Reviewerly/exHarmony huggingface dataset:
Attention-guided and resource-saving modules for semantic segmentation

Self-attention has been proven to be a quite powerful yet calculation-intensive method for scene semantic segmentation. Even though many efforts have been made to explore more effective and resource-saving ways to apply self-attention, there is still space in reducing the calculation consumption. Meanwhile, since self-attention is good at fusing information, its application should be extended to multi-scale-feature-fusion, which is barely researched while the information exchange paths between features in different resolutions are mostly addition and concatenation. A special partition method decreasing the computational complexity of self-attention is investigated, and a multi-scale-feature-attention (MFA) module fusing low-resolution features containing semantic information with high-resolution features having detailed information is presented at the same time. To be specific, the proposed multi-scale-partition-attention (MPA) module and MFA module are inserted into the backbone in sequence to fuse information among all the pixels in one highly extracted feature and the pixels from features with different resolutions, respectively. Extensive experiments are carried out on semantic segmentation benchmarks including PASCAL-Context and Cityscapes to demonstrate that these two improved modules can improve the performance of the backbone in scene semantic segmentation tasks that contain multiple classes and objects in both big and small sizes.
Securing Databases: A Comparative Study on the Impact of Implementing SSL on MySQL 8.0.33

The increasing reliance on online communication and data storage has made protecting sensitive information a top priority for businesses and individuals. This study investigates the effectiveness of different security protocols in safeguarding databases from external threats. By comparing the performance of databases that use SSL and those that do not, this project provided valuable insights into the importance of securing data in transit and the impact of security protocols on database performance. The results of this research could be helpful for companies and individuals looking to improve the security of their databases and protect sensitive information. These research findings demonstrate that enabling SSL encryption leads to longer average execution times for database operations. This latency can be attributed to the computational workload associated with SSL's encryption and decryption processes. However, the trade-off between performance and security is essential to safeguarding data in transit.
A Study on Blockchain Technologies for Security and Privacy Applications in a Network

Integrating the Internet into many applications has made securing users' data and maintaining their privacy a significant concern.In recent years, blockchains (BC) have garnered much attention due to their distinctive properties, which include decentralization, immutability, anonymity, security, and auditability.BC technology was utilized in various nonfinancial applications, like the Internet of Things (IoT), wireless sensor networks (WSN), and cloud computing.The objective of this study is to conduct an analysis of previously published research and provide a summary of the efforts put into researching BC applications for network security.In this study, many networking technologies, including IoT, Industrial IoT, Cloud, WSN, VANET, and MANET, were used in conjunction with BC technology to investigate applications for network security.This study presents an analysis of network security, along with its limitations and contributions, with an overview of the BC evolution, BC architecture, its working principle, and its application, as well as the advantages and disadvantages associated with BC.In this study, recently published articles on BC-based solutions for network security and privacy preservation that were published between 2018 and 2022 are analyzed.The surveyed articles are categorized according to the network application, methodology, and contribution.In conclusion, an analysis of the implementation of BC technology across various networks and their issues and challenges are presented.
Practice and Discussion on Teaching Reform of Modern Control Theory Course

The rapid development of knowledge economy poses a challenge to higher education.To cultivate high-quality innovative talents, colleges and universities must carry out educational reforms and improve teaching quality.Modern control theory is the core course of automation major.In view of the characteristics of this course, such as its strong theory and relatively abstract nature, this paper focuses on teaching structure, teaching system, teaching content, teaching methods, bilingual teaching and the construction of teaching materials, and the author optimizes teaching content.And the selection of teaching materials, the reform of teaching methods, the strengthening of the connection with practical problems, the addition of experimental links, the reform of examination methods, and the combination of teaching and scientific research to discuss the reform ideas of this course.
Quantum Motif Clustering

We present three quantum algorithms for clustering graphs based on higher-order patterns, known as motif clustering. One uses a straightforward application of Grover search, the other two make use of quantum approximate counting, and all of them obtain square-root like speedups over the fastest classical algorithms in various settings. In order to use approximate counting in the context of clustering, we show that for general weighted graphs the performance of spectral clustering is mostly left unchanged by the presence of constant (relative) errors on the edge weights. Finally, we extend the original analysis of motif clustering in order to better understand the role of multiple `anchor nodes&amp;apos; in motifs and the types of relationships that this method of clustering can and cannot capture.
Exploring the Impact of Code Clones on Deep Learning Software

Deep learning (DL) is a really active topic in recent years. Code cloning is a common code implementation that could negatively impact software maintenance. For DL software, developers rely heavily on frameworks to implement DL features. Meanwhile, to guarantee efficiency, developers often reuse the steps and configuration settings for building DL models. These may bring code copy-pastes or reuses inducing code clones. However, there is little work exploring code clones’ impact on DL software. In this article, we conduct an empirical study and show that: (1) code clones are prevalent in DL projects, about 16.3% of code fragments encounter clones, which is almost twice larger than the traditional projects; (2) 75.6% of DL projects contain co-changed clones, meaning changes are propagated among cloned fragments, which can bring maintenance difficulties; (3) Percentage of the clones and Number of clone lines are associated with the emergence of co-changes; (4) the prevalence of Code clones varies in DL projects with different frameworks, but the difference is not significant; (5) Type 1 co-changed clones often spread over different folders, but Types 2 and 3 co-changed clones mainly occur within the same files or folders; (6) 57.1% of all co-changed clones are involved in bugs.
A Universal Optimization Framework for Learning-based Image Codec

Recently, machine learning-based image compression has attracted increasing interests and is approaching the state-of-the-art compression ratio. But unlike traditional codec, it lacks a universal optimization method to seek efficient representation for different images. In this paper, we develop a plug-and-play optimization framework for seeking higher compression ratio, which can be flexibly applied to existing and potential future compression networks. To make the latent representation more efficient, we propose a novel latent optimization algorithm to adaptively remove the redundancy for each image. Additionally, inspired by the potential of side information for traditional codecs, we introduce side information into our framework, and integrate side information optimization with latent optimization to further enhance the compression ratio. In particular, with the joint side information and latent optimization, we can achieve fine rate control using only single model instead of training different models for different rate-distortion trade-offs, which significantly reduces the training and storage cost to support multiple bit rates. Experimental results demonstrate that our proposed framework can remarkably boost the machine learning-based compression ratio, achieving more than 10% additional bit rate saving on three different representative network structures. With the proposed optimization framework, we can achieve 7.6% bit rate saving against the latest traditional coding standard VVC on Kodak dataset, yielding the state-of-the-art compression ratio.
AUTOMATING LIBRARY PROCESSES USING THE OSS

Organization of library work is related to such sciences as pedagogy, psychology, sociology, computer science, economics, cybernetics, linguistics. Integration with these scientific processes allows solving a number of problems of library work. Automation of library services and the use of modern software are necessary to increase efficiency and effectiveness. In addition, it is very important for libraries not only to have appropriate software and technologies, but also to be able to use them effectively. Library automation benefits both library staff and users, as it increases staff productivity and improves remote and timely delivery of up-to-date information to users. This article is mainly aimed at determining the relevance of using modern software for library automation. To achieve this goal, the document is divided into sections that highlight the relevance of library automation, and the main issues that should be considered when choosing software for libraries. Recommendations are made on the key factors that should prevail in order to achieve successful automation of library services through software. These recommendations will be useful to the future specialist as a methodological guide for his work on library automation.
Teaching of English at Secondary Level: A Comparative Study of Public Schools and Madaris of District Mianwali

The study was designed to compare Public Schools and Madarisof District Mianwali regarding teaching of English as a subject and to find out satisfaction of students with English teachers. Multi-stage sampling method was used to draw sample. At first stage 6 public schools were selected through simple random sampling method and all 6 Madaris where English was taught were selected. At second stage, 120 students of 8th and 9th class were selected from each school and Madrisa by giving equal representation. At third stage, 12 English teachers of 8th and 9th class were selected through simple random sampling from public schools and 12 teachers from Madaris were selected. Results of the study showed that reading and writing were given some attention by the English teachers of both Public Schools and Madaris while listening and speaking skills were not given attention by teachers. Teachers of English of both types of institutions were using simple method of grammar translation but students of Public Schools were able to understand easy English without translation in Urdu as compared to students of Madaris who were not able to do that. Moreover, students were satisfied with their teachers. It is recommended that professional training of teachers should be arranged in order to update them about English language
Media Big Book Alphabet Recognition in Early Childhood

Students at kindergarten can learn the alphabet through a Big Book-based lesson. It helps them master the alphabet. The goals of this research are to (1) determine the extent to which Big Book media are being used to teach the alphabet in kindergarten, (2) examine the ways in which teachers are using Big Book media to teach the alphabet, and (3) draw conclusions about the implications of utilising Big Book media to teach students to recognise individual letters. Because kindergarteners often struggle to recognise and recall the alphabet, teachers often resort to Big Book media. To aid children's memorisation and speed with letter recognition, the Big Book includes a section devoted to the alphabet. Qualitative methods of description were used in this investigation. Observation, interviews, and thorough documentation are the backbone of the data collection process. The three phases of data analysis are reducing data, presenting data, and making conclusions. The study's findings suggest that (1) Big Book media improves children's letter recognition when compared to other media. (2) In this kindergarten, teachers use Big Book media for teaching letter recognition since it is more engaging and straightforward for young students. Three implications of Big Book media, which is used as a medium to teach kids the alphabet in kindergarten, based on markers of progress towards quicker and easier letter recognition.
Exploring students’ online self-regulated learning on writing skills at Pesantren Education in the pandemic era

Several theoretical findings have revealed that self-regulated Learning strategies can contribute to making good writers. This study focuses on using an online SRL strategy to improve the writing skills of pesantren students. A self-regulated Learning strategy is used for learners to set a goal, plan, and integrate their learning independently. Data are collected from 131 university students of Pesantren for 16 (sixteen) weeks. Two groups: experimental and control are the same duration, but only the experimental group receives the intervention. All groups get pre-and post-test writing as well as the SRL questionnaire. The findings demonstrate that when learners use SRL in writing skills on metacognitive, social behavior, and motivational control tactics, they are more engaged and driven. Furthermore, the SRL method can raise the level of linguistics. Keywords: Academic writing; online learning; self-regulated learning; Pesantren education
Evolutionary Augmentation Policy Optimization for Self-Supervised Learning

Self-supervised Learning (SSL) is a machine learning algorithm for pretraining Deep Neural Networks (DNNs) without requiring manually labeled data. The central idea of this learning technique is based on an auxiliary stage aka pretext task in which labeled data are created automatically through data augmentation and exploited for pretraining the DNN. However, the effect of each pretext task is not well studied or compared in the literature. In this paper, we study the contribution of augmentation operators on the performance of self supervised learning algorithms in a constrained settings. We propose an evolutionary search method for optimization of data augmentation pipeline in pretext tasks and measure the impact of augmentation operators in several SOTA SSL algorithms. By encoding different combination of augmentation operators in chromosomes we seek the optimal augmentation policies through an evolutionary optimization mechanism. We further introduce methods for analyzing and explaining the performance of optimized SSL algorithms. Our results indicate that our proposed method can find solutions that outperform the accuracy of classification of SSL algorithms which confirms the influence of augmentation policy choice on the overall performance of SSL algorithms. We also compare optimal SSL solutions found by our evolutionary search mechanism and show the effect of batch size in the pretext task on two visual datasets.
InkGAN: Generative Adversarial Networks for Ink-And-Wash Style Transfer of Photographs

In this work, we present a novel approach for Chinese Ink-and-Wash style transfer using a GAN structure. The proposed method incorporates a specially designed smooth loss tailored for this style transfer task, and an end-to-end framework that seamlessly integrates various components for efficient and effective image style transferring. To demonstrate the superiority of our approach, comparative results against other popular style transfer methods such as CycleGAN is presented. The experimentation showcased the notable improvements achieved with our proposed method in terms of preserving the intricate details and capturing the essence of the Chinese Ink-and-Wash style. Furthermore, an ablation study is conducted to evaluate the effectiveness of each loss component in our framework. We conclude in the end and anticipate that our findings will inspire further advancements in this domain and foster new avenues for artistic expression in the digital realm.
Enhancing Image Caption with LSTMs and CNN

With the advancements in machine learning, the fields of computer vision and natural language processing have gathered large amount of attention and development. One particular application that makes use of both of these technologies is image captioning. Image captioning is a process where the computer is trained to understand the visual contents of an image. Meaningful descriptions with high-level image semantics can be generated that not only recognize the object and the scene of an image but also draw meaning about the relationship between these objects. Image captioning is a complicated and difficult task, and large amounts of improvements and significant developments have taken place in the last few years. In this project, we use a combination of computer vision and natural language processing to create an accurate and efficient method for image captioning using deep neural networks and also perform a comprehensive comparative study on these models. We also introduce the representation of the different types of data and models used for this task, describe the evaluation metrics, and summarize the benefits of each model and the major challenges that come with it.
Analysis of Supervised Domain of Cybersecurity for Fraud Detection Through Machine Learning

In the current scenario a constant rise of cybercrime has been observed in the cyberspace. There has not only been an increase in the number of cyber criminals in the past few years but also an increase in the level of technology and methods used to commit such malicious attacks in the cyberspace. This paper discusses algorithms used in the supervised domain of machine learning such as regression algorithms especially linear regression, polynomial regression, logistic regression as well as other algorithms in the supervised domain of machine learning like Naive bayes, K-nearest neighbors (KNN), Support vector machines (SVM), Decision Trees, Random Forests and other algorithms have been discussed. It can be used for identification, classification, and optimization of malware control techniques of cybersecurity systems and also identify the particular type of algorithm in which it is best suited for dealing. The kinds of malware as well as the drawbacks of certain kinds of algorithms in certain situations and how they can be improved or made more efficient have also been discussed.
The Application of Mind Mapping in Junior High School English Reading Teaching from the Perspective of Core Literacy

From the perspective of core literacy, junior high school English discipline attaches importance to the cultivation of students' language ability, thinking quality, cultural character and learning ability.As a visual thinking tool, the application of mind map can effectively realize the interconnection and comprehensive improvement of these four aspects in reading teaching.This paper mainly starts from the connotation of the core literacy of junior high school English subjects, summarizes the application significance of mind mapping in junior high school English reading education from three perspectives, namely, cultivating language ability, cultivating cultural awareness and discovering reading blind spots.On this basis, combined with the author's own teaching experience, this paper proposes the application strategy of mind mapping in junior high school English reading teaching from the perspective of core literacy.That is to say, it carries out specific analysis and discussion on three levels, namely, combing the surface information of the article, summarizing the main content of the article and constructing the hierarchical catalog of the article, in order to promote the innovation of teaching concept, teaching form and teaching means under the background of the new era.
Investigation and Analysis on the Current Situation of Career Development of College Teachers

Career development planning is a key issue in the development of university teachers, and it is also an important foundation for their success in the field of education and contributions to society.After investigating and analyzing some universities, it was found that university teachers face challenges such as a lack of incentive mechanisms, low sense of belonging, and lack of understanding of career planning.Over time, this can lead to instability in the teaching staff and insufficient personal potential development.The development of teachers in higher education is not only related to the survival and development of universities, but also closely related to the survival interests of teachers themselves.In order to change this situation, this article conducts a detailed analysis and proposes countermeasures: strengthening confidence in responding to teacher career development, establishing an effective teacher incentive mechanism in the university system, establishing an evaluation system beneficial to teacher career development, and establishing a lifelong learning awareness.The best positioning of a university teacher is to plan their career in a timely manner, utilize their strengths and knowledge, make contributions to the construction of socialist education with Chinese characteristics within their own abilities, and gain a sense of achievement and honor in the pursuit of teaching career.
“I think this could be a big success” – A mixed methods study on practitioner perspectives on the acceptance of a virtual reality tool for preparation in MRI

Introduction A key part of a radiographer's role within MRI is providing the required emotional support to help patients succeed with a scan. Being informed is important; whilst information leaflets and videos are commonly used, these can be limited in their representation of the experience. Virtual reality tools are being shown to reasonably replicate a scan experience, having a positive impact on patient satisfaction and anxiety. The aim was to obtain the views of practitioners on the use and implementation of such a tool in practice. Methods A mixed methods study was conducted looking at the use of a virtual scan experience for patients prior to MRI. Nine radiographers attended two focus group sessions to see the tool and undergo a virtual experience. Following this, a survey based on the technology acceptance model was completed along with a semi-structured discussion about its use. Results Perceived usefulness, ease of use, attitude and intention to use were all positive towards the virtual scan tool. All practitioners saw value in such a tool and how it could be implemented within practice, highlighting areas for improvement and development. Conclusion The practitioner's perspective was that access to such a virtual scan experience could be of use to better prepare and support those patients needing extra support before a real scan. Acknowledgement of having time to discuss patient concerns was noted and this could provide a means of doing so away from busy scanning lists whilst not taking up additional time. Implications for practice Use of VR tools could be a conduit through which trust and rapport are built in advance away from busy scanning lists, thereby not impacting on operational throughput and hindering efficiency.