Reviewerly/exHarmony huggingface dataset:
Precise Segmentation for Children Handwriting Analysis by Combining Multiple Deep Models with Online Knowledge

We present a strategy, called Seq2Seg, to reach both precise and accurate recognition and segmentation for children handwritten words. Reaching such high performance for both tasks is necessary to give personalized feedback to children who are learning how to write. The first contribution is to combine the predictions of an accurate Seq2Seq model with the predictions of a R-CNN object detector. The second one is to refine the bounding box predictions provided by the detector with a segmentation lattice computed from the online signal. An ablation study shows that both contributions are relevant, and their combination is efficient enough for immediate feedback and achieves state of the art results even compared to more informed systems.
Intelligent prediction method of construction cost based on GA-BP algorithm

In order to solve the problem of low prediction accuracy of traditional construction cost prediction methods, a new intelligent prediction method of construction cost based on GA-BP algorithm is proposed in this paper. First of all, make clear the composition and main content of construction cost. Secondly, the main structure of basic BP neural network is analyzed, and the BP neural network is optimized by genetic algorithm. The main process of optimization is to optimize the weight and threshold of BP neural network through crossover and mutation in genetic algorithm, so as to improve the calculation performance of BP neural network. Finally, based on the optimized BP neural network, the construction cost prediction model of construction engineering is constructed, and the model is solved to complete the intelligent prediction of construction cost of construction engineering. The experimental results show that under the set parameters, when the number of iterations reaches 108, the mean square difference does not change, and the mean square difference is 3.5924×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> . Moreover, the proposed prediction method has high cost prediction accuracy, and the prediction accuracy of the proposed method is always 96%.
Information Resources Management in the Twenty-First Century: Challenges, Prospects, and the Librarian’s Role

This study looks at the problems and possible futures of information resource management (IRM) in libraries in the 21st century, as well as the role of librarians in making sure that library and information resources are used well.This study used a mix of methods, including a thorough literature review, qualitative content analysis, technology trend analysis, and thematic analysis, to give a full picture of IRM in the present day.A qualitative content analysis was done on the literature study results to look at the different ways people see IRM.This meant finding and putting together themes about the role of IRM in making organizations more competitive, improving the way decisions are made and making information management better.Information resources management was searched in Scopus.Only articles were searched and 1085 records were found whereas the word 'information management' is the most relevant item.A thematic analysis was done on the results of the literature study to figure out what the biggest problems are for IRM in the twenty-first century.This meant finding and putting into groups themes about information security, the complexity of information systems, and the management and study of data.The results of the different studies were put together to give a full picture of IRM including its views, prospects, and challenges.The mixed-methods approach used in this study is a thorough and systematic way to learn about how IRM is complicated and has many different parts.Researchers and practitioners who work in this rapidly changing area can learn a lot from the results.Based on what was already known about the problem, the study suggested some potential solutions.
Laboratory Access Implementing QR Code Authentication Using OTP

Laboratories in colleges are used to give lectures to students, but what about after working hours? Students can get many benefits from these labs after working hours. For example, students can study and do their homework after working hours. In this project, we have proposed a new technique to control the access of these laboratories. Our idea is to use an encrypted QR code with an TOTP authentication that will be connected to LAN network to guarantee that each student enter can only use a single PC. Each student will have his own encrypted QR code that differs from other students. The TOTP code will be sent to student’s university email. The encryption method used encrypted method used AES encryption to encrypt the data inside the QR code to guarantee the confidentiality, integrity, non-repudiation, and authenticity of the date.
Index/Impressum: Vakuum in Forschung und Praxis 4/2023

Vakuum in Forschung und PraxisVolume 35, Issue 4 p. 51-51 Magazin Index/Impressum: Vakuum in Forschung und Praxis 4/2023 First published: 15 August 2023 https://doi.org/10.1002/vipr.202370409AboutPDF 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. Volume35, Issue4August/September 2023Pages 51-51 RelatedInformation
A Model for Troubleshooting Automation Based on Text Similarity

Troubleshooting problems within an organization are recurring situations that often require a substantial workforce to resolve issues within a troubleshooting system. While complex issues require human intervention, automating the resolution of simple and frequent issues offers the opportunity for substantial savings in time and money. One key way in which organizations try to address this challenge is by storing textual data about historical incidents in a service desk system. The key challenge here is to effectively use this data to match new issues that do require completely new solutions by human staff. We address this challenge by using a text classification machine learning model for troubleshooting automation based on text similarity. The resulting model uses two distance metrics over text embeddings alongside an SVM model to match new issues with historical ones. We show the effectiveness of our model using a real incidents-based dataset collected by a large IT enterprise. Our approach achieves high matching accuracy and offers a promising avenue for effective troubleshooting automation.
Text Summarization with K-Means Method

Text Summarization is a tool used to generate a short form of text that contains important information that is needed by the user automatically. In this study, Text Summarization was conducted on Indonesian news using K-Means method. The news is taken from CNN Indonesia with a free topic. K-Means is used to classify sentences that already have weight in the news with 2 clusters, namely text summaries and not text summaries. The initial centroid is selected based on the sentence with the largest value and the sentence with the smallest value. The test conducted on Indonesian news with a total 50 news and tested for feasibility using a questionnaire. K-Means was successfully summarizing the news with an average 27.3 % of original news length and gain 87% good summarize based on respondents from questionnaire.
EFEKTIVITAS METODE CHAIN WRITING BERBANTUAN MEDIA INSTAGRAM PADA PEMBELAJARAN MENULIS TEKS ANEKDOT PADA PESERTA DIDIK KELAS X DI SMA PASUNDAN 7 BANDUNG

This study aims to test the chain writing method and the Instagram application as learning media to improve the ability to write anecdotal texts. This research was conducted to find out the right method to improve the ability to write anecdotal texts and learning media that can increase students' learning motivation. The ability of students in learning to write anecdotal texts increased by using the chain writing method and learning media through the Instagram application in the experimental class. This is evidenced by the average pre-test score of 60.33 and an increase in post-test results with an average of 86.17. There were differences in learning outcomes in writing anecdotal texts between the experimental class and the control class, for the experimental class the average post-test score was 86.17 and the control class received an average post-test score of 81.83. The chain writing method is effectively used in learning to write anecdotal texts compared to the brain writing method. This can be proven by the results of the test results of the Independent Sample t-test with the result of an Asymp Sig (2 tiled) of 0.649 meaning that the variances between the groups are equal, then the assumptions are met. From the data above, it can be seen that Sig (2 tailed) &lt; 0.05, namely 0.022, then the hypothesis is accepted and the chain writing method is effectively used in learning to write anecdotal texts.
Global-aware and local-aware enhancement network for person search

Person search, one of the increasingly important tasks of computer vision, performs pedestrian localization and re-identification simultaneously. Several studies incorporate anchor-free detectors into person search models and achieve good results. However, these methods ignore learning the contextual information of images, which is essential for improving the performance. To solve this problem, we propose a global-aware and local-aware enhancement network for person search. The stem network is constructed using hierarchical vision transformers to adaptively learn the global context. A local-aware enhancement module is proposed to learn the local context and incorporate multi-level features. To facilitate adequate learning on the limited training data, we design a symmetric online instance matching (SOIM) loss for end-to-end training the whole model. We conduct experiments on two benchmark datasets (the CUHK-SYSU and the PRW dataset) in comparison with the state-of-the-art methods. The results demonstrate the comparable performance of our proposed method.
An efficient pedestrian attributes recognition system under challenging conditions

In this work, an efficient pedestrian attribute recognition system is introduced. The system is based on a novel processing pipeline that combines the best-performing attribute extraction model with an efficient attribute filtering algorithm using keypoints of human pose. The attribute extraction models are developed based on several state-of-the-art deep networks via transfer learning techniques, including ResNet50, Swin-transformer, and ConvNeXt. Pre-trained models of these networks are fine-tuned using the Ensemble Pedestrian Attribute Recognition (EPAR) dataset. Several optimization techniques, including the advanced optimizer Adam with Decoupled Weight Decay Regularization (AdamW), Random Erasing (RE), and weighted loss functions, are adopted to solve issues of data unbalancing or challenging conditions like partial and occluded bodies. Experimental evaluations are performed via EPAR that contains 26993 images of 1477 person IDs, most of which are in challenging conditions. The results show that the ConvNeXt-v2-B outperforms other networks; mean accuracy (mA) reaches 85.57%, and other indices are also the highest. The addition of AdamW or RE can improve accuracy by 1-2%. The use of new loss functions can solve the issue of data unbalancing, in which the accuracy of data-less attributes improves by a maximum of 14% in the best case. Significantly, when the attribute filtering algorithm is applied, the results are dramatically improved, and mA reaches an excellent value of 94.85%. Utilizing the state-of-the-art attribute extraction model with optimization techniques on the large-scale and diverse dataset and attribute filtering has shown a good approach and thus has a high potential for practical applications.
Dephasing-assisted preparation of asymmetric steering in coupled quantum wells

It is shown that the dephasing rates are usually harmful for quantum correlations in various systems. Nevertheless, we explore that the dephasing rates in the coupled quantum wells (QWs), as a major contribution of the decay processes, can assist to generate one-way Einstein-Podolsky-Rosen (EPR) steering. By applying two strong fields to drive two dipole-allowed transitions while the other transitions are coupled with two quantized modes, the asymmetric EPR steering is possible to obtain at steady state through a single-pathway dissipation in the three-well system. According to dressed-state and Bogoliubov mode transformation, we find that the dephasing rates play a role in modifying the dressed-state populations and the dissipation rate through multiple quantum interference mechanisms. The positive effect of the dephasing rates from a nanostructure on quantum correlation is expected to find potential applications in quantum information processing.
Aspect-Level Sentiment Analysis of Graph Convolutional Networks Based on Position and Sentiment Knowledge

To address the problems that the graph convolutional network model using syntactic dependencies is susceptible to performance degradation due to the influence of syntactic noise when dealing with spoken and multivariate online comment data and the sensitivity of the model to the extraction of sentiment features of aspect words due to the existence of multiple pairs of dependencies between aspect words and contexts, this paper proposes a graph convolutional network model based on position and sentiment knowledge. Based on the syntactic dependencies combined with the positional relationships between aspect words and contexts, a positional weight adjacency matrix is constructed to reduce the influence of sentence noise. At the same time, the sentiment enhancement graph network is constructed using external sentiment knowledge to enhance the sentiment representation of contextual words that depend on aspect words to solve the problem of decreasing model sensitivity. According to the results of comparison experiments and ablation experiments on openly available datasets, the model proposed in this study executes better than the benchmark model in terms of reducing the impact of sentence noise and improving the sensitivity of sentiment feature capture.
Corpus Generation for Multi-class Bangla Email Classification and Its Evaluation

Email is an important communication tool used worldwide, but it also exposes people to various threats such as spam emails, which can result in theft, blackmailing, and phishing. Unfortunately, there is no spam email detection tool available for the Bangla language, despite it being widely used on the internet and email This makes Bengali people are vulnerable to serious threats as spammers send emails in Bangla to avoid filters. There is a huge number of emails sent in Bangla and these Bangla emails suffer from such problems. Though three is no dataset available for Multi-class Bangla Email Classiflcation, T'his paper explores generating a corpus for Bangla emails using important features that help to filter out Bangla emails into multi-classes. We generate a collection of 7000 Bangla emails to address the problem. We also evaluate the features selected of the emails for multi class classification.
Dialogues between teacher training, digital information and communication technologies (TDIC) and curriculum.

In this article I present some reflections on the use of emerging technologies by teachers and their difficulties in inserting them in their teaching methodologies. I also present some reflections about the school curriculum and the technologies as teaching-learning tools. The emerging technologies as pedagogical tools generate fears in many teachers when it comes to using them in the classroom as a mechanism for students' learning. In many cases, teachers with higher education degrees have suffered resistance to using ICT’s (Information and Communication Technology) in the classroom. We know their importance in the basic formation of the student and their effectiveness in the teaching-learning process. In this article, I present some reflections about the teachers' training, which has been a bottleneck regarding the use of technologies as part of the lesson plan formation and their use in the daily life of the teacher and the student.
Foreign Aid Promotes Human Development in Bangladesh: Empirical Evidence from Governmental and Nongovernmental Aid

Foreign aid is the international flow of money, commodity and services from governments in advanced countries and international institutions to developing ones for the benefit of the receiving country. Bangladesh is a developing country receiving a billion dollars of foreign aid in the last few decades. This paper taking the data from 1990-2021, empirically tests the effectiveness of foreign aid in the promotion of human development in Bangladesh. Using multivariable multiple regressions, this paper finds that foreign aid positively impacts HDI and its sub-indices of income, education and health. Moreover, this paper finds that foreign aid coming through the nongovernmental channel is more effective than foreign aid coming through the governmental channel though the volume of funds received by the NGOs is just one-tenth of total foreign aid. Our findings suggest that foreign donors should consider allocating more funds through nongovernmental channels in order to speed up the human development and political stability of the country.
An Improved BM3D Algorithm Based on Image Depth Feature Map and Structural Similarity Block-Matching

We propose an improved BM3D algorithm for block-matching based on UNet denoising network feature maps and structural similarity (SSIM). In response to the traditional BM3D algorithm that directly performs block-matching on a noisy image, without considering the deep-level features of the image, we propose a method that performs block-matching on the feature maps of the noisy image. In this method, we perform block-matching on multiple depth feature maps of a noisy image, and then determine the positions of the corresponding similar blocks in the noisy image based on the block-matching results, to obtain the set of similar blocks that take into account the deep-level features of the noisy image. In addition, we improve the similarity measure criterion for block-matching based on the Structural Similarity Index, which takes into account the pixel-by-pixel value differences in the image blocks while fully considering the structure, brightness, and contrast information of the image blocks. To verify the effectiveness of the proposed method, we conduct extensive comparative experiments. The experimental results demonstrate that the proposed method not only effectively enhances the denoising performance of the image, but also preserves the detailed features of the image and improves the visual quality of the denoised image.
AN ANALYSIS STUDNTS’ ABILITY IN READING COMPREHENSION AT THE SECOND SEMESTER AT EDUCATION PROGRAM OF MUHAMMADIYAH UNIVERSITY OF BENGKULU

The research was aim to describe about students’ ability levels in reading comprehension at the second semester Muhammadiyah University of Bengkulu. This method of this study was descriptive qualitative. The research instrument was a reading comprehension test to show the students’ reading ability based on the theory of (Roe, 2011). There were; main idea, specific information, reference, inference, and vocabulary. The results of the study, it was only analysis reading ability in vocabulary got very high score with the percentage 90.90%. And the four rest abilities got very low score. To be considered, reading is an important part for English language students. It is obvious that we have to be good reading ability in reading comprehension because reading is each part four language skills. The researcher are hoped to teach the students about strategy in reading subject, specially to improve reading ability in main idea, specific information, reference, inference.&#x0D; Keywords: Reading Ability, Levels, Comprehension
The Impact of Social Media, Information and Communication Technology (ICT) on Reading Habit

This systematic review study goals to carefully examine the influence of social media and information technology on the reading habit. With the fastdevelopment of technology and the universalimpact of social media in contemporary society, concerns have arisen regarding their effects on traditional reading practices. The study synthesizes existing research to provide a complete understanding of how social media and information technology have prejudiced reading habits across various individuals. By analyzing the literature, we recognize both positive and negative impacts, shedding light on possible strategies to mitigate the encounters and exploit on the opportunities presented by these technologies. The findings highlight the need for a balanced approach that harnesses the benefits of technology while preserving and enhancing reading habits
Preface: Proceedings of the Workshop on Requirements Engineering (WER2023)

Engineering Workshop at PUC-RS in Porto Alegre, Rio Grande do Sul, Brazil.The event brought together participants from both academia and industry who actively engaged in fruitful discussions over these 3 days, encompassing topics such as Usability, Transparency, Data Protection, Smart Cities, Safety Requirements, Modeling, and Knowledge Management Requirements.
Comparison of integrable Blockchain technologies in the processes of digitalization of sectors of the national economy

Today all over the world digital technologies are a priority for the development of the healthcare sector. Digitalization increases the availability of medical services and improves their quality without a significant increase in healthcare costs, while the development of digital medicine is impossible without the participation of public and private partnerships. Russia has not become an exception, where several world-wide information and analytical projects are already being implemented, one of which is Blockchain technologies. However, the question of which Blockchain technology to use is still open. In this article, the authors compare the economic and technical characteristics of the three most popular Blockchain technologies that are used in the digitalization of healthcare and give their recommendations on the digitalization of the social insurance healthcare system in the Russian Federation.