Reviewerly/exHarmony huggingface dataset:
The Application of Multi-integration Teaching Mode Based on Cloud Computing in Computer Professional Teaching

With the development of Internet + and cloud wisdom, the teaching mode of universities has changed greatly. Among them, online and offline hybrid teaching method is widely used in college teaching. Through analyzing the current teaching methods, the application of multi-integration teaching mode based on cloud computing in Computer Professional Teaching is put forward. Under the condition of existing teaching, using cloud computing technology, combining with the characteristics of cloud service platform, and constructing a learning environment. From the three main links of project driven theoretical teaching, cascading experimental teaching, and independent content expansion teaching, a cloud computing diversity auxiliary teaching platform of “Principles of Computer Organization” is built, to improve students’ interest in learning initiative, and to cultivate students’ logical analysis and reasoning ability. Through analysis and verification, this teaching mode integrates the concept and advantages of MOOC, effectively changes the traditional teaching mode of pure classroom teaching and the phenomenon of students’ cramming in listening, makes up for the shortcomings of the online and offline hybrid teaching, and improves the training mode in the computer professional education system, and achieves efficient teaching effect and teaching quality.
Using Smartphone Sensing for Recognition of Game Player Attributes During Gameplay

With the recent advances in smart sensing technologies, many people are now using smartphones to do routine tasks. In particular, the use of smartphones for gaming and infotainment purposes has increased significantly, as a result of which the gaming industry is expanding globally. Smartphones can now sense the interactions of a user with the device based on the embedded sensors. These human-smartphone interactions can be recognized using machine learning approaches to sense and automate/control different tasks being performed on the device. In particular, smartphone-based games have a lot of advantages, where the game controls can be seamlessly adjusted automatically based on the game player’s interaction to achieve a better gaming experience. In this regard, we propose a smartphone sensor-based method to recognize the attributes of a user, i.e., game player, during gameplay. The proposed scheme is based on the idea that different game players have their own ways of interacting with the device when playing games. The smartphone inertial sensors can be used to track these interactions and recognize different attributes (such as expertise level, gender, and identity) of a game player. This information can further be used in the games for adaptive control to maximize the user’s gaming experience. The proposed scheme is validated based on different experiments, and the overall average accuracy of 71.3% is achieved in the best case; thus, satisfactory results are achieved.
Comparative Analysis of Augmented Reality Software Development Kits for the Development of Educational Applications Within Unity Game Engine

In order to increase the use of educational applications and their development, many interfaces are often used for the development of applications that facilitate and simplify the process of building educational applications for Augmented Reality. The issue appears when, due to a large number of interfaces and an insufficient number of comparisons regarding their possibilities, the user cannot evaluate which interface would be the most suitable for building the application in a particular education sphere. The purpose of this research is to prove which interface is the most suitable for building educational Augmented Reality applications using their comparative analysis. The comparative analysis is based on three hypotheses, and the research process is conducted by building simple applications that use four interfaces for the development of applications. Several hypotheses were tested regarding AR SDKs for the development of AR educational applications and the results derived from practical performance and statistics are indicating some valid conclusions.
Secure Authentication and Data Transmission for Patients Healthcare Data in Internet of Medical Things

Currently, data transmission is an expanding area in healthcare, enabling health practitioners to examine, assess, and manage patients using mobile communication technologies. To identify and analyze a patient, healthcare providers need to access the physician's Electronic Medical Record (EMR), which may contain extensive audiovisual big data such as MRIs, CT scans, PET scans, X-rays, and more. To ensure accessibility and scalability for healthcare workers and consumers, the EMR needs to be stored in large data repositories on cloud servers. However, due to the sensitive nature of medical information stored in the cloud, the healthcare profession faces numerous security challenges, with data theft attacks being one of the most critical vulnerabilities. This research focuses on protecting medically sensitive data in the cloud by leveraging cloud computing facilities. The upgraded AES approach ensures that confidential data is securely accessible and stored. In addition, improved Elliptic Curve Cryptography (ECC) is utilized for key generation and validation. A hybrid optimization approach, combining robust optimization and genetic algorithms, is employed to select unique and distinct keys. Decryption is performed using deep neural networks, and Convolutional Neural Networks (CNN) enable batch encryption of multiple documents. The comparison between old methods and the proposed approach is based on encryption time, decryption time, and security strength.
An Algorithm for Constructing Support of Bent Functions by Extending a Set

Boolean functions form the fundamental components of symmetric cryptographic systems, serving as the building blocks. Modifying bent functions makes it feasible to design Boolean functions with desired properties that exhibit high non-linearity. The current study offers a comprehensive analysis of bent functions through its support, culminating in the introduction of an algorithm for the systematic construction of four variable bent functions. This algorithm enables the complete generation of all 896 four-variable bent functions. Furthermore, we introduce a methodology for constructing n-variable bent functions (where n &gt; 4), leveraging both the algorithm and an established secondary technique for bent function construction. Lastly, we examine the estimation of the count of bent functions by utilising certain properties associated with the support of bent functions.
BALANCING SERVICE PROVIDER AND END-USER REQUEST INTEREST IN AN SDN-ORIENTED DATA CENTRE NETWORK

The problem of network flow interference within a data centre network has been addressed by several scholars in the literature with a number of solutions provided.However, many of such solutions does not take into cognisance the relative cost effect of the proposals on the service providers and end-users.The solutions are either on the benefit of the service providers at the expense of the end-users and vice versa.We proposed a Multi-Criteria Optimization Crosspoint Queue which was able to address the traffic flow interference that results into both network instability and unbalanced provider-user relationship.The experimental results showed that the proposed approach is able to maintain a stable specified QoS metric as a single-parameter amidst several QoS and likewise balanced the cost effects on either side of the stakeholders.The solution is relevant to the network organization in prioritizing network quality to adapt to changing business requirements and market demands.
Analisis Bibliometrik terhadap Guru Matematika Indonesia Berbasis VOS Viewer

This study aims to determine the research trends of Indonesian Mathematics Teachers using bibliometric analysis methods. The data in this study were obtained through Google Scholar from 2013-2023 with the keywords Indonesian math teachers. Researchers analyzed 1000 original articles from 2013-2023. Researchers utilize the use of Publish or Perish software and VOS Viewer to analyze data
OVERVIEW AND DEVELOPMENT IN THE PROFESSIONAL INTERESTS OF GRADUATE STUDENTS

In this article, we will analyze the needs, interests and realization of the students of Faculty of mathematics and computer science of Konstantin Preslavsky University of Shumen, preparing to become teachers of mathematics, informatics, computer modeling and information technologies. The group we are considering are the students of Bachelor’s and Master’s degree. The data for students who choose to graduate with thesis defense are being studied. The aim is to track the trends and the reflection of current methodological directions in the interests of students. A classification is proposed in the main areas in which students choose to work. A content analysis of the protected developments in the period 2014 – 2022 was made. The realization of the students was tracked. Ideas that are to be realized in diploma thesis in the future are marked.
Optimization of Deep CNN-based Bangla Sign Language Recognition using XGBoost classifier

Deafness is one of the major health problems in Bangladesh, where almost 10% of the population falls under this category. The plight of deaf individuals to fit in with the mainstream population is significant. Unable to express themselves through sound, they have to rely on sign language to communicate.To create a more inclusive environment for this minority, we developed a model that recognizes Bengali characters and digits expressed using sign language in accordance with Bangla Sign Language (BdSL) guidelines. The model comprises two parts: a convolutional neural network that uses deep learning techniques, and an Extreme Gradient Boosting (XGBoost) classifier. Together, these components form the full architecture. The model’s execution was validated using the ‘Ishara-Lipi’ dataset, which is the first open-access digit and character dataset for BdSL. With the help of pre-processing techniques such as contrast-limited adaptive histogram equalization, using a more complex pre-trained CNN model like Inception-ResNet-v2, and optimizing the XGBoost model by using GridSearchCV, we achieved an accuracy of 86.67%, precision of 89%, recall and f1 score of 87%. Lastly, for digit recognition, we obtained an accuracy of 97.33%, precision of 98%, and recall and f1-score of 97%.
Speaker Recognition Using 3D Convolutional Neural Network and GMM

Deep learning models are now regarded as state of the art in several fields of pattern recognition, including image identification and speech recognition. This paper implements a three-dimensional convolutional neural network-based model to identify a speaker with short audio signals from the available set of speakers enrolled during the model training. In order to improve the overall operation and performance of recognition, the proposed technique uses Log-Mel spectrograms and Mel-frequency cepstral coefficients (MFCCs) with their first and second differential features as input for the 3D CNN model. Furthermore, this work comprises both the design and implementation of the CNN model and employs the same prosody elements for a Gaussian mixture model (GMM) architecture. Before being retrieved for the final speaker recognition task, the audio features are pre-processed using a pre-emphasis filter for the spectral shape compensation. As compared to the relatively complex standard GMM designs, this is a significant finding in understanding how deep learning models can be applied to the problem of speaker recognition. The CNN model is trained and tested against a freely available and comprehensive VoxCeleb1 dataset.
Exploring the Automatic Selection of Aggregation Methods in Group Recommendation

A recommender system is a software tool designed to support users to filter out useless options within a multitude of choices and provide them with the best possible ones. Group recommender systems have emerged as an important trend in recommendation since they recommend social items that are enjoyed by more than one individual, such as TV programs and travel packages, that are typically consumed in groups. Although algorithm selection in recommender systems is a research problem covered to some extent by the research community in which individuals’ information is aggregated, this contribution is focused on the automatic selection of the most appropriate aggregation function in group recommendation. Specifically, a general framework that identifies group characteristics to be matched with the most appropriate aggregation function is presented. This approach is implemented by using a fuzzy decision tree classifier, in a content-based group recommendation approach. The development of an experimental protocol illustrates the advantage of the new proposal in relation to its corresponding baselines.
Práticas baseadas em Mindfulness (on-line) para profissionais de unidades de saúde do Estado de São Paulo no contexto da pandemia COVID-19

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ASSESSMENT OF THE STRESS LEVEL OF DENTISTS BASED ON THE CORTISOL CONTENT IN SALIVA

Aim. Based on the cortisol content in the oral fluid and the assessment of emotional stress, determine the stress level of dentists.&#x0D; Materials and methods. The group consisted of 25 healthy volunteers, dentists (8 men and 17 women aged 24 to 60 years) with various work experience. Participants filled in an adapted scale of the level of emotional stress before the start of the work shift. On the same day, oral fluid was collected to assess cortisol levels in the morning and evening.&#x0D; Results. There were no statistically significant differences in the level of emotional stress among dentists, depending on gender. The level of emotional stress in general increases with increasing work experience. With an experience of more than 20 years, emotional stress is significantly (p = 0.026) higher compared to colleagues who have been in the profession for less than 10 years, the indicator was 16.43 (±3.20) and 19.00 (±1.10) points, respectively. The level of evening cortisol in the oral fluid has a statistically significant relationship of noticeable strength with the level of emotional tension only in participants with a high level of tension (18–20 points).&#x0D; Conclusions. The level of emotional stress increases with increasing work experience as a dentist. To assess the level of stress, the level of evening cortisol is more indicative than in the morning, and the interpretation of the results should be carried out in conjunction with the assessment of emotional stress, as well as considering the somatic status of the subject.
Named Entity Recognition of Medical Examination Reports Based on BiLSTM+CRF Model

Medical examination reports are typical unstructured data written in natural language. Named entity recognition (NER) is used to extract key information from medical texts, which serves as the foundation for further analysis of entity relationships and extraction of diagnostic knowledge. Currently, using either deep learning models or earlier conditional random field (CRF) models alone has their respective drawbacks, such as heavy annotation workload, overfitting, and model generalization issues. Additionally, Chinese medical text data presents greater difficulties for NER tasks due to its specialized and non-standardized structure. To address these issues, this paper proposes an integrated model for extracting structured information from medical examination reports, namely the Bi-LSTM and CRF ensemble model (BLC). BLC identifies medical entities in the reports, with the BiLSTM model determining the probability of each label for individual characters and the CRF decoding ensuring the final sequence adheres to the output standards. Real gastrointestinal endoscopy reports provided by hospitals were used as experimental data for annotation, and the Bi-LSTM+CRF model was built using the TensorFlow framework for training the experimental data. The effects of different parameters on entity recognition were compared. The results showed that under the BIOES annotation scheme, the model's recognition performance was superior to the BIO annotation scheme. Good segmentation results for entity categories with well-segmented features led to better recognition performance.
FlyDB: Query Optimization of Blockchain System Based on Hybrid Storage Architecture

The hybrid storage architecture incorporates a blockchain dual-index structure optimized for byte addressing in non-volatile memory, low-latency features, and the large capacity of SSD. This paper presents a novel LSM-Tree architecture that leverages the byte-addressing characteristics of non-volatile memory. The layers of the level <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> and level <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> are stored in non-volatile memory while employing a local key-value separation strategy to minimize read and write operations during compaction. A logic compression strategy is proposed to handle data compression tasks efficiently. A dual-index structure design is introduced for users to address the scope and multi-keyword queries in the blockchain system. During the compression process, keyword switching occurs during data reordering, ensuring that data within the same SSTable is rearranged according to user preferences. By effectively harnessing the advantages of each level in the hybrid storage architecture, this design significantly reduces both databases write time and the response time for complex queries.
Audio Source Verification Method Based on Structural Re-parameterization Network

With the development of science and technology, the application of audio data in law enforcement and judicial fields is becoming increasingly widespread. Multimedia data such as audio recordings are prone to forgery, which brings great trouble to judicial fairness. When digital recordings are used as evidence, effective techniques are needed to ensure their reliability. For example, digital audio needs to verify its recording device. In this paper, we focus on the verification problem of audio source, i.e. determining whether a piece of audio recording comes from a given target device. We propose an audio source detection framework based on a structural re-parameterized network, and with a carefully designated loss function, the recognition accuracy is improved under the noise conditions. Experiments show the proposed method achieved a TPR of 99.89% and an FPR of 4.17%, which is superior to existing audio source detection methods.
Research on the Robustness of Degree-Fitness-Distance Evolutionary Networks Based on Response Surface Methodology

This paper introduces a Degree-Fitness-Distance (DFD) evolutionary network, utilizing Response Surface Methodology to investigate the impact of degree strength, fitness strength, distance strength, and their interactions on the robustness of the DFD network. The regression equation was determined, followed by a variance analysis of different factors affecting the target response. The results show that the influence of different factors on the size of the largest connected component and the overall efficiency of the network are in the following order: degree strength > fitness strength > distance strength. When degree strength is 1, fitness strength is 2, and distance strength is 3, the size of the largest connected component of the network and the overall efficiency reach their peak values, respectively at 56.57% and 10.15%. Multi-objective optimization was performed on the DFD network; when degree strength is 1, fitness strength is 1, and distance strength is 3, the predicted size of the largest connected component is 58.39%, and the overall efficiency is 10.37%. These figures deviate by approximately 5% from the actual values, which demonstrates that the predictive model possesses high accuracy and reliability.
Spike learning based Privacy Preservation of Internet of Medical Things in Metaverse

With the rising trend of digital technologies, such as augmented and virtual reality, Metaverse has gained a notable popularity. The applications that will eventually benefit from Metaverse is the telemedicine and e-health fields. However, the data and techniques used for realizing the medical side of Metaverse is vulnerable to data and class leakage attacks. Most of the existing studies focus on either of the problems through encryption techniques or addition of noise. In addition, the use of encryption techniques affects the overall performance of the medical services, which hinders its realization. In this regard, we propose Generative adversarial networks and spike learning based convolutional neural network (GASCNN) for medical images that is resilient to both the data and class leakage attacks. We first propose the GANs for generating synthetic medical images from residual networks feature maps. We then perform a transformation paradigm to convert ResNet to spike neural networks (SNN) and use spike learning technique to encrypt model weights by representing the spatial domain data into temporal axis, thus making it difficult to be reconstructed. We conduct extensive experiments on publicly available MRI dataset and show that the proposed work is resilient to various data and class leakage attacks in comparison to existing state-of-the-art works (1.75x increase in FID score) with the exception of slightly decreased performance (less than 3%) from its ResNet counterpart. while achieving 52x energy efficiency gain with respect to standard ResNet architecture.
Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks

The variational Bayesian method solves nonlinear estimation problems by iteratively computing the integral of the marginal density. Many researchers have demonstrated the fact its performance depends on the linear approximation in the computation of the variational density in the iteration and the degree of nonlinearity of the underlying scenario. In this paper, two methods for computing the variational density, namely, the natural gradient method and the simultaneous perturbation stochastic method, are used to implement a variational Bayesian Kalman filter for maneuvering target tracking using Doppler measurements. The latter are collected from a set of sensors subject to single-hop network constraints. We propose a distributed fusion variational Bayesian Kalman filter for a networked maneuvering target tracking scenario and both of the evidence lower bound and the posterior Cramér–Rao lower bound of the proposed methods are presented. The simulation results are compared with centralized fusion in terms of posterior Cramér–Rao lower bounds, root-mean-squared errors and the 3σ bound.