Reviewerly/exHarmony huggingface dataset:
Pengembangan Media Poster Edukasi Kesiapsiagaan Bencana untuk Siswa Sekolah Dasar

This research and development aim to determine: (1) the procedure of developing educational poster media for disaster preparedness for elementary schools; (2) the feasibility level of developing disaster preparedness educational poster media for elementary school students. This research was included in Research and Development (R&amp;D) using the ADDIE development model developed by Dick &amp; Carry (1996). Researchers limit the five stages to 4 stages of research steps carried out including: (1) the analysis stage, (2) the design stage, (3) the development stage, (4) the implementation stage. The product of this research has been validated by 5 experts consisting of media experts, material experts, and linguists and conducted a small-scale FGD (Focus Group Discussion) with several students from Sebelas Maret University. The results showed that: (1) Disaster preparedness educational poster media for elementary schools had been developed through 4 stages of the ADDIE development model developed by Dick &amp; Carey; (2) the results of the media expert's assessment obtained an average of 4.8 with the "Very Valid" category, the assessment of the material expert obtained an average of 5 with the "Very Valid" category, and the assessment of the linguist obtained an average of 4.68 with "Very Valid" category.
Pengembangan Media Scrapbook untuk Meningkatkan Kemampuan Menyelesaikan Soal Cerita FPB Dan Kpk Kelas IV SD

This study is based on the potential and requirements of mathematics learning media in elementary schools for supporting the learning process. The research is purposed to produce a learning media such as Scrapbook to Improve Grade IV Elementary School Students' Ability to Solve Highest Common Factor (HCF) and Lowest Common Multiple (LCM) Story Problems. This research was a kind of Research and Development (R&amp;D) which the procedure used a Borg &amp; Gall development model that is limited to the seventh step. The subject of the research was 7 students of the 4th grade at SDN 1 Karanggede. Techniques for collecting data include questionnaires, written tests, and documentation. The study's findings are as follows: (1) Media Scrapbook was created using Borg and Gall development procedures to the seventh step; (2) Scrapbook media was declared "Very Valid" by material experts with an average of 4.46 and media experts with an average of 4.37, as well the results of the student response questionnaire assessment of 97.96% with the category of "Very good"; (3) Scrapbook media has been declared effective for improving the ability to solve HCF and LCM story problems, with an average N-Gain score of 0.8 indicating a high category and a percentage of N-Gain score of 82% indicating effectiveness.
CURSIVE HANDWRITTEN TEXT RECOGNITION USING RECURRENT NEURAL NETWORKS

The handwritten Urdu recognition system is the process of reading characters or numbers that have been written on paper and provided as input in the form of a scanned digital image. The recognition of handwritten Urdu offers abundant applications in widespread domains. Big data of offline handwritten Urdu literature written by many different Urdu authors can be made available online, with the help of this offline OCR tool. Offering a variety of applications and convenience to differently-abled people motivates us to pursue our research on the development of the Urdu OCR system. The state-of-the-art approaches employ Convolutional Neural Networks (CNN) for the recognition of handwritten Urdu text. However, in these approaches, it becomes difficult and time-consuming to recognize a long sequence of handwritten Urdu text input because CNN’s can’t learn long-term dependencies (remembering previous information). Hence to recognize a long sequence of handwritten Urdu text input, an LSTM based Recurrent Neural Network (RNN) model has been proposed to recognize any unconstrained handwritten Urdu text. The proposed approach relies on a holistic approach where features are extracted from handwritten Urdu ligature images using the RNNs and these features train multi-dimensional LSTM-based RNNs. The trained MDLSTM-based RNN is used for the classification and recognition of unconstrained handwritten Urdu text because recurrent neural networks are better able to capture the long-term dependencies in the input sequences. We have employed the benchmark handwritten Urdu dataset UNHD as well as the newly proposed handwritten Urdu dataset UHLD for feature extraction as well as training the LSTM-based RNN model and the trained model recognizes any unconstrained handwritten Urdu text with a better recognition rate of nearly 94% on a large handwritten Urdu ligature dataset of 1500 cluster classes and hence outperforms the state-of-the-art accuracy of 92.75% for handwritten Urdu recognition.
Publications should be FAIR

Over the past two decades, the open access movement has made significant progress in promoting the free &lt;em&gt; accessibility &lt;/em&gt; and &lt;em&gt; reusability &lt;/em&gt; of scholarly publications. About half of the publications from recent years are free to access and reuse. However, are accessibility and reusability sufficient for a well-functioning system of scholarly publishing?
Structuring Natural Language Processing Contributions in the Open Research Knowledge Graph

Search has long been revolutionized by knowledge-graph-powered services such as the Amazon Marketplace in e-commerce, or Open Street Maps in the cartography and navigation services domains, to name just two examples. Inspired by such knowledge graph (KG) success stories in the general domain, such technology is now being realized over scholarly knowledge as well.
How important are bibliometrics in academic recruitment processes?

The use of metrics is a hot topic in science, and scholars have criticized metrics for being more driven by data than by judgements. Both the Leiden Manifesto and the DORA-declaration, signed by thousands of organizations and individuals, have expressed concerns about the use of metrics on individuals.
Scholarly Knowledge Graphs: A Call for Participation

How scholarly knowledge is communicated – using natural language, data in tables and images as digital PDFs – severely limits the extent to which machines can help us in searching, exploring and exploiting scholarly knowledge. In the age of modern information infrastructures and digitalization, it is unsatisfactory to continue presenting scholarly knowledge solely as text-based documents.
Interpretable AI Models for Transparent Decision-Making in Complex Data Science Scenarios

Interpretable AI models have emerged as crucial tools for promoting transparent decision-making in complex data science scenarios. As artificial intelligence continues to permeate various industries, the need for models that can provide clear explanations for their decisions has become increasingly apparent. This paper outlines the significance of interpretability in AI models and highlights the challenges posed by opaque systems in handling intricate data science scenarios. We discuss various approaches and techniques aimed at enhancing interpretability, including feature importance techniques, surrogate models, local explanations, and simplified models. Moreover, we emphasize the importance of transparent decision-making in critical domains such as healthcare, finance, and criminal justice, where the consequences of AI-driven decisions can be profound. Through case studies and literature review, we elucidate the benefits and limitations of interpretable AI models and propose future research directions in this field. Our findings underscore the importance of interpretable AI models in fostering trust, accountability, and regulatory compliance, while also acknowledging the trade-offs between interpretability and performance. Overall, this paper provides insights into the role of interpretable AI models in enabling transparent decision-making and lays the groundwork for further advancements in this critical area of research.
Smoking Behavior, Smoking Motivation, Stress, Physical Condition and Health Promotion Behavior of College Students with NO Smoking Intention

This study attempted to identify the smoking behavior, smoking motivation, stress and health promotingbehaviors of college students and to analyze basic physical condition and to provide basic data for smokingcessation program of college students. The subjects of this study were 135 college students who were willingto quit smoking. Data collection period was collected from February 1, 2018 to June 30, 2018. Statisticalmethods used were frequency, mean, standard deviation, t-test and ANOVA. Also Pearson's correlation analysisand regression analysis were used. Smoking motives are smoking behavior, key. Systolic blood pressure,hemoglobin, and smoking behavior were significantly correlated with CO 2 concentration. Body weight wassignificantly correlated with height, SBP, DBP, AST, ALT, ?-GTP, and CO 2 concentration and stress. Therewas a statistically significant correlation between SBP, DBP, ALT, hemoglobin, and hematocrit. Leukocytewas significantly correlated with hematocrit, and stress and ?-GTP were also significantly correlated. Healthpromotion activities showed a significant negative correlation with smoking motivation. Blood pressure,hemoglobin, platelet, and stress were found to have a significant effect on health promotion behaviors.Therefore, in order to prevent smoking of college students and help smoking cessation, basic physicalcondition is checked to promote health promotion, smoking cessation program should be developed toreduce smoking motivation.
Realizations of the Artificial Neural Network for Process Modeling. Overview of Current Implementations

This work is intended to review the most typical realizations of Artificial Neural Networks (ANNs), implemented in a Feedforward Neural Network (FNN) as well as a Recurrent Neural Network (RNN). Essential differences in ANN architecture and basic operating principles are discussed. The problems of learning processes are presented in several cuts. The advantages of prediction using ANNs have been demonstrated in several popular fields such as adaptive educology, classification of medicine and biology, industry, etc.
A Grapheme to Phoneme Based Text to Speech Conversion Technique in Unicode Language

Text-to-speech conversion can be done with two approaches: dictionary-based (database) approach and grapheme-to-phoneme (G2P) mapping. One of the drawbacks of this approach is its performance depends on the size of the dictionary or database. In the case of domain specific conversion, a simple rule -based technique is used to play pre-recorded audio for each equivalent token. It is easy to design but its limitation is mapping with the sound database and availability of the audio file in the database. In general, grapheme to phoneme conversion can be used in any domain. Advantages are the limited size of the database required, ease of mapping and compliance with domain. However, G2P suffers from pronounce ambiguity (formation of audio output). This paper will discuss about the grapheme-to -phoneme mapping and its application in text to speech conversion system. In this work, Assamese (an Indian scheduled Unicode language) is used as the experimental language and its performance is analysis with another Unicode language (Hindi). English (ASCII) language will be used as a benchmark to compare with the target language.
Mathematics in police activity: basics, features, mathematical training

The main attention is paid to the introduction of a comprehensive academic discipline “Mathematical Methods in Police Activity” for the first time and the discussion of the content of its first module for students of higher education institutions subordinated to the Ministry of Internal Affairs of Ukraine with the aim of combining mathematical methods, as well as elements of physics, astronomy and geography, in particular topography, to solve practical problems in the professional activity of police officers. Some components of the first module of this discipline are formulated on the basis of a study of an online course on mathematics for firefighters and rescuers in the United States.&#x0D; The problem of introducing a course in mathematics or elements of applied mathematics (mathematics and methods of solving tasks for professional purposes) or applied mathematics with elements of natural science (an integrated course in astronomy, physics and geography) into the educational process of training future police officers has not been studied in a comprehensive manner. There are some recommendations on the expediency of studying statistics, but there are many branches of mathematics whose methodology is useful for solving applied problems in the fields of justice and law enforcement.&#x0D; The need for such a study is due to the lack of a universal course for police officers in the world practice that would combine practical problem solving, mathematical methods, physical laws and processes, astronomy and geography, including topography.&#x0D; The formulated components of the first module of the course are relatively complete in terms of content and form the knowledge necessary to develop the skills and abilities to perform elementary calculation professional tasks and to comply with and understand safety rules.&#x0D; The article is the first in a series of articles on the relevant topic, some of which are in the process of preparation or are preprints.
Forensic technology: concepts, objectives and principles

It is established that forensic technology is a branch of forensic science which uses a variety of methods, instruments and technical means to collect, examine and interpret physical evidence at a crime scene in order to detect, investigate and document crimes and their perpetrators. Forensic technology is based on scientific principles and technologies and includes many different methods, such as ballistics, fingerprinting, DNA analysis, document examination, photography, video recording, etc.&#x0D; The article establishes that the key tasks of forensic technology are as follows: collecting, recording and documenting physical evidence at the crime scene; analysing the collected evidence, including photo and video materials, audio recordings, DNA samples, fingerprints, etc.; examining physical evidence to prove its relevance to the crime and connection with the suspect or accused; creating a reconstruction of events to understand the course of the crime and prove the guilt of the suspect or accused; preparing expert reports and testimony for use in court proceedings; supporting investigators and prosecutors in the use of scientific methods and techniques to collect and analyse evidence; conducting research and improving forensic techniques to improve law enforcement and increase the effectiveness of crime investigations.&#x0D; The key principles that reveal the essence and content of forensic techniques have been identified and characterised. The necessity of their legislative consolidation and expansion of the content has been proven.
Problematic issues related to forensic examination of written language and handwriting in pre-trial investigation

The article retrospectively examines the genesis of theoretical and law enforcement issues, legislative and other regulatory frameworks relating to forensic examination of written language and handwriting with a view to improving them and further prospects for their use in forensic science and forensic examination. The genesis of the emergence and introduction of the first state, international, religious, economic and other types of documents, methods and types of their execution and registration, and the improvement of the historical documentary system in our country and the persons who could execute them in various fields of use in the country and in ensuring the vital activity of the population, as well as the mechanism of the first identification actions with this category of writing and handwriting are investigated. The role, essence and significance of written language, handwriting and documentary studies in their historical aspect in terms of formation, improvement and use in establishing and identifying the person who executed a certain document or written text are determined. Separate objectives have been identified to improve knowledge, including subculture and skills, on the detection, establishment and preparation for forensic examination of written language and handwriting by detectives, investigators, forensic specialists, experts, prosecutors, investigating judges and court personnel, taking into account the requirements of the time regarding the use by criminals of documents made both by conventional means and with the help of computer equipment, the Internet, mobile communications, as well as other modern telecommunications achievements and digitalisation of the pre-trial process. The article investigates the peculiarities of forensic methods of examination of written language and handwriting objects to be taken into account by officials who, in particular, conduct pre-trial investigation of criminal offences committed with the use of ordinary or forged written documents, handwriting forgery, and other criminal methods of their use with a view to qualitative preparation and conduct of relevant handwriting and authenticity examinations. Based on the results of the study, the author's scientific positions and ways of solving the existing problematic issues have been developed.
A Prompt Engineering Approach for Structured Data Extraction from Unstructured Text Using Conversational LLMs

This paper aims to extract structured information from unstructured text written in natural language. This extracted information can then be stored in a database and queried using database access languages such as SQL to derive meaningful answers to questions that might arise around a specific activity related to that unstructured text. For example, an email chain discussing a planned trip could be used to extract a record with fields such as who is taking the trip, where they are departing from, when they are departing, where they are going, and when they are arriving. This information could then be used to create a relation of trips that could be queried by SQL. This paper uses a prompt engineering approach using conversational LLMs for extracting the relevant information related to travel and store the information into relational databases which can then be queried using SQL or any other query language. This initiative holds the promise to revolutionize the storage and retrieval of information with minimal effort. Currently, unstructured text is difficult to query and analyze. By extracting structured information from this text, it becomes much easier to work with.
SCaTNet: A Novel Self-supervised Contrastive Framework with Spatial-Channel Attention and Temporal Transformer for Few-Shot Action Recognition

We introduce SCaTNet, an innovative method for few-shot action recognition tasks that leverages the synergies of contrastive learning and advanced attention mechanisms. Distinct from previous few-shot methods, SCaTNet comprehensively explores the potential value of the sample data at both high and low dimensional levels. SCaTNet integrates the Quadruplet Attention mechanism (QA) with a Multimodal Temporal Contrastive Learning (MTCL) strategy, significantly enhancing video recognition and interpretation of action features. Our vast experiments on the SSv2-Small dataset show that SCaTNet’s superior performance is competitive with existing classical state-of-the-art methods, highlighting its effectiveness and practical utility in few-shot action recognition.
Classroom Behavior Recognition Algorithm Based on Graph Convolutional Networks and Knowledge Distillation

Classroom behavior recognition plays a significant role in enhancing educational quality and implementing intelligent education systems.In this study, deep learning algorithms are employed to recognize the behaviors and actions of teachers and students in the classroom. Subsequently, the overall effectiveness of classroom teaching is analyzed based on the responses and actions of teachers and students during class.Classroom behavior recognition provides data support for improving teaching quality and building intelligent classrooms. However, it requires models to have fast inference speed, fewer parameters, and robustness. Existing action recognition models, while accurate, have nonlinear structures and large parameter sizes that are not conducive to deployment. To address this, this paper proposes the Spatio-Temporal Simplified Graph Convolutional Networks(STSGC), a classroom behavior recognition algorithm based on graph convolutional networks and knowledge distillation. The Simplifying Graph Convolutional(SGC) module simplifies the model structure, and in conjunction with knowledge distillation training, transfers the knowledge of the teacher model to the student network, enabling the STSGC model to have fewer parameters and faster inference speed. Compared to the baseline network STGCN++ on the NTU-RGB+D dataset, the STSGC model’s parameters are reduced by about 49.35% to 1.56, and its GFLOPS are reduced by about 99.022%. Experiments on the TSC dataset also show that the model’s accuracy is comparable to that of the teacher model. Additionally, the model’s inference speed on the CPU is reduced by 0.7ms, and on the GPU by 70.4ms.
Graph-level Anomaly Detection via Deep Metric Learning

Graph-level Anomaly Detection (GAD) is a crucial subset of anomaly detection that aims to identify abnormal graphs with atypical nodes and structural features. Although extensive machine learning-based methods have been proposed to improve GAD, fundamental challenges of sample imbalance, distinguishable feature representation of graphs, and evaluation paradigms to identify anomalous graphs still exist. In light of these challenges, we re-consider GAD tasks from another perspective and propose a novel end-to-end method to spot graph anomalies. Specifically, we first generate graph pairs based on the traversal matching strategy as our training data to address the sample imbalance between normal and anomalous graphs. Then, we utilize a powerful Graph Isomorphism Network (GIN) combined with Siamese network architecture, which is a classical application of deep metric learning, to build a simple and end-to-end framework to learn more comprehensive graph-level representations capable of distinguishing between normal and anomalous graphs. Finally, we design a straightforward yet efficient evaluation paradigm named "The Jury" to identify anomalous graphs. Extensive experiments conducted on ten real-world datasets from various areas,e.g., social networks, molecular structures, and protein anomalies, demonstrate that the proposed method can effectively detect graph-level anomalies and performs competitively against other state-of-the-art methods.