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The increasing rates of illicit behaviors, particularly financial crimes, e.g., bank fraud and tax evasion, adversely affect national economies. In such cases, using nondestructive methods, scientists must evaluate relevant documents carefully to preserve their value as evidence. When forensic laboratories analyze paper as evidence, they typically investigate its origin and date of manufacture. If a document’s date is earlier than the earliest availability of the paper used in its creation, then this anachronism indicates that the document has been backdated. This study investigated weave marks and drainage marks for forensic purposes. Machine learning models for forensic document examination were developed and evaluated. The partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and artificial neural network (ANN) classification models achieved F1-scores of 0.903, 0.952, and 0.931, respectively. In addition, to enhance model effectiveness and construct a robust model, variables were selected using the VIP scores generated by the PLS-DA model. As a result, the SoftMax classifier in the ANN model maintained its performance with an F1-score of 0.951 even with a 50% reduction in the number of input variables.

Herein, the challenge of accurately classifying the manufacturing origin of printing paper, including continent, country, and specific product, was addressed. One-dimensional convolutional neural network (1D CNN) models trained on infrared (IR) spectrum data acquired from printing paper samples were used for the task. The preprocessing of the IR spectra through a second-derivative transformation and the restriction of the spectral range to 1800 to 1200 cm-1 improved the classification performance of the model. The outcomes were highly promising. Models trained on second-derivative IR spectra in the 1800 to 1200-cm-1 range exhibited perfect classification for the manufacturing continent and country, with an impressive F1 score of 0.980 for product classification. Notably, the developed 1D CNN model outperformed traditional machine learning classifiers, such as support vector machines and feed-forward neural networks. In addition, the application of data point attribution enhanced the transparency of the decision-making process of the model, offering insights into the spectral patterns that affect classification. This study makes a considerable contribution to printing paper classification, with potential implications for accurate origin identification in various fields.

The anatomy of the human shoulder predisposes the neurovascular bundle to compression at different levels of the thoracic outlet during abduction of the arm. There are four possible levels of compression at the thoracic outlet pathway: at the costo-clavicular angle, the inter-scalenic angle, under the pectoralis minor muscle or at the level of the humeral head. The positional thoracic outlet compression (TOC) often remains completely asymptomatic. When symptomatic, compressions are collectively referred to as thoracic outlet syndrome (TOS) and may require surgery if physical therapy fails to improve symptoms. The “thoracic outlet compression with complications or sequelae” (the acronym of which is “TOC-CS”, which can be simplified as “TOX”) will almost invariably lead to surgery to release the compression, and other possible treatment targeting the complications as required. There is a continuum between TOC, TOS and TOX, which are simply different clinical stages of the same mechanical issue, just like the Rutherford grades represent different stages of lower extremity arterial disease. We believe that discriminating between TOC, TOS and TOX clarifies clinical definitions and their respective treatment options. TOC is to be considered as a physiological positional phenomenon, TOS requires medical or surgical treatment and surgery should be considered as a primary option in TOX.

Purpose: Paper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms' nutrient source. Mold not only can damage papers' structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper. Methods: Molds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model. Results & Conclusion: In the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation. [ABSTRACT FROM AUTHOR]

The paper has three major objectives. The first objective of the paper is to synthesize and define common categories of meta-analysis. The second objective is to propose a way to comprehend these common categories of meta-analysis through learning from their respective generic conceptual frameworks. The third objective is to point out which R packages to use to conduct a meta-analysis for each category presented in this paper. In practice, research novices may not know where to begin to learn a topic of meta-analysis for their research. Furthermore, they may not know how the topic of meta-analysis progresses from basics to advanced. This makes it almost next to impossible to use meta-analysis in their research. Certainly, this is problematic. Thus, this paper intends to help research novices overcome this problem. To achieve this, the three objectives mentioned at the beginning were implemented. To propose a synthesis of common categories of meta-analysis along with their respective definitions, a literature review was used as a method to synthesize and define the common categories of meta-analysis presented in this paper. Here, synthesizing meta-analysis categories in one figure would make it much easier for research novices to see the breadth of the subject in question. To propose an easy way to understand the concepts of meta-analysis through learning from their respective generic conceptual frameworks, personal experience was used as a method to make that offering. Here, it is encouraged that research novices should go beyond the definitions of categories of meta-analysis. They can try to frame their research by using example conceptual frameworks presented in the paper as a starting point. To point out which R packages to use with what categories of meta-analysis, a literature review was used as a method to identify those appropriate R packages available. Here, it would be useful for research novices to know which R packages they may need for their meta-analysis research. In summary, the contributions of the paper here would make research novices see and understand the common categories of meta-analysis from the bottom to the top of the pyramid, also offers an easy way to understand the common categories of meta-analysis by illustrating how to develop conceptual frameworks for the common categories of meta-analysis, and finally recommending the R packages to be used with the categories of meta-analysis. Ultimately, we hope that research novices will be able to use meta-analysis successfully in their research.

Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying classification as a higher-order thinking process that connects the two. To achieve that, we designed and developed an online constructionist gaming tool called SorBET which integrates coding and database design enabling students to interpret, organize, and analyze data through game play and game design. The paper presents and discusses the results of a pilot study that aimed to investigate the data practices secondary students develop through playing and modifying SorBET games, and to determine the impact of game modding on student critical engagement with CT. According to the results, students developed and used certain data practices such as data interpretation and data model design to become better players or to design an interesting classification game. Moreover, game modding process motivated students to question the original games' content, leading them to develop a critical stance towards the game data model and representations.

Increasingly, Artificial Intelligence (AI) is having an impact on distance-based higher education, where it is revealing multiple ethical issues. However, to date, there has been limited research addressing the perspectives of key stakeholders about these developments. The study presented in this paper sought to address this gap by investigating the perspectives of three key groups of stakeholders in distance-based higher education: students, teachers, and institutions. Empirical data collected in two workshops and a survey helped identify what concerns these stakeholders had about the ethics of AI in distance-based higher education. A theoretical framework for the ethics of AI in education was used to analyse that data and helped identify what was missing. In this exploratory study, there was no attempt to prioritise issues as more, or less, important. Instead, the value of the study reported in this paper derives from: (1) the breadth and detail of the issues that have been identified; and (2) their categorisation in a unifying framework. Together these provide a foundation for future research and may also usefully inform future institutional implementation and practice.

There are steps formally initiated by the Government of India to maintain its adherence to the norms of Inclusive Education. Such schemes, acts and policy drafts are brought into discussion in this paper along with an appraisal of their draft and scope. This include the Sarva Shiksha Abhiyan which became operational from the year 2001, The Right to Education Act, which is implemented from the year 2009 and the Action Plan for Inclusion in Education of Children and Youth with Disabilities of 2005, which was revised in the year 2009. The discussion is anchored around, though not limited to the aspect of 'Diversity' which is very pertinent to the Indian scenario and the philosophy of Inclusion. Cultural and geographical diversity of the Indian sub-continent provoke challenges in 'Doing Diversity' in classrooms and at the same time it is contributing for the teacher and the taught. Considering the scope of the concept of social inclusion and inclusive education, the paper argues that at an implementation level, inclusive education should be one that is responsive to diversities in a classroom and cultural space. Rather than reproducing social structures pertaining to the above mentioned social categories of class, gender, language etc., policy drafting and implementation of inclusive education should make certain attempts which are pro-inclusive. They need to be less privileging towards certain ways of learning and certain ways of evaluation, considering the physical and cultural diversities of the learners. In our discussion on these schemes and their scope in ensuring diversity in classrooms, questions pertaining to certain disadvantaged sections are covered including Scheduled Castes, Scheduled Tribes, Gender nonbinaries, migrants etc.

In programming problem solving activities, sometimes, students need feedback to progress in the course, being positively affected by the received feedback. This paper presents an overview of the state of the art and practice of the feedback approaches on introductory programming. To this end, we have carried out a systematic literature mapping to understand and discuss the main approaches for providing and evaluating feedback used in the learning of novice programmers in the problem-solving activity. Thus, according to a formal protocol, an automatic search was performed for papers from 2016 to 2021. As a result, 39 studies were selected for the final analysis. As a result, we propose three different categorizations: the main approaches to providing feedback, the main methods used in the evaluation and the main aspects and effects of the evaluated feedback.

The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students' particulars such as their academic background, current study and student activity records, their attended higher education institution's expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students' generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students' activity and academic records in the case higher education institution to find out three major types of patterns between the students' participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paper.

The intricate and tricky case of semantic analysis, especially of the enigmatic realm of nouns, the red thread of linguistics and literary exploration. This academic research explores the rich sphere of Russian literature in-depth to determine connections between function and the embodiment of theme through proper names. This research is aiming at studying the role of proper nouns in the literary masterpieces by focusing on the ideas of character development, thematic cohesiveness, and the narrative flow. This papers deals with profound significance of proper nouns as linguistics pointers that take you beyond their literal meaning to different levels of connotative value. It takes most of its ideas from the late linguistics and literary critique. This research gets down to serious business by cherry-picking and analysing a set of Russian masterpieces. An intensive method of semantic analysis was used by the study to properly identify and categorize all the structural features of proper nouns. Also, it conducts an examination of the usage of language by these instrumentalities so as to discover their common features and themes which lead to the invention of the main narrative. The results of this study add to our comprehension of proper noun semantics in Russian literature and provide us with a stepping-stone to broader discussions on the linguistic theory and literary studies. This cognitive effort exceeds the linguistic borders, as it takes into consideration the cultural and historical value of the proper nouns being presented. The report shows why it is necessary to reconsider the theoretical design to account for the distinctive linguistic and particular cultural features of the Russian literary heritage. This research is a step toward unravelling noun semantics' mystery and adds to the already existing linguistic aesthetics and cultural semiotics debates.

Despite undeniable advances in research on language learning strategies in the last several decades, empirical investigations of actions and thoughts that learners engage in to better understand and use grammar structures in different contexts, or grammar learning strategies (GLS), remain scarce. Moreover, there is a paucity of studies examining the effects of pedagogical interventions in this area, both with respect to the use of different types of GLS and the mastery of grammar structures in terms of explicit and implicit (automatized) knowledge. There is also no empirical evidence concerning the mediating role of individual difference (ID) factors in this respect. The paper outlines a study currently in progress that seeks to address all of these gaps. The first part is devoted to a brief overview of key issues related to the definition and classification of GLS as well as a synthesis of available research in this area. This is followed by a description of an intervention-based research project which explores the effects of strategies-based instruction (SBI) targeting GLS by English majors in Poland, also taking into account the moderating impact of selected ID variables. In particular, the methodology of the study is presented and the envisaged contributions of the intervention are considered.

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term implications. This has prompted research on fairness metrics meant to capture and quantify such biases. Nonetheless, current metrics primarily focus on predictive performance comparisons between groups, without considering the behavior of the models or the severity of the biases in the outcomes. To address this gap, we proposed a novel metric in a previous work (Verger et al., 2023) named "Model Absolute Density Distance" (MADD), measuring algorithmic unfairness as the difference of the probability distributions of the model's outcomes. In this paper, we extended our previous work with two major additions. Firstly, we provided theoretical and practical considerations on a hyperparameter of MADD, named "bandwidth," useful for optimal measurement of fairness with this metric. Secondly, we demonstrated how MADD can be used not only to measure unfairness but also to mitigate it through postprocessing of the model's outcomes while preserving its accuracy. We experimented with our approach on the same task of predicting student success in online courses as our previous work, and obtained successful results. To facilitate replication and future usages of MADD in different contexts, we developed an open-source Python package called maddlib (https://pypi.org/project/maddlib/). Altogether, our work contributes to advancing the research on fair student models in education.

Document classification is an important area in Natural Language Processing (NLP). Because a huge amount of scientific papers have been published at an accelerating rate, it is beneficial to carry out intelligent paper classifications, especially fine-grained classification for researchers. However, a public scientific paper dataset for fine-grained classification is still lacking, so the existing document classification methods have not been put to the test. To fill this vacancy, we designed and collected the PaperNet-Dataset that consists of multi-modal data (texts and figures). PaperNet 1.0 version contains hierarchical categories of papers in the fields of computer vision (CV) and NLP, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran current mainstream models on the PaperNet-Dataset, along with a multi-modal method that we propose. Interestingly, none of these methods reaches an accuracy of 80% in fine-grained classification, showing plenty of room for improvement. We hope that PaperNet-Dataset will inspire more work in this challenging area. [ABSTRACT FROM AUTHOR]

Governments, businesses, and educational institutions need to collaborate and make significant investments in order to address the growing digital skills crisis. In Europe, hundreds of digital skills initiatives have been launched with different forms of government and private industry support in the last five years alone. Consequently, digital skills initiatives have come to encompass a wide array of interventions. In this context, this paper proposes a typology of digital skills initiatives that was developed based on the analysis of over 300 initiatives listed in the European Commission's repository of best digital skills initiatives. The proposed typology consists of four categories: target group, digital skills, learning format, and sponsoring organization. In terms of target group, digital skills initiatives tend to target one or more of five distinct groups: the general public, educators, adults, seniors and youth. In terms of digital skills, digital skills initiatives tend to focus on general digital skills or specialized digital skills. In terms of learning format, digital skills initiatives tend to offer training and/or a learning resource. In terms of sponsoring organization, the initiatives tend to be sponsored by organizations that are either affiliated or unaffiliated with a technology vendor. The typology is followed by a presentation of mini cases, which highlight different archetypes of the proposed typology. The paper closes with a discussion of practical implications for policy makers, administrators, and scholars interested in digital skills initiatives and the future of work.

With the change in socio-political and educational contexts in global academia, the trend of writing academic papers has arisen among Nepali students and educators in higher education. However, many of them are still unfamiliar with the basic standard to be incorporated in an academic paper. In this context, this study examined the post-graduate students' errors in writing research proposals. A textual analysis method was employed, where quantitative phase of analysis was followed by qualitative, and the data were collected from 24 purposively selected research proposals and from the interview with 10 students. The quantitative data were gathered from content analysis of the research proposals and qualitative data were collected from the unstructured interview. The collected proposals were studied, erroneous expressions were listed and categorized in to four parameters like grammatical errors, lexical semantic errors, mechanic errors and syntactic errors. The information from the interview was recorded, noted and analyzed. The results exhibited that (1) article and preposition usages were more persistent grammatical errors; (2) run-ons and fragmentation were more reiterated syntactic errors; and (3) uses of single lexical items were more common lexical-semantic errors and punctuation was the more regular mechanical errors. These findings reveal that students have insufficient knowledge and skills for academic writing. Therefore, they need academic writing courses, training, or workshops from the early level of their schooling. The findings of this study are useful for curriculum designers, policymakers, instructors, and students because it provides significant information on the building blocks experienced by English language learners in writing research paper.

The novel coronavirus disease — COVID-19 is a historic catastrophe that has caused many devastating impacts on human life and wellness. Researchers in academia and industry strive to understand the causes of this pandemic disease and find new therapeutics combating it. Consequently, the number of COVID-19 related publications increases rapidly, and it is too difficult for medical researchers and practitioners to keep up with the latest research and development. Literature filtering and categorization, and knowledge discovery can use text mining as a powerful tool. In this paper, we propose a text mining method to explore the categories of COVID-19 related themes and identify the standard methodologies that have been used. We discuss the potential limitations of this preliminary study and present future perspectives related to COVID-19 research. This paper provides an quantitative and qualitative mixed analysis example of using some research papers by data mining method to dig out several hidden information and set up a foundation for data scientists to develop more effective algorithms to deal with COVID-19 related problems. [ABSTRACT FROM AUTHOR]

This paper focuses on the most significant empirical studies on the topic of challenges facing the communicative language teaching approach in Saudi Arabia. The communicative language teaching method has been used for more than two decades in Saudi Arabia and research conducted during this period offers some insight into the main challenges of the implementation. The main goal of this paper is to synthesize findings from research studies conducted in the past and by doing so, suggest the possible avenues for tackling these challenges. Four broad categories of challenges have been adopted from Li's (1998) comprehensive framework in an attempt to structure the challenges under meaningful labels. This, in turn, enabled us to categorize the challenges according to their source which helped our understanding of how to resolve them. The findings suggest that the most frequently observed policy-related challenges are the current exam system, overcrowded classes, no AV equipment, and low quality of in-house training programs for teachers. Teacher-related challenges mostly relate to conflicting ideas about CLT, low confidence in these methods, preference for the traditional learning model, focus on developing skills that feature in the exams, and deficiencies in teachers' English. Student-related challenges are found to be their low-level proficiency, passive learning style, lack of motivation to learn the target language, and fear of their marks being negatively affected if they are paired with weaker peers during communicative activities. This paper concludes that these challenges are all interconnected, and a systematic approach is essential in trying to tackle these with the government intervention being a potential first step.

Current paper reports the findings of a content analysis of Open Educational Resources. Twenty three studies were collected for the analysis. The studies were collected from different journals depending on open access policy. Every study was categorised by author, year of publication, title of the study, author characteristics and analysis. The analysis refers to the information about Open Educational Resources variables. Therefore the investigators categorize the research papers based on OER. An analysis of research papers was formed in conclusion and finalizing the common dimensions of content analysis of OER studies.

This paper discussed the relationship between language, ecology, and culture, and claimed that the study of linguistic communication as pragmatics should not be confined to the traditional context, but should focus on a broader ecological environment. It analyzed the context of practical communication from the perspective of language ecology beginning with the discussion of the ecological crisis in communication and found that language, like plants and animals in nature, needed the support of the external environment with certain "soil fertility". This paper classified ecological context into two types: internal ecological context (psychological-cognitive context) and external ecological context (natural environment and social environment). Based on this classification, the ecological context of pragmatics was further divided into environment-friendly context, addressee-friendly context, and speaker-friendly ecological context. This paper was an exploratory analysis of language ecology in pragmatics, aiming at helping communicative participants find their ecological niche and adopt appropriate strategies to maintain the ecological balance in pragmatic communication.

This research was triggered by the identified need in literature for large-scale studies about the kinds of designs that teachers create for mobile learning (m-learning). These studies require analyses of large datasets of learning designs. The common approach followed by researchers when analyzing designs has been to manually classify them following high-level pedagogically guided coding strategies, which demands extensive work. Therefore, the first goal of this paper is to explore the use of supervised machine learning (SML) to automatically classify the textual content of m-learning designs using pedagogically relevant classifications, such as the cognitive level demanded by students to carry out specific designed tasks, the phases of inquiry learning represented in the designs, or the role that the situated environment has in the designs. Because not all SML models are transparent, but researchers often need to understand their behaviour, the second goal of this paper is to consider the trade-off between models' performance and interpretability in the context of design analytics for m-learning. To achieve these goals, we compiled a dataset of designs deployed using two tools, Avastusrada and Smartzoos. With this dataset, we trained and compared different models and feature extraction techniques. We further optimized and compared the best performing and most interpretable algorithms (EstBERT and Logistic Regression) to consider the second goal with an illustrative case. We found that SML can reliably classify designs with accuracy > 0.86 and Cohen's kappa > 0.69.

Object-oriented programming distinguishes between instance attributes and methods and class attributes and methods, annotated by the "static" modifier. Novices encounter difficulty understanding the means and implications of "static" attributes and methods. The paper has two outcomes: (a) a detailed classification of aspects of understanding "static," and (b) a collection of questions designed to serve as a learning/practice/diagnostic tool to address those aspects. Providing answers requires learners to apply higher-order cognitive skills and, hence, to advance their understanding of the essential meaning of the concept. Each question is analyzed according to three characteristics: (a) the "static" aspects that the question examines according to a detailed classification the paper provides; (b) identification of the question according: to Bloom's revised taxonomy, to the Structure of Observed Learning Outcome (SOLO) taxonomy; and to the problem-solving keywords used in the question's formulation. Several recommendations for teaching are presented.