Reviewerly/exHarmony huggingface dataset:
FedHybrid: Hierarchical Hybrid Training for High-Performance Federated Learning

Federated Learning coordinates multiple devices to train a shared model while preserving data privacy. Despite its potential benefit, the increasing number of participating devices poses new challenges to the deployment in real-world cases. The highly limited amount of data located on each device coupled with significantly unbalanced data across different devices severely impede the performance of the shared model and the overall training progress at the same time.In this paper, we propose FedHybrid, a hierarchical hybrid training framework for high-performance Federated Learning on a wide scale. Unlike the existing work that mainly focuses on the statistical challenge, FedHybrid establishes a hierarchical hybrid training framework that effectively utilizes the fragmented and unbalanced data located on the participating devices on a wide scale. Specifically, FedHybrid consists of the following two core components, a global coordinator deployed on the central server and a local coordinator deployed on each participating device. The global coordinator organizes the participating devices into different groups through jointly considering the system heterogeneity and unbalanced training data in order to accelerate the overall training progress while guaranteeing the model performance. Within each group, a novel device-to-device (D2D) sequential training procedure is coordinated by the local coordinator to effectively utilize the fragmented and unbalanced training data in order to intelligently update the local models. At the same time, we provide the theoretical analysis of FedHybrid and conduct extensive experiments to evaluate its effectiveness. The results show that FedHybrid effectively improves model accuracy up to 27% and accelerates the whole training process by 20% on average.
Efficient and Privacy-Preserving Subgraph Matching Queries in Graph Federation

Graph technology has been attracting interest due to its ability in modeling complex network and real-world relationships in various applications. Subgraph matching queries are useful tools that can be used to extract structural insights from graph dataset. As the accuracy of subgraph matching queries increases with graph size, it is natural to consider providing subgraph matching query services over a graph federation, which can form a larger graph by combining graphs from multiple data owners. However, the downside combining data is that it may provoke privacy concerns related to the graph datasets and user queries. Although many schemes have been proposed for privacy-preserving subgraph matching queries, they either cannot be extended to graph federation scenarios or do not consider query privacy. Aiming at this challenge, in this paper we construct an efficient and privacy-preserving subgraph matching query scheme in graph federation with two data owners. In the proposed scheme, the two data owners jointly compute the neighboring signatures of all vertices without disclosing their graph datasets to each other. Upon receiving a subgraph matching query, the data owners together respond with a subgraph which includes all subgraphs matching the pattern in the combined graph. Security analysis shows that our proposed scheme can well preserve data and query privacy. Extensive experiments further demonstrate that the scheme is efficient in terms of computation and communication.
Accelerated Gossip Protocol for Incentivizing Block Propagation

The security and performance of blockchain systems can be undermined by resource-constrained nodes that elect to conserve power and communication resources by withholding the broadcast of blocks. To incentivize block forwarding and propagation, we introduce an accelerated gossip protocol that leverages a tamper-proof ledger to record the block routing path. Our solution employs a novel construct referred to as the signature chain and implements reward distribution via integrating with mining, accompanied by a Sybil-resistance reward policy. Our simulations demonstrate that the proposed protocol can mitigate blockchain forking rates and enhance scalability through expedited block propagation in the presence of selfish nodes.
Privacy-Preserving Task Allocation and Decentralized Dispute Protocol in Mobile Crowdsourcing

Mobile crowdsourcing is an emerging network architecture that can outsource tasks to a group of people or devices. Most recent literature studied the location privacy of users during task allocation in mobile crowdsourcing. But in some cases, a dispute may occur when users have an argument about the payment of the task. To adjudicate the dispute, users need to provide their private information such as credentials to the third party. Furthermore, finding a trusted third party for mobile crowdsourcing is hard in real life, raising the opportunity for decentralized disputes. However, existing work cannot avoid information leakage during decentralized dispute adjudication, and these schemes can also lower the efficiency of arbitrating. In this paper, we propose a privacy-preserving and decentralized dispute arbitration protocol, which allows the dispute adjudication to proceed without a trusted third party but prevents users' private information from disclosing. Specifically, we first propose a secure task allocation protocol for mobile crowdsourcing, which preserves users' location privacy while enabling efficient task release and allocation. Then, in order to avoid employing a trusted third party, we design a privacy-preserving and decentralized dispute arbitration protocol, which does not reveal any private information during the dispute adjudication. Security and privacy discussions show our protocol can resist forgery attacks but preserve privacy during the decentralized dispute adjudication. In addition, performance evaluation validates the efficiency of our protocol.
A New Efficient and Privacy-Preserving Hybrid Classification model for Patient-Centric Clinical Decision Support System

A lot of focus has recently been placed on clinical decision support systems, that use advanced data mining methods to assist clinicians in making wise decisions. Along with increasing diagnosis accuracy, clinical decision support systems (CDSS) have the added benefit of speeding up diagnosis. Data security is crucial in this system. In this research, we offer EPPCD (Efficient and Privacy preserving Patient-centric Clinical Decision) support system to assist physicians in predicting illness risks of patients in a privacy-preserving manner. This system is proposed to solve the privacy difficulties present in the CDSS. The fine-grained access control enabled by the novel Double Encryption Ciphertext Policy Attribute-Based Encryption (DE-CPABE) technique is found to be a potential solution to this issue. In the proposed system, the past patients’ historical data are stored in cloud and can be used to train the hybrid Rotation Forest and AdaBoost classifier. Furthermore, extensive simulations used to evaluate performance show that our technology is capable of quickly and accurately determining a patient's disease risk while maintaining their privacy. The proposed system model is divided into five parties: Cloud Platform (CP), Trusted authority (TA), processing unit (PU), data provider (DP), and undiagnosed patient (PA).
The method of grouping and classifying music curriculum teaching resources in the context of double reduction

Abstract A subsumption classification method is proposed to improve the classification accuracy of teaching resources in the music curriculum through a double reduction policy. Subject words are selected from high-similarity and high-frequency word sets, and a subject word tree is constructed using an automatic tree construction method based on a probabilistic latent semantic analysis algorithm. To complete the subsumption classification of teaching resources, an improved multi-graph kernel convolutional network is employed to group tree leaf nodes. According to the classification evaluation results, the recall, accuracy, and F1 values are 90%, 96.48%, and 88.81%, respectively, and the macro F1 value is as high as 81.59%. It can be seen that the method can effectively classify the teaching resources of music courses with the best effect of subsumption classification, which helps to improve the appropriateness and adequacy of teaching resources utilization.
Augmented Reality Technology in Early Schools: A Literature Review

In recent years, there has been a notable rise in the utilization of augmented reality within educational settings. However, there is still room for improvement in early childhood education. This study aims to investigate how current augmented reality works in early childhood education between 2017 and 2022 using library research methodology. The study's content was analyzed based on the case studies on integrating augmented reality in early school education between 2017 and 2022. 26 studies were found when the keyword "Augmented Reality in early school" was used to start the study (in primary school). With a sample size of fewer than 21 kids, early literacy discovered that Science (Animals, Newton) was the most frequently used subject. Regarding data collection techniques, testing has emerged as the dominant technique in research of "marker-based" augmented reality on mobile devices. These findings help to clarify the best ways to use this technology in early learning settings. Over the past five years, the number of schools using augmented reality in early childhood education has steadily increased.
Adding Edge Local Differential Privacy to the Dynamic Stochastic Block Model

In today’s networked systems a massive amount of data is produced every day. These data can be modelled using graphs, where the nodes typically correspond to users or devices and the edges to the connections between them. Almost all networks change over time, with new nodes and edges appearing or disappearing as the system matures. Therefore dynamic graph models are more adequate to analyse such networks than static graphs, and appropriate tools need to be implemented to protect them. In this paper we obtain an edge differentially private version of dynamic stochastic block model. We show experimentally that the trends in the dynamic stochastic block model obtained from the original data are well preserved with the additional privacy guarantees.
Peningkatan Kemampuan Siswa-Siswi SMK Negeri 1 Minas Dalam Speech Contest

This article is entitiled, “The Improvement of Student’s Ability in Speech Delivery Contest. The research was aimed at improving the ability of the students of State Vocational School I of Minas in delivering speech. After doing the treatment it was found that students were knowledgeable in identifying indicators of .speech contest and in doing exposures towards the factors of speaking in giving a speech, such as appearance/ performance, intonation, fluency, organization including time management/ accuracy, pronunciation and articulation. The result, therefore, in terms of the speaking abilitiy of the students in delivering a speech was gradualy increasing.
Content and Methodological Features of Preparing Bachelor Students for the Activities of a Class Teacher

The relevance of training bachelor students to implement the goals, objectives and functions of the class teacher is associated with modern trends and government requests in the field of ensuring the implementation of educational activities in educational organizations. The purpose of the article is to characterize the content and methodological features of preparing bachelor students at a pedagogical university to implement the activities of a class teacher. Methodology. The study was conducted using an activity-based approach, which made it possible to determine the educational and socializing role of students’ participation in the extracurricular activities of the university and its life, and to consider them as a factor in the formation of professional competencies; ideas of a multi-subjective approach that help determine the potential of relations between subjects of educational activity in the development of the subjectivity of bachelor students and their personal and professional qualities. The research methods are the analysis of scientific literature and normative documentation on the topic, which helps determine the actual content and elements of preparation (curricular and extracurricular activities, university life, internship) of bachelor students for the activities of a class teacher; a method of specification that allows us to characterize the versatility of the content of bachelor’s student training. Research results. An important task in preparing students to perform the functions of a class teacher is the formation of the subjectivity of bachelor students in the course of involving them in educational and extracurricular activities at a pedagogical university, involving them in the life of the university. The article presents the features of the organization of extracurricular activities at the Novosibirsk State Pedagogical University using the example of such areas as: student associations, university-wide and departmental events, artistic and scientific activities. The substantive aspects of preparing students for the activities of a class teacher when they master the discipline “Technology and Organization of Educational Practices (Classroom Management) and the practice program “Industrial Pedagogical Practice (Classroom Management)” are built in accordance with the requirements of federal state educational standards and an exemplary work program of education. In conclusion, it is concluded that when preparing bachelor students for the activities of a class teacher, it is important to provide a complex of elements, which include a variety of extracurricular activities and the life of the university; implementation of academic discipline and practice programs; approaches to the implementation of educational activities at a pedagogical university (event-activity, humanistic, systemic).
Velocity Analysis on Moving Objects Detection using Multi-Scale Histogram of Oriented Gradient

An autonomous car is a one-of-a-kind specimen in today's technology. It is an automatic system in which most of the duties that humans undertake in the car can be done automatically with minimum human supervision for road safety features. Moving automobile detections, on the other hand, are prone to more mistakes and can result in undesirable situations such as minor car wrecks. Moving vehicle identification is now done using high-speed cameras or LiDAR, for example, whereas self-driving cars are produced with deep learning, which requires much larger datasets. As a result, there may be greater space for improvement in the moving vehicle detection model. This research intends to create another moving car recognition model that uses multi-scale feature-based detection to improve the model's accuracy while also determining the maximum speed at which the model can detect moving objects. The recommended methodology was to create a lab-scale model that can be used as a guide for video and image capture on the lab-scale model, as well as the speed of the toy vehicles captured from the Arduino Uno machine before testing the car recognition model. According to the data, Multi-Scale Histogram of Oriented Gradient can recognize more objects than Histogram of Oriented Gradient with higher object identification accuracies and precision.
Research on College English Expansion Classroom Based on Mobile Learning in the Era of New Media

With the rapid development and popularity of intelligent and mobile devices, the new media era has entered an unprecedented climax, and various products have emerged, such as networked media platforms, digital television, digital newspapers and magazines, etc.The education model has also undergone great changes and breakthroughs.Traditional teaching philosophies, methods and resources are relatively homogeneous, with some teachers focusing on the instrumental aspects of English to the detriment of its humanistic aspects, thus limiting the enhancement of students' English literacy.In addition, the atmosphere of English learning and students' motivation also greatly affects the efficiency of English learning.This article uses mobile learning as a method to explore its effects on the extended English classroom at university, with the aim of better helping university English teachers to build an effective extended English learning classroom.Using some common English learning indicators, the aim of the article is to investigate and analyse the effectiveness of mobile learning as a way for students to develop their English language skills.The results of the experiment showed that students' ability to learn English independently at university increased after using mobile learning, with a 1.89% increase for English majors and a 5% increase for non-English majors who chose 'often learn independently'.In addition, the proportion of students who were not sure whether they would study independently dropped by 1.11% and 0.22% respectively, indicating that students have become more clear about their goals and orientation after mobile learning.All in all, mobile learning based on new media platforms has a beneficial effect on the expansion of English classes in universities.
Application of Multimedia Information Processing in English Flipped Classroom Teaching in the Age of Internet of Things

Since the implementation of the new curriculum reform, the content of English teaching has paid more attention to cultivating students' interest in learning and thinking ability, the flipped classroom has developed along with the trend of the times.Although flipped classroom gives students the initiative in the classroom, the students are generally not motivated and dare not ask questions, and the effect is not ideal.In the era of Internet of Things, multimedia assisted English classroom teaching has become a trend.Multimedia information processing technology screens, processes and displays information, stimulates students from multiple senses, and optimizes the English flipped classroom teaching process.This document has mainly studied the application of multimedia information processing technology in the English flipped classroom, and explored the teaching effect of the English flipped classroom after the introduction of multimedia technology.In this paper, multimedia information processing technology has been studied from three aspects: video technology, audio and image technology.The instantaneous frequency of the signal is calculated by the Hilbert transform, and the probability density is compared by the Gaussian mixture model, so as to improve the multimedia information processing process.Through the request test of multimedia information processing technology in English flipped classroom, the results are obtained: the learning efficiency of English flipped classroom using multimedia information processing technology has increased by 7.19%.Student academic performance has also improved.In multimedia classrooms, students are more willing to actively interact with teachers, stimulate their learning interest and have stronger experience.English teaching based on Internet of Things and multimedia information processing technology can promote the modernization of teaching methods and the diversification of teaching content.It is very beneficial to cultivate compound talents.
Consonants voicing in Surgut Khanty based on electroglottography data

This paper presents an experimental phonetic study of consonant voicing in Surgut Khanty conducted with one na-tive speaker of the Trom-Agan sub-dialect using a Rose Medical EGG-D200 laryngograph and icSpeech software. The vocal cord activity was assessed non-invasively with sensors on the speaker’s throat. The recordings were seg-mented and annotated via Praat software and then statistically processed with the Emu-SDMS corpus manager and R programming language. A voicing rate was calculated as the relative duration of the sound segment defining the fundamental frequency. Over 770 sound segments were processed. The obstruent consonants /p/, /t/, /c͡ç/, /k/, /s/, /ɬ/, and /ʎ̥/ were pronounced voicelessly, partially voiced in intervocalic position but never having sonants’ and vowels’ typical values. The obstruent consonants had the voicing coefficients of 0–0,44, with the mean values for different phonemes of 0,02–0,06, while vowels feature the voicing coefficients of 0,44–0,89 and the mean values of 0,53–0,82. The sonor consonants /m/, /n/, /r/, /j/, /ɣ/, /ɲ/, /ŋ/, and /w/ were pronounced as voiced in the initial and medial positions. In the final position, the sonor consonants were realized as devoiced or voiceless depending on the syntagmatic conditions, with the voicing coefficients of 0–0,83 and average values of 0,4–0,63. The paper provides oscillograms and glottograms for the sounds investigated. A control evaluation was conducted using the acoustic data. The comparison of the consonant voicing data from the audio recording and glottography revealed only the latter method to accurately detect a boundary of the voiced segment.
The task of digital transformation of the scientific information environment

The article discusses the issues of building interoperable scientific and informational structures based on the use of modern information technologies. Tasks related to the creation of theoretical and methodological foundations for the development of the Ukrainian fragment of the global digital interoperable environment of scientific knowledge are defined. The importance of permanent and unambiguous identification of digital resources to ensure their availability and reusability is noted. A synergistic approach to building an integrated digital environment of publishing houses and libraries is proposed. Attention is paid to the need to create a cooperative cataloging center in Ukraine to ensure one-time entry of scientific data and their subsequent multiple and multifaceted use by all interested institutions. The need for a fundamental solution to the problem of functional interaction of the conglomerate of scientific and information structures is emphasized, which will require the development of a new generation of information systems in which the problem of interoperability will be considered one of the main ones. Ukraine’s successes in creating electronic repositories are noted.
A Logical Description of Priority Separable Games

When we reason about strategic games, implicitly we need to reason about arbitrary strategy profiles and how players can improve from each profile. This structure is exponential in the number of players. Hence it is natural to look for subclasses of succinct games for which we can reason directly by interpreting formulas on the (succinct) game description rather than on the associated improvement structure. Priority separable games are one of such subclasses: payoffs are specified for pairwise interactions, and from these, payoffs are computed for strategy profiles. We show that equilibria in such games can be described in Monadic Least Fixed Point Logic (MLFP). We then extend the description to games over arbitrarily many players, but using the monadic least fixed point extension of existential second order logic.