Reviewerly/exHarmony huggingface dataset:
A deep learning approach for liver cancer detection in CT scans

In recent years, with the rapid advancements in deep learning, image processing has progressively attracted the attention of medical researchers to achieve accurate diagnoses of numerous human diseases in a non-invasive manner. Image processing systems are among the most helpful and crucial disease identification technologies. Liver cancer is the world's fifth most prevalent and leading cause of cancer deaths. Diagnoses and treatment are essential because of the high mortality rate and recurrence after treatment. Medical image analysis is extremely beneficial in the healthcare industry. In the last several years, creating and developing computer-assisted imaging systems has been increasingly vital to aid physicians or doctors in strengthening their diagnoses. This research presents a deep model to identify cancerous regions in the liver aims using Computerized Tomography (CT) scans from the Kaggle dataset. Basic preprocessing is applied to CT images from the dataset acquired from the Kaggle repository. The performance of the proposed model is evaluated using different performance metrics, and an accuracy score of 94.3% was achieved in tumour identification.
Learning filter selection policies for interpretable image denoising in parametrised action space

Abstract The denoising of images is an important research direction in computer vision. We consider the image denoising task as an estimation problem of the filtering policy related to image features, which is different from end‐to‐end image mapping. Commonly used simple filters such as gaussian filtering and bilateral filtering have fixed global denoising policies. However, the denoising policies of different filters can only adapt to limited image features. To solve this problem, we propose a method that applies different filters to different spatial ranges and adjusts the parameters of these filters simultaneously. Since not all filters can be easily transformed into differentiable forms and it is difficult to obtain paired datasets of filter action areas, we use reinforcement learning (RL) methods to estimate the spatial domain action range and adjustable parameters of filters, respectively. Furthermore, for removing higher intensity noise, simple filters can iteratively approximate higher‐order denoising policies and obtain more accurate and stable denoising results with the increase of iteration steps. Experimental results show that our proposed method can not only generate intuitive and interpretable denoising policies but also achieve comparable or better visual effects and computational efficiency than baseline methods.
Special Issue on “Decision Support Systems in an uncertain world”

International Transactions in Operational ResearchVolume 31, Issue 2 p. 1298-1299 Call for Papers Special Issue on “Decision Support Systems in an uncertain world” First published: 27 November 2023 https://doi.org/10.1111/itor.13403Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume31, Issue2March 2024Pages 1298-1299 RelatedInformation
Designing a Web-Based Tourism Package Application as a Promotional Tool for Tourism in Nusa Village

The tourism industry which is experiencing significant growth offers great potential to stimulate the economy of a region. This research aims to design and develop a web-based tour package application as a tourism promotion tool. This application uses the PHP programming language, uses the Laravel framework, and is integrated with the MySQL database. This application is designed to make it easier for users to find tour packages that suit their needs. Tourists can use this system to find out more detailed information regarding the list of available tour packages and make tour package orders. The stages in developing this application include stages such as needs analysis, interface design, system development using the PHP programming language with the Laravel framework, integration with the MySQL database, testing, and implementation.
Giving Feedback Using ‘Speedback’

Women in Higher EducationVolume 32, Issue 11 p. 9-16 Management Matters Giving Feedback Using ‘Speedback’ Laura Freebairn-Smith, Laura Freebairn-SmithSearch for more papers by this author Laura Freebairn-Smith, Laura Freebairn-SmithSearch for more papers by this author First published: 27 November 2023 https://doi.org/10.1002/whe.21338Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume32, Issue11November 2023Pages 9-16 RelatedInformation
Efektivitas Model Pembelajaran Open-Ended dan Make A Match dengan Menggunakan Teori Belajar Konstruktivistik terhadap Prestasi Belajar Siswa

Learning activities at school are important activities for students, especially learning activities that are held in the classroom. Learning activities between teachers and students must be established in harmonious interactions and relationships, so that in learning activities in the classroom there is a conducive and active atmosphere. This study used three classes, namely Experiment I class (Open-Ended), Experiment II class (Make A Match) and Control class (Descriptive/Exposure). Before giving the treatment, firstly take the value data for the initial data to match the three classes. For initial data analysis, a normality test was calculated with the Liliefors test, homogeneity test with the Bartlett test, and the average comfort test using one-way Anava. After giving the treatment and evaluation tests, the final data analysis calculations were carried out to test the research hypothesis. For the final analysis, it was carried out as the initial analysis plus the t test. The research results for hypothesis 1, there is a difference in the average learning achievement between experimental class I, II and control. For hypothesis 2, the learning achievement of the experimental class I is better than the control class. For hypothesis 3, the learning achievement of experimental class II is better than the control class. For hypothesis 4, there is no difference in learning achievement between experimental class I and experimental class II. So, learning using the Open-Ended model and the Make A Match learning model on Logarithmic Mathematics material is better than the Descriptive/Exposure learning model
Finite strain formulation of the discrete equilibrium gap principle: application to mechanically consistent regularization for large motion tracking

The equilibrium gap principle offers a good trade-off between robustness and accuracy for regularizing motion tracking, as it simply enforces that the tracked motion corresponds to a body deforming under arbitrary loadings. This paper introduces an extension of the equilibrium gap principle in the large deformation setting, a novel regularization term to control surface tractions, both in the context of finite element motion tracking, and an inverse problem consistent reformulation of the tracking problem. Tracking performance of the proposed method, with displacement resolution down to the pixel size, is demonstrated on synthetic images representing various motions with various signal-to-noise ratios.
Information Security in Social Media Sites: Sentiment Analysis of Email

In today’s digital era, email stands as a fundamental tool for communication due to its convenience, rapidity, flexibility, and comprehensive archival capabilities. However, the escalating risk of cyber threats targeting emails has introduced new challenges. This research introduces an innovative approach to identify fraudulent emails through a hybrid-based approach using sentiment analysis. The proposed method unites the lexicon-based technique using Word2Vec features with a Machine Learning (ML) classification approach. Two prominent feature extraction methods, Bag of Words and TF-IDF, are integrated to enhance the precision of the analysis. The study employed well-established ML classifiers including Random Forest, KNN, Decision Tree, SVM, Multilayer Perception, and logistic model to analyze the emails. The result of the analysis was impressive across the selected models, with Support Vector Machine and Multilayer Perception slightly surpassing others with an impressive 99% accuracy, F1-score, and Recall rate respectively. This emphasizes the effectiveness of this model in recognizing fraudulent emails.
Analysis of the Possibility of Using Commercial Virtual Systems for Educational Purposes

A qualitative analysis of ways to increase the effectiveness of education by introducing virtual learning environments into the educational process was carried out. The advantages of learning for students using virtual reality systems compared to traditional methods have been determined. Current information about modern virtual reality headsets as the main devices for immersive immersion in the educational system is provided. An analysis of the popular Oculus application store was carried out, the popularity of commercial educational systems and the types of applications that can be used in the educational process were determined.
Contemporary Aspects Of Automated Visual Object Creation In Gamification Tasks

The thesis explores various gamification tasks in the context of creating and utilizing virtual objects. Furthermore, it provides an overview of modern methodological aspects of automated visual object generation in gamification tasks and meticulously analyzes their usage. In particular, several analogs of automated virtual object creation are presented, and an assessment of the methodology for automated visual object construction in gamification tasks is conducted. Additionally, the article examines analogs of automated techniques for building virtual objects and highlights their strengths and weaknesses. Moreover, user feedback on the “fundamentals of gamification” is gathered to determine the overall level of user satisfaction with this approach as a learning tool. Based on the researched material, a conclusion is drawn regarding the effectiveness of gamification as an educational instrument. Various implementation options are suggested, taking into careful consideration the design of visual objects and user feedback. The developed methodology can be valuable for designers and developers who are creating effective (captivating) gaming approaches in the field of IT.
Tracing topic evolution in higher education: a text mining study on Italian universities

In the last decade, the fast technological and social transformations have been placing new demands on Higher Education (HE) systems to update their educational offers. Grounded in the broader discourse on the impact of such transformation on HE, this article relies on Text Mining (TM). We consider as a case study the Italian HE system, providing an overview of the university education in the last 10 Academic Years. We automatically analyse 54,535 learning outcomes of degree programmes to identify the relevant topics with Named Entity Recognition, a technique of TM. For each topic, we measured its trend in time and coverage on a geographical basis and in the disciplinary fields. We detected 6062 different topics, among these the most growing are artificial intelligence, sustainability, rare diseases, and ethics. The findings reveal that topics related to hard sciences register higher occurrence than the ones concerning humanities and social sciences. Furthermore, we observed that universities are overcoming the boundaries of disciplinary fields, promoting knowledge contamination and vertical specialisation. The proposed approach can advance studies in HE literature for effective and efficient analysis. The findings can inform policies and practices in HE on evolving learning outcomes and educational offerings, even across different countries and disciplines.
Specialized text classification: an approach to classifying Open Banking transactions

With the introduction of the PSD2 regulation in the EU which established the Open Banking framework, a new window of opportunities has opened for banks and FinTech to explore and enrich Bank transaction descriptions with the aim of building a better understanding of customer behavior, while using this understanding to prevent fraud, reduce risks and offer more competitive and tailored services.And although the usage of natural language processing models and techniques has seen an incredible progress in various applications and domains over the past few years, custom applications based on domain-specific text corpus remain unaddressed especially in the banking sector.In this paper, we introduce a language-based Open Banking transaction classification system with a focus on the French market and French language text. The system encompasses data collection, labeling, preprocessing, modeling, and evaluation stages. Unlike previous studies that focus on general classification approaches, this system is specifically tailored to address the challenges posed by training a language model with a specialized text corpus (Banking data in the French context). By incorporating language-specific techniques and domain knowledge, the proposed system demonstrates enhanced performance and efficiency compared to generic approaches.
Composing Multiple Online Exams: The Bees Algorithm Solution

Online education has gained increasing importance in recent years due to its flexibility and ability to cater to a diverse range of learners. The COVID-19 pandemic has further emphasized the significance of online education as a means to ensure continuous learning during crisis situations. With the disruption of traditional in-person exams, online examinations have become the new norm for universities worldwide. Among the popular formats for online tests are multiple-choice questions, which are drawn from a large question bank. However, creating online tests often involves meeting specific requirements, such as minimizing the overlap between exams, grouping related questions, and determining the desired difficulty level. The manual selection of questions from a sizable question bank while adhering to numerous constraints can be a laborious task. Additionally, traditional search methods that evaluate all possible solutions are impractical and time-consuming for such a complex problem. Consequently, approximate methods like metaheuristics are commonly employed to achieve satisfactory solutions within a reasonable timeframe. This research proposes the application of the Bees Algorithm (BA), a popular metaheuristic algorithm, to address the problem of generating online exams. The proposed solution entails creating multiple exam forms that align with the desired difficulty level specified by the educator, while considering other identified constraints. Through extensive testing and comparison with four rival methods, the BA demonstrates superior performance in achieving the primary objective of matching the desired difficulty level in most test cases, as required by the educator. Furthermore, the algorithm exhibits robustness, indicated by minimal standard deviation across all experiments, which suggests its ability to generalize, adapt, and be practically applicable in real-world scenarios. However, the algorithm does have limitations related to the number of successful solutions and the achieved overlap percentage. These limitations have also been thoroughly discussed and highlighted in this research.
An Information System Project Using Augmented Reality for a Small Local History Museum

the essence of the augmented and virtual reality technologies is revealed, the advantages and prospects of their use in museums are analyzed. An information system project using augmented reality technologies was created for the museum.
Semantic Models for Buildings Energy Management

The use of semantic models in building energy has been investigated. Energy management systems commonly consist of heterogeneous systems where it is highly desired to collect and manage challenging information uniformly. Throughout a building’s existence, a variety of data formats are available for the interchange of building-related information. The new ontology has been created to standardize building data management to address these issues. A semantic model for building energy management systems, taking into multiple domain relations, has been constructed. The proposed ontology’s implementation has been made.
Wavefront reconstruction for double-grating Ronchi lateral shearing interferometry with nonlinear optimization

Traditional phase retrieval methods for Ronchi lateral shearing interferometry eliminate the impact of high diffraction orders by increasing the number of phase-shifting interferograms, however, this introduces additional error in the phase-shifting process. We propose an optimization method combining a 2-frame phase-shifting algorithm to achieve accurate wavefront reconstruction. A numerical model matching the physical model is constructed and the cross-iterative gradient descent method is used to optimize the initial results obtained by the two-step phase-shifting method. The accuracy and robustness of the method are verified by simulations and experiments. The proposed method has the advantages of achieving high-precision wavefront reconstruction and correcting the phase-shifting errors, and it significantly simplifies the process of phase shift.
Precise surface form measurements of off-axis aspheric freeform optics

Freeform optical components are increasingly demanded by optical manufacturers and researchers, and are always a challenging topic for metrologists. Form errors of the freeform surfaces resulting from the manufacturing process are critical, in terms of the functionality and reliability of the freeform optics. This paper presents two methodology case studies of off-axis aspheric optics using contact profilometry and non-contact scanning point interferometry. The contact method is accomplished by use of an ultra-low noise measurement platform, combined with a patented phase grating interferometry (PGI) technology and specially developed algorithms for calibration and analysis. The study shows the capability of the proposed method for high tangential slope freeform measurement. This slope measurement capability of PGI Freeform, together with its large gauge range, enables 3D form measurements for most freeform surfaces. However, for some optical surfaces non-contact measurement is preferred due to the possible surface damage caused by the stylus force of contact method. Non-contact scanning metrology is based on a patented multi-wavelength interferometry (MWLI) technology. It provides high density 3D data in short measurement times at a highly reproducible form measurement accuracy. The long-range absolute measurement capability of the MWLI sensor, together with its ultraprecision metrology platform and improved calibration routine through which the sensor accurately follows the designed shape of optical surfaces, enables precise 3D freeform surface measurements within its tangential slope measurement range.
Color speckle deflectometry for three-dimensional surface transient measurement

Transient measurement technology has broad application prospects in fields such as mirror processing, automotive painting, and precision mechanical processing. It can help us monitor and analyze instantaneous changes in the processing process in real-time. The existing contact and non-contact measurement methods have drawbacks in both non-destructive testing and transient measurement directions. This paper develops a transient measurement system for color speckle deflectometry, aiming to improve efficiency in the field of optical measurement. The core components of the system described in this paper include a liquid crystal display (LCD) screen, a camera, and a beam splitter. By using multi-channel speckle fusion and color correction, one color image is captured to achieve the same effect as the previous three monochromatic images. The gradient of each point on the surface under test (SUT) is obtained through speckle shift, and then the surface shape of the SUT is obtained through integration. The feasibility is verified by the actual measurement with a flat mirror. By utilizing these devices and methods, transient measurement of the three-dimensional shape of a mirror can be achieved, and reliable results can be obtained in both laboratory environments and processing sites. Compared with traditional measurement methods, this system not only has a significant improvement in measurement speed, but also can achieve non-destructive and in place measurement.
A Comprehensive Review on Adversarial Attack Detection Analysis in Deep Learning

This comprehensive review investigates the escalating concern of adversarial attacks on deep learning models, offering an extensive analysis of state-of-the-art detection techniques. Encompassing traditional machine learning methods and contemporary deep learning approaches, the review categorizes and evaluates various detection mechanisms while addressing challenges such as the need for benchmark datasets and interpretability. Emphasizing the crucial role of explaining ability and trustworthiness, the paper also explores emerging trends, including the integration of technologies like explainable artificial intelligence (XAI) and reinforcement learning. By synthesizing existing knowledge and outlining future research directions, this review serves as a valuable resource for researchers, practitioners, and stakeholders seeking a nuanced understanding of adversarial attack detection in deep learning.
A Comprehensive Review on Gujarati-Text Summarization Through Different Features

This comprehensive review delves into the intricacies of Gujarati-text summarization, exploring diverse features employed in the process. With a focus on the nuances of the Gujarati language, the paper investigates various techniques and methodologies applied to extract essential information from textual content. The review systematically examines the effectiveness of distinct features such as linguistic, semantic, and syntactic elements in the context of Gujarati summarization. Additionally, the study provides insights into the challenges specific to Gujarati-language summarization and discusses advancements in natural language processing and machine learning that contribute to the refinement of summarization models. This thorough examination serves as a valuable resource for researchers, practitioners, and enthusiasts seeking a deeper understanding of the complexities and advancements in Gujarati-text summarization.