Reviewerly/exHarmony huggingface dataset:
Enhancing the Linear Probing Performance of Masked Auto-Encoders

This paper especially investigates the linear probing performance of MAE models. The recent Masked Image Modeling (MIM) approach is shown to be an effective self-supervised learning approach. These models usually mask out some patches of images and require the model to predict specific properties of those missing patches, which can either be raw pixel values or discrete visual tokens learned by pre-trained dVAE. Despite the promising performance on fine-tuning and transfer learning, it is often found that linear probing accuracy of MAE is worse than that of contrastive learning. This is concerning, demonstrating that features out of MAE network may not be linear separable. To investigate this problem, we consider incorporating contrastive learning into the MAE modeling, to examine the mechanism behind linear probing. We design specific head architectures to associate with MAE, which allows us to include additional feature constraints inspired from Barlow Twins method. The motivation behind is our hypothesis that features learned by MIM would focus more on image style and high-frequency details, while features learned by Barlow Twins will focus more on image content. Our motivation then is to select a trade-off between the two types of features in order to improve the linear probing accuracy of MAE without hurting fine-tuning and transfer learning performance. Empirical results demonstrate the effectiveness of our method. We achieve 27.7% top1 accuracy at linear probing with ViT-Tiny as our backbone, which outperforms the MAE protocol under the same settings by 1.6%.
How does an imaginary persona's attractiveness affect designers' perceptions and IT solutions? An experimental study on users' remote working needs

Purpose The “what is beautiful is good” (WIBIG) effect implies that observers tend to perceive physically attractive people in a positive light. The authors investigate how the WIBIG effect applies to user personas, measuring designers' perceptions and task performance when employing user personas for the design of information technology (IT) solutions. Design/methodology/approach In a user experiment, the authors tested six different personas with 235 participants that were asked to develop remote work solutions based on their interaction with a fictitious user persona. Findings The findings showed that a user persona's perceived attractiveness was positively correlated with other perceptions of the persona. The personas' completeness, credibility, empathy, likability and usefulness increased with attractiveness. More attractive personas were also perceived as more agreeable, emotionally stable, extraverted and open, and the participants spent more time engaging with personas they perceived attractive. A linguistic analysis indicated that the IT solutions created for more attractive user personas demonstrated a higher degree of affect, but for the most part, task outputs did not vary by the personas' perceived attractiveness. Research limitations/implications The WIBIG effect applies when designing IT solutions with user personas, but its effect on task outputs appears limited. The perceived attractiveness of a user persona can impact how designers interact with and engage with the persona, which can influence the quality or the type of the IT solutions created based on the persona. Also, the findings point to the need to incorporate hedonic qualities into the persona creation process. For example, there may be contexts where it is helpful that the personas be attractive; there may be contexts where the attractiveness of the personas is unimportant or even a distraction. Practical implications The findings point to the need to incorporate hedonic qualities into the persona creation process. For example, there may be contexts where it is helpful that the personas be attractive; there may be contexts where the attractiveness of the personas is unimportant or even a distraction. Originality/value Because personas are created to closely resemble real people, the authors might expect the WIBIG effect to apply. The WIBIG effect might lead decision makers to favor more attractive personas when designing IT solutions. However, despite its potential relevance for decision making with personas, as far as the authors know, no prior study has investigated whether the WIBIG effect extends to the context of personas. Overall, it is important to understand how human factors apply to IT system design with personas, so that the personas can be created to minimize potentially detrimental effects as much as possible.
The Application of Project-Based Teaching Based on Chinese Characteristics in High School Chemistry

China's basic education has undergone several reforms, and the new round of curriculum reform has vigorously promoted the policy of "core literacy" and cultivating high-quality talents in all aspects. Project-based teaching is a teaching mode that takes students as the main body and lets students practice in real problem situations. It plays a very important role in promoting students' problem-solving ability, developing innovative consciousness and implementing "subject core literacy". Based on Chinese characteristics, this paper takes "handmade ancient soap making" as the project theme, disassembles the task, design the teaching process, and briefly describes the application of Chinese project-based teaching in high school chemistry practice teaching
Introduction

Addressing the challenge of hate speech is a pressing problem for societies around the world. Recent advances in information technology, online communications and mass media have markedly changed the pace and reach of its spread. Across the globe, we have witnessed an alarming increase in the proliferation and impacts of hate speech directed at individuals and groups.
Multi-task Learning Method Using Emoji Prediction as Auxiliary Task for Sentiment Analysis

There are text sentiment analysis methods that target a variety of sentiments, such as joy, anger and sadness. This method takes work to perform a highly accurate analysis because creating the dataset is more costly than negative/positive binary targets. Therefore, there is a need for data augmentation in sentiment analysis. Data augmentation in sentiment analysis exists using emojis. By defining the sentiment expressed by emojis, it is possible to automatically annotate a sentiment label to text with emoji. However, conventional methods have a problem in that the definition and weighting of sentiments are subjective. Our method automatically generates training data using emojis and learns it by multi-task learning. This method aims to improve the performance of sentiment analysis by transmitting only helpful information for sentiment analysis.
The Effect of Digital Multilingual Thematic Dictionary Toward The Fifth Grade Students' Literacy Skill

This research aimed to discover whether using a digital multilingual thematic dictionary significantly affects fifth-graders' literacy skills. The author applied pre- and post-test control groups alone for its quasi-experimental design. The researcher used a multi-stage sampling technique to select the research sample. The stage consist of three steps namely: (1) determining population; (2) placement of the sampling area using the statistical matching technique proposed by Fraenkel, J.R., Wallen, N.E., and Hyun, H.H. (2012); and (3) selection of the control and experimental groups via lottery. Students in the 5A grade from SD N 1 Seririt served as the control group and fifth-graders from SD N 1 Kubutambahan served as the experimental group for the sample. The research instruments featured teaching scenarios for the control and experimental groups as well as literacy competency exams with pre- and post-test questions. The pre-test was administered before of treatment, and the post-test was done afterwards. Three treatment sessions were provided to each group using three different treatment lesson materials namely: daily activities, my school, and clothes. The acquired data were quantitatively examined using N-Gain score test analysis, inferential statistics, and descriptive statistics. Based on the findings of the N-Gain Score test computation and the descriptive analysis, the experimental group's mean N-Gain score was 70.5453%, whereas the control group's mean N-Gain score was 67.9676%. The use of multilingual thematic printed dictionaries in the control group and multilingual thematic digital dictionaries in the experimental group shared the same effect, particularly regarding that they were "moderately effective " to enhance the literacy skills of class students, according to the results of the N-Gain score test, which showed that the experimental group's average score was 70.5% and the control group's average was 68%. Hence, it is proven that multilingual thematic dictionaries, both printed and digital, have a significant impact on fifth-graders' literacy skills.&#x0D; &#x0D; Keywords: literacy skills, young learner, digital multilingual thematic dictionary
Pengaruh Pembelajaran Daring Berbasis Whatsapp Dan Metode Pemberian Tugas Terhadap Motivasi Belajar Siswa di SMA Negeri 1 Kefamenanu

The online learning implementation during the COVID-19 pandemic by using WhatsApp and assigning assignments as one of the methods used by teachers in providing material. The purpose of the study is to determine the effect of WhatsApp-based online learning and assignment methods on student learning motivation in PAK subjects at SMA Negeri 1 Kefamenanu. The research method is Quantitative Method. All students who are Christian is the population of this study.Total of 56 samples using proportional random sampling technique. The results showed that the online learning variable via WhatsApp has a positive and significant influence on students' learning motivation. The regression coefficient of 0.377 is positive with a t-count value of 3.156. The assignment method variable has positive and significant effect on students' learning motivation. The regression coefficient of 0.287 is positive with a t-count value of 2.130. The online learning variable and the assignment method simultaneously have a simultaneous effect on the F value of 4.343. While the resulting significance value is 0.018 which is smaller than 0.05 and contributes 14.1% to students' learning motivation.
Design Of A Website-Based Job Description Preparation Application System In The Human Resource Planning And Organisation Division (Study On PT Pelindo Terminal Container Surabaya)

This internship report is made to explain related to the design of a website-based job description preparation application system in the HR Planning and Organizational Planning division of PT. Pelindo Terminal petikemas Surabaya. The implementation of the Certified Student Internship Program (PMMB) batch 1 in 2022 starts from march 2022 to august 2022. Data collection uses observations, interviews, field activity reports and documentation.&#x0D; From the results of the internship it is known that one of the jobs or in making a job description still uses a manual method, starting from collecting information related to job descriptions to designing job descriptions so that it is considered very inefficient and slows down work and takes a long time while the deadlines are provided very limited, therefore the author proposes an application design called My jobdesk to compile a job description to make it easier and shorten processing time.
Pengembangan Media Pembelajaran Multimedia Interaktif Articulate Storyline 3 dalam Menganalisis Unsur Kebahasaan Teks Negosiasi Siswa Kelas X SMK AKP Galang

This study aimed to develop and test the feasibility of interactive multimedia learning media using "Articulate Storyline 3" for analyzing linguistic elements in negotiating texts among Class X students at SMK Awal Karya Pembangunan Galang. The sample consisted of 36 students from Class X RPL-1 out of a total population of 76 students. Research and Development (R&amp;D) approach was employed, following the ADDIE model, which included Analysis, Design, Development, Implementation, and Evaluation phases. The results indicated that the interactive multimedia learning media using "Articulate Storyline 3" was deemed suitable and valid for analyzing linguistic elements in negotiating texts by media experts (92%) and material experts (84.5%). The evaluation by teachers yielded a feasibility level of 94%, and students showed positive responses with an average of 92.5%. In conclusion, the developed learning media is valid and appropriate for instructional purposes.
LMConvMorph: Large Kernel Modern Hierarchical Convolutional Model for Unsupervised Medical Image Registration

Medical image registration is a crucial task for numerous medical applications, such as radiation therapy planning and surgical navigation. While deep learning methods have shown promise in improving registration accuracy, existing unsupervised registration methods based on convolutional neural networks struggle to capture long-range spatial relationships between voxels. Additionally, unsupervised registration methods based on Transformer are limited by their dependence on the induction bias of convolutional neural networks and the complexity of global attention. To address these limitations, we present LMConvMorph, a large kernel modern hierarchical convolutional model for unsupervised deformable medical image registration. LMConvMorph leverages larger receptive fields to identify spatial correspondence and employs a hierarchical design with smaller parameters to extract features at different scales, enabling effective feature extraction between moving and fixed images. Our approach yields significant improvements in registration performance. LMConvMorph is evaluated on a 3D human brain magnetic resonance image dataset, and the qualitative and quantitative results demonstrate its competitiveness with other baseline methods.
Automatic Text Extractive Summarization Based on Text Graph Representation and Attention Matrix

Automatic text summarization via representing a text as a graph has been investigated for over ten years. With the developments of attention mechanism and Transformer on natural language processing (NLP), it is possible to make a connection between the graph and attention structure for a text. In this paper, we propose an attention matrix text graph model for extractive text summarization. Specifically, an attention matrix between all the sentences of the whole text is adopted as a weighted adjacent matrix of a fully connected graph of the text where each node represents a sentence, which can be computed using the pre-training language model. The GCN is further applied to the text graph model for classifying all the nodes and finding out the salient sentences from the text to generate a summary. It is demonstrated by the experimental results on two typical datasets that our proposed model can achieve a competitive result in comparison with state-of-the-art models.
A Novel Approach to Analyzing Defects: Enhancing Knowledge Graph Embedding Models for Main Electrical Equipment

The safety of electric power grids can be threatened by defects in main electrical equipment, creating significant risks and pressures for dispatching operations. To analyze defects in main electrical equipment, we adopt a knowledge graph link prediction approach. We found that using pre-training models, such as BERT, to extract node features and embed initial embeddings significantly improves the effectiveness of knowledge graph embedding models (KGEMs). However, this approach may not always work and could lead to performance degradation. To address this, we propose a transfer learning method that utilizes a small amount of domain-specific electric power corpus to fine-tune the pre-training model. The PCA algorithm is used to reduce the dimensionality of extracted features, thereby lowering the computational cost of KGEMs. Experimental results show that our model effectively improves link prediction performance in analyzing defects in main electrical equipment.
Weak Positive Sampling and Soft Smooth Labeling for Distractor Generation Data Augmentation

Distractor generation is one of the most important and challenging tasks in the automatic generation of multiple choice questions. Previous studies usually use a few ground truth distractors as training samples, which ignores more potential usable distractors, where the strong generation ability of deep learning models might not be fully utilized. Therefore, we propose a data augmentation framework for distractor generation, which first applies the distractor ranking model on a distractor candidate set and then selects useful distractor candidates as additional training samples. Besides, we propose weak positive sampling and soft smooth labeling mechanism to ensure the sample quality and effectively use samples during the training stage. Experimental results on public benchmarks demonstrate the effectiveness of our proposed method.
Perancangan Enterprise Architecture Pada SMKN1 di Pulau Bangka Menggunakan Metode Zachman Framework

SMKN1 on the island of Bangka is a school located in the Bangka area. Currently, SMKN1 already uses and implements IS/IT to carry out its business processes. There are several problems in schools such as a website system that cannot be accessed, student attendance records are done manually so that recording errors often occur, and errors in recording the names of students who have attended extracurricular due to the large number of students. Therefore, we need an IS/IT design that can support the company's ongoing business processes. In this study, research and design were carried out with the application of enterprise architecture. The framework used in this article is the Zachman framework. In this paper, a complete perspective is not used, only using a 4x5 perspective because the process is not carried out until the implementation, the process is carried out only until the design. The result of the design is in the form of an IS/IT system blueprint. Suggested applications are school websites, e-library websites, attendance websites, procurement systems, and extracurricular websites.
Penguatan Keterampilan Pembuatan Big Book Berbasis Digital dengan Integrasi Konten Bahasa Inggris

The challenge faced by teachers today is the lack of skills in developing technology-based media. In facing the demands of an increasingly developing curriculum, teachers need to be given reinforcement in the development of professionalism, including in making digital-based media. Based on the problem, MI Muhammadiyah 2 Kedungbanteng is a school that deserves instructional media training. The obstacles faced include the absence of assistance to develop technology-based media so that the digital literacy of teachers is still low, the absence of school literacy activities through teacher-made media, and the lack of activities that can hone creativity to optimize vocabulary learning. This service wants to make a meaningful contribution to teachers with training activities in developing Canva application-based big book media. The activity is carried out through preparation, implementation, and evaluation stage. In the preparatory stage, the team collected initial information about conditions in school that could support the development of teacher competencies as well as implementation schedules and monitoring and evaluation. Furthermore, at the implementation stage, activities are carried out through material presentation, discussion, and independent assignments. For the monitoring and evaluation stage, the teachers conducted learning using the resulting big book and the community service team observed learning in class. This service was successfully carried out according to plan and made several contributions to strengthening understanding of the material and skills in designing and implementing Canva-based big book. The teachers stated that they were satisfied with this training and hoped to create other media creations in the next training.
Literature Teaching Forms Student Character

The teaching of literature involves strengthening awareness about ethical attitudes . Literature is considered less important and has less role in society. HKBP University English Education Students Nommensen Pematangsiantar are the young generation, the next generation, who will become the owners of a diverse and unique nation's future . This research aspect (research focus) is to form a commendable moral culture/character in the acquisition of literary teaching which is evident in the influence of the sensitivity factor of a person's feelings who have studied a lot in various literary works in showing which things are valuable and which are not valuable (ethical culture/character noble character/ The Character Building ) with the 3P method (touching, awakening, and developing conscience). object Research (type of material to be studied and research aspects) attitude and character of English Language Education Study Program students at HKBP Nommensen Pematangsiantar University in teaching literature. The research subjects were 15 English Language Education Students at HKBP Nommensen Pematangsiantar University. Teaching literature using the 3P method which pivots on motivation has succeeded in changing the mind-set of the way of thinking of English Language Education Students at HKBP Nommensen Pematangsiantar University so that the character of noble character is formed.
Indonesian EFL’s Learning Strategies and Personality Types in Achieving TOEFL Score Above 500

There are many English proficiency exams available, including the TOEIC, TOEFL, and IELTS. TOEFL is one of the most well-known English proficiency tests in Indonesia, which is taken for both academic and professional requirements. The average TOEFL score for Indonesian students is still below the global norm of 500. Based on the problems and the difficulties faces by most of Indonesia learners, this study tries to analyze the learning strategies used by learners who successfully achieve TOEFL scores above 500. This study also delves into the learner's personality types in order to understand the characteristics of each type that affected their learning strategy. The 15 participants are randomly selected from Indonesian EFL students between the ages of 20 to 25. The participants must have taken the TOEFL test and received the score above 500. Both quantitative and qualitative writing are used in this study. The quantitative method used in analysing the data collection, the researchers employed descriptive statistic data analysis in Excel to find out the mean. Two different types of instruments were used to collect the data: Oxford's Strategy Inventory for Language Learning and the Myers-Briggs Type Indicator. The result of this study shows; metacognitive strategy as the dominant strategy utilised by the majority of learners and there are 8 personality types such as; ISTJ, INFJ, ISFP, INFP, ENFP, ESTJ, ESFJ, and ENFJ.