Reviewerly/exHarmony huggingface dataset:
Elaborating Advanced Machine Learning Techniques in the Music Class

In music education, there are several cases where the instructor needs to set preparatory tasks and use verbal communication, both of which, nonetheless, interrupt the music continuity. These “interruptions” are considered as learning barriers. Having researched teaching communication habits on several music instruction cases, we have come up with the idea of designing a set of software blocks that, laid down together as a digital aid to the class, can generously assist music teaching by providing communication facilitators in a wide range of commonly used music teaching exercise tasks. In this direction, a range of algorithms and software blocks have been implemented at the Ionian University using the Max/MSPTM dedicated software platform, comprising the FIG set of tools. A specific subset of these software tools has included Machine Learning (ML) logic in order to promote a wiser instructor-student communication that advances class musicality and potentially facilitates deeper consolidation of musical structures.
Building a Collaborative STEM Culture through Website, Radio, and Journal Creation with High School Students from the 4th High School of Ilion: An Evaluation of a Technology-Enhanced Educational Project

In today’s digital age, educators must employ innovative and creative approaches to engage students in collaborative and interdisciplinary STEM education. Integrating technology tools like Photoshop, Audacity, and social media platforms such as Instagram offers students the chance to acquire and apply vital skills in real-world contexts. This study evaluates the effectiveness of a technology-enhanced educational project where high school students collaboratively build a formal school site, radio, and journal, fostering communication, collaboration, and project management skills. Drawing on research highlighting students’ autonomy and technology integration in education, this study examines the project’s impact on student learning outcomes. It investigates how the project influences student engagement, collaboration, and creativity. The use of technology in the project is expected to boost student motivation and enable personalized learning, ultimately enhancing the educational experience. The research questions explore the effects of the technology-enhanced project on student engagement, collaboration, and creativity within the context of building a school site, radio, and journal. Additionally, the study aims to understand students’ perceptions of the project, including their satisfaction, motivation, and self-efficacy. The findings will suggest improvements to support STEM education and enhance student learning outcomes.
Electric Signals in Machine Learning Using AppInventor and Arduino

Recent developments in Artificial Intelligence (AI) have introduced machine learning and its applications into everyday life. As technology becomes increasingly integrated into the educational system, researchers are focused on developing tools that allow students to interact with machine learning in a way that sparks their curiosity and teaches them essential concepts. Our instructional proposal, titled “Electric Signals in Machine Learning Using App Inventor,” focuses on applying learning, transfer, and classification models of audio spectrograms to teach students in the first year of high Secondary school (A’ Lyceum) fundamental concepts of machine learning. This is accomplished using MIT App Inventor and Arduino’s visual programming environments. Students will use the website “Personal Audio Classifier” to train an audio model and App Inventor to connect computer science and machine learning. In addition, with the aid of the Arduino microcontroller, students will engage in visualising Morse code signals and investigating Physical Computing, allowing them to create digital solutions that connect to the real world.
Russian Book Business of the 19th — Early 20th Century on 23rd Pavlenkov Readings

The review of the All-Russian scientifi c conference “23rd Pavlenkov Readings. Book business in Russia in the 19th — early 20th century”, held in the Russian National Library on 17—19 October 2023, presents the reports delivered at the plenary session and four sections: “Ivan Sergeevich Aksakov (1823—1886) — publisher, publicist, poet: to the 200th anniversary of his birth”, “History of publishing”, “History of book collections” and “History of censorship”. The participants of the readings were researchers from St Petersburg, Moscow, Moscow region, Nizhny Novgorod, Orel, Samara, Khabarovsk. The speakers represented various scientifi c institutions: libraries, universities, museums. Among them there were both experienced book historians and novice researchers who touched upon various aspects of the history of the book in their reports.
The Library World of Eduard Rubenovich Sukiasyan

A review is given of the biographical collection “The Great Librarian: Eduard Rubenovich Sukiasyan”, published in 2023 in the publishing house “Pashkov House”. This edition is dedicated to one of the most famous and authoritative theorists and practitioners of librarianship in our country, a truly large-scale Russian librarian, author of over a thousand scientifi c publications, Honoured Worker of Culture of the Russian Federation. The book contains unknown details of the biography of Eduard Rubenovich Sukiasyan (1937-2021) related to his youth and student years, other interesting episodes of his creative path, fragments of diary entries. The edition also includes reminiscences of E.R. Sukiasyan’s friends and colleagues and an impressive list of the scientist’s published works since 1960.
Eduard Rubenovich Sukiasyan and the Higher Library School

The article covers the main directions of the activities of a major Soviet and Russian scientist Eduard Rubenovich Sukiasyan (22.06.1937—27.02.2021): cataloguing and cataloguing, systems of library and bibliographic classifications, training of librarians in Russia and abroad. A new book from the series “The Great Librarian” dedicated to E.R. Sukiasyan is presented. It describes his contribution to the training of teachers at universities of culture and to the development of the academic discipline “Library Catalogues”, the use of his advice in the development of lectures by the author of the article, E.R. Sukiasyan’s participation in discussions and reviewing books and articles on library catalogues, library processing, training and retraining of future cataloguers.
The Custodian of Bibliographic Norms and Rules (to the 70th anniversary of G.P. Kalinina)

The article discusses the issues of standartisation and methodical provision of the domestic book business following an example of long-term activities of G.P. Kalinina who celebrated seventieth anniversary on the 9th December 2023. The main achievements of jubilee woman for almost 50 years of working at the Russian Book Chamber until its incorporation into the structure of the Russian State Library are described. The list of the 40 main publications of G.P. Kalinina is given for 2013—2023. Besides it the list of the all 25 SIBID national standards is given in developing of which G.P. Kalinina took part as the responsible executive or the working group member.
AUTOMATED SYSTEM OF SEARCHING FOR INFORMATION ON INTERNET

In this article, author examines a process of searching for information on a particular topic on the Internet. Attention is focused on features related to functioning an information retrieval system, and the operation of its typical model is studied. Such a system should provide a flexible response to an operator's request by means an interactive search and maintenance a query archive, including a thesaurus, spelling and punctuation checkers for a language query. Results of analysis carried out on existing automated information retrieval systems indicate that a query should be formed taking into account: using logical connections, restrictions on the distance between words, the possibility of setting weighting coefficients for keywords, sorting by date and document size. An approach to creating and a structural scheme of an automated system of searching for thematic information on Internet based on unique nature of operational and analytical activities of special units is proposed. Such a system should take into account particularities in operation of various Internet services used by special services analysts: thematic resource catalogs, news sites, RSS feeds and news agencies broadcasting news online. It is envisaged that development of special software based within the proposed functional structure of automatic system for searching thematic information on Internet will increase efficiency of information and analytical activities in special units due to availability of means for: searching for heterogeneous information on specific objects; identifying links between the monitored objects and related facts and events; visualization of analytical research results. Keywords: automated system; Internet; information query; information retrieval system; structural diagram.
Algebraic Operations on Spatiotemporal RDF Data

The algebraic approach has been proven to be an effective way to process queries, and algebraic operations in RDF have been investigated extensively. However, the study of spatiotemporal RDF algebra has just started and still needs further attention. This chapter aims to explore an algebraic operational framework to represent the content of spatiotemporal data and support RDF graphs. The authors define a spatiotemporal data model based on RDF. On this basis, the spatiotemporal semantics and the spatiotemporal algebraic operations are investigated. The authors define five types of graph algebras. The filter operation can filter the spatiotemporal graphs using a graph pattern. Besides this, they put forward a spatiotemporal RDF syntax specification to help users browse, query, and reason with spatiotemporal RDF graphs. The syntax specification illustrates the filter rules, which contribute to capturing the spatiotemporal RDF semantics and provides a number of advanced functions for building data queries.
Subgraph Matching of Spatiotemporal RDF Data

With the growing importance of RDF data in web data management, there is an increasing need for modeling and querying RDF data. In this chapter, firstly, the authors formally define spatiotemporal RDF data and construct a spatiotemporal RDF model st-RDF that is used to represent and manipulate spatiotemporal RDF data. Secondly, they propose the stQuery algorithm for subgraph matching of spatiotemporal RDF data. This algorithm can quickly determine whether the query result is empty for queries whose temporal or spatial range exceeds a specific range by adopting a preliminary query filtering mechanism in the query process. Thirdly, they propose a sorting strategy that calculates the matching order of query nodes to speed up the subgraph matching. Finally, they conduct experiments in terms of effect and query efficiency. The experimental results show the performance advantages of this approach.
Pengaruh Model Pembelajaran Kooperatif Tipe Group Investigation (GI) Terhadap Hasil Belajar Siswa Pada Mata Pelajaran Matematika Kelas IV SDN 0306 Sosopan T.A 2022-2023

This research was conducted with the aim of determining the Influence of the Cooperative Learning Model of Group Investigation (GI) on student learning outcomes in mathematics for fourth-grade students at SDN 0306 Sosopan. The population in this study consisted of 48 students, divided into Class IV A with 24 students and Class IV B with 24 students. The research sample consisted of 24 students selected using the Total Sampling technique. The research type employed was an experiment with a Quasi-Experimental Design of the Pretest-Posttest Control Group Design. The research procedure included preparation, implementation, and conclusion stages. Before conducting the research, an instrument validation test, reliability test, difficulty level test, and discriminant power test were conducted. Subsequently, a pretest was administered to both classes, with the experimental class receiving treatment by implementing the Group Investigation (GI) learning model, while the control class used a conventional model. Finally, a posttest was administered to both classes. Data were analyzed using SPSS 24, including tests for normality, homogeneity, and hypothesis testing. Based on the students' learning outcomes, the experimental class obtained an average score of 41.04 in the pretest, which increased to 77.08 in the posttest. Based on the research results, it can be concluded that there is an influence of the Group Investigation (GI) learning model on student learning outcomes in mathematics for fourth-grade students at SDN 0306 Sosopan.
Rancang Bangun Game Edukatif Matematika Berbasis Android pada Materi Geometri

The goal to be achieved in this research is to produce an Android-based Mathematics educational game on class VII Geometry Material. The type of research used is R&amp;D (Research and Development). The MDLC (Multimedia Development Life Cycle) method is used to complete the research stages which include concept, design, material collection, assembly, testing and distribution. The resulting application presents mathematical educational games to users, which can be used by teachers and students of SMPN 1 Baebunta Selatan. Application functional testing results where the test results of all features on the main menu and functions on interactive learning media are as expected. Where the final score obtained is 4, which means that the application assessment is in the very good category
Multimedia Interaktif IPA: Pengembangan Media Pembelajaran pada Materi Sistem Organ Tubuh Manusia

This research aims to design an interactive learning media for human organs for students at SDN 45 Padang Alipan based on desktop as a solution to problems at the research location. This research was carried out at SDN 45 Padang Alipan. The type of research used is Research and Development (R&amp;D), the research method is Multimedia Development Lyfe Cycle (MDLC) and the data collection methods are observation, interviews and literature study. This application was created using construct2 software and design using CorelDraw X7. Interactive learning media for human body organ systems displays material, learning objectives, evaluation, instructions and profiles. With this learning media, you can get to know what the organs of the human body are and it is also equipped with evaluations to hone students' abilities from the material that has been explained. The testing of this research is system testing using black box testing and media expert testing. The results of media expert testing indicate a value of 93% is in the very feasible category and the results of material expert testing indicate a value of 90% is in the very appropriate category.
Implementation of Multimedia Learning Applications to Increase Understanding of Kirana Hati Bunda Tamiajeng Special School Students

Kirana Hati Bunda Special School (SLB) is the only special school in Trawas District. This school has 19 students with special needs who come from several villages in Trawas District. The village fully bears operational costs, so the development of SLB has been quite slow. Due to limited funds, teachers are often unable to attend training to add skills and knowledge to the teaching and learning process. Teachers still have difficulties in compiling learning programs according to the different needs of students, innovating, and being creative in developing new learning media. Therefore, this program will provide training and assistance in planning, implementing, and evaluating learning for SLB Kirana Hati Bunda students. In addition, to meet the needs of teaching aids, learning applications will be made, training on application use and assistance in using multimedia learning applications with hand gesture recognition. Based on student learning results, it was found that the multimedia hand gesture recognition learning application made students more enthusiastic in learning, did not get bored of repeating exercises and the results were better when learning was done repeatedly. From two practice sessions on the same material there was a significant increase in scores&#x0D;
Good or Evil: Generative Adversarial Networks in Digital Forensics

Generative adversarial networks (GANs) are widely used in different areas that enable the generation of new or modification of existing data. Specifically, GANs can be used to improve the quality of images and videos, generate synthetic data such as fictional characters and deep fakes, and apply data adaptation. In digital forensics, GANs can be used to improve the quality of machine learning (ML) algorithms. This is because the existing ML-based forensics techniques often lack robustness due to the limited availability of specialized data required for training and the need for prior knowledge about the potential attack. In these cases, GANs are employed to complement the existing datasets with new augmented synthetic samples within a short time frame and to improve the generalizing capability of forensic classifiers. At the same time, GANs contribute to new challenges when used as anti-forensic tools to fool classifiers. The adversary may either remove or modify crucial evidence present in data or create completely new data which closely reflects the original. In this work, we present a literature overview on the application of GANs in the digital forensics domain to demonstrate their versatility. We identify the architectural commonalities and trends of GANs used for anti-forensics in different domains. We also provide a categorization of GAN methods used to improve forensic classifiers based on their deployment. We use qualitative metrics to analyze GAN-based methods designed for both forensic and anti-forensic purposes to identify the potential future research directions based on discovered benefits and drawbacks.
Linguistic Steganography and Linguistic Steganalysis

The objective of linguistic steganography is to embed additional data in text carriers for covert communication, whereas linguistic steganalysis, as a counter technology to linguistic steganography, aims at revealing the existence of additional data within unknown texts. Early linguistic steganography algorithms alter a text carrier to embed additional data, which limits the embedding payload due to the extremely small number of changeable operations. With the rapid development of deep learning and natural language processing techniques, recent advances in linguistic steganography utilize a well-trained language model to directly produce the text carrying additional data without a given text, which results in a significantly higher payload when compared with previous works and enhances the level of security. On the other hand, early linguistic steganalysis algorithms rely heavily on manually crafted statistical features, which cannot well reveal steganographic characteristics and thus limit the detection accuracy. Recent advances demonstrate that by modeling the local and global semantic, syntactic and contextual characteristics through deep learning, discriminative features can be extracted for high-accuracy detection. To trace the latest developments and trends, in this chapter, we will review advanced methodologies in linguistic steganography and linguistic steganalysis, and discuss challenges and opportunities. This chapter is intended as a tutorial of text based steganography and steganalysis in the context of media security.
Random Deep Feature Selection’s Efficiency in Securing Image Manipulation Detectors Opposed by Adversarial Attacks

We investigate whether it is possible to extend the random feature selection approach to include detectors that incorporate Deep Learning features in addition to the improvement in the robustness of forensic detectors to targeted attacks mentioned in Chen et al. (IEEE Trans Inf Forensics Secur 14(9):2454–2469, 2019). This paper specifically investigates the transferability of adversarial cases targeting the original Convolutional Neural Network (CNN) image manipulation detector compared to other detectors that essentially respond to a subset of features randomly collected from the original network, particularly from its flattened layer. Considering the following features, the results were obtained: (1) Three image manipulation detection tasks, including rescaling, average filtering, and adaptive histogram equalization. (2) Two structures of the original network. (3) Three different classes of attacks. Randomization of features was found to contribute to disrupting attack transferability, even in cases where attack transferability can be prevented by retraining the detector or simply varying the detector architecture.
Optimized Distribution for Robust Watermarking of Deep Neural Networks Through Fixed Embedding Weights

This study describes a technique to use watermarking To secure Deep Neural Networks’ (DNNs)’ intellectual property rights. To watermark data, a watermark can be inserted into the trainable parameters of the network, which is known as “white box watermarking”, or into the network’s input-output mapping according to specific inputs (black box watermarking). Watermarking makes it difficult to ensure robustness to network modifications such as fine-tuning, model compression, and transfer learning. The watermark message is encoded in the network’s fixed weights, whose placement is defined by a secret key, for this reason. These weights are set before training and are not modified during it. The spread of watermarked weights is tuned to large amplitudes to increase resilience, while being optimized to be indistinguishable from non-watermarked weights. Experiments show that the proposed approach is able to handle large payloads without significantly affecting the accuracy of the network, and is resistant to various types of network modifications and reuse.
Anti Forensic Measures and Their Impact on Forensic Investigations

This work gives an overview of a number of popular anti forensic measures on local persistent storage systems, specifically on photos and videos, and evaluates the effectiveness of the measures based on a number of different factors. We discuss the factors for the effectiveness along with the problems and anti forensic targets. We provide an overview of a number of popular anti-forensic measures that apply to media forensics, along with descriptions of every measure. We also qualitatively evaluate anti forensic measures on the conflict of interest between convenience and effectiveness of the measure.
Domain Specific Information Based Learning for Facial Image Forensics

Multimedia forensics involves the use of scientific methods for analyzing the contents such as audio, video, and digital images. Digital image forensics is one of the sub fields of multimedia forensics which involves any kind of digital images such as face, fingerprint etc. Security systems are one of the most sophisticated systems to protect assets and privacy in recent years. The demand for fast, accurate identification and authentication is increasing day by day. Face recognition is one of the trending topics in multimedia forensics. Several researchers have worked on addressing challenges such as pose variation, blurriness, low resolution, illumination, facial expression, viewing angle and lighting conditions in face recognition. In forensics, facial images are captured under adverse conditions; hence, face recognition in forensics is more advanced than normal face recognition. To identify the suspects involved in crime, forensic experts follow manual comparison which is a time-consuming task. The main objective of this chapter is to discuss the importance of forensics and the various universities, laboratories, and applications established in this discipline. Domain specific methods for facial image analysis in forensics and the datasets, metrics which can be used for automating domain specific methods is discussed.