Reviewerly/exHarmony huggingface dataset:
Relationship Between Laptop Use Behavior and Health in Midwifery Students at the University of West Sumatra

The use of laptops is generally to meet the needs of wireless communication and internet access. The use of laptops has increased very high in the world of education, both among school and college students. The majority of today's school children always bring laptops to school because of the demands of an increasingly sophisticated era. In the world of education, Australia is a pioneer country for the use of laptops in the school environment, laptops are in great demand because they can be used anywhere and also the use of laptops has a positive impact on the learning and teaching process. The data collection method was carried out by interviews using a research instrument in the form of a questionnaire. The results of this study showed that there was no significant relationship between laptop size and health complaints that were felt due to laptop use (Pvalue = 0.961), there was no significant relationship between the duration of laptop use and health complaints that were felt due to laptop use (Pvalue = 0.782), no there is a significant relationship between the frequency of laptop use and health complaints that are felt due to laptop use (Pvalue = 0.065), there is a significant relationship between body position when using a laptop and health complaints that are felt due to laptop use (Pvalue = 0.002).
Retracted: Martial Arts Competitive Decision-Making Algorithm Based on Improved BP Neural Network

[This retracts the article DOI: 10.1155/2021/9920186.].
Retracted: Research on Early Warning Mechanism and Model of Liver Cancer Rehabilitation Based on CS-SVM

[This retracts the article DOI: 10.1155/2021/6658776.].
Branch-Assisted Sign-Flipping Belief Propagation Decoding for Topological Quantum Codes Based on Hypergraph Product Structure

Topological codes, a kind of quantum error correction code, have been used for current quantum computers due to their local qubit layout and high threshold. With nearly linear complexity, syndrome-based belief propagation (BP) can be considered as a decoding candidate for topological codes. However, such highly degenerate codes will lead to multiple low-weight errors where the syndrome is identical so that the BP decoding is not able to distinguish it, resulting in degradation in performance. In this article, we propose a branch-assisted sign-flipping belief propagation (BSFBP) decoding method for topological codes based on the hypergraph product structure. In our algorithm, we introduce the criteria to enter the new decoding path branched from BP combined with a syndrome residual, which is obtained from the syndrome-pruning process. A sign-flipping process is also conducted to disturb the log-likelihood ratio of the selected variable nodes, which provides diversity in the syndrome residual. Simulation results show that using the proposed BSFBP decoding is able to outperform the BP decoding by about two orders of magnitude.
Retracted: Drone Assisted Robust Emergency Service Management for Elderly Chronic Disease

[This retracts the article DOI: 10.1155/2021/5552350.].
Anti-tobacco text warnings in Italy: Geography, language and South Tyrol

Conflicts of interest: None.
Hubungan Penggunaan Multimedia dalam Pembelajaran terhadap Peningkatan Hasil Belajar Peserta Didik

This study discusses the impact of using multimedia in learning on improving students' learning outcomes. The research used a literature review method by examining various related studies that have been conducted. The analysis results showed that the use of multimedia can increase motivation, interest, and learning outcomes of students in learning. However, the effectiveness of multimedia use in learning depends on various factors such as multimedia design and content, teaching methods, student characteristics, and available learning environments. Therefore, the use of multimedia in learning requires a holistic and integrated approach. Continuous research and development are needed to improve multimedia design and use as well as to consider the influencing factors.
Emotion Recognition in Tweets Using Optimized SVM and KNN Classifiers

Social media is reflected as a rich source of information because it is widely used by people to share their emotions, views, and opinions. Therefore, this information should be mined qualitatively for the purpose of Emotion Recognition (ER). Various other applications associated with ER are public mood detection, identifying psychological (depression/anxiety) disorders, online product sales prediction, etc. This work exploits optimized SVM (Support Vector Machine) and K-Nearest Neighbor (KNN) classifiers for recognition of emotions in textual Twitter messages. The multiple feature extraction model combined with optimal Binary Grasshopper Optimization Algorithm (BGOA)-based feature selection (FS) enhances the computation process. The meta-heuristic algorithm-based FS improves the classification accuracy with minimal computation time and speed up the classifier for recognizing the multiclass emotions. The performance measures are done at various training rates. The simulations depict the superiority of KNN and SVM classifiers over traditional Naive Bayes approach.
Correction: Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management

Correction for ‘Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management’ by Xusheng Fang et al. , Environ. Sci.: Water Res. Technol. , 2023, 9 , 890–899, https://doi.org/10.1039/D2EW00560C.
Improved duelling deep Q-networks based path planning for intelligent agents

The natural deep Q-network (DQN) usually requires a long training time because the data usage efficiency is relatively low due to uniform sampling. Importance sampling (IS) can promote important experiences and is more efficient in the neural network training process. In this paper, an efficient learning mechanism using the IS technique is incorporated into duelling DQN algorithm, and is further applied to path planning task for an agent. Different from the traditional DQN algorithm, proposed algorithm improves the sampling efficiency. In this experiment, four target points on the map are deployed to evaluate the loss and the accumulated reward. Simulations and comparisons in various simulation situations demonstrate the effectiveness and superiority of the proposed path planning scheme for an intelligent agent.
Identification of Lip Printing

The lip crease pattern is on the vermilion border of the lip, which is quite mobile and Lip Prints may vary in appearance according to the pressure, direction and method used in making the print. Although Lip Print identification may appear in the field literature there is very little science or research to support the theory that Lip Prints are individual, or to support a methodology, for the collection and comparison of Lip Prints, which has become accepted within the forensic community. The pattern of wrinkles on the lips has individual characteristics as fingerprints. The wrinkles and grooves on the labial mucosa (called sulci labiorum) form a characteristic pattern called lip prints, the study of which is referred to as Cheiloscopy. It can be defined “as a method of identification of a person based on characteristic arrangements of lines appearing on the red part of lips or as a science dealing with lines appearing on red part of the lips”. The present article is reviewed in an intention of providing various methods of recording and collection of the Lip prints at the crime scene with an emphasis on development of the latent lip Prints.
Uncertainty estimation for gradient boosting models based on Gaussian graph model and natural gradient

We study the multi-output probability regression prediction and uncertainty quantification problem in multi-output regression prediction with gradient boosting models, and propose an uncertainty estimation method based on Gaussian Graph Model (GMM) constraint and natural gradient. Under the assumption of Gaussian distribution, we employ GMM to model the conditional independence relationship of the outputs, and propose a mixed parameterization to represent the subfamily of Gaussian distributions defined by the graph model. We set this mixed parameters as the learning target of the gradient boosting model, and use natural gradient in Riemann space that based on Fisher information matrix (FIM) instead of the vanilla gradient to be the steepest descent direction. The formula to compute FIM and natural gradient for the subfamily defined by GMM is deduced. Experiments show that the proposed method is able to incorporate the priors on the outputs to optimize the search space, therefore, lead to better uncertainty estimation results on multi-output regression for gradient boosting models.
Adaptive federated learning aggregation strategies based on mobile edge computing

Federated learning is a machine learning paradigm that can protect data privacy, but the high communication cost and the arithmetic limitation of clients become one of the bottlenecks of federated learning. To address this problem, an adaptive federated learning aggregation strategy based on mobile edge computing called A-FedAvg is proposed, which first evaluates the accuracy gain metrics and cost metrics from each round of global iteration in federated learning, and then combines them into an adaptive metric by weighting parameters to indicate the number of local iterations needed for the next round of global iteration. The A-FedAvg algorithm is designed and then experimented on the MNIST and EMNIST datasets for IID and non-IID data distributions, respectively. The results show that the A-FedAvg algorithm can reduce the number of global communications compared to the FedAvg algorithm with guaranteed accuracy and without affecting the normal use of the mobile device client.
Research on message gateway based on heterogeneous system

With the rapid growth of computer communication technologies such as "Cloud Compute", "Big Data", "IOT", "Mobile Internet" and "AI", the different types of the message middleware have been widely used in multiple heterogeneous systems. In order to solve the message communication requirements between heterogeneous systems of "Dispatching and Control Systems", this paper proposes the message gateway technology of heterogeneous systems and completes the presidential architecture design of this technology. To achieve the reliable and stable message interaction between heterogeneous systems, some technologies such as message subscription and publishing technology, message persistence and cache technology, elastic expansion technology of message gateway, message communication fault location technology are proposed. These technologies significantly improve the ability of various types of data interaction between heterogeneous systems.
Federated learning on non-independent and identically distributed data

Federated Average algorithm (FEDAVG) is the preferred algorithm for federated learning (FL) because of its simplicity and low communication cost However, if all clients’s local data aren’t independent and equally distributed (that is, nonindependent and identically distributed), FedAvg will have the phenomenon of customer drift, which will lead to the slow convergence speed of the model, and then the efficient cooperative learning to realize the cooperative training of multiple clients will face the challenge of data heterogeneity and vulnerability to attack. This paper systematically summarizes three aspects: local model training improvement, server-side aggregation optimization, and personalized federated learning. The local model can be improved by adjusting the loss function and control variables. Servers can be optimized through asynchronous aggregation, hierarchical aggregation. Personalized federated learning can improve global model performance through both data-based and model-based approaches. This paper puts forward the future research direction of federated learning from all above aspects, and provides reference for the further research of non-IID in federated learning, so as to provide investigation and help for researchers in related fields.
FORMATION OF AN IDEA OF THE DISCIPLINE «PHYSICAL EDUCATION» IN PRIMARY SCHOOL STUDENTS

In the article, the question of the peculiarities of the formation of the ideas of primary students in the discipline «Physical Education» is revealed. The question of the exceptions of students' motor activity under the conditions of online education caused by the COVID-19 pandemic and a full-scale war with Russia is also outlined. Emphasis is placed on the peculiarities of motor activity of primary students under complete digitization of education. It was established that since the educational activity for students is the leading one, under the conditions of distance learning, students, teachers, and professors, parents should remember the importance of forming the correct posture and physical qualities of children, basic motor skills, and improving health in general in the process of Physical Education, observing sanitary – hygienic requirements for using a computer, gadget. The need for motor activity of students is high and determined by the age-specific features of ontogenesis; however, modern society and remote learning purposefully lead to a reduction in children's movements during the day.
Audit IT Process Po-01 to Academic Information Systemin Higher Education Polytechnic using the Cobit Framework

The role of information technology in higher education is to realize academic effectiveness. Academic Information System (SIAK) which is loaded with the use of Information Technology (IT). The PO1 IT Process Governance audit based on the COBIT standard, namely “Define a Strategic Information Technology Plan” then became a strategic issue for the success of higher education management, especially the SIAK Polytechnic campus which uses information technology-based tools in its teaching and learning process. This study aims to measure the level of IT Process PO-01 governance in relation to Academic Information Systems in Vocational Model High Schools (Polytechnics). The sample of this research is nine Vocational High Schools (Polytechnics) in Bandung. From the results of the study, it is known that the IT Process Governance PO-01 at SIAk Vocational Colleges is still at Level 2, namely "Repeatable but Intuitive". The effectiveness of governance is still determined by the competence of the individual or part of the personnel who carry out the IT Function (technical) and the assignment is ad-hoc. IT management at SIAk has not been internalized and refers to Best Practice. But on the other hand, it has begun to be seen that the Polytechnic management has a fairly good awareness of the importance of IT Governance and wants to make it an enabler for the Academic Information System (SIAk). However, there is still a fairly high gap between what is and what should be desired as in COBIT Best Practice.
Automated high-resolution aerial mapping with UAVs for seismic survey scouting

Summary With the growing trend of high-channel high trace density acquisition, the accuracy of the survey planning became critical for the best data quality and highest productivity. The satellite maps are a great source of information about ground conditions in the area of interest but often lack the required resolution and could be outdated for seismic survey planning. We propose using a small dedicated crew equipped with UAVs to obtain high-resolution imagery on-demand. This data can be processed, automatically labeled using Machine Learning, and delivered to the survey planning specialist within days.
EFFICIENCY OF EDUCATION, ESPECIALLY HIGHER ECONOMIC EDUCATION

Abel Aganbegyan analyzes quality and effectiveness of higher economic education in Russia, as well as Russia’s place in terms of education among other countries of the world and the share of the “knowledge economy” in the gross product of the country. The scientific and educational complex of Russia as a whole is not inferior to many developed countries, nevertheless, there is a need to reform education, including increasing its practical orientation, the availability of professional requirements and degrees necessary to occupy important positions, including in the civil service. This will increase its efficiency, move to socio-economic growth.
A Fractal Dimension-Gutenberg Richer Analysis for the Decatur Open-Source Seismicity Catalogue

Summary The study of the microseismic activity produced at CO2 storage sites constitutes a very important approach which helps to understand the change in the physical properties of the subsurface in response to the CO2 injection. In this study, we present the calculation of the fractal capacity dimension, the Gutenberg-Richter parameter and their interaction for the Decatur, Illinois, open-source seismicity catalogue. This study was performed in order to obtain new information concerning to the geomechanical behaviour of the fracture network and its relationship with the natural environment. The fractal dimension by itself allows to describe the geometrical development of the fracture network and at the same time, it gives new information regarding the development of long-term 2D seismogenic structures. At the same time, the b-value, even though before studied, can be additionally used in terms of fractality, helping to establish distinctions between the tectonic and the induced seismicity.
The development and validation literacy scale for pre-service mathematics teachers

The 21st century is an era in which education determines the future of society. The relevant theoretical research on core literacy is mainly carried out around students, and the research on the core literacy of pre-service mathematics teachers in ethnic areas has not yet begun. By consulting literature and existing mature questionnaires, combined with pre-service mathematics teachers' characteristics in ethnic areas, a "core literacy scale of pre-service mathematics teachers in ethnic areas" was compiled. Taking 412 pre-service mathematics teachers in Guangxi, China as the research object, using SPSS22.0 and AMOS24.0 to carry out project analysis, exploratory factor analysis, and confirmatory factor analysis on the data, the scale was revised accordingly, and the formal scale contains knowledge There are 3 dimensions of, ability, character, and the corresponding 9 factors. The scale compiled has good reliability and validity. It can be used as an effective tool to measure the core literacy of pre-service mathematics teachers in ethnic areas, and it also provides a basis for in-depth research on the current situation and strategies of training the core literacy of pre-service mathematics teachers in ethnic areas.
Framework For Load Balancing Using Multi Resource Scheduling With Minimum Time Consumption

The scheduling of appropriate resources for cloud workloads is a difficult task, as it depends on the quality of service needs of cloud applications. Due to their limited data storage and energy capabilities, loT applications demand high-speed data transfer and low latency. Many loT devices generate data continuously and want to store it on the cloud quickly and efficiently. Dynamic virtual machine (VM) allocation in cloud data centers (DCs) is taking advantage of the cloud computing paradigm. Each VM request is characterized by four parameters: CPU, RAM, disk, and bandwidth. Allocators are designed to accept as many VM requests as possible, considering the power consumption of the loT device's network. Resource scheduling and time consumption are the two most significant problems in cloud computing. To overcome this problem, in this paper, the author has extended CloudSim with a multi-resource scheduling and minimum time consumption model that allows a more accurate valuation of time consumption in dynamic multi-resource scheduling. The author proposes a new load distribution framework, TTS, which provides a better solution to other scheduling algorithms and also tries to reduce energy consumption and time to give a task to the VM.
Facial Image-based Age and Gender Classification to Enhance Recommendations in a Smart TV Environment

Advances in Machine Learning and Deep Learning have made it possible to do a myriad of tasks that were either too complex to hardcode or even deemed impossible. We have taken leaps in security, medicine, linguistics and accessibility with their help. Day by day, the number of appliances using these mechanics is increasing, with new vehicles to smart homes implementing these changes. This revolutionary technology can be utilized to detect and predict one’s gender and approximate age through the lens of a camera either in the form of a photo or through live video. This information is vital in privacy and security aspects. However, it can also be applied to the overlooked emergent device, the smart television. We want to stress the importance of the data of age and gender in today’s television climate and how it can be implemented to benefit our day-to-day life hence why we decided to use such technology to aid in a common but often overlooked task. There are on average 850 channels that can be browsed in our very own television and therefore it can be a hassle to find the channels we would like to watch. We wish to tackle this problem by using an estimated combination of deep learning and a classification-based machine learning model which will be integrated with an IoT device (microcontroller) all of which is linked by a data pipeline. By training our multi-component model on a dataset on facial images along with a custom dataset regarding the preferences of channels in different age and gender groups, we can help solve such a task.
Dual-Leak: Deep Unsupervised Active Learning for Cross-Device Profiled Side-Channel Leakage Analysis

Deep Learning (DL)-based side-channel analysis (SCA), as a new branch of SCA attacks, poses a significant privacy and security threat to implementations of cryptographic algorithms. Despite their impacts on hardware security, existing DL-based SCA attacks have not fully leveraged the potential of DL algorithms. Therefore, previously proposed DL-based SCA attacks may not show the real capability to extract sensitive information from target designs. In this paper, we propose a novel cross-device SCA method, named Dual-Leak, that applies Deep Unsupervised Active Learning to create a DL model for breaking cryptographic implementations, even with countermeasures deployed. The experimental results on both the local dataset and publicly available dataset show that our Dual-Leak attack significantly outperforms state-of-the-art works while no labeled traces are required from victim devices (i.e., unsupervised learning). Countermeasures are also discussed to assure hardware security against new attacks.