Reviewerly/exHarmony huggingface dataset:
Enhanced Pelican Optimization Algorithm with Deep Learning-Driven Mitotic Nuclei Classification on Breast Histopathology Images

Breast cancer (BC) is a prevalent disease worldwide, and accurate diagnoses are vital for successful treatment. Histopathological (HI) inspection, particularly the detection of mitotic nuclei, has played a pivotal function in the prognosis and diagnosis of BC. It includes the detection and classification of mitotic nuclei within breast tissue samples. Conventionally, the detection of mitotic nuclei has been a subjective task and is time-consuming for pathologists to perform manually. Automatic classification using computer algorithms, especially deep learning (DL) algorithms, has been developed as a beneficial alternative. DL and CNNs particularly have shown outstanding performance in different image classification tasks, including mitotic nuclei classification. CNNs can learn intricate hierarchical features from HI images, making them suitable for detecting subtle patterns related to the mitotic nuclei. In this article, we present an Enhanced Pelican Optimization Algorithm with a Deep Learning-Driven Mitotic Nuclei Classification (EPOADL-MNC) technique on Breast HI. This developed EPOADL-MNC system examines the histopathology images for the classification of mitotic and non-mitotic cells. In this presented EPOADL-MNC technique, the ShuffleNet model can be employed for the feature extraction method. In the hyperparameter tuning procedure, the EPOADL-MNC algorithm makes use of the EPOA system to alter the hyperparameters of the ShuffleNet model. Finally, we used an adaptive neuro-fuzzy inference system (ANFIS) for the classification and detection of mitotic cell nuclei on histopathology images. A series of simulations took place to validate the improved detection performance of the EPOADL-MNC technique. The comprehensive outcomes highlighted the better outcomes of the EPOADL-MNC algorithm compared to existing DL techniques with a maximum accuracy of 97.83%.
Image denoising and segmentation model construction based on IWOA-PCNN

Abstract The research suggests a method to improve the present pulse coupled neural network (PCNN), which has a complex structure and unsatisfactory performance in image denoising and image segmentation. Then, a multi strategy collaborative improvement whale optimization algorithm (WOA) is proposed, and an improved whale optimization algorithm (IWOA) is constructed. IWOA is used to find the optimal parameter values of PCNN to optimize PCNN. By combining the aforementioned components, the IWOA-PCNN model had the best image denoising performance, and the produced images were crisper and preserve more information. IWOA-PCNN processed pictures have an average PSNR of 35.87 and an average MSE of 0.24. The average processing time for photos with noise is typically 24.80 s, which is 7.30 s and 7.76 s faster than the WTGAN and IGA-NLM models, respectively. Additionally, the average NU value measures 0.947, and the average D value exceeds 1000. The aforementioned findings demonstrate that the suggested method can successfully enhance the PCNN, improving its capability for image denoising and image segmentation. This can, in part, encourage the use and advancement of the PCNN.
Trustworthy Healthcare Cloud Storage Auditing Scheme (TCSHAS) with blockchain-based incentive mechanism

Abstract The Trustworthy Healthcare Cloud Storage Auditing Scheme (TCSHAS) represents a progressive solution for resolving trust-related issues linked to third-party auditors (TPAs) within traditional healthcare cloud storage audit systems. As the healthcare industry increasingly relies on cloud storage, concerns surrounding security and privacy have grown, motivating the development of a unique incentive mechanism. This mechanism leverages the non-tamperable and traceable features of blockchain technology to encourage TPAs to uphold honesty and reliability. By organizing TPAs as a group of nodes on the blockchain, a system of mutual surveillance is established, enabling diligent monitoring and penalization of any malicious actions. TCSHAS encompasses a comprehensive system model that incorporates smart contracts to manage transaction-related matters, including dispute resolution. Performance evaluations have confirmed its efficiency and suitability for real-world healthcare applications. Our experiments demonstrate that TCSHAS performs well in terms of gas consumption and exhibits scalability as the number of participant’s increases. Compared with other common smart contracts, TCSHAS maintains a balanced level of complexity, incorporates strong security measures, offers comprehensive auditing capabilities, and remains flexible. To further enhance our research, we can explore ways to optimize TCSHAS scalability and performance, such as through shading, as the volume of healthcare data continues to grow. In addition, we can investigate the integration of advanced privacy technologies or AI-based auditing. These advancements will reinforce the role of TCSHAS in establishing trust and security in healthcare cloud storage audits, making it highly relevant in real-world healthcare settings. Ultimately, TCSHAS contributes to improving trust and security in healthcare cloud storage auditing, ensuring responsible management of sensitive healthcare data.
Women Safety Portable Hidden Camera Detector

The women's safety portable hidden camera detector is a small, easy-to-use tool that helps people—women in particular—protect their security and privacy. The gadget uses cutting-edge camera detection technologies, such as infrared and RF signal recognition, to locate hidden cameras in a variety of settings. When a hidden camera is found, the detector notifies users through both visual and auditory alerts, all while maintaining a discrete and portable design. Furthermore, this innovative device empowers individuals to reclaim their sense of security, making it an invaluable tool for personal privacy in an increasingly interconnected world. Its discreet and portable design ensures that users can carry it with ease, offering peace of mind wherever they go. With the women's safety portable hidden camera detector, one can step confidently into any environment, knowing they have a reliable shield against potential privacy breaches and safeguard their personal space.
Supervising Automated Decisions

There is a broad consensus that human supervision holds the key to sound automated decision-making: if a decision-making policy uses the predictive outputs of a statistical algorithm, but those outputs form only part of a decision that is made ultimately by a human actor, use of those outputs will not (per se) fall foul of the requirements for due process in public and private decision-making. Thus, the focus in academic and judicial spheres has been on making sure that humans are equipped and willing to wield this ultimate decision-making power. Yet, proprietary software obscures the reasons for any given prediction; this is true both for machine learning and deterministic algorithms. And without these reasons, the decision-maker cannot accord appropriate weight to that prediction in their reasoning process. Thus, a policy of using opaque statistical software to make decisions about how to treat others is unjustified, however involved humans are along the way.
GLOBALIZATION OF EDUCATION: VITAL APPROACHES IN STEM EDUCATION

The article deals with the contemporary situation in education sphere in terms of globalization process, describing the key aspects of the globalization of education, as well as the main aspects of STEM implementation. The article highlights various technologies of STEM system that are widely used all over the world. Among the most common are augmented and virtual reality, artificial intelligence, internet of things and others. Much attention is paid to recourses needed for implementation of STEM into learning process. Online Courses, Educational Websites, Open-Source Platforms, STEM Education Kits, Maker Spaces and Fab Labs, STEM Competitions and Challenges as well as many others were viewed in the article. The process of implementation of these resources and technologies was considered on the basis of “Smart Heat” educational project that can serve as a perfect example of successful combination of these approaches into educational process. It's worth noting that the selection of technologies and resources for STEM learning should align with the educational goals. They introduce practical knowledge and emphasize the importance of hands-on, experiential learning that promotes critical thinking, problem solving, and creativity.
JEJE in improving elementary students' speaking skills, what learning models and strategies are suitable?

This research has a background of 1) How are teacher teaching activities in improving speaking skills 2) How are student activities in improving speaking skills 3) How are student learning outcomes improving speaking skills in learning Indonesian? This study has the objectives (1) to determine teacher activity in learning Indonesian through the application of the Jigsaw Cooperative learning model in class, V. 2) to determine student activity in learning Indonesian, and (3) to determine the improvement in students' speaking skills. This type of research is Classroom Action Research (PTK) which is conducted in two cycles of action. The subjects of this study were teachers and all fifth-grade students at SD Qiswa An-Nafi, consisting of 17 people. The procedures in this research are (1) planning, (2) implementation, (3) observation and evaluation, and (4) reflection. This study's data type is qualitative data from observation sheets and quantitative data from student learning outcomes. After analyzing the research in cycle I, it was found that 70.58% of 12 students succeeded in achieving completeness, and 29.41% of 5 students still need to complete it. The average student score in that cycle is 63.64. In cycle II, the percentage of completeness increased to 94.11% or 16 students, while the percentage of students who did not complete was only 5.88%. The average student score in cycle II was 80.82. By using the jigsaw-type cooperative learning model
Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

The relationships among different subregions in facial images and their varying contributions to facial expression recognition indicate that using a fixed subregion weighting scheme would result in a substantial loss of valuable information. To address this issue, we propose a facial expression recognition network called BGA-Net, which combines bidirectional gated recurrent units (BiGRUs) with an attention mechanism. Firstly, a convolutional neural network (CNN) is employed to extract feature maps from facial images. Then, a sliding window cropping strategy is applied to divide the feature maps into multiple subregions. The BiGRUs are utilized to capture the dependencies among these subregions. Finally, an attention mechanism is employed to adaptively focus on the most discriminative regions. When evaluated on CK+, FER2013, and JAFFE datasets, our proposed method achieves promising results.
Hate Speech Detection in Low-Resource Bodo and Assamese Texts with ML-DL and BERT Models

Hate speech detection research is a recent sizzling topic in natural language processing (NLP). Unburdened uses of social media platforms make people over-opinionative, which crosses the limit of leaving comments and posts toxic. A toxic outlook increases violence towards the neighbour, state, country, and continent. Several laws have been introduced in different countries to end the emergency problem. Now, all the media platforms have started working on restricting hate posts or comments. Hate speech detection is generally a text classification problem if considered a supervised observation. To tackle text in terms of computation perspective is challenging because of its semantic and complex grammatical nature. Resource-rich languages leverage their richness, whereas resource scarce language suffers significantly from a lack of dataset. This paper makes a multifaceted contribution encompassing resource generation, experimentation with Machine Learning (ML), Deep Learning (DL) and state-of-the-art transformer-based models, and a comprehensive evaluation of model performance, including thorough error analysis. In the realm of resource generation, it adds to the North-East Indian Hate Speech tagged dataset (NEIHS version 1), which encompasses two languages: Assamese and Bodo.
Time Series Analysis and Forecasting of Solar Generation in Spain Using eXtreme Gradient Boosting: A Machine Learning Approach

The rapid expansion of solar photovoltaic (PV) generation has established its pivotal role in the shift toward sustainable energy systems. This study conducts an in-depth analysis of solar generation data from 2015 to 2018 in Spain, with a specific emphasis on temporal patterns, excluding weather data. Employing the powerful eXtreme gradient boosting (XGBoost) algorithm for modeling and forecasting, our research underscores its exceptional efficacy in capturing solar generation trends, as evidenced by a remarkable root mean squared error (RMSE) of 11.042, a mean absolute error (MAE) of 5.621, an R-squared (R²) of 0.999, and a minimal mean absolute percentage error (MAPE) of 0.046. These insights hold substantial implications for grid management, energy planning, and policy development, reaffirming solar energy’s promise as a dependable and sustainable contributor to the electrical power system’s evolution. This research contributes to the growing body of knowledge aimed at optimizing renewable energy integration and enhancing energy sustainability for future generations.
A New Multi-level Hazy Image and Video Dataset for Benchmark of Dehazing Methods

The changing level of haze is one of the main factors which affects the success of the proposed dehazing methods. However, there is a lack of controlled multi-level hazy dataset in the literature. Therefore, in this study, a new multi-level hazy color image dataset is presented. Color video data is captured for two real scenes with a controlled level of haze. The distance of the scene objects from the camera, haze level, and ground truth (clear image) is available so that different dehazing methods and models can be benchmarked. In this study, the dehazing performance of five different dehazing methods/models is compared on the dataset based on SSIM, PSNR, VSI, and DISTS image quality metrics. Results show that traditional methods can generalize the dehazing problem better than many deep learning-based methods. The performance of deep models depends mostly on the scene and is generally poor on cross-dataset dehazing.
Decision Support System to Determine Higher Education Resource Needs Using Accreditation Assessment Standards

Accreditation is an activity to evaluate the eligibility of programs and academic units based on predetermined criteria. In an educational institution, accreditation is an important thing to do to improve quality in the field of education because the aim is to improve quality which includes all aspects of education in the form of science, administration, as well as teaching, and educational staff. College managers really need a decision support system that can produce several alternative strategic decisions quickly and accurately. The existence of decision support system tools used to make strategic decisions makes the manager's work lighter and the results are more accurate. In this study, a decision support system approach was applied to determine the resource requirements of tertiary institutions using accreditation assessment standards consisting of a management subsystem model, data management subsystem, and user interface management subsystem. The result of the research is a design of a decision support system that can be used in an educational institution for determining the need of higher education resources.
The Problem-based Learning Model: PBL Model via Cloud Technology to Promote Programming Skills

The problem-based learning model via cloud technology (PBL model via cloud technology) is a research tool fabricated with the concepts of problem-based learning management, in which students are stimulated and enabled to foresee the problems that will arise. Also, in this learning style, teachers will define the problem situations and encourage students to develop their systematic analytical thinking skill by taking action through the cloud technology. Thus, it is believed that this learning model can be used as a guideline for the instruction management that can promote students to have thinking process and problem-solving process while developing their programming skills. The objectives of this research are (1) to synthesize the conceptual framework of the PBL model via cloud technology, (2) to develop the PBL model via cloud technology, and (3) to study the results of the PBL model via cloud technology. The results of this research show that (1) the overall elements suitability of the PBL model via cloud technology is at the highest level (Mean = 4.77, SD. = 0.44), and (2) the overall suitability of the PBL model via cloud technology is at the highest level (Mean = 4.74, SD. = 0.39). Referring to the research results above, it can be summarized that the PBL model via cloud technology can be employed as a guideline to further develop the PBL systems via cloud technology in order to promote the programming skills among vocational students in Thailand.
On hierarchical clustering-based approach for RDDBS design

Abstract Distributed database system (DDBS) design is still an open challenge even after decades of research, especially in a dynamic network setting. Hence, to meet the demands of high-speed data gathering and for the management and preservation of huge systems, it is important to construct a distributed database for real-time data storage. Incidentally, some fragmentation schemes, such as horizontal, vertical, and hybrid, are widely used for DDBS design. At the same time, data allocation could not be done without first physically fragmenting the data because the fragmentation process is the foundation of the DDBS design. Extensive research have been conducted to develop effective solutions for DDBS design problems. But the great majority of them barely consider the RDDBS's initial design. Therefore, this work aims at proposing a clustering-based horizontal fragmentation and allocation technique to handle both the early and late stages of the DDBS design. To ensure that each operation flows into the next without any increase in complexity, fragmentation and allocation are done simultaneously. With this approach, the main goals are to minimize communication expenses, response time, and irrelevant data access. Most importantly, it has been observed that the proposed approach may effectively expand RDDBS performance by simultaneously fragmenting and assigning various relations. Through simulations and experiments on synthetic and real databases, we demonstrate the viability of our strategy and how it considerably lowers communication costs for typical access patterns at both the early and late stages of design.
An Ensemble of Deep Convolutional Neural Networks Models for Facial Beauty Prediction

Facial beauty prediction is an emerging topic. The pursuit of facial beauty is the nature of human beings. As the demand for aesthetic surgery has increased significantly over the past few years, an understanding beauty is becoming increasingly important in medical settings. This work proposes a new ensemble based on the pre-trained convolutional neural network (CNN) models to identify scores for facial beauty prediction. These ensembles were originally built from the following previously trained models: DenseNet-201, Inception-v3, MobileNetV2, and EfficientNetB7. According to the SCUT-FBP5500 benchmark dataset, the proposed model obtains a Pearson coefficient of 0.9469. This reveals that the suggested EN-CNNs model can be successfully applied in a variety of face-to-face applications.
Copyright Issues in Artificial Intelligence: A Comprehensive Examination from the Perspectives of Subject and Object

In the era of rapid technological advancement, artificial intelligence (AI) has emerged as a transformative force across various domains. However, this widespread integration of AI technology has given rise to complex intellectual property challenges, particularly in the realm of copyright. This paper explores the intricate landscape of AI copyright issues, addressing disputes related to training data, the copyright status of AI entities, and ownership attribution for creative works generated by AI. From an object perspective, AI-generated creations undeniably meet the requirements of originality and merit copyright protection, irrespective of civil law or common law frameworks. However, from a subject perspective, the legal recognition of independent copyright status for AI is currently lacking in most jurisdictions. Consequently, it is argued that the relationship between AI and its human operator should be legally characterized as a tool-based association, essential for addressing ownership concerns regarding AI-generated works. Furthermore, AI gives rise to novel copyright infringement quandaries, such as data utilization and incorporation of copyrighted works, presenting challenges in definition and enforcement within existing legal frameworks. Thus, there is a pressing need to refine and enhance relevant laws to adequately address these emerging challenges. As AI technology continues to progress, the copyright conundrums associated with it are poised to evolve, necessitating profound and extensive discourse within the realms of law and intellectual property.
Multi-attention TransUNet—A Transformer Approach for Image Description Generation

Image description generation is a task that requires the translation of images into sentences that describe the image. In deep learning terms, this task is described as an efficient connection of vision and language. This paper proposes an efficient and fast transformer-based approach for image description generation using TransUNet and multi-attention decoder. The vision-based TransUNet extracts local as well as global contexts of the image, thus providing detailed embedding information for the decoder architecture to predict words using context. This helps the multi-modal architecture to understand the relationship between the image and the sentences, thus performing the task efficiently and faster than other transformer-based counterparts. The experimental results have been compared to existing published works demonstrating that the approach used in this paper has achieved state-of-the-art (SOTA) performance in terms of multi-model architecture for the given task.
Statistics of Russian Book Publishing in the First Half of 2023

The authors present the main statistical indicators of domestic book publishing for the first half of 2023. 13 tables with comments give a clear picture of the number of book titles and brochures, their circulation, themes, types and types of publications, the main publishers, printers and the most popular authors in the field of fiction for adults and children. Particular attention is paid to the problem of electronic copies of printed legal deposits of publications. The decline of two main statistical figures of the first half of 2023 in comparison with the same period of 2022 came to 11,37% for the total number of titles of books and pamphlets and 17,48% for their total print runs.