Reviewerly/exHarmony huggingface dataset:
Construction of a secure and efficient content key distribution framework for DRM systems

Summary Recent advances in networks and digital technology have provided many user‐friendly environments such as telemedicine, e‐banking, e‐content, and e‐commerce. It has revolutionized the multimedia industry and made it accessible anytime and anywhere. However, its limitation also appeared in digital content piracy, which is more prevalent nowadays. Digital rights management (DRM) systems were developed to control the illegal spreading of digital content. Protocols are being developed to enable effective and secure communication in DRM systems. As security and efficiency are essential parameters, the approach should be two birds with one stone; i.e., the protocol should achieve efficiency and security together. Our investigation identified that secure mechanisms either have increased computation/communication overhead or fail to meet specified security criteria when discussing efficient content key delivery mechanisms. So, an efficient protocol design with the right security attributes is required. We have worked on designing a content delivery mechanism keeping in mind the desirable attribute of security and without decreasing overhead. The suggested solution targets “two birds with one stone.” We proved the security of the scheme in the random oracle model. We verify the security against active attacks using the formal simulation tool AVISPA. Detailed analysis is performed for the evaluation of computational and communication overhead. The comparative study is also presented to understand the gains in efficiency and security.
Dynamics of Dispersive Measurements of Flux-Qubit States: Energy-Level Splitting Connected to Quantum Wave Mechanics

Superconducting flux qubits have many advantages as a storage of quantum information, such as broad range tunability of frequency, small-size fabricability, and high controllability. In the flux qubit-oscillator, qubits are connected to SQUID resonators for the purpose of performing dispersive non-destructive readouts of qubit signals with high fidelity. In this work, we propose a theoretical model for analyzing quantum characteristics of a flux qubit-oscillator on the basis of quantum solutions obtained using a unitary transformation approach. The energy levels of the combined system (qubit + resonator) are analyzed in detail. Equally spaced each energy level of the resonator splits into two parts depending on qubit states. Besides, coupling of the qubit to the resonator brings about an additional modification in the split energy levels. So long as the coupling strength and the tunnel splitting are not zero but finite values, the energy-level splitting of the resonator does not disappear. We conclude that quantum nondemolition dispersive measurements of the qubit states are possible by inducing bifurcation of the resonator states through the coupling.
GBT-CHAIN: A System Framework for Solving the General Trilemma in Permissioned Blockchains

As a subset of distributed systems, blockchain technology is equally bound by the CAP trilemma. Specifically, no distributed system can satisfy consistency, availability, and partition tolerance simultaneously. Informally, the innovative blockchain trilemma has been theorized to include security, scalability, and decentralization. While many software approaches on blockchains claim to solve the trilemma problem, almost all known approaches have been proven to involve trade-offs and fail to reach the upper bounds, meaning they do not achieve the theoretical limit value in each term of a trilemma. In this paper, we propose a general trilemma based on the above two. For permissioned blockchains, we then describe a system framework called GBT-CHAIN to solve the G eneral B lockchain T rilemma. In the proposed framework, the consensus and data layer, the network layer and the physical layer work in cooperation. According to our theoretical proof and experimental analysis, the three properties of the trilemma consisting of consistency, scalability, and partition tolerance are perfectly satisfied without trade-offs. Our results demonstrate that the general blockchain trilemma is resolved by combining consensus algorithms and physical topologies, in particular for permissioned blockchains.
Integrated Photonics for Computing and Artificial Intelligence

In this paper, we review the progress of integrated photonics in both digital computing and analog neuromorphic computing. We introduce methods to design scalable, area-efficient, and energy-efficient integrated photonic computing chips for computing and artificial intelligence acceleration with experimental demonstrations.
Attention Based Graph Neural Networks

The performance of graph neural networks (GNNs) in a variety of graph-related tasks, such as node categorization, has been remarkably good. Existing GNN models, especially when working with big and sparse graphs, are constrained in how well they can capture complicated graph topologies. In order to overcome this issue, we incorporate an attention mechanism into the GNN design in this study. During the message-passing phase, our proposed model, Attention-Based Graph Neural Networks (AB-GNN), uses a learned attention mechanism to differentially weight the significance of surrounding nodes. Using numerous benchmark datasets for node classification, we test the performance of the AB-GNN and demonstrate that it outperforms current state-of-the-art GNN models. Our tests specifically show that AB-GNN improves accuracy by up to 1% in comparison to the top baseline model. According to our findings, the attention mechanism enhances the model's capacity to detect critical aspects in the graph, resulting in more precise node classification on Cora and CiteSeer datasets in our case. Comprehensively, our work demonstrates the potential of attention mechanisms to enhance the functionality of GNN models and offers directions for further study in this field.
SET, SORT! A Novel Sub-stroke Level Transformers for Offline Handwriting to Online Conversion

We present a novel sub-stroke level transformer approach to convert offline images of handwriting to online. We start by extracting sub-strokes from the offline images by inferring a skeleton with a CNN and applying a basic cutting algorithm. We introduce sub-stroke embeddings by encoding the sub-stroke point sequence with a Sub-stroke Encoding Transformer (SET). The embeddings are then fed to the Sub-strokes ORdering Transformer (SORT) which predicts the discrete sub-strokes ordering and the pen state. By constraining the Transformer input and output to the inferred sub-strokes, the recovered online is highly precise. We evaluate our method on Latin words from the IRONOFF dataset and on maths expressions from CROHME dataset. We measure the performance with two criteria: fidelity with Dynamic Time Warping (DTW) and semantic coherence using recognition rate. Our method outperforms the state-of-the-art in both datasets, achieving a word recognition rate of $$81.06\%$$ and a 2.41 DTW on IRONOFF and an expression recognition rate of $$62.00\%$$ and a DTW of 13.93 on CROHME 2019. This work constitutes an important milestone toward full offline document conversion to online.
Musescore Software Training for the Development of TPACK-Based Music Learning in Elementary Schools

Digital literacy and the skills of elementary school teachers in choosing and using scoring applications (musical notation) as a medium as well as a learning resource for students are essential to do. Elementary school education is experiencing several problems today, such as the government's inability to provide training on using applications for the learning process. In addition, not all teachers have the confidence in choosing and determining computer application software that can support learning in class, especially in the subject matter of Cultural Arts and Crafts (SBdP) material in the field of music. Elementary school teachers in the Laweyan sub-district of Surakarta do not yet have optimal insight regarding developing music applications to support material delivery in class. This community service activity is focused on training elementary school teachers to use music scoring (notation) software with the Musescore application. The results of this training found the formation of teacher skills using the Musescore music application to support music learning in elementary schools.
Continuous-Time Random Matching

Continuous-time random matching with a large (continuum) population is widely exploited in the literature, but has not had a rigorous formulation, nor a demonstration of its key assumed properties. This paper provides the first probabilistic foundation for this approach by presenting a mathematical model of continuous-time random matching and showing its existence and properties. The agents' types, which can change due to random matching and random mutation, form a continuum of independent continuous-time Markov chains. Using the exact law of large numbers, we show how the cross-sectional distribution of agent types evolves deterministically according to an explicit ordinary differential equation. Nonstandard analysis is used in proving the main theorem.
Aplikasi Analisis Sentimen Komentar Pengguna Genshin Impact Di Play Store

Google Play functions as the official app store for the Android operating system, allowing users to browse and discover applications developed with the Android software development kit (SDK) and published through Google. Google Play also serves as a digital media store, offering music programs, books, movies, and television shows. It previously offered Google hardware for purchase until it introduced a separate online hardware store, Google Store, on March 11, 2015.The research will utilize a web-based application development tool that uses Flask and JavaScript as the application interface, and the Pandas library from Python for data manipulation. Naïve Bayes will be employed as the methodology for analyzing sentiments based on words, and K-Fold Cross Validation will be used to strengthen the accuracy of the analysis results.Sentiment analysis typically classifies opinions into three categories: positive and negative. However, applications that can perform the process of creating training and testing sets from consumer opinion data, simultaneously analyzing consumer sentiment and dynamically measuring the accuracy of the analysis results, are still scarce. This study aims to develop an application capable of analyzing consumer sentiment with the mentioned functionalities, wherein Naive Bayes is used as the classification method.
THE PREFERENCES OF STUDENTS AND THEIR VIEW ON THE EDUCATION OF ECONOMIC AND MANAGEMENT SUBJECTS AT A TECHNICALLY ORIENTED UNIVERSITY

The contribution is a partial output of KEGA research task no.011STU-4/2022 "Creating a model of education supporting the increase of competencies of students of a non-economically oriented university in the field of innovative, entrepreneurial thinking and business support" conducted at the Institute of Management of the STU in Bratislava.The research presented in this article was conducted on a sample of 45 students of the Slovak University of Technology in Bratislava, Faculty of Chemical and Food Technology, who successfully completed the master level of education in 2022.The students were presented with a questionnaire consisting of 6 questions.The questionnaire contained both open and closed questions and its objective was to find out the preferences of students and their view on the education of economic and management subjects at a technically oriented university.Data collection took place online, through the Academic Information System -AIS, and in person on the basis of printed questionnaires that students received at their final exams.The results of the survey were compared with a similar survey conducted in 2017 and we found that up to 20% more students than in 2017 think they do not have sufficient knowledge of economic and managerial subjects for their future career.Even fewer students think (77%) that they do not have enough knowledge to start a business, despite the fact that almost 49% of them consider that they would like to start a business in the future.
Empowering Topic Classification Accuracy by Integrating Transformer-Based with Machine Learning and Deep Learning Techniques

Identifying the topic of a piece of text is a crucial responsibility in natural language processing and the analysis of written language. This study investigates the performance of two approaches - machine learning and deep learning - for topic classification. In the machine learning approach, we tested ten different classifiers after applying various pre-processing techniques to our dataset and used T5 X-Large as the pre-trained embedding model. The best result was achieved using the Support Vector Machine (SVM) classifier with minimal pre-processing, i.e., only removing special characters. This method obtained an accuracy of 89%. While in the deep learning approach, we experimented with different pre-trained models, pre-processing techniques, and input layers for our neural network architecture. The optimal configuration was found using a Recurrent Neural Network (RNN) as the input layer, applying lowercase as the only pre-processing technique, and employing T5 X-Large as the embedding model. This configuration achieved an accuracy score of 89.91%. The results demonstrate that both approaches have their merits, but the deep learning approach offers a slight improvement in classification performance. This research provides valuable insights for researchers and practitioners working on topic classification tasks in natural language processing and text analysis.
The Effect of Using Tangible User Interfaces Compared to Traditional Learning for Teaching Programming in Higher Education: An Experimental Study

There is a lot of interest in employing tangible user interfaces (TUIs) to enhance teaching programming, but there is conflicting evidence for their advantages since implementing them in educational settings offers more difficulties than traditional learning. This paper examines the effect of using TUIs in learning and compares it to traditional learning methods. The study measures the difference in learning rate on university students majoring in computer science. The study was conducted on 20 university students aged 17 to 22 who participated in a 45-minute learning activity. The activity used a Tangible User Interface (TUI) and paper notes from a textbook. Pre-test and post-test results were collected as part of the study's between-groups design, and the TUI group outperformed the traditional learning group in terms of learning gains. The TUI group had an increase of 25.4% in their scores following the three learning sessions and a 1.5% decrease when the retention was measured two weeks later. The traditional paper learning group had increase of 9.1% in scores and a decrease of 7.2% in retention.
A Novel Robust Spread Spectrum Watermarking Scheme for 3D Video Traitor Tracing

The 3D video traitor tracing approach is an effective technique for securing 3D content. This approach involves the protection of the multimedia content and the prevention of each malicious manipulation by embedding the Collusion-Secure Fingerprinting (CSF) codes. Traitor tracing aims also at retrieving back the actors who contributed to the construction of an illegal release of a multimedia product by applying an efficient tracing scheme. Depending on the position of embedding the CSF codes, the current secure 3D video content schemes can be classified into three categories: the first one embeds the fingerprint into the 2D video, the second one into the depth maps’ content, but each of these two categories protects only one component of the 3D video which gives rise to the proposal of a third category which embeds the fingerprint in both the two components of the 3D video and protects independently and simultaneously the 2D frames and the depth maps contents. This paper proposes a novel traitor tracing technique to protect the two essential components of 3D videos by embedding the CSF in the 2D frames and the depth maps by applying a novel and robust spread spectrum watermarking scheme. Experimental assessments demonstrate the effectiveness of this proposed technique by applying it to a case study of 3D games and show promising results in terms of speed and tracking accuracy constraints.
Comparative Analysis in Utilizing Different Diagnostic Methods for Plasmodium falciparum Detection within Owerri Municipal, Imo State, Nigeria

Malaria is one of the major public health issues in Africa as its affects human beings at different levels. However, any wrong diagnosis of malaria infection can lead to severe ill health, and death of the infected individual. This study was carried out to compare different diagnostic methods of malarial detection within owerri municipal in Imo State Nigeria. Blood samples were collected from patients and diagnosed via microscopy, antigen assay technique and polymerase chain reaction. About 200 blood samples were obtained utilizing the Ethylenediamine tetracetic acid (EDTA) bottles and examined. In 200 blood samples, microscopy had 75 total positive and Antigen assay had 83 while, PCR had 94 However, a percentage prevalence of about 37% was obtained from microscopy though, about 41.5% were observed in Antigen Assay and 47% for a Polymerase chain reaction. There was high malaria prevalence recorded in relation to age within the age range of 31 to 40. The Sensitivity and specificity of the three diagnostic methods show that PCR had the highest for both while, microscopy had the least sensitivity followed by RDT. Moreover, positive predictive value and negative predictive value assessments were analyzed. The results showed that Microscopy has a 0.2% chance of missing a truly positive and antigen assay has 0.1% while, PCR has 0%. Therefore, more emphasis should be given to PCR as the possibility of an error during diagnosis is less.
Saxophone Disassembly

Abstract Chapter 4 contains step-by-step instructions for the disassembly of the saxophone.
Saxophone Assembly

Abstract Chapter 3 contains step-by-step instructions for the assembly of the saxophone.
EDUCATION MODEL FOR FUTURE

The modern work environment requires many new abilities.Hence, there are often called 21st-century skills.It can be caused to the increasingly common use of digital tools or by more and more individualization (fitting) occurring in all aspects of life.These skills are not only subject-matter skills but auxiliary such as time planning, flexibility, ability to contact and discuss with others, social skills, etc.These skills are often called "hard" and "soft" skills.Many professions introduce these skills to help employees adjust to future changes.All industries had to follow changing world, mainly from an economic point of view.Banking, manufacturing, tourism, hotel industry, and sometimes administration, medicine, and healthcare focus on customization.Doing work quicker and more flexibly in all these areas is essential.Unfortunately, education is one of the arenas more resistant to these changes.There are "traditional" exam forms, "traditional" lecture forms, etc.The pandemic has introduced changes, but now there is a great willingness (from teachers, administration, and some students) to return to the previous situation.University is an old institution.There was a time when the university was used to individual development.There were unitsstudents studied chosen subject in their own time and developed themself in the desired direction (Humboldt University model).Most important was feeling like part of science society.After this, the following modelthe French university, shows that students should learn to be a valuable element of the state.Students' feelings were not significant.Students can be treated as the "labor force".They learn to fulfill state expectations.What university model is desired now?What university model is practical enough to give work knowledge and prepare for new challenges?Are some pedagogical theories better matched to unknown current and future abilities?What is essential for students, and what is vital to the state?In this paper, we would like to discuss some of the possibilities of different university models and different pedagogical theories with a focus on flexibility and further self-development of students
DEVELOPING AN EFFECTIVE MOBILE APP FOR IMPROVING ENGLISH SPEAKING SKILLS

This study aims to create a mobile application that will facilitate learners' practice of the Test of English as a Foreign Language (TOEFL) speaking and improve their speaking skills.The study follows a design-based research (DBR) framework to delineate the phases and principles of developing the application.The TOEFL exam is a requirement for international students to apply to universities in the USA.However, challenges emerge even after international students are admitted to colleges.All lectures and discussions within classrooms are conducted in English, and students need to communicate and negotiate all daily issues with others by themselves.While there are a number of mobile apps in the market targeting English Speaking skills in general, only a small number of apps address TOEFL speaking specifically with their own limitations.The four steps of DBR--Planning, Developing, Testing, and Redesigning--were followed in designing the mobile application.This DBR framework proved to be a successful model for designing and developing the mobile application, which can be applied to other applications as well.The Visualize TOEFL Speaking mobile application demonstrated that its learning activities could support pedagogical goals.Students found the application engaging, useful, and interactive, and they reported that it helped them improve their English-speaking skills, particularly in the TOEFL speaking test and its sub-categories.Although the interface and functions of the mobile learning application need to be refined, it is a highly valuable tool.
PERANCANGAN SISTEM PENDUKUNG KEPUTUSAN UNTUK MEMUDAHKAN PEMILIHAN KEDAI KOPI

A decision support system (DSS) for selecting coffee shops is a technological solution that can help coffee enthusiasts find coffee shops that suit their preferences. The purpose of this research is to design a decision support system to facilitate the selection of coffee shops. The methodology used in this research is the waterfall method which consists of needs analysis, design, implementation, and system testing. Needs analysis is carried out by conducting surveys and interviews with respondents who are coffee consumers. The results of the needs analysis will be used as the basis for system design. The system design is carried out using the ERD and DFD models, as well as the selection of the right programming language and database. Implementation is done by developing the system according to the design that has been made. Finally, system testing is carried out to ensure that the system can run properly and according to user requirements. It is hoped that the results of this study can provide benefits for coffee fans in choosing coffee shops that suit their preferences.