Reviewerly/exHarmony huggingface dataset:
UnlockArt: Cultural Mediation at the Museum Through an Augmented Reality Escape Room

In the context of a museum, cultural mediation facilitates access to the works of art for the visitors which not always share the culture the pieces of art come from. Tools and resources such as catalogs, guided tours, or the work of the curators are all part of the museum's cultural mediation, which highlights and informs the visitors of the works on display. In this case study we present the game design process and results of UnlockArt, a project developed as a cultural mediation tool at the Museo delle Culture (MUSEC) in Lugano, Switzerland. The project studied and applied the educational escape room format to enhance the museum experience for visitors, and to encourage the younger ones to approach the collection through a ludic activity. By the means of the Star Model (Botturi and Babazadeh, 2020), the project designed an educational escape room for the "JAPAN. Arts and Life. The Montgomery Collection" exhibition, a collection of Japanese objects from the period between the 12th and the 20th century. Educational escape rooms are a format that works well in class, but may be burdensome to apply in a museum, which intrinsically imposes many constraints. Players should be silent, the escape room should always be ready to be played and easy to be rebooted, and it should not impact the exhibition for visitors that wish not to engage in the experience. For this reason, we opted for an augmented reality educational escape room (AR EER) which can be played on tablets. Such approach only requires QR codes placed around the exhibition, which hide puzzles and hints tied to the works exhibited in the museum, and does not need any rebooting phase as the puzzles are to be solved in augmented reality.
A Comprehensive Bibliometric Analysis of Deep Learning Techniques for Breast Cancer Segmentation: Trends and Topic Exploration (2019-2023)

The objective of this study is to perform a comprehensive bibliometric analysis of the existing literature on breast cancer segmentation using deep learning techniques. Data for this analysis were obtained from the Web of Science Core Collection (WOS-CC) that spans from 2019 to 2023. The study is based on a comprehensive collection of 985 documents that cover a substantial body of research findings related to the application of deep learning techniques in segmenting breast cancer images. The analysis reveals an annual increase in the number of published works at a rate of 16.69%, indicating a consistent and robust increase in research efforts during the specified time frame. Examining the occurrence of keywords from 2019 to 2023, it is evident that the term "convolutional neural network" exhibited a notable frequency, reaching its peak in 2021. However, the term "machine learning" demonstrated the highest overall frequency, peaking around 2021 as well. This emphasizes the importance of machine learning in the advancement of image segmentation algorithms and convolutional neural networks, which have shown exceptional effectiveness in image analysis tasks. Furthermore, the utilization of latent Dirichlet Allocation (LDA) to identify topics resulted in a relatively uniform distribution, with each topic having an equivalent number of abstracts. This indicates that the data set encompasses a diverse range of topics within the field of deep learning as it relates to breast cancer image segmentation. However, it should be noted that topic 4 has the highest level of significance, suggesting that the application of deep learning for diagnosis was extensively explored in this study.
Machine Learning Centered Energy Optimization In Cloud Computing: A Review

The rapid growth of cloud computing has led to a significant increase in energy consumption, which is a major concern for the environment and economy. To address this issue, researchers have proposed various techniques to improve the energy efficiency of cloud computing, including the use of machine learning (ML) algorithms. This research provides a comprehensive review of energy efficiency in cloud computing using ML techniques and extensively compares different ML approaches in terms of the learning model adopted, ML tools used, model strengths and limitations, datasets used, evaluation metrics and performance. The review categorizes existing approaches into Virtual Machine (VM) selection, VM placement, VM migration, and consolidation methods. This review highlights that among the array of ML models, Deep Reinforcement Learning, TensorFlow as a platform, and CloudSim for dataset generation are the most widely adopted in the literature and emerge as the best choices for constructing ML-driven models that optimize energy consumption in cloud computing.
Malayalam Handwritten Character Recognition using CNN Architecture

The process of encoding an input text image into a machine-readable format is called optical character recognition (OCR). The difference in characteristics of each language makes it difficult to develop a universal method that will have high accuracy for all languages. A method that produces good results for one language may not necessarily produce the same results for another language. OCR for printed characters is easier than handwritten characters because of the uniformity that exists in printed characters. While conventional methods find it hard to improve the existing methods, Convolutional Neural Networks (CNN) has shown drastic improvement in classification and recognition of other languages. However, there is no OCR model using CNN for Malayalam characters. Our proposed system uses a new CNN architecture for feature extraction and softmax layer for classification of characters. This eliminates manual designing of features that is used in the conventional methods. P-ARTS Kayyezhuthu dataset is used for training the CNN and an accuracy of 99.75% is obtained for the testing dataset meanwhile a collection of 40 real time input images yielded an accuracy of 95%.
Challenges in reading English academic texts for non-English major students of an Indonesian university

This study examines the barriers to reading academic texts among university students for whom English is a foreign language. While many previous studies have focused on instructional design for building academic reading skills, this study focuses on the fundamental issues that need consideration before setting up the instructional design for English academic reading. Taking an interpretive phenomenological viewpoint, this study applied a qualitative method through an online survey and interviews. Due to the Covid-19 pandemic, online data collection was the most accessible means of approaching the students. Ninety-five students from various non-English study programs (courses) at a private university voluntarily responded to the open-ended online questionnaire, providing survey data. Five students provided further data through individual interviews on their academic reading challenges. A thematic analysis of the survey data revealed four themes and eight subthemes representing the students’ challenges, which were explored further in the interviews. These challenges and the relationships among them are discussed. The results suggest that most students depended on single-word meanings as their prime strategy for achieving comprehension. However, this strategy also represented the most notable challenge in their effective reading of English academic texts. They failed to comprehend the text effectively because their translation did not make sense. Furthermore, the nature of the reading strategies of the student cohort had an impact on their baseline reading proficiency.
The validity and reliability of writing attitude scale for EFL learners in the Indonesian context

Having a positive attitude in writing is very important since the positive attitude will make the students approach writing in a good way and will enable them to finish the writing task well. Thus, an appropriate instrument to measure the writing attitude is needed. Yet, scarce research is dedicated to investigating the validity and reliability of the writing attitude scale. This research is intended to investigate the validity and reliability of the writing attitude scale adapted from Podsen (1997) which was originally developed from the Daly-Miller Writing Apprehension Test (Daly Miler, 1975b). The design of this study was an observational, non-experimental study with a cross-sectional design, in which the researchers focused on analyzing Podsen’s (1997) Writing Attitude Scale (WAS). The population of the study was 96 students who took the Essay Writing course in the English Department, at a public university in East Java. The data were in the form of numbers on an ordinal scale. The researchers used the Content Validity Ratio (CVR) to analyze the validity and Cronbach alpha to check the inter-item correlation coefficient in SPSS 25. The result of the study shows that the CVR analysis was 1, and all items in the questionnaire were valid (p=.000≤.000) as shown in the Pearson product-moment analysis. Secondly, the internal consistency of Podsen’s (1997) Writing Attitude Scale was high (r.877). This implies that the instrument is applicable because it possesses validity and is highly reliable in measuring students’ writing attitudes.
Assisted Requirements Selection by Clustering using Analytical Hierarchical Process

Keywords: Requirements prioritization, Next release plan, Software product planning, Decision support, MoSCoW, AHP, K-Menas, GMM, BIRCH, PAM, Hierarchical, Clustering, Clusters Evaluation
General Quantum Gravity

General Quantum Gravity (GQG) is a formalization of quantized gravity that emerges from General Relativity through Quantum Mechanics. GQG is formalized in three different aspects, such as: Semi-quantum Minkowski GQG, Quantum Minkowski GQG, and Quantum Non-Minkowski GQG. Every observable (3+1)D spacetime in GQG must have internal hidden unobservable (n+1)D spacetime inside it which yields extra hidden dimensions by a closed continuous mapping, so the overall system must acquire Supersymmetry. Strings are natural and universal but forever hidden inside every (3+1)D observable spacetime. Strings have eleven-dimensions by nature in GQG. If we replace Minkowski spacetime with an internal hidden spacetime, then M-theory acquires strings inside of this observable (3+1)D spacetime. Obtaining type IIA string theory from M-theory by dimensional reduction is now non-restricted for the 11th direction but universally for any nth direction. Instead of introducing the cosmological constant in Einstein field equation, we yield Dark Energy as well as Dark Matter from GQG quite naturally. We have developed here Gravitational Electroweak Dark Energy interactions (GED), where gravity and Dark Energy are combined with electroweak symmetry. Likewise, in Gravitational Chromodynamic Dark Energy interactions (GCD), we have combined QCD with gravitational and Dark Energy symmetries. Further in a Dark Matter gauge symmetry model, we have combined Dark matter along with GED and GCD. Finally, from GED, GCD and Dark Matter gauge symmetries, we have developed a Universal Model, SU(3)_{GED}\otimes SU(4)_{GCD}\otimes SU(5)_{DM}\subset SU(7)_{UM}, where it is clear that Dark energy field is homogeneous, as well as non-decaying, in all kind of matter fields.
Development of a Robust Dataset for Printed Tamil Character Recognition

Despite the fact that many character datasets for several languages are publicly available, there are only a very few standardized datasets for Tamil characters. This article presents a subset of the Mepco Tamil Character database, a Tamil font isolated character dataset representing the printed characters. This dataset includes 124 glyphs representing the 247 characters of Tamil language. This dataset is tested for its robustness using multiple experimentations using SVM classifier and is compared against UJTDchar, another dataset available for Tamil language. Also we have verified the robustness of DIGI-Net, CNN architecture for this Tamil character recognition problem using the UJTDchar dataset and the Mepco Tamil Character dataset. We report an accuracy of 90.59% and 97.66% while using SVM and DIGI-Net CNN on our newly created dataset.
A Framework for Migration of Microservices based Applications to Serverless Platform with Efficient Cold Start Latency

Serverless computing has become a very popular cloud-based solution for designing, testing and deploying the applications as serverless functions. Serverless computing has become a buzzword in both industry and academia as many large IT giants have rolled out their own serverless platforms. Independent design, deployment and auto scalability are its major features with Function-as-a-Service (FaaS) as its popular implementation. On the other hand, microservices are an emerging trend for design of large enterprise applications as many companies like Amazon, Uber and Spotify have already migrated their existing applications to microservices style. However, with the advent of serverless platforms, to design efficient applications with simpler infrastructure management, reduced operational overhead and cost factor, migrating microservices to serverless has become inevitable. Additionally, existing studies report that serverless platforms suffer from cold start latency. Therefore, in this paper, a framework is proposed which has two phases: (i) an empirical investigation to find out the impact of programming language on the cold start of serverless functions and (ii) an approach for migration of microservices to serverless platforms. The evaluation results help us to identify the platform with low cold start latency and also recommend the choice of programming language with lower latency. After migrating the containerized microservices to serverless, a comparison in terms of performance is conducted. The applications designed as serverless functions exhibit better response time and throughput compared to containerized microservices.
Techniques in Convolutional Depth Measurements

&lt;p&gt;This study evaluates the effectiveness of diverse focal metrics for generating depth maps from focal stacks in both Electro-Optical (EO) and Radio-Frequency (RF) domains. The paper also discusses the potential applicability of these methods to address a broader range of laboratory requirements. The techniques examined include Sobel Convolution, Laplacian Variance, the Tenengrad Method, the Histogram Method, Fourier Analysis, and Haar Wavelet Transforms. The inherent limitations of specific methods are highlighted through empirical data collected from various example datasets.&lt;/p&gt;
Quantum-Enabled Discrete Distribution Sampling for Post-Quantum Lattice-Based Cryptography

&lt;p&gt;Lattice-based cryptography is considered the most promising candidate for post-quantum public key cryptography such as key encapsulation and digital signature schemes. Accurate and efficient sampling of matrix / vector elements and polynomial coefficients from specified discrete probability distributions is crucial to the security and efficiency of lattice-based cryptography protocols. In this work, a design methodology is proposed to implement these sampling operations using currently available quantum computing hardware. Quantum circuits for sampling from uniform, trinary, binomial and discrete Gaussian distributions are presented. Implementation results obtained from simulation as well as measured from real cloud-based quantum hardware are also presented and analyzed. Although the proposed circuits require only few qubits, they implement practical distribution parameters used by various lattice-based protocols. This clearly demonstrates the immediate relevance of such quantum circuits in the context of currently available small-scale quantum computers, and they have the potential to enhance post-quantum cryptography implementations on quantum-enabled cloud environments.&lt;/p&gt;
Special Issue on “Decision Support Systems in an uncertain world”

International Transactions in Operational ResearchVolume 31, Issue 1 p. 618-619 Call for Papers Special Issue on “Decision Support Systems in an uncertain world” First published: 05 October 2023 https://doi.org/10.1111/itor.13377Read 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, Issue1January 2024Pages 618-619 RelatedInformation
Correction: Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

[This corrects the article DOI: 10.1371/journal.pone.0257901.].
Mass and Size Characterization of FRACEP Composite Elementary Fermions

The FRACEP model characterizes the entire set of Standard Model elementary fermions as composite particles having a preon-like fractal-based structure. The internal components have several levels of complexity, beginning with the fractal structures of the momentum carriers, and the dynamic charge carriers and spin carriers. These base structures combine in a series of nested 3-element preon-like components that build up the composite fermions (the subject of this paper) and composite bosons. The entire structure is based on only two fundamental particles forming the basis of a Dual Universe containing positive mass particles, negative mass particles, and mixed mass particles. This system offers an intuitive explanation for the observed decay of the particles that are otherwise assumed to be fundamental because only positive mass particles or negative mass particles are long-term stable. Repulsion within a mixed mass particle is only quasi-stable for very short times. The estimate masses for the composite fermions are consistent with the Particle Data Group’s 2016/2020-update estimates, and the estimated radii are consistent with published expected upper size limits for the Standard Model’s fundamental elementary fermions.
PENGEMBANGAN MODEL PROJECT BASET LEARNING UNTUK MENSTIMULASI KEMAMPUAN BERPIKIR TINGKAT (HOTS) ANAK USIA DINI USIA 5-6 TAHUN

Problems with the learning model used in schools, where the learning model used is the conventional learning model which is still teacher center in nature and in learning it still uses children's worksheets (LKA) without concrete objects so learning is not yet high level thinking skills (HOTS) early childhood aged 5-6 years. The type of research used is Research and Development which adapts the Borg &amp; Gall model with 10 steps which are simplified into 5 steps, namely conducting preliminary research, developing initial products, conducting product validations, conducting trials, products. The limited trial subjects involved group B teachers at Pembina State Kindergarten 2, Jambi City, namely 4 teachers and Independent Kindergarten, Jambi City, namely 1 teacher. Research and development data were obtained from several sources, namely: material expert validation questionnaire, media expert validation questionnaire. Product feasibility is obtained from the results of limited field trials. The results of research conducted by material experts at the final stage obtained a percentage of 100% with the category very feasible to try out. The results of the assessment were obtained from media experts at the final stage obtaining a percentage of 96.15%. The level of validity of the project based learning model to stimulate higher order thinking skills (HOTS) for early childhood aged 5-6 years from teacher respondents as product users scored a percentage of 87.5% in the very decent category. The conclusion of this research is that the Project Based Learning Model to Stimulate Higher Order Thinking Ability (HOTS) for Early Childhood Children Aged 5-6 Years is very feasible and can be tested.
ANALISIS FAKTOR PENGHAMBAT KEMAMPUAN BERBICARA SISWA KELAS RENDAH DI SEKOLAH DASAR

This research aims to describe the factors inhibiting the speaking ability of lower grade students in elementary schools. This research is a type of qualitative research with a case study type of research. The data sources for this research are class III teachers and class III students at SD Negeri 139/IV Jambi City. The results of this research are what factors hinder the speaking ability of lower grade students in elementary schools. Factors that hinder lower grade speaking abilities in elementary schools consist of 2 categories, namely, internal factors and external factors. Internal factors consist of the student's condition, both the physical and mental condition of the student, the student's passivity during the teaching and learning process, lack of self-confidence, always feeling afraid that the opinion or idea he conveys is wrong. Then there are external factors consisting of parenting patterns, parents' education and social background and teachers' methods of learning.
Seafloor spreading and the delivery of sulfur and metals to Earth’s oceans

Abstract Circulating fluids within Earth’s mid-ocean ridge system cool and alter the oceanic crust, contribute distinct chemistry to the ocean, and generate economically and geologically important metal-sulfide deposits at the seafloor. Yet, we have few constraints on the characteristics of these fluids at peak subseafloor pressure and temperature conditions or how the primary variable, seafloor spreading, affects these fluids’ delivery of metals and sulfur to seawater. Here, we develop a new, robust technique for estimating the peak endowment of heat and dissolved sulfur, iron, and copper in subseafloor hydrothermal fluids and determining their fate as these superheated fluids rise to the seafloor. Calculations using this technique indicate that &amp;gt;20%–70% of sulfur, iron, and copper dissolved at peak subseafloor conditions are lost during upflow due to cooling and concomitant decreases in sulfide mineral solubility. The interpretation of these estimates within the geologic context of vent fields allows us to demonstrate a strong inverse relationship between seafloor spreading rate and peak pressure-temperature conditions, subseafloor heat loss, and the magnitude of subseafloor sulfide mineralization. Our results demonstrate the extent to which the secular variation of Earth’s mid-ocean ridge system over geologic time has impacted sulfide deposition rates and hydrothermal fluxes of sulfur and metals to the ocean.
Grouping Patient Data Based On Work And Place Of Residence On Perceived Complaints

Every day the Sawit Seberang Health Center serves many patients with various kinds of disease complaints from various areas in Sawit Seberang District. The number of patients can even reach tens of people in one day resulting in a large number of patient visit data. Limited information regarding the spread of diseases that are often suffered by patients in several areas at the Sawit Seberang Health Center has resulted in less optimal policy action, anticipation of treatment and prevention of disease in the community. To find information about grouping patient data based on work and place of residence for perceived complaints, a large or large data mining technique is needed, namely data mining techniques using the clustering method. The purpose of this study is to process and cluster patient data based on work, place of residence and complaints that are felt using the Clustering method, to analyze the results of applying data mining using K-Means Clustering in grouping patient data based on work, place of residence and complaints that are felt and find out the results of the settlement grouping patient data based on work and place of residence on perceived complaints using clustering and data mining methods.