Reviewerly/exHarmony huggingface dataset:
A Study on College English Reading Teaching for Multimodal Teaching Model

Abstract In order to effectively improve college students’ English reading ability, this paper introduces multimodal teaching to analyze the way of teaching English reading in college. This paper firstly dismantles the multimodal teaching model, which is divided into multimodal teaching and the incorporation of the convolutional neural network, and analyzes the current situation of college English reading teaching. Secondly, we select the college English reading teaching modality, construct a multimodal fusion model for college English reading teaching and optimize this model to analyze the convergence and time complexity. Through the analysis of the fusion performance, we found that the accuracy and the pick-up degree of the pre-fusion increased by 22.25% and decreased by 18.22% compared with the unimodal fusion, and the number of participants increased by 61.51 Mb, which was not much different from the middle and late comparison, so the pre-fusion was selected to have the highest practicality. Finally, through the survey statistics on the application of multimodal teaching of college English reading, more than 80% of students and teachers think that multimodal teaching of college English reading can improve their English reading ability and teaching ability. So this teaching method improves their reading ability and enriches the diversity of teaching modes at the same time, which is worth promoting.
Building a Panopticon Through Nodal Governance: Mass Surveillance and Plural Policing in China’s COVID-19 Lockdown

At one time monitoring over 900 million people, China’s health code system is arguably the most controversial invention of the pandemic. This study explores how the system emerged and its implications for security governance in urban communities. By analysing 9,533 social media posts published during three key weeks, the study revealed that early pandemic responses in China were heavily shaped by private nodes, such as estate management companies, private security guards and homeowners. Homeowners’ demands for extra security clashed with migrants’ and tenants’ demands for mobility. The health code system was presented as a ‘solution’ to these conflicts. The findings of this study highlight the limitations of consumer-driven pluralisation in policing. Such pluralisation offered limited opportunities for democratisation. Instead, the radical pursuit of ‘club goods’ by consumer-denizens reinforced existing inequalities. Entrenched inequalities tempted marginalised social groups to accept ‘indiscriminate’ surveillance, which paved the way for a neo-panopticon. The study also warns against the alliance of state nodes and big-tech companies. Through collaboration, these powerful players can replace political dynamics in the community with data-driven modulation, thus destroying the foundation of nodal governance.
The Mediating Effects of Playfulness on the Relationship between Six Personality Factors(HEXACO) and Creativity in Early Childhood Teachers

Objectives The purpose of this study was to investigate the mediating effect of playfulness on the relationship between Six Personality Factors(HEXACO) and creativity in early childhood teachers.&#x0D; Methods For this purpose, measures of our study variables were obtained from 232 early childhood teachers in various regions including Busan and Ulsan. The collected data was analyzed by SPSS 21.0 and The PROCESS macro v.4.0 for SPSS.&#x0D; Results First, The characteristics of the early childhood teachers who participated in this study were the highest emotionality and the lowest agreeableness, and the high level of playfulness and creativity. Second, playfulness mediated the effect of Six Personality Factors on creativity. Also, statistical significance was verified through 5,000 bootstrapping.&#x0D; Conclusions The result of this study implicates the importance of personality factors(Extraversion, Openness to Experience) and playfulness about creativity for teachers in ECEC. This study suggested that it is necessary to support the improvement of ‘extraversion’ and ‘openness to experience’ tendencies and playfulness in order to promote the creativity of early childhood teachers.
The mediating effect of the principal's praising leadership recognized by the early childhood teachers between the turnover intention and organizational commitment of the early childhood teachers

Objectives The purpose of this study is to examine the mediating effect ofthe principal's praising leadership recognized&#x0D; by early childhood teachers between the turnover intention and organizational commitment of early childhood&#x0D; teachers.&#x0D; Methods To this end, a survey was conducted on 343 early childhood teachers in Busan, Ulsan, and Daegu. The&#x0D; collected data were analyzed using descriptive statistics, correlation analysis, and the stepwise regression analysis,&#x0D; utilizing the SPSS 21.0(ver.) statistical program. The mediating effect was examined using the Sobel test.&#x0D; Results Firstly, significant correlations were found between overall turnover intentions and its sub-variables,&#x0D; overall organizational commitment and its sub-variables, and overall praise leadership and its sub-domains.&#x0D; Secondly, the perceived praise leadership by principals was found to mediate the relationship between turnover&#x0D; intentions and organizational commitment among early childhood teachers.&#x0D; Conclusions Even if early childhood teachers have turnover intentions, the effective display of praise leadership&#x0D; by principals positively influences their organizational commitment. These findings highlight the importance of&#x0D; continuous research on enhancing the praise leadership of educational institution principals.
Automating the Evaluation of Datasets for FAIR and CARE Principles

The FAIR and CARE data principles are critical to ensuring widespread and equitable access to open data. They provide guidelines for what should be contained in metadata and how certain types of data should be handled. This study examines how large, collective data hubs such as the WIFIRE Data Commons can implement the FAIR and CARE data principles through evaluating the datasets hosted on the platform. An automation pipeline was developed to check for specified criteria in these principles, allowing fast integration of the principles on a large scale data hub. This pipeline can be expanded to check for all FAIR and CARE criteria, and similar pipelines can be created for a variety of other data hubs. Automating for the FAIR and CARE principles will help simplify organization of open data, allowing for a greater expansion of open science.
Analyzing a Semantics-Aware Bug Seeding Tool's Efficacy: A qualitative study with the SemSeed tool

Software developers can benefit from machine learning solutions to predict bugs. Machine learning solutions usually require a lot of data to train a model in order to achieve reliable results. In this context, developers use bug-seeding approaches to generate synthetic bugs, which should be similar to human-made bugs. A recent state-of-the-art tool, called SemSeed, uses a semantics-aware bug seeding approach in order to hopefully achieve more realistic bugs. In this study, we report on the investigation of SemSeed’s efficacy. We create a survey that shows developers a bug and asks whether it is a Real or Synthetic bug. We collected and analyzed the answers from 47 developers, and we show that SemSeed can be very accurate in seeding realistic bugs.
A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning

Federated Learning (FL) enables potentially a large number of clients to collaboratively train a global model with the coordination of a central cloud server without exposing client raw data. However, the FL model convergence performance, often measured by the job completion time, is hindered by two critical factors: non independent and identically distributed (non-IID) data across clients and the straggler effect. In this work, we propose a clustered FL framework, MCFL, to minimize the job completion time by mitigating the influence of non-IID data and the straggler effect while guaranteeing the FL model convergence performance. MCFL builds upon a two-stage operation: i) a clustering algorithm constructs clusters, each containing clients with similar computing and communications capabilities to combat the straggler effect within a cluster; ii) a deep reinforcement learning (DRL) algorithm based on soft actor-critic with discrete actions intelligently selects a subset of clients from each cluster to mitigate the impact of non-IID data, and derives the number of intra-cluster aggregation iterations for each cluster to reduce the straggler effect among clusters. Extensive testbed experiments are conducted under various configurations to verify the efficacy of MCFL. The results show that MCFL can reduce the job completion time by up to compared with three state-of-the-art FL frameworks.
An Efficient Approach for (Multi-)CDN Identification

As the use of Content Delivery Networks (CDNs) continues to grow, it has become increasingly difficult to accurately identify which CDNs are being used by a particular website or domain. In this paper, we propose a new method for detecting CDNs, including multiple-CDN deployments, behind domains using a combination of DNS and HTTP-based measurements. Our key observation is that using a few vantage points can detect nearly all CDN(s) behind the websites. Thus, we first design a heuristic algorithm to select representative vantage points and retrieve DNS and HTTP(S) information to identify patterns that are indicative of the use of CDNs. We run our method on the top 10k websites in tranco list and detect 6,392 domains hosted on CDNs, of which 200 are hosted on multiple-CDN, accounting for 2.0% of the total.
Course Design and Tool Development for Functional Programming Integrating with Program Verification: from the Perspective of the Interactive Theorem Prover

Functional programming, as a highly reliable programming paradigm, was once valued in the last century, but it was not popularized in education and practical applications. In recent years, due to the increase of security-critical information infrastructure and information systems, functional programming has been widely used in key areas by virtue of its advantages in program verification. There is an urgent need to provide students with a supportive learning environment, but the steep learning curve and immature ecosystem can present certain limitations and challenges for learning and practice of students. Combining functional programming languages with concrete tools is widely believed to allow for quick and practical learning, so we chose to utilize Interactive Theorem Provers (ITPs) to help students understand and apply functional programming. In this poster, we design a functional programming course and develop a tool FPAid to relieve the difficulty of getting started and improving teaching method. While students have a better comprehension of the teaching content, they can also use functional programming languages to solve problems with high quality.
Analysis of the Technical Debt of Software Projects Based on Merge Code Comments

Developers use code comments for various reasons, such as explaining the produced code, documenting specifications, communicating with other developers, and highlighting future tasks. Software projects with minimal documentation often have a significant number of comments. In this regard, code comment analysis techniques can be used as tools to examine more complex aspects of software projects, such as technical debt generated by merge conflicts. Technical debt resulting from the resolution of merge conflicts occurs when the resulting code contains comments indicating tasks to be performed in the future. No studies directly linking merge conflicts and technical debt were found in the literature. The objective of this work is to identify and analyze code comments generated during the resolution of merge conflicts from this perspective. This process can lead to improvements in software quality and assist in managing technical debt. To achieve this, an exploratory analysis was conducted on 100 software projects, specifically focusing on task annotations originating from the merge conflict resolution. The results revealed that 60.61% of the analyzed projects have at least one code comment indicating the creation or maintenance of technical debt.
A Study on UX Design Guidelines and Visitor Profiling for Virtual Museum

Following the changes in the museum environment in Korea and abroad owing to COVID-19, this study examines how visitors behave and react in a virtual museum that has appeared frequently in recent years. In this study, we aim to profile categorized museum visitors, present usability evaluation tools and UX design guidelines for improving virtual museums. To achieve this, it was first necessary to classify the viewers of the virtual museum into several types. Next, the usability, affection, and presence of the virtual exhibition space of the participants who visited the 360-degree VR and 3D VR museums were measured. Finally, we attempted to derive needs, pain points, and insights through affinity diagram. We suggest a virtual museum UX design guideline by deriving what is lacking for each virtual museum and suggesting what needs to be supplemented in the future.
<i>Oct 14, 2023 Annular Solar Eclipse</i>

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Dan MacIsaac; Oct 14, 2023 Annular Solar Eclipse. Phys. Teach. 1 October 2023; 61 (7): 639. https://doi.org/10.1119/10.0021083 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
Feature-Based Question Routing in Community Question Answering Platforms

&lt;p&gt;Community question answering (CQA) platforms became popular and indispensable sources of information in diﬀerent domains. The success of these platforms relies heavily on the timely contribution of their expert users who would answer questions. There are many questions that remain unanswered for a long time, if not ever, on CQA platforms. In this dissertation, the problem of question routing on CQA platforms is addressed, which aims to connect experts to the right questions. We introduce and semantically classify 67 features and then train a learn to rank framework over ﬁve different CQA datasets. Results show that features based on tags, topics, user characteristics and user temporality are eﬀective for question routing. Also the proposed approach outperforms the state-of-the-art neural matchmaking methods, that lack the interpretation of features, without compromising interpretability. The interpretation of features’ inﬂuence helps future research in this ﬁeld to address the question routing problem more eﬀectively.&lt;/p&gt;
Multidomain active defense: Detecting multidomain backdoor poisoned samples via ALL-to-ALL decoupling training without clean datasets

Deep learning is vulnerable to backdoor poisoning attacks in which an attacker can easily embed a hidden backdoor into a trained model by injecting poisoned samples into the training set. Many prior state-of-the-art techniques for detecting backdoor poisoning attacks are based on a potential separability assumption. However, current adaptive poisoning strategies can significantly reduce ’distinguishable behavior’, making most prior state-of-the-art techniques less effective. In addition, we note that existing detection methods are not practical for multidomain datasets and may leak user privacy because they require and collect clean samples. To address the above issues, we propose a multidomain active defense approach that does not use clean datasets. The proposed approach can generate diverse clean samples from different domains and decouple neural networks round by round using clean samples to disassociate features and labels, making backdoor poisoned samples easier to detect without fitting clean samples. We demonstrate the advantage of our approach through an extensive evaluation of CIFAR10, CelebA, MNIST & MNIST-M, MNIST & USPS & MNIST-M, MNIST & USPS & SVHN and CIFAR10 & Tiny-ImageNet.
Grammar and Vocabulary Skills in Relation to the Maritime Students’ Speaking Abilities

English proficiency is an important component of a thriving and modern society. This study explored the levels of grammar and vocabulary skills in relation to the speaking abilities of maritime students. This study was conducted in one of the higher education institutions in Ozamiz City. The descriptive-correlational research design was used. One hundred twenty students served as the respondents of the study. They were chosen through stratified random sampling. The researcher-made tests used in gathering the data were the Grammar Skills Test, Vocabulary Skills Test, and Speaking Abilities Test. In addition, Frequency, Percentage, Mean and Standard Deviation, and Pearson Product-Moment Correlation Coefficient were used in analysing the data gathered. The results revealed that the levels of the students' grammar skills and speaking abilities were very satisfactory. On the other hand, the students' vocabulary skills were satisfactory. Furthermore, the students’ grammar and vocabulary skills do not correlate with each other. The study concludes that the students have performed well in grammar, are good at vocabulary, and are proficient in speaking. In addition, their grammar and vocabulary skills do not greatly impact how they deliver their speaking abilities. It is recommended that the teachers develop learning strategies to improve their proficiency and promote the development of grammar, vocabulary, and speaking abilities.
Review of Economidou-Kogetsidis, Savić &amp; Halenko (2021): Email Pragmatics and Second Language Learners

Preview this online first article: Review of Economidou-Kogetsidis, Savić & Halenko (2021): Email Pragmatics and Second Language Learners, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/10.1075/ps.00070.yan/ps.00070.yan-1.gif
Improving Students’s Narrative Writing Through Fairy Tales at the Second Year of MTs. DDI Al-Mujahidin Bendoro

The objective of the research was fine out whether or not the use of fairy tales improving the students writing narrative text. This research was applied quasi-experimental design. It involved two groups: the experimental group and the control group. The population of this research was the second years students in academic year 2014-2015 of MTs. DDI Al-Mujahidin Bendoro. The total number of population is 48 students, where the researcher take all the population on sample by using census techniqui. The researcher selects VIII.2 as the experimental group and VIII.1 consists of 24 students so the total sample of the research is 48 students. The researcher applied Fairy Tales on the experimental group adn for the control group the student taught using conventional technique. There was one techniques of data collecting applied in this study; they were writing test. The test is use to compare students’ writing achievements. The application of Fairy Tales improve students’ ability Narrative Writing in the Second Year of MTs.DDI Al-mujahidin Bendoro. The achievement in writing narrative text of the students who applied Fairy Tale and those who did no apply Fairy tale had significant difference. It was proved by the mean score in post-test of experimental group was higher than te mean score of control group (79.1667&gt;65.9583) and the t-test value on post-test was higher than t-table(5.058&gt;2.021). base on the data above the researcher conclude the the using Fairy Tales increase students’ writing narrative text.
Research on the construction of the evaluation index system of teacher morality in universities under the background of big data

Abstract This paper constructs the evaluation index system of university teachers’ ethics from the hierarchical structure diagram of the evaluation system. In order to reduce the subjective feelings of the evaluators, a genetic algorithm is introduced to optimize the consistency test of the weights of the hierarchical analysis method. Taking ten colleges and universities in GD province as examples, the GAOHP-AHP model was used to calculate the index weights and then obtain the comprehensive evaluation scores of teacher ethics and teacher morality of each college and university. The results showed that the evaluation scores of ten universities were 3.975, 3.946, 4.075, and 4.078 for love for work, teaching and education, rigorous learning, and being a teacher, respectively. The overall evaluation scores of teacher Q in school 1 increased by 7.13% and 12%, respectively, compared with the other two teachers. This shows that the evaluation index system of teacher morality and teacher style in universities can effectively evaluate the teacher morale and teacher style quality of teachers.
Automatic Machine Learning Applied to Electrical Biosignals: A Selective Review

Electrical biosignals, stemming from potential differences in specific tissues like muscles, play a pivotal role in medical diagnoses, aiding in identifying ailments from arrhythmias to brain traumas. The burgeoning realm of Automatic Machine Learning (AutoML), integrating automatic optimization with machine learning, is ushering in novel methodologies to decipher these signals with minimal preliminary data comprehension. This review delves into the contemporary discourse on AutoML techniques tailored for electrical biosignal quandaries, drawing from articles spanning 2018 to 2022 from six technology and machine learning-centric databases. Consequently, insights into biosignal comprehension, the apt AutoML solutions, and prevailing challenges in the domain are delineated.
Note on Romanization, Transliteration, and Translation

Extract The subject of this study presents particular difficulties in Romanization and transliteration. It covers a period of time when the older Wade-Giles system and the use of traditional (complex) Chinese characters gave way to the Hanyu pinyin system and simplified characters on the Chinese mainland. In Hong Kong, traditional and simplified characters are commonly used but may represent the sounds of Cantonese rather than Mandarin; in Taiwan, many older forms have continued in use. In general, I have presented proper nouns and titles in the transliteration used by the person or publisher. I have kept particularly common names in the forms that I hope will be most readily recognized by readers: Mao Zedong, Chiang Kai-shek, Cao Zheng, Dou Wun, and Liang Tsai-ping, for example. Although I have benefitted from the help of numerous native speakers and readers of Chinese in my attempts at translation, they are not foolproof. Where a translation has been supplied by the author or publisher, I have allowed this version to stand. Yuzhou chang wan (鱼舟唱晚) is rendered as Evening Song from a Fishing Boat, and Huangcheng lai ke (荒城来客) is translated as Visitors of the Desolate City, as shown on So Chun-bo’s recording of his composition.