Reviewerly/exHarmony huggingface dataset:
AU-Net: an image segmentation for complex scenes

The continuous advancement of artificial intelligence technology has made autonomous driving possible. However, duo the lack of sufficient data to train a good deep learning model, the current smart driving system can only rely on the driver for autonomous control, which may have serious consequences in the event of an accident. In practical applications, smart driving systems not only need autonomous driving technology, but must also be able to recognize obstacles and accurately avoid them without relying on manual manipulation, making the integration of autonomous driving features into vehicles a very promising research direction. To address this problem, we propose a novel segmentation method, AU-Net, which is capable of achieving accurate and complete segmentation of complex scenes by introducing an axial attention mechanism. We evaluate the performance of our model on the dataset Camvid, which improves 0.54%, 0.47%, 0.32% and 1.54% in the miaou, accuracy, percision and recall metrics, respectively, and the results show that our model is well adapted to complex scenes in intelligent driving detection.
PENGARUH PEMBELAJARAN MENGGUNAKAN TABLET BERBASIS QR-CODE UNTUK MENINGKATKAN MOTIVASI BELAJAR SISWA KELAS IV SDN 1 REJOSO

Learning is very meaningful for students, learning requires learning media, learning resources and appropriate learning methods to be applied by teachers to achieve learning objectives. This research is a Classroom Action Research (PTK). This class action research was conducted in class IV of SDN 1 Rejoso which aims to see the effect of using tablet for learning media on students' learning interest. Based on the data in table 1 above, it can be concluded that the data from the pretest control class has a minimum pretest value of 48, a maximum pretest value of 81, a mean value of 62.89 and a standard deviation value of 9.8. Based on the table. 2 above it can be concluded that the data from the control class pretest has a minimum pretest value of 48, a maximum pretest value of 81, a mean value of 65.89 and a standard deviation value of 9.1. Based on the MANOVA test, a significance value of 0.5 was obtained. If the sign value is less than 0.5 then Ho is rejected, then Ha is accepted which shows the influence of the learning media used. Based on the research conducted, there is a good effect on the results of the application of video learning media, quizzes, games and reflection by scanning barcodes. Good student enthusiasm will give good results, if students are enthusiastic about participating in learning then they will understand what is being learned can also provide motivation and interest in learning so that student learning outcomes can increase
Perspectiva docente frente a la transición de ambientes virtuales de aprendizaje

Education in the world changed radically as consequence of the health emergency experienced in 2020; the confinement posed -among others- the challenge of urgently implementing digital tools to teach at a distance with the resources available up to that moment, and limited teacher training in these topics. This article explored, through a survey applied to 1,009 academics of the School of Medicine, their perception regarding the type of electronic devices, the quality of Internet connection and the use of electronic tolos they had at the beginning of the confinement, as well as their willingness to develop online classes and activities. The results show that less than half of the academics surveyed had previous experience in implementing distance classes; practically all of them agreed to train and teach their classes online. These results provide guidance on the general strengths and weaknesses that need to be addressed to enrich the preparation of academics in the teaching-learning process towards a hybrid model. Keywords: Virtual modality; virtual learning environments; online classes; technological barriers in education; teaching practice in the health emergency.
Retracted: Analysis of O2O Teaching Assistant Mode of College English in MOOC Environment

[This retracts the article DOI: 10.1155/2022/8164934.].
Differentiable Bootstrap Particle Filters for Regime-Switching Models

Differentiable particle filters are an emerging class of particle filtering methods that use neural networks to construct and learn parametric state-space models. In real-world applications, both the state dynamics and measurements can switch between a set of candidate models. For instance, in target tracking, vehicles can idle, move through traffic, or cruise on motorways, and measurements are collected in different geographical or weather conditions. This paper proposes a new differentiable particle filter for regime-switching state-space models. The method can learn a set of unknown candidate dynamic and measurement models and track the state posteriors. We evaluate the performance of the novel algorithm in relevant models, showing its great performance compared to other competitive algorithms.
Semantic Segmentation in Thermal Videos: A New Benchmark and Multi-Granularity Contrastive Learning-Based Framework

Video semantic segmentation has achieved great success, which is significant for road scene understanding. However, semantic segmentation remains challenging in poor illumination and inclement weather. Thermal camera, highly invariant to light and highly penetrating to rain and fog, enables semantic segmentation to work under challenging conditions. Thus, this paper explores semantic segmentation in thermal videos to broaden the scope of the application of road scene understanding. We offer the first thermal video semantic segmentation dataset TVSS including 1695 thermal videos with 50850 frames in road scenes. It is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://xzbai.buaa.edu.cn/datasets.html</uri> . TVSS is finely annotated by 17 categories at the frame rate of 1fps, with a labeled pixel density of 98.9%. Existing video semantic segmentation methods rely on the amount of labels and the representation power of backbones, which cannot achieve ideal results on thermal videos. Thus, we introduce a multi-granularity contrastive learning based thermal video semantic segmentation model (MGCL), which explores the abundant unlabeled frames to boost the supervised segmentation. Specifically, MGCL constructs multi-granularity self-supervised signals on unlabeled thermal videos by contrastive learning, including the intra-frame context generalization loss, the intra-clip temporal consistency loss, and the inter-video category discrimination loss. In addition, a hard anchor sampling strategy is introduced to focus on hard-classify pixels for further performance improvement. Extensive experiments on TVSS demonstrate the superior performance of MGCL in both accuracy and efficiency. Compared to the 12 state-of-the-art semantic segmentation methods, MGCL achieves 2.8% to 8.1% gains in mIoU performance while maintaining the inference speed.
APPLICATION OF CUTTER–JORDAN–BOSSEN METHOD FOR DATA HIDING IN THE IMAGE SPATIAL AREA

The article deals with the main methods of digital steganography and presents a classification scheme. Special attention is paid to Cutter–Jordan–Bossen method for hiding data in the spatial area of the image. Aim. The study of digital shorthand methods, as well as the assessment of their applicability for hiding information in images. The main task is to analyze the Cutter–Jordan–Bossen method for hiding data in the spatial area of the image and evaluate its effectiveness under various conditions. Materials and methods. In this work, various methods of digital shorthand were used, including the Cutter–Jordan–Bossen method. Images of various types and quality, as well as various embedding parameters were used for testing. Results. As a result of the study, it was revealed that the Cutter–Jordan–Bossen method is effective for hiding information in the spatial area of the image. The dependence of the data extraction quality on the embedding parameters was tested, which showed that the optimal parameters depend on the type of image and its quality. The resistance of the information hidden by this method to distortion during compression was also tested. The test results showed that JPEG compression, even at low and high energy values, leads to the destruction of information hidden in the container. It was found that the best results are achieved when using the Cutter–Jordan–Bossen method with optimal embedding parameters, which allows you to save hidden information when compressing an image. Conclusion. In conclusion, we can say that the study of digital shorthand methods and their application to conceal information in images is an urgent and important topic. The Cutter–Jordan–Bossen method has shown good results in hiding information in the spatial area of the image, but for each specific case it is necessary to choose the optimal embedding parameters. It was found that JPEG compression can significantly affect the quality of information extraction, so it is necessary to take this factor into account when choosing a method for hiding data in an image. In general, the study of digital shorthand techniques and their application to conceal information in images can be useful for various fields, such as the protection of confidential information and digital watermark.
Research on the evolution and path of college English teaching reform in China since the 1980s

This article mainly discusses the evolution of college English teaching reform in China and the reform measures for the problems, and puts forward some suggestions for the path.
Efficient Face Recognition in Real Time for Locating Missing Person

Abstract: This paper presents a web-based application system that utilizes deep learning as the core technology of a Face Recognition System, with the primary objective of enhancing the accuracy and efficiency of locating missing individuals. Every day, a significant number of individuals go missing due to various factors such as old age, mental health issues, or conditions like Alzheimer’s. The conventional methods employed for searching for missing persons are typically slow, expensive, and involve protracted physical searches lasting weeks or even months. In contrast, deep learning-based technologies offer a promising solution by rapidly analyzing substantial volumes of data within minutes or hours. By leveraging facial recognition technology, which is an application of deep learning, our proposed system aims to compare images and videos obtained from surveillance cameras with pictures of missing persons to identify potential matches. Specifically, we employ the Resnet deep learning algorithm to examine the images of missing individuals, thereby improving the accuracy and speed of identification and making the process more reliable and efficient. To provide a comprehensive solution, we have developed a user-friendly webbased application system that facilitates the search for missing persons. The application efficiently collects and stores information about missing individuals in a centralized database. Whenever a missing person is identified in a CCTV video stream, our application actively tracks their location. Once the missing person is successfully identified within the video stream, the application promptly sends location details via email to the person’s relative
ANALYSIS OF FUNDING AND EFFICIENCY IN THE FIELD OF EDUCATION AND SCIENCE IN KAZAKHSTAN

Today, significant amount of attention is paid to the development of science and education because information and knowledge are the main drivers of progress. This study aims to analyze the financing and implementation of such documents of the state planning system as: the State Program of Education and Science Development of the Republic of Kazakhstan for 2016-2019, the State Program of Education and Science Development of the Republic of Kazakhstan for 2020-2025, and the National Project «Quality Education «Educated Nation». As a result of the analysis, it was found that over the two years of the National Project «Quality Education «Educated Nation», its annual actual funding is less than that of each year of the entire period of 2016-2020, but the share of fulfilled benchmarks and measures is generally higher than in the first year of the State Program of Education and Science Development of the Republic of Kazakhstan for 2020-2025. Recommendations were formed on the presentation of implementation of the main parts of the National project «Quality Education» Educated Nation» in the form of the information system.
Exploring Dialog Act Recognition in Open Domain Conversational Agents

Recognizing dialog acts of users is an essential component in building successful conversational agents. In this work, we propose a dialog act (DA) classifier for two of our open domain dialog systems. For this, we first build a hierarchical taxonomy of 8 DAs suitable for classifying user utterances in open-domain setting. Next, we curate a high-quality, multi-domain dataset with over 24k user dialogs and annotate it with our 8 DAs. Next, we fine-tune our pretrained BERT-based DA classifier on this dataset. Through extensive experimentation, we show that our proposed model not only outperforms the baseline SVM classifier by achieving state-of-the-art accuracy but also generalizes extremely well on previously unseen data.
CUI-Net: a correcting uneven illumination net for low-light image enhancement

Abstract Uneven lighting conditions often occur during real-life photography, such as images taken at night that may have both low-light dark areas and high-light overexposed areas. Traditional algorithms for enhancing low-light areas also increase the brightness of overexposed areas, affecting the overall visual effect of the image. Therefore, it is important to achieve differentiated enhancement of low-light and high-light areas. In this paper, we propose a network called correcting uneven illumination network (CUI-Net) with sparse attention transformer and convolutional neural network (CNN) to better extract low-light features by constraining high-light features. Specifically, CUI-Net consists of two main modules: a low-light enhancement module and an auxiliary module. The enhancement module is a hybrid network that combines the advantages of CNN and Transformer network, which can alleviate uneven lighting problems and enhance local details better. The auxiliary module is used to converge the enhancement results of multiple enhancement modules during the training phase, so that only one enhancement module is needed during the testing phase to speed up inference. Furthermore, zero-shot learning is used in this paper to adapt to complex uneven lighting environments without requiring paired or unpaired training data. Finally, to validate the effectiveness of the algorithm, we tested it on multiple datasets of different types, and the algorithm showed stable performance, demonstrating its good robustness. Additionally, by applying this algorithm to practical visual tasks such as object detection, face detection, and semantic segmentation, and comparing it with other state-of-the-art low-light image enhancement algorithms, we have demonstrated its practicality and advantages.
Pengenalan Kosa-Kata Bahasa Inggris dalam Pembuatan Makrame kepada Karyawan Cleaning Service Politeknik Negeri Samarinda

Purpose: This Community Service held by the Engineering Department tim of Politeknik Negeri Samarinda had the aim to give an introduction to English vocabulary used in making macrame products to female cleaning service employees of PT. Galina Citraraya Mandiri. Methodology: Fifteen female cleaning service employees attended this macrame training to make a hanging vase. Result: This community service tried not only to give training but also to give a little knowledge of English that the participant can use in their daily conversation. Before the training began, the team had given an introduction to the tools and materials used in making a hanging vase in English. In spite of the short training, the participants enjoyed following the training until the end of the training. The participants were also given some materials to make a hanging vase so that they could continue making their own creations in their homes. The team hopes that this training can help the participant to have new experiences in making a hanging vase that later could be used as their financial source by selling their own macrame products.
The Synergies of Context and Data Aging in Recommendations

In this paper, we investigate the synergies of data aging and contextual information in data mining techniques used to infer frequent, up-to-date, and contextual user behaviours that enable making recommendations on actions to take or avoid in order to fulfill a specific positive goal. We conduct experiments in two different domains: wearable devices and smart TVs.
Students' Perception on Public Speaking Lecture for Their Speaking Ability

The purpose of this study is to identify students’ perception on public speaking lecture for their speaking ability. The writer used a qualitative descriptive design to provide an overview of information obtained from the data. A sample of 10 students of fourth-semester students from the English Education Department in the Academic Year 2021/2022 who are studying Public Speaking Lectures will take part in this study. The interviews were the process of obtaining information for research by way of questions and answers while looking face to face respondents using an interview guide. An interactive model of analysis was used to analyze the data in this study. The results of student perceptions fou`1%nd that students stated that public speaking lecture is very influential on their speaking ability, the use of public speaking lecture can also make them more able to express their feelings, students can explore and increase their vocabulary, and influence people with their words.&#x0D; &#x0D;
Identification of Suspected Tuberculosis Using A Pharmamed Chatbot Based on Health Services in The City of Padang

Introduction: TB disease is the first of the 10 leading causes of death in the world, and Indonesia is the 3rd highest country after India and China. The Minister of Health said there were 824,000 people suspected of having TB and asked all health officials to prioritize surveillance efforts to find people with TB detected by name by address. The TB cure rate in West Sumatra Province in 2020 is 76.9% and has not reached the national target of 85% and the city of Padang is only 23%. Objective: The purpose of this study is to find suspected cases of TB using the Pharmamed Chatbot, by name and by address so that they are easy to find to overcome TB. Methods: This type of research is quantitative descriptive of suspected TB cases using a 20-question chatbot to obtain social data by name and by address and suspected cases of TB. Results: The results of 838 respondents obtained 91 people suspected of TB, the composition of respondents BPJS 78.5% and 22.5%, not BPJS. BPJS respondents of productive age 15-24 years 26.91% and age range 45-64 years 34.4%. Chatbots are relatively successful and innovative as digital health in obtaining patients with suspected TB and the initial steps for TB control. It is necessary to develop a more complete chatbot and further research on TB disease prevention in Indonesia
On the Preservation of Soft-Biometric Information in Biometric Face Embeddings

The development of deeply-learned features is the foundation for the success of contemporary face recognition systems. These embeddings are designed to encode a person's identity so that they can be used for identification. Recent studies, however, have demonstrated that these embeddings also store information on demographics, image qualities, and social traits in addition to the user's identity. This brings up issues with prejudice and privacy in facial recognition. We examine the predictive power of 73 various soft-biometric features on three well-liked face embeddings with various learning philosophies. The tests were run on two databases that were accessible to the general public. We developed a huge attribute classifier that can accurately express the confidence in its predictions as part of the evaluation process. As a result, we are able to construct more complex statements concerning the property predictability. The findings show that most of the attributes under investigation are encoded in face embeddings. For instance, a robust encoding for accessories, accessories, and accessories was discovered. We discovered that these characteristics are particularly easy to anticipate from face embeddings, despite the fact that face recognition embeddings are taught to be resilient against non-permanent elements. Our research is intended to inform future efforts to create better bias-reducing and privacy-preserving face recognition technology. IndexTerms - Face recognition, bias, fairness, soft-biometrics, analysis, privacy, biometrics
Imprint: Geomechanics and Tunneling 4/2023

Geomechanics and TunnellingVolume 16, Issue 4 p. 447-447 ImprintFree Access Imprint: Geomechanics and Tunneling 4/2023 First published: 09 August 2023 https://doi.org/10.1002/geot.202370407AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume16, Issue4August 2023Pages 447-447 RelatedInformation
A multi-scale branch convolutional neural network for denoising

Images, being significant carriers of memories and information, are valued by people. To restore images, it is necessary to perform noise reduction processing to eliminate noise generated by camera equipment and other factors. Traditional denoising technology such as wavelet transform is used to help engineer restore a image. And in recent years, the introduction of convolutional neural networks has accelerated the progress of noise reduction research. Many classic models have been developed by researchers using U-shaped networks and other techniques. Researchers often use multi-scale approaches to obtain multiple feature maps and enhance their network with these features. Our work enhanced denoising network by introducing large convolutions, small convolutions, and Fast Fourier convolutions to capture feature information at different scales. Additionally, we used an SE block to introduce attention mechanisms into the network. As evidenced by experimental results, our network achieved outstanding performance.