Reviewerly/exHarmony huggingface dataset:
Bridging the Gap: Towards Linguistic Resource Development for the Low-Resource Lambani Language

Language technology development is crucial for many downstream applications such as machine translation and language understanding. The lack of linguistic resources makes it challenging for technology development of under-resource languages. This paper aims at developing linguistic tools for Lambamni, an under-resourced tribal language of India through corpora creation, annotation, and transfer learning from contact language. Based on the annotated corpora, we develop the Lambani language tagset and our investigation focused on various methods for developing a Part-of-Speech (POS) tagger and also creating a morphology dictionary for Lambani. A total of eight BIS tagset is found to be present for Lambani language. The experimental results revealed that the statistical approach with GMM-HMM (Gaussian Mixture Model - Hidden Markov Model) achieved POS tagging accuracy of 96% despite the limited dataset containing 6,893 sentences. This success in a low-resource setting highlights the promising potential of GMM-HMM in overcoming challenges posed by the scarcity of annotated data in under-resourced languages. The experiments not only showcase the effectiveness of the proposed methods for low-resource language processing but also shed light on their applications and open new directions for research in language revitalization and the development of digital tools for zero-resource languages.
An Application of Anomaly Detection to Financial Fraud Using Machine Learning

With the increasing rate of adoption of financial technologies otherwise known as fintech applications, financial fraud has also morphed and infiltrated the financial systems we have today. Financial transactions in today’s world are divided into card-present and card-not-present transactions. Card-not-present transactions are especially prone to these fraudulent attacks. However, as fraudulent transactions become more undetectable with the increase in the skill set of the perpetrators, it is important to identify a model capable of detecting these fraudulent cases accurately. This paper utilized supervised learning algorithms such as support vector machine algorithm, logistic regression, and Naïve Bayes algorithms to classify clean transactions and fraudulent transactions. A model for identifying fraudulent transactions was derived. It was identified that the SVM approach was the most efficient with an accuracy of 99.5%, a recall of 1, and a precision of 0.5. This is essential to improve our means of tracking and identifying fraudulent transactions as they occur in the financial system.
Source Camera Identification Using GGD and Normalized DCT Model-Based Feature Extraction

In recent decades, blind feature-based source camera detection has drawn a lot of attention. In literature, researchers used a specific sort of distortion, such as vignetting effects, chromatic aberration, and radial lens distortion, etc., to distinguish between different camera models. Therefore, it becomes specific to the distortions present in the particular camera model. However, distortion-specific approaches perform poorly in the absence of the specific distortion in an image. To develop a source camera identification system, we introduce a non-distortion-specific methodology using normalized DCT coefficients, a generalized Gaussian distribution (GGD) model-based blind features. DCT is performed on the sub-images of N × N blocks after the image is normalized using mean subtracted contrast normalization (MSCN). Over three scales, the DCT features are extracted. To perform camera model identification, a multi-class SVM is used to utilize all the extracted features. The experiment was conducted on the Dresden image database, which contains 10,173 images captured by 43 devices distributed over 12 camera models. The proposed method showed a maximum accuracy of 98.82% on the dataset.
Malignancy Identification from Cytology Images Using Deep Optimal Features

Automated cytology image classification using computer-aided diagnosis (CAD)-based system is an important task for diagnosing cancer at early stage. For the last few decades, researchers have remain involved in the research of cytology domain. Optimal features selection is an utmost importance to enhance the performance of cytology image classification. In this article, we have proposed artificial electric field algorithm to find the optimally appropriate features by discarding the less relevant ones and have boosted the performance of cytology image classification. The features are first extracted from traditional ResNet-18 model. Finally, these optimal subsets of features are classified by SVM classifier and achieved the accuracy 85%. The relevant codes of the proposed classification model are publicly available on GitHub .
Recent Challenges and Opportunities of Multilingual Natural Scene Text Recognition and Its Real World Deployment

Multilingual natural scene text recognition is difficult due to its complex text font style, difficult image background, multilingual text formats, etc. In vision-based applications, natural scene text plays an important role in industrial automation, robot navigation, application software for visually impaired persons, instant translation of multilingual access, etc. Although deep learning techniques have produced encouraging results recently, there are still several issues with multilingual natural scene text recognition tasks that need to be resolved. The main objective is to summarize recent challenges and future opportunities of multilingual natural scene text detection and recognition in this paper. We have proposed a real-world deployment model of multilingual natural scene text recognition. The system employs a text localization method using MSER-based region detection technique involving Niblack local thresholding method with some modification. This paper also suggests the future research directions for the multilingual natural scene text detection and recognition tasks.
A Fast Restoration of Weather Degraded Images

Real-time haze removal from weather corrupted image is a challenging process. Due to presence of atmospheric particles, light coming from the object scatters and results in hazy images. In this research work, a novel dehazing method is presented by introducing parallel calculation of transmission coefficient (TC) and atmospheric light (ALE), followed by a contrast enhancement method. This results to improve image quality and reduce computational complexity. Effectiveness of this method is presented by comparative analysis with respect to existing methods in terms of the statistical parameters like MSE, SSIM, PSNR, Correlation, fast Computational Complexity.
A study on investors' attitude towards cryptocurrency investment with special reference to Tamilnadu, India

Cryptocurrency is an investment option for investors. It is a digital or virtual currency. Many cryptocurrencies are decentralized networks based on blockchain technology. The main benefits of cryptocurrency are faster money transfers, decentralized systems, and cost-effectiveness. Most investors are aware of and are showing more interest in investing in cryptocurrency. The aim of this study is to find out the association between demographic factors and investors attitudes towards investment in cryptocurrencies, then rank the various sources that investors use to make the investment decision on the basis of their preferences. This study has been conducted with the use of primary data collection with a structured questionnaire and also secondary data from internet sources. For this study, the target respondents are those who have bought cryptocurrencies such as Bitcoin, Ethereum, Litecoin, Cardano, etc. This research is focused only on Tamil Nadu, a state in India. The collected data was analyzed by statistical tools and, based on the findings, measured the level of investors' attitude and preference of sources for investment decisions. This study gives awareness and suggestions to potential investors on how to play an effective role in cryptocurrency.
Quantum Key Distribution in Access Networks

This chapter presents a literature survey of data security in optical fiber networks. The evolution of data security in optical fibers starts from PHY-layer encryption based on ITU-T G.709 optical transport networks (OTN) standards, where the security of classical cryptography is based on computational complexities of intractable mathematical problems. The emerging quantum computers make threats to classical cryptography. Two strategies are developed to address these threats, i.e., post-quantum cryptography (PQC) and quantum key distribution (QKD). PQC algorithms are still based on computational complexities, whereas QKD provides information-theoretic security guaranteed by quantum mechanics. A comprehensive literature review of QKD is presented with a focus on quantum networks in optical fibers. Three issues hindering QKD technologies from wide commercial deployment include distance limit, noise interference, and QKD in passive optical networks (PONs). The key rate of a QKD link scales linearly with the channel transmittance. In optical fibers, the channel transmittance decays exponentially with fiber distance due to the absorption of photons. This limits the distances of fiber-based QKD links to hundreds of kilometers. To extend distances, trusted and untrusted relay technologies were developed, depending on key exposure at the relay node. Since QKD links are vulnerable to noise and interference from classical data traffic, most reported QKD systems need dedicated/dark fibers. But this is cost-prohibitive and impractical in real deployments. The only chance for the commercial success of QKD technologies is integration into existing fiber networks and sharing the same fibers with classical data traffic, which in turn brings the second issue, spontaneous Raman scattering (SpRS) noise from classical channels. Several noise mitigation strategies are discussed based on wavelength/time-division multiplexing (WDM/TDM) techniques. Finally, as the last mile of optical fiber networks, PONs are not only the bottleneck of data throughput but also the most vulnerable segment of data security. Most eavesdropping occurs in PONs leveraging the point-to-multipoint (P2MP) topology, where the downstream data are broadcast to all users. In PONs, the imbalanced attenuation to classical downstream data and the upstream QKD channel makes the downstream data in the feeder fiber become the dominant source of SpRS noise. A dual-feeder fiber method is introduced to mitigate SpRS noise by isolating QKD links in a dedicated feeder fiber.
Postdigital Literacies

Literacy researchers across various perspectives have demonstrated a notable agility in responding to the emergence of digital technologies and their impacts on literacy practices. As digital technologies permeate human existence, writing and other literacy practices occur in postdigital literacy ecologies. Examining how literacy is bound up postdigitally in everyday life, offers opportunities to comprehend the complex relationships between technology, society, and individuals. It also reflects ongoing efforts in Literacy Studies to understand the social, epistemological, and ideological nature of mediatized representation as occurring at the level of everyday literacy.
Postdigital Publishing

Publishing is the practice of preparing informational content and issuing that content to the public. Types of content include datasets, text, image, film, sound, software, websites, and so on. Published content can be copyrighted or in the public domain; physical (print books, gramophone records) and / or electronic (websites, streaming services); readable by humans (literature) and/or readable by machines (datasets); etc. Publishing consists of many dialectically intertwined aspects (technical, legal, social, etc.) and carries significant consequences in diverse areas including knowledge development, social justice, and democracy. Late twentieth-century digitalization has significantly transformed traditional publishing. This entry describes the formation of postdigital publishing, its main features and implications.
Questioning Optimization Strategies of High School English Reading Classroom based on Deep Learning

With the development of the times, the requirements for education are constantly increasing, and deep learning is crucial for students' development and progress. This paper integrates the concept of deep learning into the classroom and explores optimization strategies for questioning in high school English reading classes. By exploring the current situation of questioning in high school English reading classrooms, it was found that there are shortcomings and deficiencies in teacher questioning and feedbacks. Based on the requirements of deep learning and core competencies for English learning, this paper reflects questioning in English reading classrooms. On this basis, strategies such as clarifying the purpose of questioning, respecting individual differences, increasing classroom participation, and encouraging and guiding students are proposed for high school English teachers in order to promote students' deep learning and better development.
Study on the Learning Strategies of Chinese High School Students Using English as a Second Language

By analyzing the theory of second language learning strategies, this paper discusses the common mistakes of senior high school students in the process of English learning, and puts forward the problems that should be paid attention to in the application of second language learning strategies in senior high school teaching.
Semantic Construction of Internet Buzzwords from the Perspective of the Prototype Theory

In recent years, internet buzzwords have attracted increasing attention from researchers. However, there is currently relatively little research on the semantics of buzzwords. By organizing the development trends of prototype category theory at home and abroad, this article provides a theoretical basis for semantic analysis in the following text. Based on the analysis of some 2022 Chinese internet buzzwords in terms of prototype meaning, new meaning, and semantic construction process, this article believes that the semantics of internet buzzwords are derived from new meanings through metonymy, metaphor or phonetic metaphor, irony metaphor, and other means of prototype meaning, and some prototype constructions will also emerge. Its semantic construction is a process centered around the prototype and constantly expanding its scope.
Knowledge, Skills and Attitudes of Chinese University Teachers in Digital Applications

This study explores the knowledge, skills and attitudes of Chinese university teachers in digital adoption. The study is conducted from various aspects, including respondents' profiles, knowledge levels, skills, attitudes, and challenges they face in improving their digital application capabilities, with the aim of identifying areas for improvement. In addition, differences in assessment will be explored based on the respondents' profiles, which will help to gain insights into the perceptions of Chinese university teachers on digital adoption. Ultimately, based on the findings, the study aims to propose a robust Digital Application Capability Training Program (DACTP), for Chinese university teachers. The training program will serve as a practical guide for educators, provide valuable insights for comprehensively improving teachers' digital application competence, and provide data support for China's education reform and innovation, as well as for improving education quality and efficiency.
Ensuring Parameter Estimation Accuracy in 3PL IRT Modeling: The Role of Test Length and Sample Size

The objective of this simulation study was to evaluate the accuracy of item parameters estimation when employing the 3PL IRT model, mainly focusing on sample size and the length of the test (number of test items). The investigation used six datasets produced by WinGen, each comprising 5000 responses and varying test lengths within 10 to 40 items. For each dataset, the study conducted simulations and re-analyzed the data 15 times, generating a total of 2025 data subsets and estimating 225 parameters for each item. The results revealed that smaller sample sizes led to more pronounced biases, emphasizing a recommended minimum sample size of 3000 for precise parameter estimation. Additionally, the study found that a limited number of items (short test) yielded biased estimations and proposed a minimum of 25 or 40 test items for accurate estimation using the 3PL IRT model. These findings offer valuable insights for test developers in making informed decisions regarding sample sizes and test length, ultimately ensuring reliable and accurate parameter estimates.
Cross Disciplinary Teaching Cases of Physical Education and English based on Cultural Background

In many interdisciplinary fields, the relationship between physical education (PE) and English is increasingly close, which makes the interdisciplinary teaching of PE and English a subject with research value and practical significance. Cultural differences, including values, beliefs, habits and traditions, may have a certain impact on interdisciplinary teaching of PE and English. In the current teaching practice, the role of cultural factors in the interdisciplinary teaching of PE and English has not been paid enough attention. This article will first summarize the interdisciplinary teaching of PE and English under the cultural infiltration, and discuss the problems and challenges it faces. Then, it analyzes the urgency of infiltrating traditional culture into the interdisciplinary teaching of PE and English. Finally, through the analysis of actual instructional cases, the role of cultural background in interdisciplinary teaching of PE and English is demonstrated. Teachers need to actively develop and utilize other instructional resources, such as network and multimedia, so as to enrich instructional contents and forms and help students better understand and master relevant knowledge.
Ruminations on the Teaching Methods of Choral Training for College Students in Higher Education

Nowadays, college students' chorus has been attached importance by colleges and universities, and has become an important form of teaching activities, students in the process of chorus can understand the team spirit, enhance the patriotism, and promote the growth and development of students to a certain extent. This paper mainly focuses on choral teaching methods to explore, through the combination of macro training and micro training, to optimize the current stage of college students choral training problems, improve the quality of choral.
Labor Education Curriculum Implementation in the Perspective of Artificial Intelligence: Dilemmas and Optimization Paths

The continuous development and sustained progress of artificial intelligence has made it an important driving force for future educational change. Labor education, as an important part of China's education policy, must focus on the deep integration with artificial intelligence, and the key to the development of labor education lies in the construction and implementation of the labor education curriculum. Based on the labor education curriculum standard, a labor education curriculum system integrating "general theory courses, public practice courses and literacy development courses" has been constructed. Through the perspective of artificial intelligence, the implementation of labor education curriculum faces the practical dilemmas of lagging curriculum awareness, one-sided curriculum setting, scarcity of curriculum resources, and single curriculum evaluation. Starting from the realistic dilemma, the optimization path of labor education curriculum implementation in the field of artificial intelligence is proposed in order to promote the deep integration of labor education curriculum and artificial intelligence, and carry out useful exploration for realizing the modernization of education and helping diversified talents cultivation.
Education dimensions relevant to successful electronic levy mobilization in resource-rich yet poor countries in Africa

First and foremost, the study explored why countries in Africa are rich in natural resources yet resort to e-levy legislation for more revenues. In addition, the study investigated dimensions of education needed to facilitate successful mobilization of e-levy revenue in resource -rich yet poor countries in Africa. Qualitative exploratory design, semi-structured interviews, judgmental and snowball sampling techniques were used for the study. Twelve (12) scholars from US (N = 3), Uganda (N = 3) Canada (N = 3), Ghana (N = 3) were interrogated. The paper was guided by the natural resource-cursed and social learning theories. Thematic analyses were used to analyse the data. It was found that although African countries are rich in natural resources yet they face challenges generating revenue from natural resources due to mismanagement, poor leadership and weak governance. They also find it difficult to mobilize revenues from e-levy too because of the informal nature of the economy, lack of financial inclusion, corruption, the disinterest of the public in the e-levy legislation as well as inadequate education on the e-levy concept. But the advanced economies are successful in generating revenue from e-levy. Proactive leadership and governance in managing natural resources, addressing mismanagement, and dealing with corruption and its negative effects are required to make things happen in Africa. African economies need to be more formalised and financial inclusion deepened. Proper accounting of state revenues to the citizenry must be enforced. E-levy education, civic education, digital literacy, ethics and legal education, can significantly contribute to the success of e-levy revenue generation in Africa.
Research on the Teaching Reform of Online and Offline Hybrid Teaching Mode Based on Smart Classroom in the "Development of Android for the Internet of Things"

This article aims to explore the teaching reform of the online and offline hybrid teaching mode based on smart classrooms in the course of "Internet of Things Android Application Development". Through in-depth analysis of the characteristics and advantages of smart classrooms, combined with specific cases, the effectiveness and value of this teaching model in practical applications are elaborated. This study is of great significance for improving the quality of curriculum teaching, enhancing students' practical abilities, and promoting teacher-student interaction. It also provides useful reference for promoting teaching reform.