Reviewerly/exHarmony huggingface dataset:
A novel approach to enhance the quality of health care recommender system using fuzzy-genetic approach

In recent generations of the digital world medical data in Recommender Systems. Health Care Recommender System (HCRS) analyses the medical data and then predicts the user’s or patient’s illness. Nowadays, healthcare data is used by various users or patients in recommendation systems which are useful for everyone. Analysing and predicting medical data provides awareness to users and these data predictions may be enriched using various techniques of RS. Machine learning techniques are used to make sure that health data is reliable and of high quality. In every RS the issues are targeted such as scalability, sparsity and cold start problems. In many social networking applications, these issues are resolved using ML algorithms. However, there is a significant gap between IT systems and medical diagnosis. The fuzzy genetic method is used in HCRS in order to bridge the gap between IT and healthcare applications. Through the use of the mutation and crossover operators, a real-value genetic method is used in this to compute similarity. With the user’s extra personalized information, fuzzy rules are later generated for the database. The Hybrid fuzzy-genetic method, also known as this situation, combines both techniques to improve recommendation quality. Utilizing this method will improve the quality of the recommendation process by discovering the most precise similarity measures among different users. Six factors are subjected to fuzzification, including age, gender, employment, height, weight, and region. Genre-interesting measure weights are then used, including Very Light, Light, Average, Heavy, and Very Heavy. Finally, the evaluation metrics used MAE and RMSE to evaluate the recommendation accuracy which showed the best results in comparison with baseline approaches such as Convolutional Neural Networks and Restricted Boltzman Machine.
A Study of Optimization in Deep Neural Networks for Regression

Due to rapid development in information technology in both hardware and software, deep neural networks for regression have become widely used in many fields. The optimization of deep neural networks for regression (DNNR), including selections of data preprocessing, network architectures, optimizers, and hyperparameters, greatly influence the performance of regression tasks. Thus, this study aimed to collect and analyze the recent literature surrounding DNNR from the aspect of optimization. In addition, various platforms used for conducting DNNR models were investigated. This study has a number of contributions. First, it provides sections for the optimization of DNNR models. Then, elements of the optimization of each section are listed and analyzed. Furthermore, this study delivers insights and critical issues related to DNNR optimization. Optimizing elements of sections simultaneously instead of individually or sequentially could improve the performance of DNNR models. Finally, possible and potential directions for future study are provided.
Observations on the Intertextuality of Selected Abhidharma Texts Preserved in Chinese Translation

Textual reuse is a fundamental characteristic of traditional Buddhist literature preserved in various languages. Given the sheer volume of preserved Buddhist literature and the often-unmarked instances of textual reuse, the thorough analysis and evaluation of this material without computational assistance are virtually impossible. This study investigates the application of computer-aided methods for detecting approximately similar passages within Xuanzang’s translation corpus and a selection of Abhidharma treatises preserved in Chinese translation. It presents visualizations of the generated network graphs and conducts a detailed examination of patterns of textual reuse among selected works within the Abhidharma tradition. This study demonstrates that the general picture of textual reuse within Xuanzang’s translation corpus and the selected Abhidharma texts, based on computational analysis, aligns well with established scholarship. Thus, it provides a robust foundation for conducting more detailed studies on individual text sets. The methods employed in this study to create and analyze citation network graphs can also be applied to other texts preserved in Chinese and, with some modifications, to texts in other languages.
Enhancement of broadband entangled two-photon absorption by resonant spectral phase flips

Broadband energy-time entanglement can be used to enhance the rate of two-photon absorption (TPA) by combining a precise two-photon resonance with a very short coincidence time. Because of this short coincidence time, broadband TPA is not sensitive to the spectrum of intermediate levels, making it the optimal choice when the intermediate transitions are entirely virtual. In the case of distinct intermediate resonances, it is possible to enhance TPA by introducing a phase dispersion that matches the intermediate resonances. Here, we consider the effects of a phase flip in the single photon spectrum, where the phases of all frequencies above a certain frequency are shifted by half a wavelength relative to the frequencies below this frequency. The frequency at which the phase is flipped can then be scanned to reveal the position of intermediate resonances. We find that a resonant phase flip maximizes the contributions of the asymmetric imaginary part of the dispersion that characterizes a typical resonance, resulting in a considerable enhancement of the TPA rate. Due to the bosonic symmetry of TPA, the enhancement is strongest when the resonance occurs when the frequency difference of the two photons is much higher than the linewidth of the resonance. Our results indicate that broadband entangled TPA with spectral phase flips may be suitable for phase-sensitive spectroscopy at the lower end of the spectrum where direct photon detection is difficult.
Density peak clustering based on improved dung beetle optimization and mahalanobis metric

The density peak clustering algorithm (DPC) quickly divides each cluster based on high-density peak points and shows better clustering performance. In order to address the issue that the local density is constrained by the preset cut-off distance in DPC and the Euclidean distance cannot capture the possible correlation between different features, a DPC algorithm based on improved dung beetle optimization (IDBO) and Mahalanobis metric is proposed, called IDBO-MDDPC. The IDBO algorithm enhances the ball dung beetle individual by incorporating nonlinear dynamic factors to increase the search and development capabilities of the algorithm and by incorporating an adaptive cosine wave inertial weight strategy to more precisely determine the optimal position of the thief dung beetle in order to improve the convergence speed and accuracy of the algorithm. The IDBO algorithm is simulated on eight benchmark functions, and the results demonstrate that it is superior to other comparison algorithms in terms of convergence speed and accuracy. In the DPC algorithm, the Mahalanobis metric is used to capture the correlation between features to improve clustering performance. The IDBO algorithm is integrated with the DPC algorithm, and the F-Measure evaluation index is used to design the objective function so that the optimal value of the cut-off distance can be automatically selected. In order to evaluate the efficiency of the algorithm, three sets of artificially synthesized datasets and five sets of UCI standard datasets were chosen for studies. Experimental results show that the IDBO-MDDPC algorithm can automatically determine a better cut-off distance value and ensure higher clustering accuracy.
Approximate Invariance Testing in Diagnostic Classification Models in the Presence of Attribute Hierarchies: A Bayesian Network Approach

This paper demonstrates the process of invariance testing in diagnostic classification models in the presence of attribute hierarchies via an extension of the log-linear cognitive diagnosis model (LCDM). This extension allows researchers to test for measurement (item) invariance as well as attribute (structural) invariance simultaneously in a single analysis. The structural model of the LCDM was parameterized as a Bayesian network, which allows attribute hierarchies to be modeled and tested for attribute invariance via a series of latent regression models. We illustrate the steps for carrying out the invariance analyses through an in-depth case study with an empirical dataset and provide JAGS code for carrying out the analysis within the Bayesian framework. The analysis revealed that a subset of the items exhibit partial invariance, and evidence of full invariance was found at the structural level.
Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality: 3D Reconstruction and Segmentation Using 3D U-Net Architecture

Osteosarcoma is a common type of bone tumor, particularly prevalent in children and adolescents between the ages of 5 and 25 who are experiencing growth spurts during puberty. Manual delineation of tumor regions in MRI images can be laborious and time-consuming, and results may be subjective and difficult to replicate. Therefore, a convolutional neural network (CNN) was developed to automatically segment osteosarcoma cancerous cells in three types of MRI images. The study consisted of five main stages. First, 3692 DICOM format MRI images were acquired from 46 patients, including T1-weighted, T2-weighted, and T1-weighted with injection of Gadolinium (T1W + Gd) images. Contrast stretching and median filter were applied to enhance image intensity and remove noise, and the pre-processed images were reconstructed into NIfTI format files for deep learning. The MRI images were then transformed to fit the CNN's requirements. A 3D U-Net architecture was proposed with optimized parameters to build an automatic segmentation model capable of segmenting osteosarcoma from the MRI images. The 3D U-Net segmentation model achieved excellent results, with mean dice similarity coefficients (DSC) of 83.75%, 85.45%, and 87.62% for T1W, T2W, and T1W + Gd images, respectively. However, the study found that the proposed method had some limitations, including poorly defined borders, missing lesion portions, and other confounding factors. In summary, an automatic segmentation method based on a CNN has been developed to address the challenge of manually segmenting osteosarcoma cancerous cells in MRI images. While the proposed method showed promise, the study revealed limitations that need to be addressed to improve its efficacy.
Adoption of Robots in Open and Distance Learning University Libraries: A Global Overview Based on Review of Literature

AbstractAbstractThe use of robots in open and distance learning university libraries is a relatively new and innovative concept that has emerged in recent years as a response to the increasing demand for remote access to library resources and services. However, there seem to be limited studies that focused on the adoption of robots in open and distance learning university libraries. The study through a review of literature from a global perspective examined the adoption of robots in open and distance learning university libraries. The study drew data and information from the literature gathered manually from various databases including Google Scholar, research gate, and Scopus. The study discussed the adoption of robots in distance learning university libraries, examples of distance learning university libraries adopting robots per region of the world, the factors open and distance learning university libraries consider for adopting robots in their services, services distance learning university libraries use robots to offer, data on distance learning university libraries adoption of robots, benefits of using robots in open and distance learning university libraries, ethical and practical concerns in the adoption of robots in distance learning university libraries, and challenges of using robots in open and distance learning university libraries. The study concludes that the adoption of robots in open and distance learning university libraries can be beneficial for both library operations and users. However, libraries must carefully evaluate their needs and goals before adopting the technology and take steps to address any potential challenges that may arise. Based on the conclusion, the study recommends among others that open and distance learning university Libraries should carefully consider their specific needs and goals before adopting robots and ensure that the technology is implemented in a way that benefits both library staff and users.Keywords: Open and distance learningopen and distance learning universitiesuniversity librariesrobotic technologyrobotsliterature review
A conceptual architecture for simulating blockchain-based IoT ecosystems

Abstract Recently, the convergence between Blockchain and IoT has been appealing in many domains including, but not limited to, healthcare, supply chain, agriculture, and telecommunication. Both Blockchain and IoT are sophisticated technologies whose feasibility and performance in large-scale environments are difficult to evaluate. Consequently, a trustworthy Blockchain-based IoT simulator presents an alternative to costly and complicated actual implementation. Our primary analysis finds that there has not been so far a satisfactory simulator for the creation and assessment of blockchain-based IoT applications, which is the principal impetus for our effort. Therefore, this study gathers the thoughts of experts about the development of a simulation environment for blockchain-based IoT applications. To do this, we conducted two different investigations. First, a questionnaire is created to determine whether the development of such a simulator would be of substantial use. Second, interviews are conducted to obtain participants’ opinions on the most pressing challenges they encounter with blockchain-based IoT applications. The outcome is a conceptual architecture for simulating blockchain-based IoT applications that we evaluate using two research methods; a questionnaire and a focus group with experts. All in all, we find that the proposed architecture is generally well-received due to its comprehensive range of key features and capabilities for blockchain-based IoT purposes.
The usefulness of the Munsell colour indices for identification of drained soils with various content of organic matter

Abstract Purpose The aim of the study was to determine the usefulness of the Munsell colour indices for identification of drained soils with various content of organic matter, developed on the sandy substrate. Methods The analysed soils, according to the Polish Soil Classification (PSC 2019), belong to thin murshic soils (WRB 2022: Murshic Histosols/Histic Gleysols), typical semimurshic soils (Mollic/Umbric Gleysols (Arenic, Drainic, Mulmic)) and to postmurshic soils (Umbric Gleysols (Arenic, Drainic, Nechic)). The following dry colour indices: value (V), chroma (Ch), V+Ch, V+0.5Ch, V×Ch and V/Ch, were correlated with soil variables (LOI, C org. , N tot. , C:N and Fe HCl , Mn HCl —elements extracted with 0.5 M HCl). Results The strongest correlation with the SOM content was displayed by the Munsell value, which allows one to estimate the SOM, C org. , N tot. content in the soils studied. The classification and regression trees (C&amp;RT) revealed that the analysed soil materials could be successfully divided based on the Munsell value alone. The V/Ch quotient demonstrated significant correlations with LOI, C org., N tot. , C:N, Fe HCl and Mn HCl . This quotient equalled 1–2 for murshic (≥ 12% C org. ) and semimurshic (≥ 6.0 C org. &lt; 12.0%), but varied greatly (1–5) for postmurshic (≥ 0.6 C org. &lt; 6.0% C org. ) soil materials. Conclusion The analysed soil materials had the Munsell value differentiated enough to enable their identification. The V/Ch quotient can help to trace the origin of postmurshic soils. Its narrow value (1–2) indicates that the postmurshic soil developed through advanced transformation of murshic soil materials, whereas a broader value (2–5) indicates that the postmurshic soil originated from dewatered Gleysols.
Enhanced Night-to-Day Image Conversion Using CycleGAN-Based Base-Detail Paired Training

Numerous studies are underway to enhance the identification of surroundings in nighttime environments. These studies explore methods such as utilizing infrared images to improve night image visibility or converting night images into day-like representations for enhanced visibility. This research presents a technique focused on converting the road conditions depicted in night images to resemble daytime scenes. To facilitate this, a paired dataset is created by augmenting limited day and night image data using CycleGAN. The model is trained using both original night images and single-scale luminance transform (SLAT) day images to enhance the level of detail in the converted daytime images. However, the generated daytime images may exhibit sharpness and noise issues. To address these concerns, an image processing approach, inspired by the Stevens effect and local blurring, which align with visual characteristics, is employed to reduce noise and enhance image details. Consequently, this study contributes to improving the visibility of night images by means of day image conversion and subsequent image processing. The proposed night-to-day image translation in this study has a processing time of 0.81 s, including image processing, which is less than one second. Therefore, it is considered valuable as a module for daytime image translation. Additionally, the image quality assessment metric, BRISQUE, yielded a score of 19.8, indicating better performance compared to conventional methods. The outcomes of this research hold potential applications in fields such as CCTV surveillance systems and self-driving cars.
Sentiment Analysis of Online New Energy Vehicle Reviews

Massive online reviews of new energy vehicles in China are deemed crucial by companies, as they offer valuable insights into user demands and perceptions. An effective analysis enables companies to swiftly adapt and enhance their products while upholding a positive public image. Nonetheless, the sentiment analysis of online car reviews can pose challenges due to factors such as incomplete context, abrupt information bursts, and specialized vocabulary. In this paper, an enhanced hybrid model is introduced, combining Enhanced Representation through kNowledge IntEgration (ERNIE) and a deep (Convolutional Neural Network) CNN, to tackle these challenges. The model utilizes fine-tuned ERNIE for feature extraction from preprocessed review datasets, generating word vectors that encompass comprehensive semantic information. The deep CNN component captures local features from the text, thereby capturing semantic nuances at multiple levels. To address sudden shifts in public sentiment, a channel attention mechanism is employed to amplify the significance of crucial information within the reviews, facilitating comment relationship classification and sentiment prediction. The experimental results demonstrate the efficacy of the proposed model, achieving an impressive accuracy rate of 97.39% on the test set and significantly outperforming other models.
Application of blockchain technology to 21st century library services: Benefits and best practices

The fourth industrial revolution has paved the way for emerging technologies, and among them, blockchain stands out for its unprecedented ability to create and trade value in library organizations. This research paper explores the potential application of blockchain technologies in 21st-century library services. By conducting a systematic analysis of the literature, this study examines how libraries can harness blockchain to support innovative services and meet global demands. The study suggests that the recent advancements in blockchain have led to a fourth generation of the technology, which possesses disruptive capabilities across diverse fields, including library and information science. The paper proposes that blockchain can enhance library services such as collection development, circulation services, research, data management, and storage. It is important to note that this paper represents the original ideas of the authors and does not rely on copyrighted materials. Furthermore, it highlights that blockchain remains a vast and underexplored area of research, presenting both challenges and opportunities for library professionals seeking to provide diverse library services.
Yet another cybersecurity risk assessment framework

Abstract IT systems pervade our society more and more, and we become heavily dependent on them. At the same time, these systems are increasingly targeted in cyberattacks, making us vulnerable. Enterprise and cybersecurity responsibles face the problem of defining techniques that raise the level of security. They need to decide which mechanism provides the most efficient defense with limited resources. Basically, the risks need to be assessed to determine the best cost-to-benefit ratio. One way to achieve this is through threat modeling; however, threat modeling is not commonly used in the enterprise IT risk domain. Furthermore, the existing threat modeling methods have shortcomings. This paper introduces a metamodel-based approach named Yet Another Cybersecurity Risk Assessment Framework (Yacraf). Yacraf aims to enable comprehensive risk assessment for organizations with more decision support. The paper includes a risk calculation formalization and also an example showing how an organization can use and benefit from Yacraf.
Building Rooftop Extraction Using Machine Learning Algorithms for Solar Photovoltaic Potential Estimation

Green cities worldwide are converting to renewable clean energy from natural sources such as sunlight and wind due to the lack of traditional resources and the significant increase in environmental pollution. This paper presents an approach of two stages for photovoltaic (PV) potential estimation of solar panels mounted on buildings’ rooftops. The first stage is rooftop detection from satellite images using a series of image pre-processing algorithms, followed by applying machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). The second stage is the solar PV potential estimation using the PVWatts calculator, PVGIS, and ArcGIS. Satellite images for the B6 division of Madinaty City in Egypt were evaluated in this paper. The precision, recall, and F1-score of rooftop detection were 91.2%, 98.6%, and 94.7% from SVM, while those from NB were 86.6%, 98.3%, and 92.2%, respectively. About 290 rooftops were extracted, with a total area of 150,698 m2 and a relative root mean square error of 10.6%. The usable area of rooftops was utilized to estimate the annual PV potential of 21.1, 24.9, and 22.9 GWh/year from the PVWatts calculator, PVGIS, and ArcGIS, respectively. According to the estimated PV potential, replacing traditional energy sources reduced the amount of CO2 by an annual average value of 62%.