Reviewerly/exHarmony huggingface dataset:
Enhancing classification performance through multi-source online transfer learning algorithm with oversampling

Multi-source online transfer learning uses the tagged data from multiple source domains to enhance the classification performance of the target domain. For unbalanced data sets, a multi-source online transfer learning algorithm that can oversample in the feature spaces of the source domain and the target domain is proposed. The algorithm consists of two parts: oversampling multiple source domains and oversampling online target domains. In the oversampling phase of the source domain, oversampling is performed in the feature space of the support vector machine (SVM) to generate minority samples. New samples are obtained by amplifying the original Gram matrix through neighborhood information in the source domain feature space. In the oversampling phase of the online target domain, minority samples from the current batch search for k-nearest neighbors in the feature space from multiple batches that have already arrived, and use the generated new samples and the original samples in the current batch to train the target domain function together. The samples from the source domain and the target domain are mapped to the same feature space through the kernel function for oversampling, and the corresponding decision function is trained using the data from the source domain and the target domain with relatively balanced class distribution, so as to improve the overall performance of the algorithm. Comprehensive experiments were conducted on four real datasets, and compared to other baseline algorithms on the Office Home dataset, the accuracy improved by 0.0311 and the G-mean value improved by 0.0702.
Deep Learning Based Enhanced Text Recognition System

The branch of artificial intelligence called “deep learning” enables the computer to take on and learn new rules. Deep learning algorithms are capable of recognizing objects, photos, messages, observations, and other structures. The underwhelming performance of the current scene recognition algorithms has motivated many researchers in the computer vision field in recent years, yet scene text recognition still need improvement. This study offered an innovative method for scene text recognition that uses a CraftNet model with enhanced learning capabilities. In the proposed approach, edge information is first collected from the images after image enhancement using a median filter. The deep learning model is given these edge characteristics, so it can recognize text area and build a bounding box around it. The discovered region is next transformed into text and then, using a text-to-speech conversion model, into voice. The efficacy of the suggested model is confirmed by comparison with the existing models.
Bisimulations for Fuzzy Geometric Models

The purpose of this paper is to develop coalgebraic fuzzy geometric logic by incorporating modalities into the fuzzy geometric logic language. The modal operators are introduced in the fuzzy geometric logic language using coalgebraic techniques. We introduce the idea of fuzzy-open predicate lifting in order to define the modal operators. Based on coalgebras for an endofunctor T on the category $${\textbf {Fuzzy-Top}}$$ of fuzzy topological spaces, we build models for the coalgebraic fuzzy geometric logic. We study the relationship between the concepts of behavioural equivalence and modal equivalence on the class of fuzzy geometric models. Furthermore, the paper studies bisimulation for fuzzy geometric models.
Jordan may soften its new cybercrimes law slightly

Headline JORDAN: Cybercrimes law may soften slightly
FAST: A Journey Toward Sustainability in Subject Indexing at the British Library

This article provides an update on progress since the partial roll-out of FAST in 2015 at the British Library. It discusses developments to the product and the provision of community interaction with FAST via a FAST funnel, ensuring the vocabulary is robust and flexible enough to meet the continued needs of Legal Deposit workflows. It describes the planning and implementation methods used in rolling out FAST to the majority of cataloging workflows at the British Library leading to extensive training over the autumn of 2022.
Dictionary-Based Transfer Learning for One-Class Text Classification

One-class classification requires only positive samples to build the model. In this paper, we propose a novel method for one-class text learning based on transfer learning and dictionary learning (DTOC). We integrate transfer learning and dictionary learning into one-class text model. In order to so
Research on Node Control of Blockchain Network Based on Reputation Model

The rapid development of the Internet has driven the network technology to all walks of life, and the generation of massive data flow has caused huge pressure on the traditional centralized data management platform.The data information management system built can provide a better solution for data control nowadays.To this end, this paper researches the consensus algorithm to make the nodes of blockchain network reach consistency, addresses the lack of node behavior control problem of practical Byzantine fault tolerance (PBFT) algorithm, quantitatively evaluates the node reputation by combining the custom reputation model with PBFT algorithm, optimizes the selection of master nodes in the consensus process, and completes the real-time update and full monitoring of node reputation, which is essential for It has a good role in improving the security and reliability of blockchain system consensus.The scheme has certain theoretical and practical significance to solve the problem of safe storage of data in the market and improve the governance system of data.
Overview of Computer Networks for Data Transmission at Pondok Kopi Islamic Hospital, Jakarta, Indonesia

Network quality monitoring can provide services in the provision of computer network performance in the provision of data transmission according to the level of aspects related to the performance of network infrastructure systems and the number of clients operating for each computer. During the day, at certain hours, many subscribers use the network when the connection is blocked, and the graphics on the screen reach their highest levels. By monitoring computer network installation graphics, Axence netTools software needs to know bandwidth management to overcome network congestion by analyzing the QoS (quality of service) of computer network data transmission. The purpose of this study is to provide quality of service presentation material on LAN infrastructure performance using the TIPHON standard to measure LAN performance and analyze the factors that influence the results of quality of service measurements and obtain packet loss, delay, jitter based on class classification and average bandwidth value, according to TIPHON. The research method used is observation, interviews, literature study, monitoring, and qualitative descriptive research methods. The results showed that the percentage value of quality of service based on the TIPHON standardization was 66.66%. This result was classified as "unsatisfactory" because the average packet loss was 40%. The average percentage of delay is 115ms, and the average percentage of jitter is 7.5ms. The factors that affect the QoS index results are the distance of computer network transmission facilities and the need to monitor computer networks when adding and maximizing bandwidth according to TIPHON standards.
An Extended Labanotation Generation Method Based on 3D Human Pose Estimation for Intangible Cultural Heritage Dance Videos

To address the issues of low accuracy in existing 3D human pose estimation (HPE) methods and the limited level of details in Labanotation, we propose an extended Labanotation generation method for intangible cultural heritage dance videos based on 3D HPE. First, a 2D human pose sequence of the performer is inputted along with spatial location embeddings, where multiple spatial transformer modules are employed to extract spatial features of human joints and generate cross-joint multiple hypotheses. Afterward, temporal features are extracted by a self-attentive module and the correlation between different hypotheses is learned using bilinear pooling. Finally, the 3D joint coordinates of the performer are predicted, which are matched with the corresponding extended Labanotation symbols using the Laban template matching method to generate extended Labanotation. Experimental results show that, compared with VideoPose and CrossFormer algorithms, the Mean Per Joint Position Error (MPJPE) of the proposed method is reduced by 3.7[Formula: see text]mm and 0.6[Formula: see text]mm, respectively on Human3.6M dataset, and the generated extended Labanotation can better describe the movement details compared with the basic Labanotation.
FEFR: Feature early fusion and reconstruction for online knowledge distillation

Online knowledge distillation breaks the pre-determined strong and weak teacher-student models, it provides a new way of thinking about knowledge distillation. However, the current online methods often use the Logits-based prediction distribution, and the features containing rich semantic information are rarely used. Even if the feature-based methods are used, they only operate on the last layer of the network, without further exploring the representation knowledge of the middle layer feature map. To address the above issues, we propose an innovative feature early fusion and reconstruction (FEFR) method for online knowledge distillation which entails four essential components: multi-scale feature extraction and intermediate layer feature early fusion, reconstruction of features, dual-attention and overall fusion module in this paper. We propose early fusion by “sum” operation for feature matrices between different layers and advance fusion to improve the feature map representation. In order to enhance the communication ability between groups to obtain features, the features were reconstructed. We create a dual-attention to enhance the critical channel and spatial regions adaptively in order to collect more accurate information. The previously processed feature maps are combined and fused using feature fusion, which also aids in student models training. A study of the network architectures of CIFAR-10, CIFAR-100, CINIC-10 and ImageNet 2012 shows that FEFR provides more useful characterization knowledge for refinement and improves accuracy by about 0.5% compared to other methods.
PENGEMBANGAN MEDIA PEMBELAJARAN P3 (PAPAN PUTAR PINTAR) PADA MATERI PROKLAMASI KEMERDEKAAN DI INDONESIA UNTUK PEMBELAJARAN IPS KELAS IV SDN UNGGULAN KARANGGENENG

Development of P3 Media (Smart Playboard) on the Material of the Proclamation of Independence in Indonesia for Class 4 Social Studies Learning at SDN Unggulan Karanggeneng. Thesis, Elementary School Teacher Education Program. Faculty of Teacher Training and Education. Muhammadiyah Gresik University (supervised by Mr. Arya Setya Nugroho and Mr. Nanang Khoirul Umam, M.Pd). This study aims to describe the process of development and quality of P3 media on the material for the Proclamation of Independence for fourth grade elementary school students. In 2021/2022. The research model used in this development research is the ADDIE model. The subjects in this study were fourth grade students at SDN Unggulan Karanggeneng, totaling 18 students. The learning model used during implementation uses the first 3 models TGT, IBL, Discussion. The data collection technique for this research is to use validation sheets and student questionnaires. Analysis of the data obtained from the validation sheet of media experts, the graphic aspect obtained a percentage of 90% and the linguistic aspect obtained a percentage of 83.3%. while the presentation aspect material expert validation obtained a percentage of 91.6% and the content feasibility aspect obtained a percentage of 91.6% and the criteria were very good.
Google and Microsoft find new quasiparticle

The existence of so-called non-Abelian anyons has been reported by four independent teams, but plenty of challenges remain before they can transform quantum computing. Philip Ball explains.
Postmodern Artistic Discourse in the Training of Students in Pedagogical Specialties

This text puts the primary emphasis on the relationship of postmodern time and artistic discourses in the context of the Higher School. It attempts to analyse the advantages and disadvantages of postmodernism in relation to artistic texts that can be used in pedagogues’ training. Postmodernism is a whole movement of ideas that changes perspectives on both learning and teaching, and it is up to us to use them successfully. Particular attention has been paid to reading as a cultural and pedagogical phenomenon, which significantly determines the direction in understanding and interpreting artistic texts for instructional purposes. The message of this study is that the artistic pedagogical discourse and texts of postmodernism have new characteristics and issues that are to be known and applied correctly by teachers in their professional thinking
LO SCRITTORE SULLA SOGLIA: IL MOTIVO PIRANDELLIANO DELL’INCONTRO CON I PERSONAGGI IN ALCUNI RACCONTI DI PRIMO LEVI

This paper starts from the textual cooperation postulated by Umberto Eco’s interpreta- tive semiotics (1989) and from the notion of Bakhtin’s Menippean satire (2001), together with the theoretical instruments of the narrative semantics of Lubomir Doležel (2008). We ana- lyze some stories of Primo Levi and the presence of the motif of the author’s encounter with the characters, considered a variant of the topos of the encounter with the dead in its twenti- eth-century declination (Luperini: 2007) which characterized Pirandello’s work. We believe this approach could be fruitful and promising for future research, both for Levi’s other works and those by other authors, such as Elio Vittorini, Đorđe Lebović and Danilo Kiš, as well.
Application Exploration of Encouraging Teaching Method in EFL Teaching from the Perspective of Krashen's Input Hypothesis

The independence of the students in their own characters make the effect of foreign language acquisition different in English foreign language teaching; to put it another way, English is more difficult to learn than other foreign languages for those students who acquire English as a second language.Previous studies have shown that emotion is one of the most important factors affecting foreign language acquisition.Emotional factors take many forms, such as self-esteem, risk-taking, anxiety, empathy, extroversion/introversion. To study the effect of Encouraging Teaching Method on students' ability of foreign language acquisition in English teaching, this paper explores the application of "Encouraging Teaching Method" in English teaching based on Krashen's Input Hypothesis theory, explains and analyzes its strengths and weaknesses in more detail, and finally concludes that the proper use of "Encouraging Teaching Method" can better improve the quality of education and instruction.
Preliminaries

The first sections in this chapter provide necessary mathematical background for the rest of the book. As we shall soon see, we will reduce some musical problems to problems regarding numbers. The last sections are a crash course in music, necessary as well to provide a common language which will be used throughout the rest of the book. If the reader is already familiar with the material, quickly browsing through will do.
The pricing decisions for the cloud service supply chain: service quality and power structure

The cloud service supply chain is a current paradigm that enables cloud service to meet the complex and diverse combination needs of cloud users and promote the development of the cloud computing industry. In this research, we construct a two-level supply chain structure composed of infrastructure service providers and cloud software service providers and study the balanced pricing decisions of all cloud service supply chain entities under different subject positions. This paper analyses the impact of different market structures, competition intensity, service quality, and other factors on the equilibrium price. We can conclude that when the market competition intensity is strong, the supply chain members are more willing to choose their leading structure; the replacement rate of products will increase the level of cloud service quality and the price. Under different market competition intensities, when the cloud software service provider dominates, it will choose a lower-price strategy to reduce the impact of the increase in service substitution rate. Our conclusions can help enterprises formulate pricing strategies to meet the needs of different users' service quality.
The Pigeonhole Principle

InPigeonhole principle this chapter we begin to introduce the mathematics necessary to understand why it is that the musical scale we just saw on the pianoPiano keyboard with seven white and five black keys, 12 notes, has exactly that number of notes. Why not less or more? We shall soon understand why.
Research teacher in middle school: the theory and cultivation strategies

Becoming a research teacher is an important goal for every teacher's professional development.This paper firstly introduces the definition of research teacher, then discusses the meaning and development direction of being a research teacher from a theoretical level, and finally discusses effective strategies for becoming a research teacher from a practical level, including the teacher's perspective and the school' s perspective.
A Novel Framework for Satellite Image Denoising and Super Resolution Using CNN-GAN

Denoising and Super-Resolution are two challenging tasks in the area of Computer Vision. Benefits of the results are applicable in many domains. Many applications of Deep Learning and other methodologies have made significant achievements in this field. Generative Adversarial Network advancement takes researcher attention towards this area due to results obtained by GAN. In this paper, We focused on the speckle noise denoising and super-resolution of satellite images. Speckle noise is multiplicative noise captured in satellite images by airborne acquisition devices. Here, we propose a Generative Adverasarial Network which is responsible for noise removal as well as 4-times magnification of satellite images. Generative adversarial network proposed in this paper consists two layered generator as well as discriminator network. The first Layer of generator which is multi layered CNN is responsible for extracting noise from given input images, for learning noise mapping from image sample, we used mean square error function for learning this mapping. While lower or second layer of generator are used for magnification of images which employed combined mean loss function and adversarial loss function. Apart from generator network, we used discriminator network responsible for separating high-resolved images and real images, which make generator more robust. Our discriminator network also used adversarial loss function. The Generative adversarial network reconstructed more realistic textured images from heavily degaraded quality images. Standard satellite database images are used for experiments, and results generated by the GAN are superior performance of state-of-art in both the term of quantitavely as well as visually over existing similar methods.
Peran Pengelola Perpustakaan dalam Menumbuhkan Budaya Gemar Membaca Siswa Tunarungu di Perpustakaan SLB-B Pangudi Luhur Jakarta

The decline in reading culture in school students is an issue that comes to the fore today. The government and schools have made various efforts to develop a reading culture. Many roles should be involved so that the student is in an environment that always encourages him to read hard. Library managers are an essential part of having a role in fostering a culture of reading. This study aims to determine the role and constraints of library managers in motivating the reading interest of deaf students in the library of SLB-B Pangudi Luhur a Jakarta. This study aims to determine library managers' roles and constraints in motivating the reading interest of deaf students. This research is descriptive with a qualitative approach and the case study method. Data collection techniques are carried out through interviews and literature reviews. Using B. F. Skinner's Operant Conditioning Learning Theory, research shows that library managers play their role in motivating deaf students' reading interest through mandatory library programs. The activities carried out by library managers in the program include storytelling, using picture books, composing for students to imagine, and collaborating with teachers. Library managers carry out these activities to encourage deaf students to read, especially when there are obstacles in the process of absorbing their ear language and difficulties in generating emotions such as anxiety, fear, anger, depression, and low communication resulting in a lack of self-confidence. Obstacles experienced by library managers in motivating students include the lack of adequate human resources and the lack of availability of new books. The manager of the SLB-B Pangudi Luhur Jakarta Library applies the oral/aural verbal communication method in communicating to support and make it easier to motivate the reading interest of deaf students.
All-function Integrated Silicon Photonic Tensor Core (PTC) AI Accelerator

Here we present our architecture for Silicon Photonic Tensor Core (PTC) capable of accelerating computational needs of neural networks and augmented/virtual reality applications. We present a novel fully-integrated PTC including chip-based lasers, modulators, and photodetectors.
Autobiographical Memory Functions of Healthy Older Adults &amp; Their Associations With Self-Rated Health

Abstract Date Presented 04/22/2023 Autobiographical memory function is categorized into three types according to the purpose of recalling personal episodic memories. We found a negative association between autobiographical memory function and self-rated health of healthy older adults. Primary Author and Speaker: Inhye Kim Additional Authors and Speakers: Hyunseo An, Sangmi Park Contributing Authors: Jiwon Shin
External-Memory Sorting with Comparison Errors

We provide several algorithms for sorting an array of n comparable distinct elements subject to probabilistic comparison errors in external memory. In this model, which has been extensively studied in internal-memory settings, the comparison of two elements returns the wrong answer according to a fixed probability, $$p_e<1/2$$ , and otherwise returns the correct answer. The dislocation of an element is the distance between its position in a given (current or output) array and its position in a sorted array. There are various existing algorithms that can be utilized for sorting or near-sorting elements subject to probabilistic comparison errors, but these algorithms do not translate into efficient external-memory algorithms, because they all make heavy use of noisy binary searching. In this paper, we provide new efficient methods that are in the external-memory model for sorting with comparison errors. Our algorithms achieve an optimal number of I/Os, in both cache-aware and cache-oblivious settings.