Reviewerly/exHarmony huggingface dataset:
Enhancing Personalized Book Recommender System

Recommender systems (Rs) are widely used to provide recommendations for a collection of items or products that may be of interest to user or a group of users. Because of its superior performance, Content-Based Filtering (CBF) is one of the approaches that are commonly utilized in real-world Rs using Time-Frequency and Inverse Document Frequency (TF-IDF) to calculate document similarities. However, it computes document similarity directly in the word-count space. We propose a user-based collaborative filtering (UBCF) method to solve the problem of limited in content analysis which leads to a low prediction rate for large vocabulary. In this study, we present an algorithm that utilises Euclidean distance similarity function, to solve the identified problem. The performance of the proposed scheme was evaluated against the benchmark scheme using different performance metrics. The proposed scheme was implemented and an experimentally tested by using the benchmark datasets (Amazon review datasets). The results revealed that, the proposed scheme achieved better performance than the existing recommender system in terms of Root Mean Square Error (RMSE) which reduces the errors by 29% and also increase the Precision and Recall by 51.4%, and 55.8% respectively in the 1 million datasets.
Comprehensive Analysis of Deep Learning Methods on Gender Identification Using Speech Signals

Human-Computer Interaction Systems have been becoming ubiquitous owing to ever-extending information generation. However, the rising numbers also breed credibility questions about these systems. As one of the fundamental information carried by speech signals, the gender identity of the person bears the potential to solve many authentication problems. In this study, we surveyed several Convolutional Neural Network architectures in the binary gender identification task. As our experimental results demonstrated, the VGG network showed superiority over the other model by achieving 86.01% weighted F1 in the CommonVoiceTR-9.0 Turkish dataset and 88.32% weighted F1 in the IEMOCAP English dataset. To the best of our knowledge, this is the first study surveying deep learning methods for the speech gender identification task in Turkish. Moreover, the capabilities of the models in language independency were questioned in this study.
Cost-Effective Spot Instances Provisioning Using Features of Cloud Markets

Cloud computing offers a variable-cost payment scheme that allows cloud customers to specify the price they are willing to pay for renting spot instances at much lower costs than fixed payment schemes, and depending on the varying demand from cloud customers, cloud platforms could revoke spot instances at any time. To alleviate the effect of spot instance revocations, applications often employ different fault-tolerance mechanisms to minimize or even eliminate the lost work for each spot instance revocation. However, these fault-tolerance mechanisms incur additional overhead related to application completion time and deployment cost. This article proposes a novel cloud market-based approach for provisioning spot instances using features of cloud markets to reduce the deployment cost and completion time of applications. The simulation results show that the approach reduces the deployment cost and completion time compared to approaches based on fault-tolerance mechanisms.
Research on the Design of Paper Cutting Patterns and Digital Preservation Strategy of Non-heritage Based on Deep Learning

With the continuous development of science, combining art and science has become a new means of conservation.This paper first analyzes the application of deep learning in the field of NRM conservation, and then implements paper cutting pattern design in three aspects: generating fractal patterns, network architecture design, and perceptual loss function.Finally, this paper proposes a strategy of digital conservation of paper cutting non-heritage based on deep learning by cultivating intelligent interpreters, constructing a paper cutting database, and reconstructing a paper cutting virtual field to develop ideas for designing paper cutting patterns as well as spreading, protecting, and passing on paper cutting non-heritage.
Distributed System Based on Software Components and Middleware Technology

With the wide application of Internet technology, more and more application systems use a dynamic and open network environment as a platform for computing and operation, and the functional requirements of the system are becoming more and more complex.Due to its strong real-time, openness, scalability and loose coupling characteristics, distributed component-based systems have been widely used in many fields such as civil industrial systems and ship equipment.The main purpose of this paper is to study the design and implementation of distributed systems (DS) based on software components (SC) and middleware technology (MT).In this paper, through the in-depth analysis of the MT, the middleware that conforms to the automatic testing technology is developed, and the instrument control, process communication, data management and other services of the middleware are realized, and the interaction between the application programs and the platform are tested.Experiments show that the average transmission efficiency of the new system using MT for data integration can be almost 8 times higher than that of the original system.After testing with data (the maximum amount of data per day does not exceed 20,000), when the supervision server is idle, the time required to transmit data every day does not exceed 4 minutes, which can basically guarantee the normal operation of the system.
Grid Computing Method for Bioaerosol Concentrations Coupled with Human Health Risk Values in the Application of Vaccine Enterprise Layout and Site Selection to Reduce Environmental Risks Caused by Bioaerosol Leakage: A Case Study of Leakage Accident in Zhongmu Lanzhou Biopharmaceutical Plant of Gansu Province, China

Pathogenic bacteria pose a great threat to global public health from environmental and public health perspectives, especially regarding the impact of the COVID-19 pandemic worldwide. Thus, the increased exposure risk of pathogenic bioaerosols causes a considerable health burden and raises specific concerns about the layout and location of vaccine manufacturers. This study proposed a grid computing method based on the CALPUFF modeling system and population-based environmental health risks to reduce bioaerosol-related health risks. We have earlier used the CALPUFF model to quantitatively simulate the diffusion level, the spatial distribution of emissions, and potential health risks of bioaerosol leakage in the Zhongmu Lanzhou biopharmaceutical plant of Gansu province from July 24, 2019, to August 20, 2019; the credibility was verified by combining it with publicly reported test data. Based on our previous research, the CALPUFF model application combined with the environmental population-based environmental health risks in two scenarios: the layout and site selection, was explored by using the leakage accident of Zhongmu Lanzhou biopharmaceutical plant of Gansu province as a case study. Our results showed that the site selection method of scenario 2 coupled with the buffer area was more reasonable than scenario 1, and the final layout site selection point of scenario 2 was grid 157 as the optimal layout point. The simulation results demonstrated agreement with the actual survey. Our study results might help global bioaerosol manufacturers with appropriate layout and site selection to reduce bioaerosol-related potential environmental risks.
Psychological counseling assistant system based on virtual reality technology

In recent years, as the pace of society continues to accelerate, from elementary school students who learn to speak in the classroom to middle school students who face the high school entrance examination and college entrance examination, and even adults facing the pressure of life, there are varying degrees of psychological pressure and people’s psychological pressure. It keeps increasing, but there is nowhere to vent. You can only bury it in the bottom of your heart. These people can more or less have some psychological problems. This paper conducts a detailed investigation and analysis of the traditional psychological counseling methods, understands the shortcomings and shortcomings of the traditional psychological counseling methods, and then finds out the corresponding methods to solve them, in order to lay the foundation for the development of a psychological counseling assistance system based on virtual reality technology. Then the psychological counseling assistance system of virtual reality technology has been systematically analyzed from three aspects: function, data analysis and data practice. This article focuses on the four aspects of users, system functions, technology and hardware peripherals required by the system. A comprehensive demand analysis was carried out. Experimental research shows that in the data practice of a psychological counseling assistance system based on virtual reality technology, a four-year case summary of a university is analyzed, and the problems and proportions faced by college students at that time are obtained from it, and then extended to participate in work. After the crowd, find out their psychological problems and summarize them.
The Use of Public Cloud Services Among Students for Online Learning

A public cloud is a computing service offered by third-party service providers to Internet users. Some cloud service providers offer both free and paid services, where the prices are scaled to meet the needs and offered more benefits to clients than free cloud services. This paper aims to study the familiarity of public cloud services among respondents of multiple age groups and to find out the advantages and disadvantages of using the public cloud. The survey was taken randomly through google form media. Respondents are the public who use cloud services in Malaysia. A Google Form is used to gather data from the public among multiple age groups, from individuals aged 12 to 45 years and above. Based on the conducted survey, respondents show positive responses towards their knowledge and the usage of public cloud services. Findings from the survey revealed that almost 90.3% of the respondents use public cloud services, and about 75% of them think that the public cloud is reliable and safe to store data. Most respondents have indicated that Google Drive is their preferred public cloud, and the majority them use the public cloud for academic purposes. In conclusion, public cloud services are rather efficient for users with diverse usage necessities. Some public cloud services are reliable and affordable for students, as students need cloud storage for academic purposes, especially during pandemics where everything is conducted online.
Pedagogy for Applying Mathematical Principles in Software Engineering

Software engineering educators face the challenge of conveying the relevance and importance of mathematical principles to system requirements specification, analysis, and design. There have been attempts to address this issue and this report presents an example of one such attempt. The approach taken here is a research-based software engineering course that implements formal specification techniques, as an integral component of the curriculum. The expected outcome is that the students will acquire skills in applying mathematical concepts in a seamless manner.
Operating safety of horizontal biogas sets

The Development Program of the Chelyabinsk region noted that one of the main directions in the region's energy sector is the improvement and reconstruction of existing generating power plants, the construction of modern power sources based on gas turbine and gas piston plants, the development of renewable energy sources, as well as the decentralization of heat supply. The implementation of the program will ensure operational safety and reliability of power supply to agro-industrial consumers in the Chelyabinsk region. With this, one of the main issues is to ensure the safe operation of industrial biogas sets (BGS). For this, it is necessary to completely exclude the leakage of the final product from the BGS. The probability of damage to the BGS structure should be obtained using a preliminary probabilistic assessment.
Machine Learning Analysis of Government's Public Risk Communication During COVID-19 Lockdown in Wuhan, China

COVID-19 pandemic has caused millions of deaths worldwide since 2020, and has led to significant lives and economic loss. It was first identified in Wuhan, China, while China has one of the lowest death rates. Understanding Chinese governments’ focuses and strategies can offer insights into early pandemic control in the future. This study aimed to explore the strategies and practices of government risk communication adopted during the COVID-19 lockdown in Wuhan, China; and provide implications for effective health risk communication at the early stage of epidemic response. The 90 government press conference records during the Wuhan lockdown (from 22 January 2020 to 1 May 2020) were divided into three batches and preprocessed. Topic modeling, i.e., the Latent Dirichlet Allocation, was used to computationally extract the topics in each batch. We identified important topics early in the lockdown period such as 'medical team’s work', 'assuring supplies for society', 'patients detection and isolation'; in the middle batch such as 'patient treatment and hospitalization', 'enterprises’ resumption of work and production', 'epidemic prevention and control'; and later in the lockdown including “policies supporting enterprises', 'ensuring employment', as well as 'blood donation'. We found that the Chinese government demonstrated responses that changed during the lockdown period aiming at allowing the society to resume usual activities at the pre-vaccination stage. Our results implied that the epidemic control relied on not only the effectiveness of public health policies, but also the collaboration and collective actions across different systems in the society.
Realtime Visual Object Recognition using Support Vector Machine comparing with K- Nearest Neighbor algorithm for improving accuracy

Aim:The aim of this research is to recognise objects by using machine learning algorithms from the images with improved accuracy.Materials and Methods: A total of 104 samples are there for the two groups.Novel Support Vector Machine is considered as group 1 and K-Nearest Neighbor Algorithm is considered as group 2. Group 1 consists of 52 samples and Group 2 also consists of 52 samples and the G power is 80%.Results: The accuracy for the Novel Support Vector Machine algorithm (92%) is more than that of the K-Nearest Neighbor algorithm (83%).The mean accuracy detection is ±2SD and the significance value is 0.000 (p<0.01) which shows the hypothesis is correct and it is carried out using an independent sample T test.Conclusion: Hence, the accuracy of Novel Support Vector Machine is found to be 92% which is more than the accuracy of the K-Nearest Neighbor algorithm which is 83%.
DEAR: Dual-Level Self-attention GRU for Online Early Prediction of Sepsis

Sepsis is one of the leading causes of death in intensive care units (ICUs). Online early prediction of sepsis has the potential for application to support clinicians. Despite the great success of deep neural networks in modeling electronic health records (EHRs), many architectures are incapable to be applied to online early prediction scenarios due to two major limitations. First, they overlook the earlier signs of the disease which are vital for the early prediction of sepsis. Second, they are unable to provide interpretation of prediction results. To tackle the above limitations, we propose a Dual-level sElf-Attention Gated Recurrent Unit Networks model, DEAR. On the one hand, DEAR is able to directly identify and strengthen the important time steps from the precedent memory. Specifically, the cell of DEAR straightforwardly fusion the history of both feature level and temporal level into the current step. On the other hand, DEAR provides interpretability with multi-head attention. Experimental results in the real-world sepsis dataset demonstrate that our model outperforms state-of-the-art methods in terms of both utility and balanced accuracy. In addition, the visualization of multi-head attention weights also indicates that DEAR reveals the importance of different time steps in the early prediction of sepsis onset.
Knowledge Extraction Method to Support Domain Integrated Design Methodology

Abstract Nowadays, bio-inspiration has enhanced the creation of sustainable and innovative solutions to modern engineering problems. Nature could inspire mechanical engineers to develop innovative ideas as a great source for multifunctional and optimized designs. However, it is very difficult to extract desired design knowledge from primarily text-based databases and mainly focus on describing the biological system. The main objective of this study is to build a multi-label classification system to classify bio-inspired designs to support the Domain Integrated Design methodology. The proposed system integrates NLP and text mining with several machine learning models to learn and predict the functionalities of bio-inspired design. Various design functionalities were summarized based on the available resources from the AskNature database, then the main information extracted from the database, and they were labelled with corresponding multi-functionalities. Due to the high complexity of the multi-label classification system, multi-label classifiers were built based on different combinations of baseline classifiers and trained to classify selected AskNature pages. One case study was conducted to verify the impact of the proposed system. The results showed that the proposed system is feasible and would be a solution for classifying the bio-inspired design and functional basis knowledge extraction method to support DID methodology.
Deep neural network-based physical distancing monitoring system with tensorRT optimization

During the COVID-19 pandemic, physical distancing (PD) is highly recommended to stop the transmission of the virus. PD practices are challenging due to humans' nature as social creatures and the difficulty in estimating the distance from other people. Therefore, some technological aspects are required to monitor PD practices, where one of them is computer vision-based approach. Hence, deep learning-based computer vision is utilized to automatically detect human objects in the video surveillance. In this work, we focus on the performance study of deep learning-based object detector with Tensor RT optimization for the application of physical distancing monitoring system. Deep learning-based object detection is employed to discover people in the crowd. Once the objects have been detected, then the distances between objects can be calculated to determine whether those objects violate physical distancing or not. This work presents the physical distancing monitoring system using a deep neural network. The optimization process is based on TensorRT executed on Graphical Processing Unit (GPU) and Computer Unified Device Architecture (CUDA) platform. This research evaluates the inferencing speed of the well-known object detection model You-Only-Look-Once (YOLO) run on two different Artificial Intelligence (AI) machines. Two different systems-based on Jetson platform are developed as portable devices functioning as PD monitoring stations. The results show that the inferencing speed in regard to Frame-Per-Second (FPS) increases up to 9 times of the non-optimized ones, while maintaining the detection accuracies.
Critical Reflections on the Hot Issue of “Education Industrialization” in China

In recent years, a fervor for education industrialization has overwhelmingly impacted education of all levels and all types in China, resulting in huge shocks to educational decision-making, theories and practices. It is the responsibilities of Chinese theories in the field of education to explore the ways of examining the origin and development of this fervor, and find out its concerns, misconceptions and risks, in order to insist on the correct path for educational reform and development in China according to the law of education, to safeguard education positioned as the greatest public good, to ensure the implementation of strategically prioritizing educational development with financial resources by the central government, and to continuously balance and innovate quality education.
Application of Ice Breaker to Improve Student Learning Outcomes in Elementary School PKN Lessons

The purpose of this study was to determine the effect of Ice Breaking to improve student learning outcomes in Civics lessons for second graders of SD Quality Krembung. This researcher uses quantitative research with experimental methods and uses a form of pre-experimental design type One-Shot Case Study. The population used as the object of research is the second grade students of SD Quality Krembung. The sampling technique used in this study is a saturated sampling technique. The sample used in this study were all second grade students of SD Quality Krembung, totaling 28 students. The instrument used in this research is posttest. From the results of the study, the lowest student posttest score was 64 and the highest student score was 96 so that their average score was 82.07. It can be concluded that Ice Breaking has an effect on student learning outcomes in Civics lessons for second grade students of SD Quality Krembung.
Image-Based Visual Servoing under Field of View Constraints for Robot Manipulators<sup>*</sup>

The challenge in image-based visual servoing is to deal with the visibility constraints, which require the image features to always remain in the field of view (FOV) of the camera. In this paper, a novel constraint function is proposed to address this problem. The FOV constraint function represented by the coordinates of the features and the region boundaries defined in the image is added to the classical proportional controller, which leads the image features from the margin to the center of the image. Simulation results verify the efficiency of the modified controller by controlling a robot manipulator with eye-in-hand configuration.
An Efficient Facial Expression Recognition System Using Novel Supervised Machine Learning by Comparing CNN over Google Net

Aim:The main aim of this research article is classification of facial expression recognition with improved accuracy by using Convolutional Neural Network(CNN) comparison with GoogleNet.Materials and Methods: The data of the facial expressions is taken from the FER2013 available on kaggle.The most widely utilized technique for accurately assessing photos is the convolutional neural network (CNN), and GoogleNet is also employed here to compare the accuracy of Novel image classification.Results: The Convolution neural network(CNN) produces 82.14% accuracy in predicting facial expressions on the dataset,whereas GoogleNet produces 75.09% accuracy.Convolutional neural network(CNN) is better than GoogleNet.Between the study groups, there is a statistically significant difference (p<0.05).Conclusion: Convolutional Neural Network provides better outcomes in accuracy rate when compared to GoogleNet for predicting facial expressions.
On the Development and Application of Computer Artificial Intelligence Technology

With artificial intelligence (AI) technology as the focus of present research and application, the new cross-technology formed by AI and multiple technologies has found wide application in mobile Internet, finance, medical care, education and other industries.Currently, countries around the world are constantly accelerating the development and application of AI technology by increasing policy support and funding for its development.This paper analyzes the development direction and application potential of AI technology by studying the development of AI technology and exploring the key technologies of AI.