to analyze their learning behavior and data. Finally, through data analysis, it was found how to use network information resources to improve the quality of English blended learning courses.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
End-to-end Visual Grounding Based on Query Text Guidance and Multi-stage Reasoning

&lt;p&gt;Visual grounding locates target objects or areas in the image based on natural language expression. Most current methods extract visual features and text embeddings independently, and then carry out complex fusion reasoning to locate target objects mentioned in the query text. However, such independently extracted visual features often contain many features that are irrelevant to the query text or misleading, thus affecting the subsequent multimodal fusion module, and deteriorating target localization. This study introduces a combined network model based on the transformer architecture, which realizes more accurate visual grounding by using query text to guide visual feature generation and multi-stage fusion reasoning. Specifically, the visual feature generation module reduces the interferences of irrelevant features and generates visual features related to query text through the guidance of query text features. The multi-stage fused reasoning module uses the relevant visual features obtained by the visual feature generation module and the query text embeddings for multi-stage interactive reasoning, further infers the correlation between the target image and the query text, so as to achieve the accurate localization of the object described by the query text. The effectiveness of the proposed model is experimentally verified on five public datasets and the model outperforms state-of-the-art methods. It achieves an improvement of 1.04%, 2.23%, 1.00% and +2.51% over the previous state-of-the-art methods in terms of the top-1 accuracy on TestA and TestB of the RefCOCO and RefCOCO+ datasets, respectively.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
How to Build a Blogging Website

Abstract: This paper explores the variegated domain of blogging, with focusing on the platform OmniSpectra. Here, I present an in-depth explanation of website’s UI/UX design, usage, architecture, and backend mechanisms. The study explores the unique characteristics of OmniSpectra that distinguishes it from other popular blogging websites, highlighting its inventive method in nurturing inclusive and compelling virtual communities of both content consumers and content creators. The paper will show in-depth workings of different techniques such as blog recommendation, data collection and curation that are used in the website to provide a benchmark user experience. The paper investigates the website’s role in shaping individual’s narrative and perception based to users’ interactions to different blogs, this has been achieved by getting a constant sentiment analysis feedback from users as well as the content created by the creators using AI models. All in all, after reading this paper, the reader will get a high-level idea of how to create a blogging website and add to it different modules according to their specific requirements. All this is explained by taking the example of OmniSpectra