Reviewerly/exHarmony huggingface dataset:
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation

In recent years, continuous latent space (CLS) and discrete latent space (DLS) deep learning models have been proposed for medical image analysis for improved performance. However, these models encounter distinct challenges. CLS models capture intricate details but often lack interpretability in terms of structural representation and robustness due to their emphasis on low-level features. Conversely, DLS models offer interpretability, robustness, and the ability to capture coarse-grained information thanks to their structured latent space. However, DLS models have limited efficacy in capturing fine-grained details. To address the limitations of both DLS and CLS models, we propose SynergyNet, a novel bottleneck architecture designed to enhance existing encoder-decoder segmentation frameworks. SynergyNet seamlessly integrates discrete and continuous representations to harness complementary information and successfully preserves both fine and coarse-grained details in the learned representations. Our extensive experiment on multi-organ segmentation and cardiac datasets demonstrates that SynergyNet outperforms other state of the art methods, including TransUNet: dice scores improving by 2.16%, and Hausdorff scores improving by 11.13%, respectively. When evaluating skin lesion and brain tumor segmentation datasets, we observe a remarkable improvement of 1.71% in Intersection-over Union scores for skin lesion segmentation and of 8.58% for brain tumor segmentation. Our innovative approach paves the way for enhancing the overall performance and capabilities of deep learning models in the critical domain of medical image analysis.
Development and Effects of Customized Learning Program Using Flipped Learning for Nursing Students

Objectives The purposes of this study were to validate the effects of customized learning activities using flipped learning on the self-directed learning ability, self-efficacy, and learning attitude of nursing students.&#x0D; Methods This is a quasi-experimental study, using a single-group pretest-posttest design, to confirm the effects of customized learning activities. The study was conducted for 13 weeks, from February to June 2022, among 141 pediatric nursing students. Based on the ADDIE model, customized learning activities using flipped learning were developed.&#x0D; Results Self-directed learning (t = 4.89, p &lt;.001), self-efficacy (t = 9.70, p &lt;.001), and learning attitude (t = 2.70, p =.008) improved after education. Significant positive correlations were observed between self-directed learning ability, self-efficacy, academic attitude, and educational satisfaction.&#x0D; Conclusions Customized learning activities using flipped learning are an effective approach for improving stu-dents’ self-directed learning, self-efficacy, and learning attitude. We believe that the competency of nursing stu-dents can be effectively improved by considering the generation and subject characteristics.
Reconstruction with robustness: A semantic prior guided face super-resolution framework for multiple degradations

Despite the rapid advancements made with the support of deep learning, Face Super-Resolution (FSR) methods still suffer from challenges under multiple degradations. These challenges significantly impede the practical applications of FSR methods in real-world scenarios. Incorporating facial priors could potentially relieve this issue. However, ground truth priors are not feasible in real-world applications, meanwhile the accuracy of predicted priors is difficult to guarantee, especially for low-resolution faces under multiple degradations. Hence, it is worth exploring how to effectively leverage facial priors for improving the robustness of FSR under multiple degradations. To tackle these problems, we propose RSemFace, a robust semantic prior guided FSR framework to reconstruct multiple degraded faces. In RSemFace, we design the Degradation Stage to synthesize multiple degraded low-resolution faces with a variety of interpolations, noise levels, blurring kernels, and even the real-world interference. The Generation Stage generates Coarse-SR faces, and extracts semantic features from the Coarse-SR as priors, which are used to the reconstruction of Fine-SR faces with the support of Semantic Feature Attention Blocks (SFABs) and Semantic Loss. Both quantitative and qualitative results show the better robustness of our RSemFace for content recovery and perceptual quality in simultaneously handling multiple degraded faces compared with other state-of-the-art methods. Lastly, faces reconstructed by RSemFace are proven to improve the high-level vision task due to better recovered identities.
DRJLRA: A Deep Reinforcement Learning-Based Joint Load and Resource Allocation in Heterogeneous Coded Distributed Computing

In this paper, we introduce the DRJLRA algorithm, a load and resource allocation scheme based on deep reinforcement learning (DRL) for a generic multi-master, multi-worker coded distributed computing (CDC) system. Our aim is to minimize the combined delay of communication and computation for a set of matrix-vector multiplication tasks. The proposed DRL-based approach has several unique features that set it apart from existing literature. Firstly, it is applicable to general CDC systems with multiple masters and workers. Additionally, it considers multi-task CDC systems with stochastic task arrivals, takes into account the heterogeneity of workers with random computation and communication delays, and utilizes the state-of-the-art soft actor-critic (SAC) DRL algorithm, making it versatile and efficient in handling complex and dynamic CDC environments. Our results demonstrate that DRJLRA outperforms benchmark schemes significantly. It is thus well-suited for real-world CDC systems with diverse and dynamic workloads.
Optimal Visual Coverage for Wireless Camera Sensor Networks using Evacuation Plan

Wireless Camera Sensor Networks (WCSN) have emerged as a promising technology in recent years, due to their extensive applications in various fields, including critical contexts such as evacuation. However, optimizing a WCSN layout to reduce its cost while maximizing coverage is tedious. Cameras can be interconnected thus may require connectivity, and the evacuation context requires full-view of the targets and zones of interest to cover in priority. In this paper, we propose a new method for optimizing WCSN indoor layouts under k-connectivity constraint while considering full-view requirement and predefined zones of interests. This method utilizes an ISO 23601 evacuation plan as input to generate grid environments and an Evacuation-Oriented Multigraph. This allow to model the full-view requirement and build a relevant set of initial solutions for a Pareto-based multi-objective genetic algorithm, which computes the optimized WCSN layout. The performance evaluation is conducted using numerical and simulation tools on scenarios with different connectivity constraints and fields of view. The obtained results show that the proposed solution outperforms existing solutions and random layouts in terms of coverage.
Research Population and Sampling in Quantitative Study

The study underscores the paramount importance of meticulous population selection and sampling strategy in research design. Providing researchers with a comprehensive overview of population considerations and sampling methods, it offers a valuable resource for enhancing the robustness and applicability of research outcomes across diverse disciplines. Researchers discuss the unit of analysis, unit of observation, population of interest, target population, sampling framework, and sampling methods in light of employee work engagement in Malaysia. Simple random sampling, stratified random sampling, systematic random sampling, cluster sampling (single-stage, double-stage, and multi-stage), phase sampling (two-phase and multiphase), convenience sampling, purposive sampling, quota sampling, snowball sampling, and volunteer sampling have been discussed for selecting the appropriate sampling method for the research titled Revisiting of JD-R Theory and the effect of leadership style and meaningful work on employee work engagement among the full-time operational employee in Malaysia. According to the discussion on population and sampling methods, researchers use non-probability sampling, specifically convenience sampling techniques, based on the accessibility and availability of the full-time operational employees of successful organisations in Malaysia. Researchers and practitioners alike can leverage the insights presented in this review to make informed decisions about population selection and sampling methods, ultimately contributing to the advancement of credible and impactful research.
Deep Shape and SVBRDF Estimation using Smartphone Multi‐lens Imaging

Abstract We present a deep neural network‐based method that acquires high‐quality shape and spatially varying reflectance of 3D objects using smartphone multi‐lens imaging. Our method acquires two images simultaneously using a zoom lens and a wide angle lens of a smartphone under either natural illumination or phone flash conditions, effectively functioning like a single‐shot method. Unlike traditional multi‐view stereo methods which require sufficient differences in viewpoint and only estimate depth at a certain coarse scale, our method estimates fine‐scale depth by utilising an optical‐flow field extracted from subtle baseline and perspective due to different optics in the two images captured simultaneously. We further guide the SVBRDF estimation using the estimated depth, resulting in superior results compared to existing single‐shot methods.
Leveraging Smart Contracts for Decentralized Healthcare Data Sharing

Abstract: Healthcare data sharing is essential for enhancing healthcare service quality and efficiency. However, traditional healthcare data sharing systems are often centralized, opaque, and inefficient. This can pose security and privacy concerns, as well as delays and errors in data access. The integration of blockchain-based smart contracts into the healthcare sector has introduced a transformative paradigm for decentralized healthcare data sharing. This research paper explores the multifaceted landscape of leveraging smart contracts to enhance the security, efficiency, and accessibility of healthcare data sharing. By offering secure, transparent, and automated mechanisms, smart contracts address critical challenges in patient data management, consent verification, and interoperability across healthcare systems.
Cooperative Cloud-Edge Computing for Integrated Sensing and Communication in Internet of Vehicles

With the evolution of wireless communication technology and the popularity of intelligent vehicles, diverse computation-intensive services in Internet of Vehicles have emerged and brought enormous challenges to the computing and communication capabilities of vehicular networks. Therefore, integrated sensing and communication for cooperative cloud-edge computing in Internet of Vehicles are getting more attention. This chapter introduces collaborative cloud-edge-end computing for deep learning training and analyses the commonly used methods, such as early-exit policy, convolutional neural network splitting, and communication compression, which are used to improve user satisfaction and robustness. Also, this chapter establishes a general mathematical for the joint communication and computation resource allocation problem and solves it with a deep reinforcement learning-based solution. Moreover, this chapter discusses future research directions.
The Urgency of Reading Cross-Cultural Fiction

Globalization and increased migrationmigration demand cross-cultural knowledge for citizens, vital in personal and professional contexts. The United Nations Educational, Scientific and Cultural Organization (UNESCO)UNESCO and the Council of Europe (CoE)Council of Europe address xenophobiaxenophobia through education, encouraging discourse on intercultural understandingintercultural understanding. Literature serves as a tool for fostering dialogue and empathyempathy, shaping perceptions, and transcending cultural boundaries.
THE EFFECT OF REPORT TEXT TO STUDENT'S WRITING QUALITY BY USING INSTAGRAM POST

This study aims to determine students' ability to understand how to write a report text using Instagram posts in eighth-grade students at SMPIT AL-FAKHRI SUNGGAL. This research is an experimental research. This study's population was all class VIII students at SMPIT AL-FAKHRI Sunggal. The sample for this study was taken from 30 students in the experimental group (class VIII-A) and 30 students in the control group (class VIII-B). Data collection instruments are in the form of tests and observations. After analyzing the data, the writer gets the highest average value obtained in the experimental class is 2,247. For the control class, the value in the table is around 1,935, so the value received is higher than the table. This means that there is a significant effect of using Instagram posts to write a report text.
Activity-Based Approach to the Teaching and Psychology of Insightful Problem Solving: Scientific Concepts as a Form of Constructive Criticism

This article is dedicated to the 100-year anniversary of the birth of N.F. Talyzina and contains an assessment of the prospects for developing ways to master a scientific concept, in theories of learning according to the activity approach. The assessment takes into account achievements following the approach of L.S. Vygotsky, the "conceptual changes" approach, and theories of the psychology of insightful problem solving.To demonstrate the necessity and productivity of the activity approach to scientific concepts that students learn as forms of constructive criticism.A comparative analysis of the theories of the activity approach to learning, the approach of L.S. Vygotsky, the "conceptual changes" approach, and theories of the psychology of insightful problem solving, from the standpoint of identifying the most effective way of mastering scientific concepts.The main substantiated thesis of the article is that mastery of a scientific concept is most effective when it is presented as a form of constructive criticism of another concept.Taking into account the conceptual forms of constructive criticism allows us to outline the actual paths of development of the activity approach to learning. These forms were developed, on the one hand, through the methodology of science, and on the other, in a less developed way, through the psychology of insightful problem solving, with reliance on certain forms of "critical" action. In particular, when using a special kind of obstacle to teach a task, it is proposed to use the analysis of "full insight" to reveal a special type of reason for an erroneous action.
Kernel-Based Independence Tests for Causal Structure Learning on Functional Data

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert-Schmidt independence criterion (HSIC) and its d-variate version (d-HSIC), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (CPT) based on the Hilbert-Schmidt conditional independence criterion (HSCIC), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socio-economic data.
Creative Development and Design of APP for the New Type of Connected Bus Transfer System

In recent years, countries have gradually paid attention to the issue of carbon emissions and started to improve plans. Reaching net-zero emissions by 2050 has become the common goal of most countries. In order to achieve this goal, countries are also vigorously promoting the use of public transportation instead of driving by themselves. When actually interviewing people who take a bus, it is found that people usually spend the most time on bus transfers when taking a bus. Most of the people who need to transfer are because the journey to the destination is relatively long, and it takes a long time to wait at the transfer point when transferring to the bus. This research wants to improve from this point to provide the public with a better travel experience.
An Analysis on the Social Participation of Adolescents for Digital Citizenship

Objectives The development of digital technology has contributed to lowering the barriers for adolescents to par-ticipate as members of society. adolescents can participate in social participation activities online and offline. This study aims to identify the social participation of adolescents and to seek the directions forward to let them cultivate digital citizenship. Focusing on social participation activities using digital media, this study observes its benefits and drawbacks and suggests educational policies and practices related to digital citizenship.&#x0D; Methods This study conducts an online survey of 6,153 middle and high school students in Gyeonggi-do. This survey consists of questions asking their participation in online and offline spheres according to the types of social participation activities and their thoughts related to it. The data were used for analysis.&#x0D; Results First, adolescents were mainly participating in school activities among the types of social participation activities. Second, adolescents usually engage in social participation activities through recommendations from parents, friends, teachers, and school classes dealing with social issues. Third, adolescents perceived that social participation activities led to individual civic growth. Fourth, adolescents tend to participate in social participation activities through online rather than offline in the future. Fifth, the problem of current social participation activities was a lack of interest and fun, and social problems such as group conflict and hate speech.&#x0D; Conclusions The results described here could highlight a need for youth participation programs based on school curricula, and student agency, practicing classes dealing with social issues, and strengthening media literacy education. Through social participation activities using digital media, adolescents are expected to cultivate digital citizenship and lead to the development of a democratic society.
PACuna: Automated Fine-Tuning of Language Models for Particle Accelerators

Navigating the landscape of particle accelerators has become increasingly challenging with recent surges in contributions. These intricate devices challenge comprehension, even within individual facilities. To address this, we introduce PACuna, a fine-tuned language model refined through publicly available accelerator resources like conferences, pre-prints, and books. We automated data collection and question generation to minimize expert involvement and make the data publicly available. PACuna demonstrates proficiency in addressing intricate accelerator questions, validated by experts. Our approach shows adapting language models to scientific domains by fine-tuning technical texts and auto-generated corpora capturing the latest developments can further produce pre-trained models to answer some intricate questions that commercially available assistants cannot and can serve as intelligent assistants for individual facilities.
NON-NATIVE ENGLISH TEACHER REQUESTING IDENTITY TO MOTIVATE STUDENTS IN EFL CLASSROOM

Non-native English teacher in the context of teaching is in two languages and two cultures. This condition is the basis for further study which is very useful for research on developing students' pragmatic competence (Achiba, 2018), because there are indications that teachers in the two-cultural context use request with excessive supportive moves utterances and lack of knowledge in using internal modifications (Ahangari &amp; Shoghli, 2011). Data source used to obtain data on requests used by the teacher when teaching in class. Observations were made for all grade levels of senior high school by recording while the teacher was in the teaching process. Participant teacher with non-native English background was involved in the research. The transcriptions obtained from the recording in the teaching learning process were analyzed based on requesting speech theory (Blum Kulka, S., House, J., &amp; Kasper in Olshtain, 1989). Interview was also done to explore why the teacher uses request modifications in giving instructions to her students. The results show that there is a modification of requests with the use of English and Indonesian in the delivery of requests used by the teacher when giving instructions. This modification was also strengthened by the repetition of the words please and ok which were indicated because of the influence of the teacher's first language culture. This culture correlates with the politeness foundation of the teacher's first language culture. These modified models are further strengthened by the teacher's belief that this is to show identity as an English teacher which can motivate students to always participate in teaching in class. In other words, the teacher wants to show himself to be a teacher figure who is not being feared by students. Furthermore, this research can be deepened by conducting an analysis on the types of request strategies or how the process of developing students' pragmatic competence can develop with interactions using these utterances based on the perspectives of teachers and students.
Factors Influencing Post-COVID-19 Virtual Education and Its Impact on University Students: Analysis Using Structural Equation Models

The transcendence of the COVID-19 pandemic in education has transformed the way students use information and communication technologies (ICT) to take virtual classes since the closure of universities, so this research aims to describe how students in the School of Administrative Sciences at the Autonomous University of Baja California use ICT in times of COVID-19 to take their classes. The results obtained show that ICT management directly and indirectly influences students’ collaborative work, and in a direct way, the responsible use of ICT in the teaching–learning process. It also includes a construct validation of the ICT factor in the teaching–learning process and COVID-19 by means of confirmatory factor analysis (CFA), an analysis procedure in structural equation modeling (SEM).