Reviewerly/exHarmony huggingface dataset:
GAN, CNN and ELM Based Breast Cancer Detection

The most prevalent kind of cancer among females is known as breast cancer. One of the most significant challenges facing modern medicine is improving the accuracy of breast cancer screenings. In addition to lowering costs associated with treatment, accurate diagnosis of cancer, regardless of whether it is benign or malignant, is of utmost importance. Image processing is a tool that can detect breast cancer in its early stages without causing any damage. The processing of images comes with a number of inherent uncertainties that can be caused by a variety of factors, including the sampling to noise ratio, the initial digitalization, the intensity, and the special domain. We put the Extreme Learning Machine (ELM) to work classifying, identifying, and instructing. This research suggests a GAN-CNN-ELM-based technique for detecting breast cancer. This method produces an accuracy of about 99% which outperforms the GAN-CNN model.
Blockchain-IoT based device authentication protocol for Healthcare Multimedia

This study employed a secure location-based de-centralized device to device (D2D) authentication model where IoT devices would authenticate mutually, utilizing a blockchain network. For this authentication, Elliptical Curve Cryptography features are integrated for secure data transmission. This ar-ticle considers Ethereum blockchain technology for IoT-device registration, their authentication process, data storage, and attestation. This model is utilized in the Healthcare system for transmitting video and image data. The performance of the pro-posed authentication IoT framework was evaluated concerning parameters such as throughput, response time, count of error data packets, and energy consumption rate compared with other fog-nodes and cloud servers. This analysis proved the better efficiency of the proposed framework.
Analysis of international research trends in online music education

본 연구는 온라인 음악교육의 기초 연구로서 해외 온라인 관련 음악교육의 연구 동향 고찰을 목적으로 한다. 국제 전문 학술지에 수록된 온라인 음악교육 관련 연구 75편을 연도와 국가, 연구 주제 및 대상, 연구 방법의 범주에서 분석한 결과는 다음과 같았다. 첫째, 2000년대 이후 출간된 온라인 관련 음악교육 연구는 2000년대 10.7%, 2010년대 41.3%로 증가하였으며, 2020년대에는 2020년부터 2022년의 3년간임에도 불구하고 48.0%를 차지하는 급격한 증가를 보였다. 둘째, 온라인 관련 음악교육 연구가 가장 활발하게 이루어지는 나라는 미국으로 전체 연구의 29.3%를 차지하였으며, 다음으로 영국과 다국적 연구가 각각 9.3%를 차지하였다. 셋째, 가장 많이 연구된 주제는 온라인 교수·학습 방법으로 41.3%를 차지하였으며, 다음으로 온라인 교육에 대한 인식 및 경험이 29.3%로 나타났다. 이외에도 온라인 음악교육에서 미디어와 기술 측면을 고찰한 연구와 교사교육 연구도 수행되었다. 넷째, 연구 대상은 두 가지 이상의 연구 대상이 참여한 복합 대상의 비중이 28.0%로 가장 높았으며, 다음으로 교사가 25.3%로 나타났다. 다섯째, 연구 방법은 질적 연구가 54.7%로 과반수를 차지하였고, 다음으로 양적, 질적 자료를 모두 활용한 혼합 연구가 24.0%로 나타났다.This pilot study aims to examine international research trends in online music education. The following categories of online music education studies published in professional journals were analyzed: year of publication, author's country, research topic, subject, and research method. The results were as follows: first, a total of 75 studies on online music education published showed a steady increase of 10.7% in the 2000's and 41.3% in the 2010's, followed by a rapid increase during the 2020's. Papers published from 2020-2022 accounted for a high proportion, 48%, of the total articles. Second, research on online music education is most active in the United States, accounting for 29.3% of the total research. Next, British and multinational studies accounted for 9.3% each. Third, the most frequently examined topic was teaching and learning methods, 41.3%, followed by perceptions and experience of online education, 29.3%. Studies on media and technology also accounted for 16%, and research on teacher education accounted for 13.3%.ﾠA variety of subjects, such as students, teachers, professional musicians, and adults of various ages and vocations, participated in the studies and the results showed that a combination among the groups was the highest (28%), followed by teachers at 25.3%. Fifth, qualitative research accounted for the majority, 54.7%, followed by mixed method, 24%.
Aspect-level sentiment analysis model based on syntactic information and part of speech encoding

Aspect-level sentiment analysis aims to predict the sentiment polarity of various aspect words in a sentence. Recent studies have pointed out that the sentiment polarity of aspect words often depends on the local context that is highly correlated with aspect words. However, there are still some problems in the existing work: a) syntactic information is not fully utilized; b) the method of weight assignment to local context is not scientific enough; c) the importance of parts of speech is ignored. Aiming at the existing problems, an aspect-level sentiment analysis model based on syntactic information and part-of-speech encoding is proposed. The model innovatively proposes a pre-training module that integrates syntactic information, introduces a part-of-speech encoding component, and designs a new dynamic weight assignment method. The proposed model is experimented on three publicly available datasets, and the experimental results show that the model achieves better results on a macro scale than existing models.
Improved YOLOv5 badminton detection algorithm and embedded implementation

Aiming at the requirements of real-time badminton classification detection with high accuracy and lightweight under the limited resources of embedded platform, a badminton classification detection method based on improved YOLOv5 is proposed. (1) The Ghost Net network structure is adopted to compress the overall architecture of YOLOv5's backbone network layer, reduce the complexity and computation of the network, and realize the lightweight design of the algorithm; (2) The attention mechanism CBAM is introduced into C3 module to enhance the feature perception of badminton target; (3) Use a smoother Mish function to improve the Swish activation function of YOLOv5, so that feature information can flow in a deeper network layer. The experimental results show that the proposed method obtained batter performance than original YOLO V5. The detection frame rate is 55 frames/second, which shows that the algorithm is effective and meets the requirements of real-time high-precision detection.
A Survey on Few-Shot Class-Incremental Learning

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta-learning based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective.
M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation

Cross-domain recommendation (CDR) is an effective way to alleviate the data sparsity problem. Content-based CDR is one of the most promising branches since most kinds of products can be described by a piece of text, especially when cold-start users or items have few interactions. However, two vital issues are still under-explored: (1) From the content modeling perspective, sufficient long-text descriptions are usually scarce in a real recommender system, more often the light-weight textual features, such as a few keywords or tags, are more accessible, which is improperly modeled by existing methods. (2) From the CDR perspective, not all inter-domain interests are helpful to infer intra-domain interests. Caused by domain-specific features, there are part of signals benefiting for recommendation in the source domain but harmful for that in the target domain. Therefore, how to distill useful interests is crucial. To tackle the above two problems, we propose a metapath and multi-interest aggregated graph neural network (M2GNN). Specifically, to model the tag-based contents, we construct a heterogeneous information network to hold the semantic relatedness between users, items, and tags in all domains. The metapath schema is predefined according to domain-specific knowledge, with one metapath for one domain. User representations are learned by GNN with a hierarchical aggregation framework, where the intra-metapath aggregation firstly filters out trivial tags and the inter-metapath aggregation further filters out useless interests. Offline experiments and online A/B tests demonstrate that M2GNN achieves significant improvements over the state-of-the-art methods and current industrial recommender system in Dianping, respectively. Further analysis shows that M2GNN offers an interpretable recommendation.
Entanglement-assisted quantum error-correcting codes from subfield subcodes of projective Reed-Solomon codes

We study the subfield subcodes of projective Reed-Solomon codes and their duals: we provide bases for these codes and estimate their parameters. With this knowledge, we can construct symmetric and asymmetric entanglement-assisted quantum error-correcting codes, which in many cases have new or better parameters than the ones available in the literature.
Special Issue on Recent Advances in Machine Learning and Computational Intelligence

Machine learning and computational intelligence are currently high-profile research areas attracting the attention of many researchers [...]
Deep Unrestricted Document Image Rectification

In recent years, tremendous efforts have been made on document image rectification, but existing advanced algorithms are limited to processing restricted document images, i.e., the input images must incorporate a complete document. Once the captured image merely involves a local text region, its rectification quality is degraded and unsatisfactory. Our previously proposed DocTr, a transformer-assisted network for document image rectification, also suffers from this limitation. In this work, we present DocTr++, a novel unified framework for document image rectification, without any restrictions on the input distorted images. Our major technical improvements can be concluded in three aspects. Firstly, we upgrade the original architecture by adopting a hierarchical encoder-decoder structure for multi-scale representation extraction and parsing. Secondly, we reformulate the pixel-wise mapping relationship between the unrestricted distorted document images and the distortion-free counterparts. The obtained data is used to train our DocTr++ for unrestricted document image rectification. Thirdly, we contribute a real-world test set and metrics applicable for evaluating the rectification quality. To our best knowledge, this is the first learning-based method for the rectification of unrestricted document images. Extensive experiments are conducted, and the results demonstrate the effectiveness and superiority of our method. We hope our DocTr++ will serve as a strong baseline for generic document image rectification, prompting the further advancement and application of learning-based algorithms. The source code and the proposed dataset are publicly available at https://github.com/fh2019ustc/DocTr-Plus.
An Automated Human Action Recognition and Classification Framework Using Deep Learning

Human activity recognition has captivated the interest of researchers due to its significant applications such as smart home health care systems in which this technology can be applied to enhance the patients' rehabilitation. Different sensors can be used in a variety of ways to recognize human activity in a smartly managed environment. It is also used in pedestrian detection, robotics and human-computer interface, etc. Hence, with the development of Artificial Intelligence, researchers are keen to solve problems related to human action recognition and classification. We propose a novel method using Deep Learning (DL) algorithm for the task of human action recognition. The suggested framework is trained and evaluated on a publically available database containing recorded movements performed by both male and female participants. We tested various DL architectures and their parameters by changing epochs, learning rates, batch size, and optimizers before reaching the final architecture. The optimal architecture consists is trained on 6 epochs, a mini-batch of 128 on an adam optimizer, and a 0.001 learning rate. The system attained the highest accuracy of 98% on unseen test samples. The results prove the method's robustness and can be deployed for real-time human activity recognition and classification.
An efficient CNN-based Automated Leukemia diagnosis Using microscopic blood smear images and Subtypes Classification

Leukemia is a form of blood cancer that damages the cells in the blood and bone marrow of the human body. It produces cancerous blood cells that disturb the human's immune system and significantly affect bone marrow's production ability to effectively create different varieties of blood cells like red blood cells (RBCs) and white blood cells (WBC), and platelets. Different kinds of manual methods have been used, but all these techniques are slow, labour-intensive, inaccurate, and need a lot of human experience and dedication. To deal with such manual methods, different researchers used different machine learning algorithms to classify the cells into normal and blast cells. However, still, the problem is complex blood characteristics. In this paper, we have proposed a robust diagnosis system to classify leukemia and its subtypes. Acute lymphocytic leukemia (ALL) is classified into subtypes based on FAB classification, such as L1, L2 and L3 types with better performance. Our model outperformed as compared to other state-of-the-art approaches.
Detailed Investigation on Blockchain Smart Contracts Application and its Usefulness

The blockchain and digital industry has emerged in the last decade as a result of the swift advancement of blockchain technology and virtual currencies, which has had an impact on the banking sector. However, owing to the advent of smart contracts, which seem to be the computer systems created to naturally enable, monitor, & execute the negotiations and agreements between several unreliable individuals, successful decentralised apps without engaging a reliable third party have arisen. Despite the good aspects of smart contracts, proliferation is still hindered by a number of obstacles, including risks, weaknesses, and statutory provisions. This article provides a thorough intellectual and applied examination of block chain-enabled smart contracts. Moreover, the study gives a classification of current blockchain -enabled smart contract solutions, and discusses the emergence of smart contract-based research by reviewing the existing research works. Based on the study's findings, a variety of challenges & open issues that must be investigated are highlighted. Notably, security and privacy challenges along with rising costs, scalability and lack of adoption are identified as the key challenges of blockchain smart contract application. Ultimately, the potential patterns are emphasized.
Side-Informed Steganography for JPEG Images by Modeling Decompressed Images

Side-informed steganography has always been among the most secure approaches in the field. However, a majority of existing methods for JPEG images use the side information, here the rounding error, in a heuristic way. For the first time, we show that the usefulness of the rounding error comes from its covariance with the embedding changes. Unfortunately, this covariance between continuous and discrete variables is not analytically available. An estimate of the covariance is proposed, which allows to model steganography as a change in the variance of DCT coefficients. Since steganalysis today is best performed in the spatial domain, we derive a likelihood ratio test to preserve a model of a decompressed JPEG image. The proposed method then bounds the power of this test by minimizing the Kullback-Leibler divergence between the cover and stego distributions. We experimentally demonstrate in two popular datasets that it achieves state-of-the-art performance against deep learning detectors. Moreover, by considering a different pixel variance estimator for images compressed with Quality Factor 100, even greater improvements are obtained.
A New Velocity Planning Method for Autonomous Robots with Varying Mass

Mobile robots are designed to perform different tasks in many different fields such as production, fast delivery, defence industry and space exploration.Advances in mobile robots are crucial as they can perform tasks that are difficult, dangerous or inefficient for humans.In this context, we discuss the improvement of velocity planning in the rapidly evolving field of autonomous mobile robots.In this study, we focus on mobile robots with high variance in their weight.Mobile robots can transport loads or autonomous personal transportation vehicles such as wheelchairs, which can be given as an example of these types of systems.This study focuses specifically on linear velocity planning for providing the same performance for all payloads on the mobile robot considering comfort and safety.For this aim, we propose a new velocity planning method based on fuzzy logic which has several advantages over previous methods.The suggested algorithm is tested in simulations using certain performance measurement metrics.After the observation of successful results, realworld experimental tests are performed to prove the real-time applicability of the proposed approach.
Bibliographic Index of the Contents of the Journal “Bibliotekovedenie”. To the 70th Anniversary of the Periodical

In 2022, the Publishing House “Pashkov Dom” of the Russian State Library released the bibliographic work “Journal ‘Bibliotekovedenie’: Index of Contents 1952—2021”. The publication was prepared by the Research Department of Bibliography of the Russian State Library for the 70th anniversary of the scientific and practical journal. The article briefly describes all the milestones of the leading professional publication: from the periodical scientific collection “Libraries of the USSR. Work experience” up to the contemporary journal “Bibliotekovedenie”. Materials of the periodical have significant scientific value. The compilers of the bibliographic index ordered and systematized publications, as well as characterized previous similar works — cumulative in-journal indexes of contents and the only index of 1984 until 2022 published by the V.I. Lenin State Library of the USSR. The author highlights the number of differences between the previous indexes dedicated to the journal and the 2022 edition, among them are the completeness of coverage of the material, the structure and methodology of systematization, the availability of reference apparatus, etc. The reference apparatus of the new edition consists of five auxiliary indexes (index of names, personalia, library and information organizations, peer-reviewed publications and library and information serials). The book includes four reference appendices (the list of editors-in-chief, members of the editorial board and the editorial council; bibliographic list of publications about the journal in the professional press; the list of acronyms of library and information organizations; the list of bibliographic sources). In the process of compiling the index, the compilers encountered difficulties at the stage of grouping the material. Over the entire period of the journal existence, publishing standards and drawing up of the publication have changed many times. The compilers executed the painstaking work to identify the authorship of individual publications, as well as the identity in the names of repeatedly renamed library and information organizations, and to reveal data on the personalia mentioned in the articles. The article gives the examples of drawing up of the index. The index of publications of the main professional periodical from 1952 to 2021 reflects the processes of development of science, evolution, transformation of scientific knowledge and makes the basis for the work of researchers at present, being the significant mechanism of scientific activity.
Analysis of the Influence of Effective Organization Training on Physical Fitness Test of College Students

In May 2022, the author had the honor to participate in the physical health test of students in Zhejiang Province with the tutor, and took this opportunity to make statistics and analysis on the test results of 984 students in five colleges and universities based on the physical health test data of nine colleges and universities in Zhejiang Province, according to the relevant requirements of the Student Physical Health Standards, using literature, project testing, expert interviews, mathematical statistics, logical analysis and other methods.And from the perspective of tester organization and management, this paper understands and analyzes the current situation of physical health of college students in Zhejiang Province, as well as the shortcomings of the school management in the early effective organization, management and preparation of provincial physical health tests, as well as physical health spot checks and tests.From a point to area perspective, it timely adjusts the school physical education curriculum, revises unreasonable parts of textbooks, and adjusts the school management to provide relevant opinions and suggestions.