Reviewerly/exHarmony huggingface dataset:
Photometric redshift estimation of quasars with fused features from photometric data and images

ABSTRACT The redshift is a crucial parameter of quasars and performs a very important role in cosmological studies. In this paper, we propose a network called a quasar photometric redshift (photo-z or zphoto) estimation network (Q-PreNet) that integrates images and photometric data to estimate the redshifts of quasars. To enhance the information richness, we use optical and infrared data, from the Sloan Digital Sky Survey (SDSS) and the Wide-field Infrared Survey Explorer (WISE), respectively. In Q-PreNet, on the one hand, an image feature extraction network (IfeNet) is designed to obtain image features, and, on the other hand, magnitudes after extinction and their mutual differences are taken as the features of photometric data. The two features are then concatenated to form fused features. Finally, a regression network to estimate photo-z (RegNet-z) is proposed based on a mixture density network, because of its ability to provide uncertainty information. To measure the uncertainty, two quantitative metrics are proposed. Experimental results show that the performance of Q-PreNet is superior. While using fused features, the proportion of samples with |Δz| = |(zspec − zphoto)/(1 + zspec)| (spectroscopic redshifts, spec-z or zspec) less than 0.15 can reach 86.3 per cent with a reduction of 8.15 per cent and 9.37 per cent, which is compared with separately using images and photometric data only. Compared with the literature, Q-PreNet offers a substantial improvement in the redshift estimation of quasars and this is significant for large-scale sky surveys.
THE EFL STUDENTS’ PERCEPTIONS OF THE QUALITY OF THE ENGLISH LANGUAGE TEXTBOOK

The purpose of this study is to elicit students' perceptions about the quality of English textbook used by Indonesian EFL students. Although numerous teaching researchers have undertaken research on textbooks, it is still uncommon to conduct textbook research to ascertain students' perceptions on the quality of textbooks. This study employed a mixed-methods approach and gathered data from 300 senior high school students in grade eleven. They were chosen at random among 674 students. These students come from SMA 12 Medan and SMK 3 Parulian Medan. Each school was represented by 150 students in the eleventh grade. Google forms were utilized to collect data. Simple statistics are used to calculate the number and percentage in research data analysis, while interactive data analysis is used to examine qualitative data. The results of this study show that the textbook "Bahasa Inggris" meets the criteria for a textbook of appropriate quality in terms of external appearance, content quality, language or vocabulary used, scoring system, and positive impact. However, the quality of the textbook's look and content may be enhanced. The findings of this study provide a unique viewpoint on the difficulties studied in comparison to earlier textbook research.
NEEDS ANALYSIS OF PALEMBANG-TOURIST-DESTINATION RECOUNT TEXT READING MATERIALS IN THE 21ST CENTURY LEARNING

The specification of the materials taught is one of the characteristics of teaching English at vocational high school. The materials should be appropriately designed or developed for the students to enhance their comprehension and to facilitate subject matter. The aim of this research is to look into students' needs for developing English reading materials based on Palembang culture, as determined by the feedback of students and teacher. Through a questionnaire and interview, the data were obtained. The data was quantitatively and qualitatively analysed. The results of the study revealed that 1) students are still experiencing some difficulty in reading comprehension, 2) a recount text is necessary; 3) English reading materials need to be integrated with students' local culture; and 4) no English reading material has been made available to incorporate the students' local culture, particularly in the field of tourism destination of Palembang. Due to needs, reading materials must be developed through the integration of local culture that matches the level of reading and supports the content of the topics.
Evaluation of Some Haematological Metrics among Smokers in Port Harcourt, Rivers State, Nigeria

Introduction: Smoking is extremely toxic and has had a significant negative impact on society. One of the leading contributors to preventable illness and mortality has been found to be cigarette smoking.&#x0D; Aim: The goal of this study is to evaluate a few haematological metrics among smokers in Port Harcourt, Rivers State, and to verify the idea that smoking cigarettes either has a negative or favorable impact on these variables.&#x0D; Methodology: In this cross-sectional study, 100 participants between the ages of 20 and 45 were included, 50 of whom were smokers and 50 non-smokers. Venipuncture was used to obtain blood samples from the patients, which was then put into an EDTA vial for a full blood count (FBC) and other haematological analysis. The full blood count and erythrocyte sedimentation rate (ESR) were done using Haemo Auto Analyzer, Model XP-300 KOBE Japan by SYSMEX and the Westergreen method respectively. In order to analyze the data, Graph Prism Pad 6.2 and Microsoft Office Excel 2016 were both used. Using the student's independent t-test, a comparative study of mean and standard deviation values for the various parameters for test and reference ranges was conducted.&#x0D; Results: According to the findings, the average age of 50 smokers was 43.6200± 9.7250 years and that for the 50 non-smokers was 42.7800± 6.5440 years, which was statistically insignificant with p valve of 0.6130. Smokers’ haemoglobin level was 14.5080 ± 1.5590 (g/dL) and non-smokers was 12.1340 ± 0.70410 (g/dL). According to statistics, the level of Hb was significantly higher in smokers compared to non-smokers (P&lt;0.0001). In comparison to non-smokers, the RBC count dramatically increased in smokers (P&lt;0.0001) greater than in non-smokers at 5.2550 ± 0.6629 (x1012/L) and 4.6340 ± 0.5530 (x1012/L) respectively. While the total leucocyte count (TLC) in smokers is 8.0500 ± 1.8796 (x109/L), compared to 6.8580 ± 1.2454 (x109/L) in non-smokers. Statistics show that smokers have a higher total leucocyte count than non-smokers (P&lt;0.0002). Smokers' platelet count is 255.7600±61.8351 (x109/L) while non-smokers' is 216.5800±35.5752 (x109/L).The study has statistically shown that smokers' platelet counts rose considerably in comparison to non-smokers (P&lt;0.012).&#x0D; Conclusion: It may be concluded that uninterrupted smoking has an undue negative impact on haematological parameters such that increase occur in values of Hb, RBC, TLC and platelets. Significantly, these changes may increase the risk of serious health issues such as heart related defects, hardening of the arteries, Vaquez-Osler disease. It is recommended that smokers have these metrics regularly monitored.
PENGARUH PENGGUNAAN MEDIA AUDIO VISUAL TERHADAP PRESTASI BELAJAR SISWA DALAM PEMBELAJARAN MATEMATIKA

This study aims to determine the effect of the use of audio-visual media on the learning achievement of fourth-grade students in learning mathematics at MI Tembongsari Cihampelas. The method used in this research is an experimental method with a one-group pretest-posttest design. Data collection techniques used include observation, testing, and documentation. While the data analysis technique used is the analysis of observation results, the N-Gain test, and the T-Test test. The results of this study indicate that the use of audio-visual media significantly affects student achievement in learning mathematics. Through the N-Gain test, the results obtained were 0.67 with a percentage of 67%, which was interpreted as quite effective, and through the T-test, the t-count results were greater than the t-table (24.75 &gt; 2.035). This result means that the null hypothesis (Ho) is rejected and the alternative hypothesis (Ha) is accepted, namely that there is a positive effect of the use of audio-visual media on student achievement in learning mathematics in class IV MI Tembongsari Cihampelas.
Analysis of Students' Relational Understanding Ability Through the Application of Blended Learning Method

The students' relational understanding by applying blended learning is described in this study. The research method is descriptive quantitative that was performed by pre-experimental with a one-shot case study design. This study involves 33 class VIII students in one of the Karawang State Junior High Schools, which was selected by purposive sampling. There are five questions on the post-test that use as instrument tests. The quantitative data analysis is based on the statistical descriptive and statistical tests. The results show that students who have a high category are six students (18%), the medium category is 25 students (76%), and the low category is two students (6%). The average of all students in the sample class is 61.36, still below the KKM=76. The median test obtained 0,149 ≥ 0,025, such that it is rejected or accepted. It suggests that at the 95% confidence interval, the median student's relational understanding ability after applying the blended learning method could not reach the KKM.
Making Research FAIR With a PID-centric Workflow

Persistent identifiers (PIDs) are unique, machine-readable codes assigned to research entities that allow them to be easily discoverable. PIDs, along with their accompanying metadata, are crucial enablers of the FAIR principles (Findable, Accessible, Interoperable, and Reusable). PIDs ensure that digital objects can be located, accessed, and reused by humans and machines alike, while metadata provides essential information about research objects, including their origin, content, and format. In the research ecosystem, each stakeholder has a role to play in integrating PIDs into their workflows. Publishers, for example, can assign DOIs (Digital Object Identifiers) to articles, books, and other publications, making them easily findable and citable. Repositories can assign PIDs to datasets, making them discoverable and accessible. Researchers can use PIDs to link their data to their publications, ensuring that their data is discoverable and can be reused in future research. Despite the importance of PIDs and metadata, it's not always clear to researchers how to take advantage of the existing infrastructure and make their outputs FAIR. Being aware of the available PIDs, such as DOIs, ORCIDs, and RORs, and how they can be used to identify, connect, and cite various types of outputs and resources can help researchers plan and execute sensible data management, sharing, and publishing decisions that are efficient and beneficial in the long term. In the Implementing FAIR Workflows Project, DataCite works with a team of researchers at the Max Planck Institute for Empirical Aesthetics to follow along a neuroscience PhD project from the beginning, to design and plan for a series of workflows that put the FAIR principles into practice, so that they become an inherent part of the research process, instead of an afterthought. The FAIR workflows researcher is undertaking in the project include data management planning, experiment preregistration, domain-specific metadata capturing and archiving, data and code sharing, preprinting, and open access publishing. We have also been tracking the time spent on various types of FAIR and Open activities, hoping to shed a light on the actual time commitment expected for a FAIRly conducted research project. We share our experience so far implementing these workflows with the Edinburgh Open Science community - the approach we used, the steps we’ve taken, and the outcomes and challenges that surfaced during the process. We are also preparing a guide for researchers to take on FAIR research workflows in their day-to-day work based on the lessons learned in the project, we look forward to taking the opportunity to hear from the community whether it resonates, and how can we format it in a way that’s most useful.
Teaching the Best Research Data Management Practices to PhD Students

Good Research Data Management (RDM) practices make it easier for researchers to conduct their research and ensure their data is preserved and stored securely for the long-term. Archiving is one of the last stages of Research Data Lifecycle and deals with depositing and preserving datasets at the end of a project in a suitable storage location (repositories, local server, or hard drive). Students may not find data management topic as interesting as their core course topics. This can result in lack of students’ knowledge of the importance and benefits of good data management practices for their studies. At Warwick, I design and deliver Research Data Management &amp; Planning (RDM&amp;P) case study sessions to PhD students. Students learn different elements of RDM and importance of RDM for long-term preservation. Students work in groups to develop and present Data Management Plan (DMP) and put RDM&amp;P learning into practice. In this presentation, I briefly introduce the above case study, feedbacks received from students across different disciplines and further steps.
Trends and Future Directions of Accreditations in Higher Education : Bibliometric Analysis

Purpose : The study aimed to highlight the importance of accreditation in higher education and its relevance in enhancing the quality and standardization of the education system. The purpose of this research was to analyze the trends and future directions of accreditation in higher education using bibliometric analysis. Specifically, the study aimed to identify the relationship between accreditation-originating countries and top publishing countries in this domain, assess the relevance of accreditation over time, and propose future research areas through citation analysis and thematic evaluation. Design/Methodology/Approach : The bibliometric method was used in our research to study and explore accreditation in higher education research. Bibliometric analysis is famous for quantitative data analysis. Using the relevant keywords in the Scopus database, we performed a bibliometric analysis on 1,544 articles. We used the biblioshiny tool through “R” open-source software for performance analysis and thematic mapping analysis, and VOSviewer was used for co-occurrence keyword analysis and network analysis. Findings : The findings of the analysis substantiate with literature to infer the relationship between accreditation-originating countries and top countries publishing in this domain. The findings revealed that the relevance of accreditation has been increasing over the years, and the accreditation-originating countries were leading the research through publications in this area. The study proposed future research areas using citation analysis and thematic evaluation. Practical Implications : The implications are relevant to higher education institutes, policymakers, and other stakeholders. The study is expected to be a catalyst in driving the research in the area of accreditation in higher education. Novelty : This study's novelty is its use of bibliometric analysis to explore the trends and future directions of accreditations in higher education. The study also highlighted the increasing relevance of accreditation in enhancing the quality and standardization of the education system. The study is expected to be a catalyst for driving research in the area of accreditations in higher education.
On the modulo 2 +1 addition and subtraction for weighted operands

In this paper, investigation is performed on the design of efficient modulo 2n+1 adders, subtractors and add/subtract units for weighted operands. The existing modulo 2n+1 adder, subtractor architectures are reviewed and compared. A new efficient modulo 2n+1 adder architecture for weighted operands is also presented. The proposed modulo 2n+1 adders and one of the existing most efficient modulo 2n+1 subtractors are combined into a modulo 2n+1 add/subtract unit. The proposed modulo 2n+1 add/subtract units exhibit decreased area and power complexity when compared to existing ones.
Developing Dataset Management of Speech Recognition Based Automatic English-Speaking Skill Testing System

Speech recognition technology has been widely used to aid English language testing, such as for pronunciation and speaking practice for various use cases including job interview simulation. We developed an app, powered by speech recognition technology and regex text processing, that automatically score participants’ speaking performance. However, this application lacks dataset management for the speaking test maker to manage the indicators of accepted answers. A feature to effectively manage the dataset and efficiently ease the speaking test creation is needed. This paper presents the development of dataset management feature to support the speech recognition based automatic English-speaking skill testing system. All elements that support the feature in development were tested through Blackbox testing with various scenarios. The elements were proven to work as intended to support the existing system. The new feature has brought efficiency for the English-speaking test maker.
Impact of the extended version of Technology Acceptance Model on predicting learners’ adoption to synchronous online learning in Sri Lanka

Synchronous online learning has played the prime role in controlling the spread of the COVID-19 throughout the world. Despite the technological advancement, the immediate shift towards the synchronous online learning has challenged availability and accessibility to the quality learning due to the over-reliance on the class-room based teaching and learning. Thus, this study focuses on investigating the impact of factors affecting adoption to synchronous online learning amidst the COVID-19 in Sri Lanka. As the consequence, institutions can pay attention on the essential factors to enhance the adoption to synchronous online learning. In this study, Technology Acceptance Model (TAM) has extended with the external variable of perceived self-efficacy and tested its impact on all other core constructs of TAM. This study used online questionnaire to collect the data from undergraduates of Sri Lankan state universities. Consequently, the study received 382 valid responses. For the data analysis, IBM SPSS statistics 25 and SmartPLS 4 were used and hypotheses were tested using PLS-SEM. The study has identified five variables namely perceived usefulness, perceived ease of use, attitude, perceived self-efficacy and behavioral intention to use. Based on the statistical evidence, all the proposed relationships were supported except attitude and perceived usefulness towards behavioral intention to use. Notably, this study has both theoretical and practical implications. To the best of author’s knowledge, this study firstly attempted to investigate the adoption synchronous online learning with the extended version of TAM using PLS-SEM. Most of the previous studies explored the online learning in general. Furthermore, the findings can be used to establish policies to make synchronous online learning more effective, specifically if the any pandemic occurs in future. &#x0D; Keywords: Synchronous online learning; TAM; Sri Lanka; Adoption to synchronous online learning; Intention to use
HeteFed: Heterogeneous Federated Learning with Privacy-Preserving Binary Low-Rank Matrix Decomposition Method

Federated learning is a machine learning paradigm where many clients collaboratively train a machine learning model while ensuring the nondisclosure of local data sets. Existing federated learning methods conduct optimization over the same model structure, which ensures the convenience of parameter updates. However, the same structure among clients and the server may pose risks of privacy leakage as parameters from one’s model can fit in others’ models. In this paper, we propose a heterogeneous federated learning method to preserve privacy. Each client utilizes neural architecture search to determine distinct models via local data and update the server model via a federated learning framework with knowledge distillation. Besides, we develop a privacy-preserving binary low-rank matrix decomposition method (Blow), i.e., decomposing the output matrix into two low-rank binary matrices, to further ensure the secrecy of distilled information. A simple but efficient alternating optimization method is proposed to address a key subproblem arising from the binary low-rank matrix decomposition, which falls into the category of the Np-hard bipartite boolean quadratic programming. Based on extensive experiments over the image classification task, we show our algorithm provides satisfactory accuracy and outperforms baseline algorithms in both privacy protection and communication efficiency.
Graph Pooling based Human Detection Method for Industrial Application

Human detection is an important problem which has many applications in collaborative manufacturing, such as employee counting and human fatigue detection. Recently, some methods based on convolutional neural network (CNN) have been proposed to solve this problem, but they often suffer from poor generalization due to the lack of real-world datasets from industrial scenarios. In this paper, we propose a graph pooling based human detection method to improve the generalization of existing CNN models. First, we segment an image and abstract it as a graph where nodes represent the segmented regions and the features of nodes are pixel values of the segmentation. Second, a graph pooling model is used to extract coarse-grained but informative features from the graph. Next, the pooled graph is reconstructed to image and input to a popular CNN model (i.e., YOLOv5) for human detection. We perform experiments on data containing a great number of images from industrial scenarios. Experimental results show that the proposed method outperforms the popular CNN model and has better generalization performance.
Scoring prediction model focusing on local feature interaction of text

In the era of information explosion, recommender systems are widely studied and applied to explore users’ preferences. Reviews often reflect users’ real thoughts and play an important role in modeling user preferences. However, most existing review-based recommendation methods assign the same weight to words with different levels of importance, which degrades the recommendation performance. To address these problems, we propose a novel model that uses an adjusted cosine similarity to compute the preference matrix of user, then uses an incentive compression network to weight the preference matrix to focus on important local interaction features and achieve user rating prediction of items based on that interaction feature. The experimental results show that our model outperforms previous mainstream recommendation models.