Reviewerly/exHarmony huggingface dataset:
Seismic-phase detection using multiple deep learning models for global and local representations of waveforms

SUMMARY The detection of earthquakes is a fundamental prerequisite for seismology and contributes to various research areas, such as forecasting earthquakes and understanding the crust/mantle structure. Recent advances in machine learning technologies have enabled the automatic detection of earthquakes from waveform data. In particular, various state-of-the-art deep-learning methods have been applied to this endeavour. In this study, we proposed and tested a novel phase detection method using deep learning, which is based on a standard convolutional neural network in a new framework. The novelty of the proposed method is its separate explicit learning strategy for global and local representations of waveforms, which enhances its robustness and flexibility. Prior to modelling the proposed method, we identified local representations of the waveform by the multiple clustering of waveforms, in which the data points were optimally partitioned. Based on this result, we considered a global representation and two local representations of the waveform. Subsequently, different phase detection models were trained for each global and local representation. For a new waveform, the overall phase probability was evaluated as a product of the phase probabilities of each model. This additional information on local representations makes the proposed method robust to noise, which is demonstrated by its application to the test data. Furthermore, an application to seismic swarm data demonstrated the robust performance of the proposed method compared with those of other deep learning methods. Finally, in an application to low-frequency earthquakes, we demonstrated the flexibility of the proposed method, which is readily adaptable for the detection of low-frequency earthquakes by retraining only a local model.
Evaluating the Performance of Teaching Staff in Applied Colleges Using Fuzzy Logic

Using conventional approaches that evaluate teaching and research activity primarily based on numerical scores makes it harder and harder to evaluate academic success at various levels. The need for sophisticated computer models, such as the expert systems based on fuzzy Logic proposed in this study, which address the evaluation of teachers even in the face of ambiguous information, has been brought on by the indexing of academic performance in numerous international databases with impact indices at different scales. The development of a fuzzy Logic model in this study, which makes a unique contribution, involved simulating and implementing an algorithm in MATLAB, in which the membership functions and inference of fuzzy Logic rules designed. This pilot study's outcome was to test and validate the suggested model using a graphical interface, providing the results in accordance with minimum requirements and with more explanations.
Depth-bounded Epistemic Logic

Epistemic logics model how agents reason about their beliefs and the beliefs of other agents.Existing logics typically assume the ability of agents to reason perfectly about propositions of unbounded modal depth.We present DBEL, an extension of S5 that models agents that can reason about epistemic formulas only up to a specific modal depth.To support explicit reasoning about agent depths, DBEL includes depth atoms E d a (agent a has depth exactly d) and P d a (agent a has depth at least d).We provide a sound and complete axiomatization of DBEL.We extend DBEL to support public announcements for bounded depth agents and show how the resulting DPAL logic generalizes standard axioms from public announcement logic.We present two alternate extensions and identify two undesirable properties, amnesia and knowledge leakage, that these extensions have but DPAL does not.We provide axiomatizations of these logics as well as complexity results for satisfiability and model checking.Finally, we use these logics to illustrate how agents with bounded modal depth reason in the classical muddy children problem, including upper and lower bounds on the depth knowledge necessary for agents to successfully solve the problem.
Convolutional Neural Network Deep Learning Model for Improved Ultrasound Breast Tumor Classification

Breast cancer is one of the greatest frequent tumours among females in Iraq. Medical ultrasound imaging has become a common modality for breast tumour imaging because of its ease of use, low cost, and safety. In the present study, Convolutional Neural Network (CNN) feature extraction approaches were used to classify breast ultrasound imaging. The CNN model used is composed of four-layer for breast cancer ultrasound image analysis. Two types of free datasets were used. These data were divided into groups A and B. Group A has three classes, namely benign, malignant and normal, while group B has two classes, namely, benign and malignant. The proposed technique was assessed based on its accuracy, precision, F1 score and recall. The model's classification accuracy for data A was 96%, whereas for data B was 100%.
Right for the Job or Opposites Attract? Exploring Cross-Generational User Experiences with “Younger” and “Older” Voice Assistants

Efforts to diversify the design of virtual assistants (VAs) are inspiring new work on voice as a factor of user experience (UX). One trajectory is voice agedness. The Computers are Social Actors (CASA) paradigm suggests that humanlike computer agents are perceived in line with models of people. Notably, work on ageism and similarity attraction theory would predict that agents with “younger” and “older” voices may be treated differently. As a first effort, we explored the UX of a novel “older adult” text-to-speech (TTS) system. Younger (n=16) and older (n=18) people evaluated a VA that switched voices (“young” and “old”) in a storytelling activity. While most preferred the VA's younger-sounding voices on most measures, all tended to rate the “elder” VA as more appropriate within the storytelling context, challenging expectations based on similarity-attractiveness theory. This work provides initial clues about the relationship between age characteristics and age stereotypes for voice UX.
A methodological framework for using cloud-based mobile mathematical environments in higher education

This research aims to develop a methodology for utilizing cloud-based technologies in the teaching of higher mathematics. The study tasks involve identifying the features of mobile mathematical environments as tools for cloud-based technologies, developing a mobile mathematical environment specifically designed to support mathematics learning for students in economic disciplines, and formulating a methodology for employing the mobile mathematical environment. The research focuses on the process of teaching higher mathematics to students in economic disciplines. The subject of investigation encompasses the use of cloud-based learning technologies in the teaching of higher mathematics. The research methods employed include studying, summarizing, and systematizing scientific, methodological, and psycho-pedagogical literature; analyzing current educational standards and curricula; conducting targeted pedagogical observations, testing, and surveys; and analyzing the experience of using cloud-based learning technologies in the teaching of higher mathematics. The findings analyze the main features of mobile mathematical environments as tools for cloud-based technologies. The ``Higher Mathematics'' mobile mathematical environment, designed to support the learning of higher mathematics for students in economic disciplines, is examined. Elements of the methodology for utilizing the ``Higher Mathematics'' mobile mathematical environment in the teaching of higher mathematics are identified. The study identifies instructional formats through which the implementation of the ``Higher Mathematics'' mobile mathematical environment occurs in the teaching of higher mathematics. The main conclusions and recommendations emphasize that the use of the ``Higher Mathematics'' mobile mathematical environment in the teaching of higher mathematics enables students to master the necessary educational material and contributes to the development of cognitive activity, the formation of independent work skills, and the consolidation and systematization of knowledge.
From static to dynamic information containers

In today's fast-paced construction industry, collaboration is crucial for success.The Information Container for Linked Document Delivery (ICDD) is one of the efforts being made to support collaboration.It is used to store and exchange semantically linked heterogeneous application models.However, it is a static information container, which complicates synchronizing processes in an advanced digital twin environment.In this paper, we propose an approach that enhances information container implementation with dynamic features by automatically updating the linksets whenever a new document version is added to the container.This feature is expected to expand the workability of the ICDD and enhance its usefulness beyond pure data exchange.
M-dimension hybrid algorithm for scientific workflow in cloud computing

Cloud computing is emerging with growing popularity in workflow scheduling, especially for scientific workflow. With the emergence cloud computing, can benefit from virtually unlimited resources with minimal hardware investment. Scheduling the submitted Scientific Workflow Application (SWFA) tasks to the available computational resources while optimizing the cost of executing the SWFA is one of the most challenging processes of Workflow Management System (WfMS) in a cloud computing environment. Several cost optimization approaches have been proposed to improve the economic aspect of SWFS in cloud computing. The main goal of the paper is to present a new M-dimension hybrid algorithm, which uses a meta-heuristic algorithm such as Completion Time Driven Hyper-Heuristic (CTDHH), Hybrid Cost-effective Hybrid-Scheduling (HCHS), particle swarm optimization (PSO) and genetic algorithm (GA) and using heuristic algorithms such as the IC-PCPD2 and IC-Loss algorithms. Based on the results of the experimental comparison, the proposed method has proven to yield the most effective performance results for all considered experimental scenarios.
Digital Pedagogy Policy in Technical and Vocational Education and Training (TVET) in Malaysia: Fuzzy Delphi Approach

Digital platforms are oneof thetechnology adoptionthat is beginning to be used in most vocational colleges today. Services provided through digital platforms are an alternative to conventional methods. However, the use of digital platforms nowadays, among users consisting of vocational college students in Malaysia is still at a less than satisfactory level. Previous studies also focused a lot on the use and acceptance of technology platforms among users in schools only but not in vocational colleges (VC). The aim of the research is to determine the experts’ consensus on the objective of the digital pedagogy policy for Technical Education and Vocational Training (TVET) students especially in vocational colleges which is currently under development. Vocational colleges is one of the Technical Education and Vocational Training (TVET) institutions in Malaysia that offers certificate level and skill diploma education to high school students. Therefore, it is important to identify the policy that will be used. The Fuzzy Delphi Technique (FDT) was used in this research. Ten experts were chosen by utilizing the purposive sampling method. Analysis of the data was using the Triangular Fuzzy Number and Defuzzification process. The findings showed 100% consensus was achieved by the experts for objectives 1 and 3, and 90% consensus was achieved by the experts for objective 2. All of the experts agreed on the elements in the objectives of the digital pedagogy policy for TVET students. Further development of this policy hopefully will help the teachers in improving their knowledge of digital pedagogy and increase their competency pedagogy in order to produce skills oriented workforce among TVET students in Malaysia. Therefore, the results of this study are expected to be used to strengthen the development of e-learning models in the future to help students and educators to use digital platforms in vocational colleges in particular.
Plasmodium Knowlesi Malaria

Plasmodium knowlesi is a zoonotic Malaria parasite. It is now becoming very important. The vector is anopheles mosquito of different species. Many cases have also been found in offshore island areas of India. It is clinically similar to Falciparum malaria.
THE CORRELATION BETWEEN SIMPLE PRESENT TENSE MASTERY AND WRITING ABILITY IN SELF-INTRODUCTION TEXTS OF FEMALE AND MALE STUDENTS

Grammar is very important in learning and using language s so that using correct grammar will indicate clearly what the writer says. The objective of this research is to find out if the correlation between simple present tense mastery and writing ability in self - introduction text s of the tenth grade students of SMK N 1 Kalinyamatan Jepara is 1) significant for female students, and 2) no t significant for male students . This correlational research was conducted in SMKN 1 Kalinyamatan Jepara with 30 students from Class X Tata Boga 2 (BG 2) for female and 30 students from Class X Teknik E lektronika Industri 1 (EI 1) for male as the samples . Pearson Product Moment Correlation was used t o analyze the data . T he findings show that the correlation of simple present tense mastery and writing ability in self - introduction text s of female students is significant while that of male students is not significant .
A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems

Background:As cyber-physical systems (CPS) continue to grow, it is progressively crucial to address the challenges of data protection using artificial intelligence (AI). Objective:The goal of this research is to provide an up-to-date overview of the security and privacy issues of CPS that incorporate AI techniques. Method:To achieve this, the author conducted a systematic literature review, focusing on 35 relevant articles. Results:The data collected from these studies was then categorized into three main areas: 1) different security and privacy issues, 2) application areas and vulnerabilities, and 3) the AI techniques used to address security and privacy concerns. The literature review highlights that intrusion detection and cyberattacks are the most commonly studied areas in CPS, while Machine Learnning (ML)-based attacks and vessel trajectory are less explored. The review identifies various CPS applications such as water treatment, energy, healthcare, and transportation that address security and privacy concerns. However, a relatively small proportion of studies focused on the manufacturing domain. The review also notes that while supervised machine learning algorithms under the classification category are commonly used to address data protection issues, there are comparatively fewer studies that have implemented automation processes using robots and deep learning. Limitations:The articles related to blockchain-based research were not included in this review to focus solely on AI techniques. Conclusion:The results of this analysis indicate that there is a significant need for innovative AI/ML techniques to protect intelligent systems and networks from ML-based security threats.
Underwater Image Restoration and Enhancement: A Comprehensive Review of Recent Trends, Challenges, and Applications

In recent years, underwater exploration for deep-sea resource utilization and development has a considerable interest. In an underwater environment, the obtained images and videos undergo several types of quality degradation resulting from light absorption and scattering, low contrast, color deviation, blurred details, and nonuniform illumination. Therefore, the restoration and enhancement of degraded images and videos are critical. Numerous techniques of image processing, pattern recognition and computer vision have been proposed for image restoration and enhancement, but many challenges remain. This survey presents a comparison of the most prominent approaches in underwater image processing and analysis. It also discusses an overview of the underwater environment with a broad classification into enhancement and restoration techniques and introduces the main underwater image degradation reasons in addition to the underwater image model. The existing underwater image analysis techniques, methods, datasets, and evaluation metrics are presented in detail. Furthermore, the existing limitations are analyzed, which are classified into image-related and environment-related categories. In addition, the performance is validated on images from the UIEB dataset for qualitative, quantitative, and computational time assessment. Areas in which underwater images have recently been applied are briefly discussed. Finally, recommendations for future research are provided and the conclusion is presented.
A Richness Estimator Based on Integrated Data

Species richness is a widely used measure for assessing the diversity of a particular area. However, observed richness often underestimates the true richness due to resource limitations, particularly in the small-sized sample or highly heterogeneous assemblage. Estimating species richness in a large-scale region typically involves an integrated data set consisting of subsamples collected independently from different subregions. However, the pooled sample of integrated data is no longer a random sample from the entire region, and the use of different sampling schemes results in variations in data formats. Consequently, employing a single sampling distribution to model the pooled sample becomes impractical, rendering existing richness estimators inadequate. This study theoretically explains the applicability of Chao&amp;#039;s lower bound estimators in estimating species richness for large-scale areas using the pooled sample. Additionally, a new nonparametric estimator is introduced, which adjusts the bias of Chao&amp;#039;s lower bound estimator by leveraging the Good-Turing frequency formula. This proposed estimator only utilizes the pooled sample&amp;#039;s singleton, doubleton, and tripleton richness. Simulated data sets across various models are employed to demonstrate the statistical performance of the estimator, showcasing its ability to reduce bias and provide accurate 95% confidence intervals. Real data sets are also utilized to illustrate the practical application of the proposed approach.
Towards the Development of Research Skills of Physics Students through the Use of Simulators: A Case Study

The objective of this research is to evaluate the impact of using the simulators on students’ achievement of competencies, such as: Inquire, through scientific methods to build knowledge. The methodology used consisted of qualitative research whose data collection was carried out through an experiment with interactive Physics Educational Technology (PhET) simulators. The experiment was carried out through a simple random sampling of 25 students from a population of 100 students of physics subjects. With this sample, four learning sessions were developed using the PhET interactive simulator, which were implemented in a way that helped the development of skills according to learning indicators oriented to the achievement of the competence, Inquire, through scientific methods to build knowledge. A pretest and a posttest were applied. The results of the study show the benefits of the use of simulators in the teachinglearning processes, contributing to the achievement of the competence inquire, through scientific methods to build knowledge, developing capacities, such as: Problematize situations to inquire, design strategies to inquire, generate and record data or information, analyze data and information, and evaluate and communicate the process and results of their inquiry. In conclusion, 100% of the students develop inquiry competencies through the development of capabilities.
Multi-wavelength network: Predicted-illumination for phase unwrapping in quantitative phase imaging

Multi-wavelength interferometry improves the measurement range, and provides an effective unwrapping method for steep steps or complex measurement samples. However, it requires multiple wavelengths for illumination and the noise is amplified. Here, we propose a three-wavelength phase unwrapping approach by designing a multi-wavelength network (MW-Net) for prediction of illumination wavelength. Learning the mapping relationship of interferograms at different wavelengths enables the designed MW-Net to predict interferograms at two different wavelengths from only one interferogram at a single wavelength illumination. As a result, this MW-Net with increased layers for feature extraction to solve the vulnerability of small difference between two output characteristics in the network. Using this new network to implement three-wavelength phase unwrapping, we realize both the phase imaging of thicker sample and the measurement range extension under single-wavelength illumination without increasing noise. Both the simulation analysis and experimental results demonstrate the effectiveness and practicability of the MW-Net based three-wavelength phase unwrapping method.
A Review of Convolutional Neural Network based Image Denoising Algorithms

Currently, image-denoising algorithms based on convolutional neural networks (CNN)have been widely used and have achieved good results. Compared with traditional image-denoisingmethods, it has powerful learning ability and efficient algorithms. This paper summarizes traditionaldenoising methods and CNN-based image denoising methods, and introduces the basics of imagedenoising in detail, which is helpful for readers who are starting with image denoising processing.In addition, this paper also summarizes some commonly used datasets in the field of imageprocessing, which makes it easier for us to denoise images. Finally, some suggestions for improvingthe performance of CNN image denoising are presented, and possible future research directions arediscussed.
A simple coin for a 2d entangled walk

We analyze the effect of a simple coin operator, built out of Bell pairs, in a $2d$ Discrete Quantum Random Walk (DQRW) problem. The specific form of the coin enables us to find analytical and closed form solutions to the recursion relations of the DQRW. The coin induces entanglement between the spin and position degrees of freedom, which oscillates with time and reaches a constant value asymptotically. We probe the entangling properties of the coin operator further, by two different measures. First, by integrating over the space of initial tensor product states, we determine the {\it Entangling Power} of the coin operator. Secondly, we compute the {\it Generalized Relative R\'{e}nyi Entropy} between the corresponding density matrices for the entangled state and the initial pure unentangled state. Both the {\it Entangling Power} and {\it Generalized Relative R\'{e}nyi Entropy} behaves similar to the entanglement with time. Finally, in the continuum limit, the specific coin operator reduces the $2d$ DQRW into two $1d$ massive fermions coupled to synthetic gauge fields, where both the mass term and the gauge fields are built out of the coin parameters.