Reviewerly/exHarmony huggingface dataset:
An alternately optimized generative adversarial network with texture and content constraints for deformable registration of 3D ultrasound images

Abstract Objective. 3D ultrasound non-rigid registration is significant for intraoperative motion compensation. Nevertheless, distorted textures in the registered image due to the poor image quality and low signal-to-noise ratio of ultrasound images reduce the accuracy and efficiency of the existing methods. Approach. A novel 3D ultrasound non-rigid registration objective function with texture and content constraints in both image space and multiscale feature space based on an unsupervised generative adversarial network based registration framework is proposed to eliminate distorted textures. A similarity metric in the image space is formulated based on combining self-structural constraint with intensity to strengthen the robustness to abnormal intensity change compared with common intensity-based metrics. The proposed framework takes two discriminators as feature extractors to formulate the texture and content similarity between the registered image and the fixed image in the multiscale feature space respectively. A distinctive alternating training strategy is established to jointly optimize the combination of various similarity loss functions to overcome the difficulty and instability of training convergence and balance the training of generator and discriminators. Main results. Compared with five registration methods, the proposed method is evaluated both with small and large deformations, and achieves the best registration accuracy with average target registration error of 1.089 mm and 2.139 mm in cases of small and large deformations, respectively. The performance on peak signal to noise ratio (PSNR) and structural similarity (SSIM) also proves the effective constraints on distorted textures of the proposed method (PSNR is 31.693 dB and SSIM is 0.9 in the case of small deformation; PSNR is 28.177 dB and SSIM is 0.853 in the case of large deformation). Significance. The proposed 3D ultrasound non-rigid registration method based on texture and content constraints with the distinctive alternating training strategy can eliminate the distorted textures with improving the registration accuracy.
Solar Irradiance Prediction and Methods Used in Prediction Studies

Solar energy is considered one of the most important renewable energy sources and is recognized as the fastest-growing energy source worldwide. The power generated in solar energy facilities primarily depends on the amount of radiation reaching the surface of photovoltaic (PV) panels. Prior knowledge of solar radiation is crucial for reliable planning and efficient design of solar energy systems. Therefore, solar radiation forecasting is a highly significant topic. Various techniques can be employed for solar radiation prediction, including fundamental physical and statistical methods, as well as ensemble methods obtained by combining different approaches. However, the remarkable success of artificial neural networks, a form of artificial intelligence application that enhances learning algorithms, in various applications has attracted researchers to this field. The promising potential of this field is evident in the richness of proposed methods and the increasing number of publications. The main objective of this study is to review artificial intelligence-based techniques found in the literature for solar radiation prediction and to identify research gaps by examining radiation predictions using machine learning-based methods and hybrid models created by their combination with other techniques, which have gained popularity recently. Additionally, the aim is to provide an analysis that guides future improvements and understanding of recent advancements in this field. To facilitate and enhance research in this area, a comprehensive review of various artificial intelligence-based prediction methods employed in solar radiation prediction studies, particularly focusing on the most commonly used artificial intelligence-based approach published recently, is presented. Furthermore, information on the required data parameters in solar radiation prediction studies is provided. All research details, fundamental features, and specifics are summarized in tabular and shape formats for a comprehensive overview.
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction

Risk prediction, usually achieved by learning representations from patient’s physiological sequence or user’s behavioral sequence data, and has been widely applied in healthcare and finance. Despite that, some recent time-aware deep learning methods have led to superior performances in such sequence representation learning tasks, such improvement is limited due to a lack of guidance from hierarchical global view. To address this issue, we propose a novel end-to-end H ierarchical G lobal V iew-guided (HGV) sequence representation learning framework. Specifically, the Global Graph Embedding (GGE) module is proposed to learn sequential clip-aware representations from temporal correlation graph (TCG) at instance level. Furthermore, following the way of key-query attention, the harmonic β-attention (β-Attn) is also developed for making a global tradeoff between time-aware decay and observation significance at channel level adaptively. Moreover, the hierarchical representations at both instance level and channel level can be coordinated by the heterogeneous information aggregation under the guidance of global view. Experimental results on both healthcare risk prediction benchmark and SMEs credit overdue risk prediction task from the real-world industrial scenario in MYBank, Ant Group, have illustrated that the proposed model can achieve competitive prediction performance compared with other known baselines. The code has been released public available at: https://github.com/LiYouru0228/HGV.
Optimal Conditioning of Growth Media White Oyster Mushroom (Pleurotus ostreatus) to Increase Productivity

The cultivation of white oyster mushrooms is the difficulty of making mushroom seedlings, especially the technology is quite expensive and the nature of mushroom growth which has two different temperatures, namely low temperature when making seedlings and medium temperature when forming mushroom fruit bodies. Therefore optimal conditioning of the growth medium of white oyster mushroom (pleurotus ostreatus) to increase production yield. Judging from the business opportunity, oyster mushrooms are among the most popular compared to other types of mushrooms. In general, white oyster mushroom cultivation is considered easy to do, as long as the living environment is well considered. Although mushrooms are more optimal to grow in the highlands (cool areas), cultivation in the lowlands can also be done, as long as the place of maintenance is conditioned the same as its natural habitat.
Critical History of Entrepreneurship in Nigeria

Entrepreneurship is a slippery concept that has been used and abused for various purposes. This article summarizes the basics of entrepreneurship and its historical development to demystify misconceptions about this important field of knowledge and practice in Nigeria. Understanding the history of entrepreneurship extends to highlighting the entrepreneurial achievements of three ethnic groups in Nigeria. Seven (7) theories of entrepreneurship that provide logical explanations for why and how individuals in different contexts become entrepreneurs (with or without opportunities) are discussed to support professional learning and academic entrepreneurship. Ultimately, when the strengths of traditional entrepreneurship are combined with modern entrepreneurship, entrepreneurship in Nigeria can be reinvigorated to improve the country's ability to create wealth, create jobs, redistribute income, reduce poverty, and improve the country's gross domestic product for maximum effect. This will be possible if all stakeholders provide the necessary institutional support to the three ethnic groups and others to accelerate the promotion of entrepreneurship in Nigeria, particularly in the areas of agriculture, livestock, retail, and services.
Retrospect and Prospect

This article reflects the initiation, establishment, and development of the International Journal of Librarianship (IJoL), an open access academic journal published by the Chinese American Librarians Association (CALA). Sustainability and high quality have been set as the top priorities by the core editorial team throughout the journey of the creation, promotion, and advancement of this journal. The opportunities, challenges, and success are discussed along with the future directions of IJoL. This article contributes to the literature in the creation and development of academic journals, especially the open access publications in library and information science (LIS).
Natural Language Processing and Text Analytics: Techniques and Applications

The combination of NLP and Text Analytics provides a powerful toolset for businesses to gain insights from their text data. This includes analyzing customer feedback, social media posts, news articles, and other text-based sources to understand customer sentiment, identify emerging trends, and gain competitive insights. However, there are challenges in the field of NLP and Text Analytics, such as understanding the context and ambiguity of natural language, dealing with noisy and unstructured data, and ensuring data privacy and security. These challenges require the development of advanced NLP and Text Analytics techniques and algorithms to address them. Text Analytics, on the other hand, is a broader term that refers to the process of extracting insights and meaning from unstructured text data. It involves the use of NLP techniques, as well as statistical and machine learning methods, to identify patterns, trends, and relationships in text data. Text Analytics can be applied to a wide range of applications, including social media analysis, customer feedback analysis, market research, and fraud detection.
Deep Learning and Neural Networks: Methods

Deep learning is a subset of machine learning that involves training artificial neural networks to perform complex tasks, such as image recognition, natural language processing, and speech recognition. Neural networks are computational models that are inspired by the structure and function of the human brain. They are composed of layers of interconnected nodes, or neurons, that process and transmit information. Deep learning algorithms allow these networks to learn and improve over time by adjusting the weights and biases of the neurons. Deep learning has revolutionized many industries, including computer vision, natural language processing, speech recognition, and autonomous driving. Its ability to learn and improve from large datasets has made it a powerful tool for solving complex problems that were previously thought to be impossible. However, deep learning also presents many challenges, such as the need for large amounts of data, high computational resources, and a lack of interpretability. These challenges have spurred research in areas such as transfer learning, explainable AI, and adversarial attacks. Despite these challenges, the potential of deep learning and neural networks is vast, and it is likely that they will continue to play an important role in shaping the future of technology.
Deep Learning and Neural Networks: Methods and Applications

Deep learning is a subfield of machine learning that involves training artificial neural networks to learn from large amounts of data. It has become an increasingly popular approach for solving complex problems in fields such as computer vision, natural language processing, and robotics. Neural networks are the foundation of deep learning, and they are used to model the relationships between input data and output predictions. Deep learning refers to the use of deep neural networks to learn and model complex relationships between input data and output predictions. Neural networks are inspired by the structure and function of the human brain, with layers of interconnected nodes that process information and make predictions. Deep neural networks are characterized by their depth, with multiple layers of nodes that enable them to model more complex relationships between inputs and outputs.
Report of 16th International Conference on Webometrics, Informetrics and Scientometrics (WIS) &amp; 21st COLLNET Meeting

The 16th International Conference on Webometrics, Informetrics, and Scientometrics (WIS) and the 21st COLLNET Meeting, 2022 which was scheduled to be held from 10 to 12 November 2022 at Chulalongkorn University, Bangkok, Thailand was organized after a two-year break, due to the Corona restrictions, by COLLNET in association with the Society for Library Professionals (SLP) and Asian Chapter, Special Libraries Association (SLA) and Chulalongkorn University, Bangkok, Thailand for the professional growth of the library and Information Science professionals. The broad focus of the conference was on collaboration and communication in science and technology; scientometric research on Covid19; techniques for collaboration studies; informatics law and distributions; mathematical models of communication and collaboration; quantitative analysis of s&amp;t innovations; evaluation indicators; collaboration in science and technology from both quantitative and qualitative points of view; bibliographic control bibliometric research data analysis and data mining; technology &amp; innovations in libraries and their impact on measurement on learning, research and users technology transfer and innovation in library management through metrics. The aim of the conference was therefore to contribute to evidence-based knowledge on scientific research and practices, which in turn can contribute to institutional, regional, national and international policy-making in the field of research and innovation. This event was part of the successful COLLNET conference series (www.collnet.de). Previous COLLNET conferences were held in Berlin (Germany) in 2000, New Delhi (India) in 2001, Sydney (Australia) in 2001, Beijing (China) in 2003, Roorkee (India) in 2004, Stockholm (Sweden) in 2005, Nancy (France) in 2006, New Delhi (India) in 2007, Berlin (Germany) in 2008, Dalian (China) in 2009, Mysore (India) in 2010, Istanbul (Turkey) in 2011, Seoul (South Korea) in 2012, Tartu (Estonia) in 2013, Ilmenau (Germany) in 2014, Delhi (India) in 2015, Nancy (France) in 2016, Kent (UK) in 2017, Macau 2018 and Dalian (China) 2019.
Teaching English vocabulary using the audio-lingual method

Language is a system of spoken, manual (signed), or written symbols in which human beings express themselves as members of a social group and participants in its culture. The audio-lingual teaching method is defined as a method that emphasizes on repetition of words to help the students to understand the use of the target language communicatively. The objective of this thesis is to measure the studentsâ€™ accomplishment in increasing their English vocabulary after using the audio-lingual method and to find the benefits of using the audio-lingual method in increasing the vocabulary for students. In finishing this research, the method used is class action research by applying mixed methods. The population of this research was students in 3A Generous of SD Shalom Semarang, which consist of 25 students. The writer used the theory of Larsen-Freeman in this research. The instrument of the research were multiple-choice tests of 15 items and short essay tests of 5 items. In finding, the score of the post-test (84,6%) is higher than the score of the pre-test (75,8%). It means that using the audio-lingual method in teaching English vocabulary gives enough effect on the third-grade studentsâ€™ accomplishments.&#x0D;
Adaptive Huffman Algorithm for Data Compression Using Text Clustering and Multiple Character Modification

Adaptive Huffman algorithm is a popular data compression technique that creates a variable-length binary code for each symbol in a message. However, the original algorithm may not be efficient in compressing text data, particularly when dealing with long sequences of repeated characters. In this study, we propose a novel approach to enhance the compression ratio of the Adaptive Huffman algorithm by utilizing text clustering and multiple character modification. The proposed method first clusters the text data into groups of similar words or phrases. Then, it modifies multiple characters in each group to reduce redundancy and increase the frequency of the most common characters. This modification enables the Adaptive Huffman algorithm to produce shorter codes for the modified characters and effectively compress the clustered text data. Experimental results on a benchmark dataset show that the proposed method achieves better compression ratios than the traditional Adaptive Huffman algorithm and other state-of-the-art compression methods. The proposed method can be applied to various text data, such as documents, emails, and chat messages, and can significantly reduce storage and transmission costs.
Factors Affecting Self-Confidence in Tertiary Education Students: A Study of English Classroom-Based Policies

English is a language that many people used in speaking and writing. This language is widely used in business, law, and education. This study determines the factors affecting self-confidence among Bachelor of Secondary Education students, addressed to first-year students in utilizing English as a primary language of instruction in Classroom-based English policy. The research followed a quantitative design and adopted a purposive sampling technique. The study was conducted virtually and took place within a month. The study findings indicated that the lack of self-confidence and apprehension of committing errors were the only factors that had a notable correlation with the student’s level of confidence in speaking English within the classroom setting. Furthermore, the study found no significant difference between self-confidence levels and the respondents’ profile characteristics. However, the findings showed that the characteristics of the students, such as gender, age, first language, parents’ educational attainment, school type, and educational resources at home, did not have an impact on their level of self-confidence. As a result, the study’s general conclusion was that the students showed a limited level of self-confidence when speaking English in the classroom environment. To enhance their speaking ability and confidence in front of peers or others, students might have to recognize their areas of weakness and focus on developing their language proficiency. The researchers also recommend conducting a symposium on boosting the self-confidence of the students.
Admission to Gongju Women’s Normal School during the Japanese Colonial Period and the Characteristics of Japanese Studying Abroad

본고는 관립공주여자사범학교의 입학전형과 일본인 유학이 지닌 특징을 파악하는데 목적을 둔다. 충청도 지역의 가장 큰 도시였던 공주지역은 근대문물을 빨리 받아들인 곳에 속한다. 전시체제기 조선총독부는여성교원 양성을 목표로 전국의 주요도시에 여자사범학교를 세웠는데그 중 한 곳이 공주여자사범학교이다. 총독부의 방침에 따라 공주여자사범학교는 전국적 학생모집과 일본에 소재한 고등여학교 졸업생의 공주여자사범학교 입학을 권장하였다. 이로 인해서 공주여자사범학교는명문학교로서 전국적인 인기를 구가하였다. 그 이유는 첫째, 우수한 인재 선발을 이유로 공주여자사범학교 입학전형 기준이 매우 높았기 때문이다. 1차로 각 도를 대표하는 학생을 우선 선발하고 다시 그 학생들을 모아서 공주여자사범학교에서 최종 선발하는 방식이었다. 둘째, ‘일본 정신’을 가르칠 여성교원으로 가장 적합한 사람은 일본인 여학생으로 여겼다. 실제로 공주여자사범학교는 일본의고등여학교에 공주여자사범학교를 홍보하는 입학지원서를 보내면서 학생모집을 홍보하였다. 이에 따라 공주지역이 유학의 최적의 장소라는인식이 국내, 국외(일본, 대만)에까지 퍼졌다. 1932년 충남도청 이전으로 행정도시의 기능을 상실하고 교육도시로의 발돋움이 필요한 지역에서는 매우 긍정적으로 인식되었다. 지역사회는 이 학교를 시작으로 교육도시라는 이미지 형성을 시도하였고 해방 후에도 교육도시라는 이미지를 확립하는 데 중요한 요인으로 작동되었다.The purpose of this paper is to understand the entrance process of the government Gongju Women’s Normal school and the characteristics of Japanese studying in Gongju. In Gongju, which was the largest city in Chungcheong-do, is one of the places that quickly embraced modern things. The Japanese Government-General of Korea established Women’s Normal Schools in major cities across the country for the purpose of fostering female teachers during the war, one of which is Gongju Women’s Normal School. Gongju Women’s Normal School encouraged students from all over the country and Japanese high school graduates to enter Gongju Women’s Normal School in accordance with the government-general’s policy. As a result, Gongju Women’s Normal School gained national popularity as a prestigious school. The reason is that, first, the admission standard for selecting excellent talents at Gongju Women’s Normal School was very high. First, repre-sentative students guaranteed by provincial governors across the country were selected first, and students were gathered again to pass the second test and finally selected for Gongju Women’s Normal School. Second, it was considered that the most suitable person as a female teacher who teaches‘Japanese spirit’ was a Japanese female student. In fact, Gongju Women’s Normal School sent applications for admission to Japanese girls’ high schools to promote student recruitment. As a result, the perception that Gongju is the best place to study abroad has spread to Korea and overseas(Japan and Taiwan). Before the relocation of the Chungnam Provincial Government in 1932, the function of the administrative city was lost and it was recognized very positively in the area where it should become an educational city. Starting with this school, the local community attempted to form an educational city image, and even after liberation, it served as an important factor in forming an educational city image.
Tinjauan Atas Penggunaan Sistem Komputerisasi Akuntansi Di Pt. Andalan Fluid System

Computerized accounting system is an accounting system where the computer as information technology is useful to run applications used in processing accounting transactions and at the same time to generate financial statements in a company. With the computerized system in the company's operational activities, especially in the recording of financial statements can facilitate and help the computer user complete his activity report effectively and efficiently. The purpose of this discussion is to find out how to use computerized accounting system in PT. Mainstay Fluid system as well as in running using the system whether there are perceived obstacles, especially in the financial part.The results of the discussion showed that the computerized accounting system used by PT. Fluid System's mainstay is to use microsoft office excel in helping to make the company's financial statements. &#x0D; &#x0D; Keywords : Computerized Accounting System, Microsoft Office Excel.
AN OVERVIEW OF STRENGTHENING ELEMENTARY EDUCATION FOR NATIONAL DEVELOPMENT: A LITERATURE REVIEW

This paper focused on empowering elementary education as an effective tool for national development. The purpose of the study is to nd out the strategies to strengthen elementary education. Education is an overall process of not only personal growth but an enriching growth of society and nation. It is the foundation of development of any nation. Educated youth have the ability to think beyond their stagnating lives and contribute to the development of their societies. Education arms individuals with the knowledge of their rights and duties. It is informed citizenry which forms the base for the development of any society (Ram 2020).