Reviewerly/exHarmony huggingface dataset:
Reference-based super-resolution with texture transformer and residual network

Due to the large amount of information loss in low-resolution images, it is a challenging task to further develop super-resolution reconstruction techniques for single images. Reference-based super-resolution techniques are considered feasible in recovering high-frequency details of images, etc., but most existing algorithms do not consider the color distribution and illumination distribution of the input image and its reference image. Different, so as not to get very good visual needs. In response to the above problems, we propose a super-resolution model based on texture transfer combined with residual network. By introducing perceptual loss, adversarial loss, reconstruction loss and texture loss to form a new loss function, adjusting the weights of different loss items to optimize the loss function, the ability to reconstruct image details is improved. Extensive experiments show that our proposed model has higher PSNR and SSIM than other methods on the CUFED5, Sun80 and Urban100 test sets.
Design and implementation of disaster prevention science popularization system based on We Chat mini program

With the advancement of technology, there are more and more information available and access to information. However, due to the clutter of official information, the public cannot obtain emergency knowledge and disaster information in a timely and effective manner. Early warning and popular science publicity of meteorological disasters through WeChat will be a new development direction. By analyzing the current status and defects of the disaster science popularization platform on the market, the functions of the disaster prevention science popularization system are analyzed and designed on the basis of sorting out and analyzing various disaster-related knowledge and protection information. Based on the official WeChat development platform, it adopts the Spring Cloud framework, develops using Java language, and uses databases and cloud functions for background data storage. The system can provide a wider platform for users to understand and browse disaster information, and facilitate the management of administrator background information.
Security situation awareness algorithm of network information transmission based on intelligent computing

Through data mining with different network security technologies, we analyze the security situation curve, grasp the dynamic changes of the network security situation, and make reasonable predictions of the security situation. In order to solve the shortcomings of existing research on network information transmission security situation awareness, this paper discusses the selection of situation assessment indicators, experimental environment and data sources based on the discussion of network information transmission situation awareness, Bayesian network reasoning, and LSTM neural network algorithm, and it discusses the design of situation awareness assessment model of network information transmission security based on Bayesian network algorithm and LSTM neural network algorithm prediction model. Finally, the two algorithms proposed in this paper are compared with other algorithms. It can be seen from the data results that the thinking accuracy, detection rate and recall rate of Yates network algorithm and LSTM neural network algorithm are more than 99%. Therefore, it is verified that the network information transmission security situation awareness algorithm based on intelligent computing has a certain use value.
The Use of Mobile-Based Self Learning to Enrich Children’s English Vocabularies

The aim of this study was to examine self-learning using mobile-based to help children enrich their English vocabularies. This learning process used an apps called ‘English for Children’ (Bahasa Inggris Untuk Anak). It consists of twenty (20) vocabulary categories. They are Alphabet, Numbers, Colors, Shapes, Family, Body parts, Days of the week, Months of the year, Fruits, Vegetables, Animals, Birds, Food, Clothes, Kitchen, Bathroom, Living room, School, Sports, and Professions. Every word category is followed by four (4) learning sections such as ‘Learn’, ‘Writing’, ‘Find the word’, and ‘Find the picture’. Every section is served with pictures (visualization). Exercises can also be found in the ‘Writing’, ‘Find the word’, and ‘Find the picture’ sections as a benchmark of learning progress. The participants were 10 children age 10-11 years old. This type of learning was using student-centered model. The qualitative research method is used in this research. The systematic analysis of textual data was obtained via interviews, surveys, focus groups and diary entries. The result can be seen through the score of the exercise. Surprisingly, the score has risen significantly. Children could pass the exercise easily with the help of pictures. It means that by applying learning English vocabularies through mobile-based self-learning indicated that there was any significant difference before and after using this method.
Network Defense Model Based on Multi scale Feature Fusion for Countering Cycle Generation

The deep neural network has been applied well in many fields at present, but the security problem of the deep model has become increasingly prominent.Recent research shows that the neural network model will output the wrong classification by confronting samples.Therefore, confrontation samples are a major obstacle that must be overcome for the further development of deep neural networks.At present, the main task is to design an efficient and powerful defense model with strong robustness that can defend against multiple attack algorithms.This paper proposes a defense model based on multi-scale feature fusion circular confrontation generation network by combining the generation of generic adversarial networks (GAN) with existing attack algorithms.First, use the confrontation samples generated by the attack algorithm as the training samples of GAN, and use the unique network structure of CycleGan to make the reconstructed image closer to the clean image and remove potential disturbances.On the generator side, this paper uses multi feature fusion TernausNet structure to ensure that the feature information of the image can be restored as much as possible during the process of 黄楷铭 等：基于多尺度特征融合的对抗循环生成网络防御模型 http://www.sciandeng.comdisturbance removal, and adds attention mechanism to the discriminator side to increase the receptive field, establish global dependency, and train a more robust discriminator to help GAN training through experiments on CIFAR-10 and ImageNet datasets.It is proved that after the training, the model can directly classify the original samples and confrontation samples correctly, and achieve good defense effect for all kinds of confrontation attack algorithms.Compared with the existing methods, the defense effect is good, and the robustness of the depth model is enhanced.
Exploration on the Practice of &amp;quot;Project Based Learning&amp;quot; in the Field of Mathematics for Primary School Students

Project-based learning is a learning method that can implement the goal of educating people, can effectively promote the improvement of students' various abilities, and promote the cultivation of core literacy.Project based learning advocates that students are the main body, guide students to go out of the classroom, combine various learning methods to obtain knowledge, pay attention to students' actual ability, and develop ability.Project based learning has been supported by nearly 40 years of academic research and data, with rich theoretical research results, but there are few case studies on the practice of project-based learning in primary school mathematics.The 2022 new curriculum standard takes project-based learning as the implementation way to implement the goal of education, and provides some primary school mathematics project-based learning topics.In this paper, the author selected the second grade mathematics "Play with" Multiplication Formula "as the research topic, and carried out a practical study on the 王丹 等：小学数学"项目式学习"实践探索 http://www.educationrd.comapplication of project-based learning in primary school mathematics teaching.According to the general process of "project-based learning", conduct practical research from the six steps of determining the project subject, analyzing project elements, designing driven problems, implementing project activities, publishing project results, and evaluating project learning, summarize the key links to carry out project-based learning, summarize the practical experience of developing project-based learning, and explore a practical path of project-based learning that conforms to the content of the discipline and the actual situation of the school.The implementation o f project-based learning activities has improved students' interest in learning, developed their abilities, and yielded rich results in project -based learning.This study is expected to provide some reference and reference for the later development of the second classroom education of primary school mathematics.
Special Issue on Human–Computer Interactions 2.0

Human–computer interaction (HCI) research involves the design and use of computer technology, focusing in particular on the interfaces between people (users) and computers. HCI researchers observe the ways in which humans interact with computers and design technologies that allow them to interact in novel ways. As HCI evolves into HCI 2.0, user experiences and feedback become ever more relevant. This Special Issue, “Human Computer Interactions 2.0”, presents 11 excellent papers about topics related to human–computer interactions. It aims to provide a broad international forum for world researchers, engineers and professionals in human–computer interaction research for the discussion and exchange of various scientific, technical and management discoveries across the world.
Görüntü Sınıflandırma Görevi için CNN Kanal Dikkat Modüllerinin Performans Analizi

Increasing the representation power of convolutional neural networks, a popular deep learning model, is one of the hot study topics recently. Channel attention is a common strategy followed in this regard. In this strategy, the inter-channel relationship is exploited by a module placed after the convolution operation. Recently, successful channel attention modules are proposed in this context. In this article, a performance analysis of three popular channel attention structures which are Squeeze-and-Excitation Networks (SeNet), Efficient Channel Attention Networks (Eca-Net), and Convolutional Block Attention Module (CBAM), is performed using five different image datasets for the classification task. According to the obtained results, SeNet is the most successful channel attention module surpassing the other’s performance in the majority of the experiments. In experiments with the ResNet18 and ResNet34 base models, the SeNet module showed the highest performance in three of the five datasets. For the ResNet50 baseline, SeNet was the channel attention module with the highest accuracy values for all datasets.
Credit Card Fraud Detection Using LSTM

As credit card becomes the most popular payment mode particularly in the online sector, the fraudu- lent activities using credit card payment technologies are rapidly increasing as a result. The purpose of this work is to develop a novel system for credit card fraud detection based on sequential modeling of data, using attention mechanism Long Short Term Mem- ory(LSTM) deep Recurrent Neural Networks(RNN) and Synthetic Minority Oversampling Technique Edited Nearest Neighbour(SMOTE-ENN). The pro- posed approach aims to capture the historic purchase behavior of credit card holders with the goal of im- proving fraud detection accuracy on new incoming transactions. Experiments show that our proposed model gives strong results and its accuracy is quite high. keywords: Credit Card, SMOTE-ENN, FraudDetection, Machine learning,LSTM.
Figure S6 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer

&lt;p&gt;Correspondence between slide level IHC score and slide level EMO predictions.&lt;/p&gt;
Figure SF1 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer

&lt;p&gt;Supplementary comparison of model performance&lt;/p&gt;
Application of Machine Learning in Software Quality: a Mini-review

The digital transformation underway in Sa-helian countries is accompanied by a growing interest in software engineering products in both the formal and informal sectors. The developed software mostly meet the needs of the customers, but it is not excluded that they suffer from compliance to the software quality standard. Thus, having intelligent systems that can assess whether important quality attributes are verified may be helpful. In this paper, we propose a mini-survey of different research works focusing on the improvement of software quality based on Machine Learning (ML) methods. The lessons learnt show that software quality management through ML methods is an active research field. Software defect prediction and maintainability prediction are problems that had been treated by ML methods. Datasets, software metrics and ability of ML algorithms to build such predictive models have been the main target of the studies.
Combining multitask and transfer learning with deep Gaussian processes for autotuning-based performance engineering

We combine deep Gaussian processes (DGPs) with multitask and transfer learning for the performance modeling and optimization of HPC applications. Deep Gaussian processes merge the uncertainty quantification advantage of Gaussian processes (GPs) with the predictive power of deep learning. Multitask and transfer learning allow for improved learning efficiency when several similar tasks are to be learned simultaneously and when previous learned models are sought to help in the learning of new tasks, respectively. A comparison with state-of-the-art autotuners shows the advantage of our approach on two application problems. In this article, we combine DGPs with multitask and transfer learning to allow for both an improved tuning of an application parameters on problems of interest but also the prediction of parameters on any potential problem the application might encounter.
The Advanced Dietetics Module Development in Improving the Nutrition Diploma III Students’ Learning Outcomes of Kendari Health Polytechnic of the Ministry of Health

A successful learning process at schools must be supported by adequate facilities and infrastructure, including up-to-date learning resources and teaching materials that can support the learning process. The appropriate method in development research is a mixed method, which ensures that all research procedures are collected. Data analysis at the stage of the advanced dietetics module development process used analytical techniques in the forms of reviewing suggestions and corrections to the module content, presentation, language, and graphics. These processes were further conducted through a qualitative descriptive analysis. Material validation questionnaire sheets, linguists, and media were analyzed descriptively and quantitatively. In this case, calculation of the Likert Scale score was used to calculate this percentage. In addition, quantitative and descriptive analysis was carried out on student response questionnaires. The effectiveness of the advanced Dietetics module was evaluated quantitatively through quasi-experimental and non-equivalent control group research design. The expert team's assessment on the aspects of cover, content feasibility, presentation, module layout, graphics, and language aspects showed that all aspects were categorized as very feasible. Furthermore, in terms of appearance, presentation of material, and benefits, the results of the student's assessment on the module were categorized as very understandable. This study showed that the group of students who were given the dietetics module advanced had a higher average score. The researchers suggest that advanced dietetic modules need further development with regard to the media used to support teaching materials, for example interactive computer-based modules and multimedia products in digital formats (e-learning)
PENGGUNAAN METODE ANALYTICAL HIERARCY PROCESS (AHP) DALAM MENENTUKAN PLATFORM ONLINE FOOD DELIVERY TERBAIK

Decision support system is one part of the benefits of information systems that can be a guide for users in determining the best choice from several available options. Some consumer considerations in making purchases online illustrate that there are factors that influence consumer decisions in making purchases of goods. This study refers to the level of decision and consumer satisfaction in choosing the best online food delivery service platform so that it can provide an alternative problem solving in making a decision. The use of the AHP (Analytical Hierarchy Process) method in this study was used to determine the choice of the best online food delivery platform from the 3 available options, namely Grabfood, Gofood and Shopeefood. Data collection by distributing questionnaires to 50 respondents aged 18-40 years to analyze and test the 5 (five) specified criteria including price, promotion, service, security and speed. Assessment of the selection of online food delivery service platforms with these criteria uses pairwise comparison scale with an importance level from 1-9 intervals. The research results based on the weight of each of these criteria, namely speed 0.161, security 0.253, service 0.251, promotion 0.174 and price 0.161 as well as the percentage of online food delivery platforms chosen by respondents are as follows: Grabfood 36.02%, Gofood 34.14 %, and Shopeefood 29.84%. From this study, it can be concluded that Grabfood is an online food delivery platform that is most in demand by the current millennial generation.
A Comprehensive Study of Federated Learning Schemes for the Artificial Intelligence of Things

Massive amounts of data are produced continuously by billions of Internet of Things (IoT) devices and analyzed via Machine Learning (ML) models to serve a wide variety of needs. However, the high communication cost of traditional ML approaches coupled with data privacy issues makes them unpractical for many IoT applications, especially in healthcare where medical records contain sensitive information that can compromise patient privacy. As a result, current research has begun to investigate Federated Learning (FL) as a new paradigm addressing these critical concerns by training ML models without sharing private data. This paper provides a comprehensive study of existing FL algorithms and discusses their applicability in a medical IoT context using publicly available datasets. This is proceeded by integrating such schemes in the FedML framework using real-world medical datasets in both simulated and on-device federated settings to investigate the impact of clients number, communication loss, and data compression on model performance, energy, time, and code footprint. The paper also makes suggestions on open research issues that need to be addressed by the community.
Green Upgrade: Research on the Renovation and Reconstruction Engineering of Traditional Data Centers

The traditional data center built in the early stage has gradually no longer adapted to modern green requirements and performance requirements, so it needs to be updated.This paper takes a data center in Shanghai, China as an example, through the author's practical engineering research, summarizes the engineering measures of transforming a traditional data center into a modern green data center, and proposes the update of the ceiling, ground, wall, air, waterproof and fire protection system in the data center.Suggestions for renovation.
Augmented Reality at Gedung Sate Museum: Bring The Past To The Present

The purpose of this study is to collect and explain the importance of applying Augmented reality technology to museums to increase the visitors' space experience. The method used is descriptive qualitative, which is supported by a literature review. The author will collect the various advantages of using augmented reality technology in museums with visitors' experience when this technology is seen from previous studies. The Cultural History Museum is a facility that presents cultural heritage. These artifacts and cultural heritage are sometimes presented in incomplete form, and historical stories are sometimes written or told in a rather dull form. AR is a technology that can be a solution so that cultural heritage and artifacts from the past can be presented in a more interactive and fun form. Gedung Sate Museum is one of the museums that uses AR technology for one of its exhibition spaces, discussed in this paper. From observations, there are two kinds of spaces enjoyed by visitors, namely the actual exhibition space and the virtual space presented on the screen. When a visitor looks at the screen, what is visible is an enhanced visual. The situation from the past is represented in a visual form that is combined with the actual state of space to present the past presented on a virtual screen and support from the atmosphere of the room, which is also essential to evoke emotions, memories, and thoughts of visitors. The results of this study can be used to add references to the application of Augmented reality technology, which is suitable to be applied in cultural history museums in Indonesia
Empirical Study on Dust Distribution and Prevention in Thermal Power Plant Based on Big Data and Cloud Computing

In order to reduce the dust concentration in front of the boiler room in thermal power plants, a dust distribution monitoring system for thermal power plants based on cloud computing and big data technology was proposed.With real-time dust distribution data monitoring as the core, the system realizes the unified management of information collection and information receiving, storage, analysis and processing on a system platform, avoiding the independent operation of each system; The system can monitor the dust distribution status in a timely and effective manner, and realize the information interaction among the monitoring plan, monitoring implementation, monitoring summary, monitoring report and project management module, thus forming the information integrated management of the collection work.After analyzing and testing the generation process, concentration value and free silica content of dust in thermal power plants, the results show that the dust concentration value increases with the increase of boiler height.The average dust concentration value detected at the top of the boiler is 7.42 mg/m3, which is higher than the average dust concentration value of 4.21 mg/m3 in the middle of the boiler and 3.85 mg/m3 at the bottom.Therefore, the effective prevention and control of dust hazards in thermal power plants should be carried out in strict accordance with relevant national regulations.