Reviewerly/exHarmony huggingface dataset:
Fostering creativity and cultural bonds: revitalizing kasab crafts through arts education and community engagement in West Aceh, Indonesia

This study aims to examine the school's efforts in introducing and preserving Kasab in West Aceh through art education and community collaboration. Kasab is gold thread embroidery on velvet cloth which is widely used in Acehnese weddings as a cultural product. Kasab was introduced to art education at the West Aceh High School through arts and culture subjects. This study used a qualitative approach. The research was conducted in five high schools in West Aceh District, Indonesia. Data collection techniques were carried out through interviews with students, teachers who teach arts and culture, and Kasab craftsmen. In addition, observations were also conducted to observe the collaborative learning process of the community and teachers, as well as literature studies on community collaboration and art education in the field of crafts. The results of the study show that collaborative art education in schools and communities regarding the introduction and process of making Kasab crafts is taught in the fine arts course, which begins with making decorative motifs. Next, the Kasab crafts were embroidered with gold thread. Students and teachers performed field studies at the Kasab craftsmen's place to observe the process of making Kasab. Later on, the students practice making Kasab at school, guided by the teacher and Kasab craftsmen. This activity is very important to be performed in every high school as an effort to preserve the Kasab craft in the West Aceh district.
Doing Pronunciation Online: An Embodied and Cognitive Approach Which Puts Prosody First

The content of pronunciation teaching is increasingly based on solid research and pragmatic goals, and the aim for many teachers today is no longer to modify their students’ accents to achieve perfect Received Pronunciation/Standard British English (RP/SBE) or General American (GA), but the more pragmatic goal of intelligibility. The many constraints often associated with learning languages in French universities encourage stakeholders to seek innovative solutions, and online learning is often viewed as a solution. This paper will present a pilot study being carried out at Grenoble Alpes University which seeks to measure the progress of students using an online learning path targeting pronunciation and listening skills. The learning path is one of several created with an Initiative d’excellence, a French government scheme which awards grants for innovative pedagogical projects (IDEX) grant, and which can be used by specialist English students and non-specialists alike. The course integrates short subtitled videos and interactive activities, and is based on an articulatory approach to learning pronunciation which puts prosody front and centre. More specifically, this article will explore the problems of learning pronunciation, especially for French speakers, before presenting our pedagogical approach and the technological solutions we propose.
Nickel in Tuzla topsoils: Its distribution and impacts

The paper presents the results of geoecological - pedological research of nickel (Ni) concentrations in the soil of the city of Tuzla. The main goal of the research was to determine the extent to which the soil of the city of Tuzla is contaminated with nickel and to determine the origin of pollutants. The 240 soil samples were collected during the terrain research, covering an area of 303 km2. The nickel concentration testing in soil samples was performed by mass spectrometry (ICP-MS) with a detection range of 0.1 - 10000ppm. Exceeding the maximum allowable concentrations of nickel, defined by the Ordinance on determining the allowable amounts of harmful and dangerous substances in the soil and methods of their testing was recorded in 99.16% of samples.&#x0D; The range of determined nickel concentrations is 30 - 1437.7ppm, and the average value of all measured concentrations in the samples is 297.91ppm. The highest concentration of nickel was recorded in sample 64 (1437.7ppm), collected in the area of Lipnica. Out of 240 total analyzed soil samples, nickel concentrations were increased in 238 samples, in some places several tens of times larger than the allowed limit. Elevated concentrations were also recorded in sample no. 59 (1109.1ppm) in the area of Požarnica - Kovačica and in sample 189 (1005ppm) in the area of Slavinovići. At twenty locations, nickel concentrations vary from 500-1000ppm.
Strategic Placement of Servers in Mobile Cloud Computing: A Comprehensive Exploration of Edge Computing, Fog Computing, and Cloudlet Technologies

Mobile Cloud Computing (MCC) has become integral to the advancement of mobile applications, necessitating strategic server placement for optimized performance. This review article explores three pivotal technologies—Edge Computing, Fog Computing, and Cloudlet Technologies—aimed at addressing the challenges posed by traditional cloud-centric models in MCC environments. The paper provides a thorough analysis of each approach, elucidating their architectural principles, benefits, and applications. Edge Computing’s proximity to end-users, Fog Computing’s intermediary role, and the localized Cloudlet Technologies are scrutinized. A comparative analysis offers insights into their strengths and limitations, aiding in determining their suitability based on diverse use cases. Real-world applications showcase the transformative impact of these technologies in enhancing mobile experiences across sectors such as healthcare, gaming, and more. The review concludes with a discussion on the challenges inherent in each strategy and proposes future research directions. This comprehensive exploration serves as a valuable resource for researchers, practitioners, and decision-makers navigating the dynamic landscape of strategic server placement in MCC, contributing to the optimization of performance and user experience in mobile applications and services.
Exploring the Landscape: A Systematic Review of Quantum Machine Learning and Its Diverse Applications

Quantum Machine Learning (QML), a confluence of quantum computing and classical machine learning, represents a revolutionary paradigm with transformative potential. This systematic review explores the landscape of QML by investigating its underlying principles, methodologies, diverse applications, challenges, and ethical considerations. Beginning with an examination of fundamental quantum computing principles, the review navigates through various QML methodologies, comparing them with classical counterparts. Real-world applications, ranging from quantum-enhanced optimization to drug discovery, are scrutinized, showcasing the practical implications of QML across industries. The paper systematically identifies challenges, including quantum hardware constraints and ethical considerations, while offering insights into current limitations and future research directions. A comparative analysis benchmarks QML against classical machine learning approaches, providing a nuanced understanding of its strengths and limitations. Ethical considerations underscore the importance of responsible AI practices in the integration of QML. The review concludes by identifying research gaps and suggesting future directions, emphasizing the need for continued exploration in this dynamic intersection of quantum computing and machine learning. This comprehensive exploration serves as a valuable resource for researchers, practitioners, and decision-makers seeking insights into the current state and transformative potential of Quantum Machine Learning.
Advancements in Weapon Detection: A Systematic Review Using Deep Learning in Surveillance Footage

Weapon detection in surveillance footage is a critical aspect of modern security systems, aiming to prevent potential threats and enhance public safety. Traditional methods of weapon detection often rely on manual inspection, which is labor-intensive and prone to errors. With the advent of deep learning techniques, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), there has been a significant shift towards automated and efficient weapon detection in surveillance footage. This systematic review provides an in-depth analysis of recent advancements in weapon detection using deep learning methodologies. Through a systematic search and evaluation of existing literature, this review examines the methodologies, datasets, performance metrics, and challenges encountered in various studies. It explores the effectiveness of deep learning models in detecting weapons with high accuracy and speed, while also addressing factors such as dataset diversity, annotation quality, and real-world applicability. Additionally, the review discusses the impact of transfer learning, data augmentation techniques, and model architectures on the performance of weapon detection systems. Furthermore, it highlights the role of domain adaptation and fine-tuning strategies in improving the generalization capabilities of deep learning models across different surveillance environments. The review also delves into the ethical considerations and privacy implications associated with the deployment of automated weapon detection systems in public spaces. By synthesizing findings from diverse studies, this comprehensive overview aims to provide valuable insights for researchers, practitioners, and policymakers involved in the development and implementation of advanced surveillance technologies for public safety and security purposes
Advancements in Weapon Detection: A Systematic Review Using Deep Learning in Surveillance Footage

Weapon detection in surveillance footage is a critical aspect of modern security systems, aiming to prevent potential threats and enhance public safety. Traditional methods of weapon detection often rely on manual inspection, which is labor-intensive and prone to errors. With the advent of deep learning techniques, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), there has been a significant shift towards automated and efficient weapon detection in surveillance footage. This systematic review provides an in-depth analysis of recent advancements in weapon detection using deep learning methodologies. Through a systematic search and evaluation of existing literature, this review examines the methodologies, datasets, performance metrics, and challenges encountered in various studies. It explores the effectiveness of deep learning models in detecting weapons with high accuracy and speed, while also addressing factors such as dataset diversity, annotation quality, and real-world applicability. Additionally, the review discusses the impact of transfer learning, data augmentation techniques, and model architectures on the performance of weapon detection systems. Furthermore, it highlights the role of domain adaptation and fine-tuning strategies in improving the generalization capabilities of deep learning models across different surveillance environments. The review also delves into the ethical considerations and privacy implications associated with the deployment of automated weapon detection systems in public spaces. By synthesizing findings from diverse studies, this comprehensive overview aims to provide valuable insights for researchers, practitioners, and policymakers involved in the development and implementation of advanced surveillance technologies for public safety and security purposes.
Text, Voice-notes, and Emojis: Exploring the use of WhatsApp as a responsive research method for qualitative studies

Unprecedented times are upon us and the need for ethically responsive online research methods is increasing exponentially. Currently, literature is showing that previously frowned upon methods such as the use of social media continue to offer solutions to traditional research challenges in times of COVID-19 and beyond. This paper describes how the features of WhatsApp were used to respond to the needs of geographically dispersed, working study participants. Tronto’s Ethics of Care framework is employed as a lens to place emphasis on the elements of care this emerging method offers. Participants and the researcher reflected on the high level of responsiveness WhatsApp offered as a method and the drawbacks are also discussed. In conclusion, this paper offers a list of recommendations that researchers need to take into account when using WhatsApp as a method in a context-sensitive manner.
Real Time Eye-Tracking Mouse Control Using Recurrent Neural Network

Machine Learning (ML) techniques, specifically Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), were employed to achieve precise and intuitive real-time eye tracking mouse control through computer vision. However, XGBoost may suffer from overfitting when dealing with a large number of features compared to the training data size, or when dealing with noisy or imbalanced data. To address this issue, this paper introduces a Recurrent Neural Network (RNN), for eye tracking mouse control in Human Computer Interaction (HCI). Gaze Direction Estimation (GDE) is initially employed to estimate gaze direction, utilizing pupil positions and camera calibration parameters. The estimated gaze direction is then used as input for the RNN to eye-tracking mouse control for HCI. The experimental results shows that the GDE-RNN has 26.03% and 8.24% superior accuracy, 24.93% and 86.76% better precision, and 27.36% and 8.75% high recall in comparison to SVM and XGBoost for eye tracking mouse control.
Contrast Limited Adaptive Histogram Equalization based Multi-Objective Improved Cat Swarm Optimization for Image Contrast Enhancement

The contrast enhancement technique is utilized to enhance the image quality which produce good quality images for various image processing tasks. The Histogram Equalization (HE) is one of the widely utilized technique for enhance the contrast and estimates the intensity frequency in an image. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is one of the local HE technique. Therefore, the CLAHE can handle the issues of classical HE algorithm. The CLAHE utilizes two various parameters such as number of tiles and clip limit, moreover the effectiveness depends on these two parameters. In this paper, Multi-Objective Improved Cat Swarm Optimization (MOICSO) is proposed to determine the optimum parameters for CLAHE to improve the image contrast. The first fitness function is utilized for estimating entropy and second is utilized for calculating Fast Noise Variance Estimation (FNVE) which produce local preservations and avoid noise amplification in an output image. The parameters like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal-to-noise Ratio (PSNR) and Computing Time (CT) are utilized to evaluate the proposed methodology. The obtained result shows that the MOICSO achieves less MAE of 6.28 which is which is better than other existing methods.
The Alfred T. Anderson <i>Journal of Geology</i> Best Paper Award

Previous articleNext article FreeNew Award AnnouncementThe Alfred T. Anderson Journal of Geology Best Paper AwardPDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreThe Journal of Geology is proud to establish a new best-paper award: the Alfred T. Anderson Journal of Geology Best Paper Award, named in honor of the journal’s coeditor and editor from 1983 to 2008. This new annual award honors Fred Anderson’s many years of service and dedication to both the journal and the earth sciences and will shine a spotlight on the journal's continued contributions to knowledge development and dissemination.The award will be given annually for the best paper published in the past three years of the Journal of Geology. The Editor and the Board of Associate Editors will serve as the selection committee. The award will be announced at the annual Geological Society of America in the fall of each year. Winners will receive a $1,000 cash prize. The first Alfred T. Anderson Journal of Geology Best Paper Award will be announced at the 2024 GSA meeting in Anaheim, California for the best paper published in the Journal of Geology in 2021–2023. Previous articleNext article DetailsFiguresReferencesCited by The Journal of Geology Volume 131, Number 1January 2023 Article DOIhttps://doi.org/10.1086/728587 © 2024 The University of Chicago. All rights reserved.PDF download Crossref reports no articles citing this article.
Enhancing Segmentation Efficiency: A 2D U-Net Approach with 3D-to-2D Conversion for Medical Image Analysis

The efficient and reliable segmentation of three dimensional (3D) medical images is a critical task in many diagnostic and therapy operations. Traditional segmentation methods frequently entail training sophisticated 3D models, which can be computationally and time-consuming. In this paper, we suggest a novel segmentation approach that involves transforming three- dimensional (3D) images into two-dimensional (2D) slices and applying a two-dimensional (2D) U-Net architecture. This change not only lowers the computing cost but also allows us to use proven 2D segmentation techniques. To generate 2D images, we slice 3D images at defined intervals and use maximum intensity projections. For segmentation, we use a modified 2D U-Net architecture that has been trained on these transformed 2D slices. Our experimental results demonstrate the usefulness of this strategy.We compare our method to traditional 3D segmentation algorithms and talk about the trade-offs between computational efficiency and accuracy. Our findings indicate that the 2D conversion method offers promise for efficient segmentation without sacrificing results quality. This study contributes to the field of medical picture segmentation by providing a fresh look at how to deal with the computational hurdles that come with 3D data.
EzStream: Video Compression and Textual Representation for Multimedia Sensor Networks

In an age characterized by the relentless growth of multimedia data, the efficient management and processing of video content have emerged as pressing challenges. This paper introduces “EzStream,” a dynamic video-to-text conversion system that transcends the conventional boundaries of compression techniques. Rather than merely reducing file sizes, EzStream delves deeper by converting video content into a textual format, thereby encapsulating the essence of the information embedded within. What sets EzStream apart is its adaptability, as it can be retrained to cater to a diverse array of applications, ensuring that the extracted text aligns precisely with specific task requirements. Our extensive experimentation, grounded in the Human Motion Database (HMDB) dataset, has unveiled a pivotal insight: the selection of an effective network architecture profoundly influences encoding efficiency. We present a comprehensive analysis of EzStream's performance, showcasing the tangible impact of architectural choices on video encoding. Our results underscore a series of transformative benefits, including remarkable reductions in file sizes, expedited processing times, and enhancements in video quality. This paper serves as a beacon, illuminating the indispensable role of network architecture in video processing. It highlights how this architectural foundation empowers EzStream to excel in encoding, ultimately reshaping the landscape of multimedia data management. In an era marked by the exponential growth of multimedia content, the innovation embodied in EzStream not only meets but anticipates the escalating demands for resource-efficient multimedia processing, promising more agile and adaptive solutions for information extraction and utilization.
Secure Vote: AI-powered Fingerprint Authentication for Next-Generation Online Voting

Democratic societies place significant importance on secure and efficient voting systems as a cornerstone of their governance. Normally, the voting process would follow a traditional ballot system involving tedious paperwork. It all takes a lot of time to manually count the votes, which may lead to human errors in counting or missing votes. However, other concerns surrounding the security and reliability of e-voting systems have initiated the development of more robust approaches. The proposed Secure Vote system presents an AI-enabled voting solution that enhances the security and reliability of the voting process by incorporating dual-factor authentication through a unique identification number and the utilisation of fingerprints. It also has the potential to provide a secure and trustworthy platform for democratic societies to exercise their voting rights.
Penerapan Model Pembelajaran Kooperatif Tipe Numbered Head Together (NHT) Berbantuan Media Interaktif Berbasis Animasi terhadap Motivasi dan Hasil Belajar Siswa UPTD SMP Negeri 1 Kokop

This study aims to reviw student learning motivation and student learning achievement tests using Numbered Head Together (NHT) learning assisted by interactive media based on animation the research method used is the Lilierfrors method assisted by Microsoft Excel. In terms of the teacher’s ability, and can increase the ability of student in learning data processing while the purpose of this research is to find out student motivation questionnaires and student learning achievement tests, this reseachis in the from of quantitative research the subject of this research is class VII-A students at UPTD SMP NEGERI 1 KOKOP the data collection instrument used on learning motivation and student response test sheets which were very positive, where 98% of students expresses their enjoyment of learning using the Numbered Heads Together (NHT) cooperative learning model through interactive media based on animation achievement tests. Positive and understand 99% of this study it is stromgly application of the Numbered Heads Together (NHT) cooperative learning model can increase student learning VII-A UPTD SMP NEGERI 1 KOKOP in data processing material. &#x0D; Keywords: animation – based interactive media &#x0D;
Semigroups on some new operations over intuitionistic fuzzy matrices

In this article, different kinds of component wise max-max operations are introduced on intuitionistic fuzzy matrix and some algebraic properties are studied together with the component wise \(\min -\min\left(\wedge_m\right)\) operation. Also some semigroup algebraic structures are constructed using these new operations on intuitionistic fuzzy matrices.
Keyword Driven Language-Independent Low-Resource Graph-Based Automatic Text Summarization of Konkani Texts

Automatic text summarization has been widely researched for the most popularly spoken languages. There is a need to extend research to other less popular and low-resource languages as well. We explore automatic text summarization for Konkani language. It is a language spoken by a relatively small population of people in the state of Goa, India, and is a low-resource language. A low-resource language has limited language processing tools, which add to the challenges faced in the automatic text summarization process. This study aims at extending the popular graph-based language-independent approach using language-independent keywords with the help of YAKE. These keywords allow us to weight the edges in a fully connected undirected graph by not only considering the influence of two vertices (sentences) but also the sentences with the overall document, represented by the keywords. Variable thresholds of relevant keywords are examined along with altering a bias parameter. The impact of these keyword thresholds along with the bias parameter is examined on the system generated summary.