Reviewerly/exHarmony huggingface dataset:
ProcNet: Deep Predictive Coding Model for Robust-to-occlusion Visual Segmentation and Pose Estimation

Systems involving human-robot collaboration necessarily require that steps be taken to ensure safety of the participating human. This is usually achievable if accurate, reliable estimates of the human's pose are available. In this paper, we present a deep Predictive Coding (PC) model supporting visual segmentation, which we extend to pursue pose estimation. The model is designed to offer robustness to the type of transient occlusion naturally occurring when human and robot are operating in close proximity to one another. Impact on performance of relevant model parameters is assessed, and comparison to an alternate pose estimation model (NVIDIA's PoseCNN) illustrates efficacy of the proposed approach.
Moral Exemplars and Moral Followers on Twitter: Human Rights Activism and Politeness

Until recently, studies on moral exemplars focused on the characteristics or roles of exemplars in moral education. However, the issue of the possible effects of moral exemplars on their followers lacked adequate coverage in studies on psychology. Thus, the current study discussed the concept of moral following in the context of social media. The study conducted a comparison between Twitter users engaged in Malcolm X and human rights activists unengaged in moral exemplars. The accounts of moral followers (N = 20) were compared with those of human rights activists (N = 20). Tweets were subjected to content analysis, and categories related to activism were determined. The groups were compared in terms of the seven categories using the Mann–Whitney U-test. Statistically significant differences were observed between the groups in terms of impoliteness (U = 83; p &lt; .01), prejudice/discrimination (U = 130; p &lt; .01) and antisocial rights/justice-seeking (U = 126,5; p &lt; .05). The results demonstrated that moral followers preferred offensive and impolite language. The findings were discussed in the context of possible explanations.
Whisper-MCE: Whisper Model Finetuned for Better Performance with Mixed Languages

Recently Whisper has approached human-level robustness and accuracy in English speech recognition, while in minor language and mixed language speech recognition, there remains a compelling need for further improvement. In this work,we present the impressive results of Whisper-MCE, our fine-tuned Whisper, which was trainedusing our self-collected dataset, Mixed Cantoneseand English (MCE) audio dataset. Whisper-MCE achieved an impressive Mix Error Rate (MER) of 14.28%, which is 35.13% lower than the original model. It also achieved 12.61% Character Error Rate (CER) in Common voice zh-HK, positioning it as state-of-the-art. However, MER and CER pose challenges when it comes to evaluating its effectiveness in mixed-language and minor language contexts. We proposed a novel evaluation metric called FAL, which assesses an Automatic Speech Recognition (ASR) system based on fidelity to the original audio, accuracy, and latency. Whisper-MCE outperformed other models in this evaluation metric, achieving a score of 90.91 FAL, further highlighting its exceptional performance. The MCE dataset and code can be found at https://github.com/Shelton1013/Whisper MCE.
Understanding Parameter Saliency via Extreme Value Theory

Deep neural networks are being increasingly implemented throughout society in recent years. It is useful to identify which parameters trigger misclassification in diagnosing undesirable model behaviors. The concept of parameter saliency is proposed and used to diagnose convolutional neural networks (CNNs) by ranking convolution filters that may have caused misclassification on the basis of parameter saliency. It is also shown that fine-tuning the top ranking salient filters efficiently corrects misidentification on ImageNet. However, there is still a knowledge gap in terms of understanding why parameter saliency ranking can find the filters inducing misidentification. In this work, we attempt to bridge the gap by analyzing parameter saliency ranking from a statistical viewpoint, namely, extreme value theory. We first show that the existing work implicitly assumes that the gradient norm computed for each filter follows a normal distribution. Then, we clarify the relationship between parameter saliency and the score based on the peaks-over-threshold (POT) method, which is often used to model extreme values. Finally, we reformulate parameter saliency in terms of the POT method, where this reformulation is regarded as statistical anomaly detection and does not require the implicit assumptions of the existing parameter-saliency formulation. Our experimental results demonstrate that our reformulation can detect malicious filters as well. Furthermore, we show that the existing parameter saliency method exhibits a bias against the depth of layers in deep neural networks. In particular, this bias has the potential to inhibit the discovery of filters that cause misidentification in situations where domain shift occurs. In contrast, parameter saliency based on POT shows less of this bias.
Differential Neural Network Identifier for Dynamical Systems With Time-Varying State Constraints

This study presents a state nonparametric identifier based on neural networks with continuous dynamics, also known as differential neural networks (DNNs). The laws for adjusting their parameters are developed using a control barrier Lyapunov functions (BLFs). The motivation for using the BLF comes from the preliminary information of the system states, which remain in a predefined time-depending set characterized by state or purely time-dependent functions. In this study, time-dependent state constraints are supposed to be known in advance continuous-time functions. The obtained learning laws require solving differential continuous-time Riccati equations and nonlinear differential equations for the learning laws that depend on the identification error and the state restrictions. The developed identifier was evaluated concerning the identifier that does not consider the state restrictions. This comparison included the numerical evaluation of the identifier for a robotic arm intended to reproduce a nonstandard flight simulator. This evaluation confirmed that the identification results were improved using the proposed learning laws and considering that the state limits were not transgressed. The quality indicators based on the mean square error were more minor by 4.2 times.
A Study on Lee Kyu-gyeong(李圭景)'s 『Oju-Pilju(五洲筆疇)』: In addition to introducing a newly discovered book

『Oju-Yeonmunjangjeon-Sango(五洲衍文長箋散稿)』 by Lee Kyu-gyeong (李圭景, 1788~1856) is an encyclopedia of Joseon in the 19th century. It is highly evaluated for not only having a lot of content, but also for making good use of previous books. A symposium with the theme of 『Ohjuyeonmunjangjeonsango』 was held, and at this time Lee Kyu-gyeong's erudition was revealed and the level of Korean intellectual history in the 19th century was proved.&#x0D; Lee Kyu-gyeong's 『Shiga-Jumdeung(詩家點燈)』 is a book that criticizes Korean and Chinese poetry and sentences. Researchers do not use it as much as 『Oju-Yeonmunjangjeon-Sango』 because it is difficult to identify characters and it is not databased, but there is no disagreement that it has a lot of content like 『Oju-Yeonmunjangjeon-Sango』. 『Oju-Yeonmunjangjeon-Sango』 and 『Shiga-Jumdeung』 make us look forward to the value of another of Lee Kyu-kyung's books that have not been discovered yet.&#x0D; 『Oju-Yeonmunjangjeon-Sango』 says “It can be seen in detail in 『Oju- Pilju(五洲筆疇)』”, so 『Oju-Pilju』, which was obviously Lee Kyu-kyung's book but had not been seen before, was newly discovered. It is a hand-held book [袖珍本] similar in size to 『Shiga-Jumdeung』. There are 20 letters in 10 lines and a total of 72 pages. The condition of the book is very good, so it is easy to distinguish the letters.&#x0D; 『Oju-Pilju』 contains military operations, which are the core of war, walls for defense, pavilions to watch military camps, armor and weapons, carts carrying munitions, other things that could be weapons, military medicine, and amulets to defeat the enemy. In other words, it is a ‘book on military strategy(兵法書)’ with quite a bit of content. Through two prefaces written by Lee Kyu-gyeong, he emphasized that as a Confucian scholar, the purpose of separately writing the book on military strategy, 『Oju-Pilju』, was to achieve a peaceful era.&#x0D; Looking at the parts related to the walls, weapons, and carts that Lee Kyu-kyung said he put effort into in the preface, it is clear that he had a high level of knowledge about ‘strategy’. Through the review of 『Oju-Pilju』, it is possible to accurately grasp the level of Joseon books on military strategy in the 19th century.
The Effects of Pre-service Teachers' Social Presence on Learning Outcomes in Scaffolding-based Online Collaborative Learning Environments: The Mediating Effects of Learning Engagement

Objectives The purpose of this study is to explore the relationship between social presence, learning engagement, and learning outcomes of pre-service teachers’ in scaffolding-based online collaborative learning environments( hereinafter referred to as OCLEs), and to verify the mediating effects of learning engagement in the relationship between social presence and learning outcomes.&#x0D; Methods For this purpose, this study conducted scaffolding-based online collaborative learning with 68 preservice teachers who took Educational Technology-related courses in a 4-year university A, Busan Metropolitan city. And then, this study surveyed social presence and learning engagement, and evaluated learning outcomes. The collected data was analyzed by conducting the hierarchical regression analysis of Baron and Kenny (1986) and the Sobel Test (1982).&#x0D; Results The main results of this study were as follows. First, In a scaffolding-based OCLEs, the relationship between the remaining variables showed a positive correlation with each other, except for the correlation between ‘affective connectedness’, ‘sense of community’, ‘emotional engagement’ and ‘learning outcomes’. Second, Pre-service teachers' ‘learning engagement’ was found to completely mediate the relationship between ‘social presence’ and learning outcomes’ in scaffolding-based OCLEs. Third, Pre-service teachers' ‘cognitive engagement’ and ‘behavioral engagement’ were found to completely mediate the relationship between ‘open communication’, ‘mutual support and empathy’ and ‘learning outcomes’ in scaffolding-based OCLEs.&#x0D; Conclusions It is meaningful in that it provided empirical grounds and basic data for scaffolding-based OCLEs design and class design that can be used in pre-service teachers’ education.
A Phenomenological Study on Engagement Experiences within the Innovation University-Gwangju and Jeonnam Collaborative Program: A Focus on the Eco-Friendly Smart Ship Convergence Major

Objectives This study explores the efficacy of leaner-centered education, spotlighting the lived experiences of participants enrolled in the “Eco-Friendly Smart Ship Convergence Major” offered under the Innovation University- Gwangju and Jeonnam Collaborative Program. The objective is to provide insights for the enhancement and tra-jectory of convergence education in the future.&#x0D; Methods Employing Giogri’s phenomenological method, the research seeks to understand the core essence of participants’ experiences and to unearth their inherent meanings. By using this method, the study facilitates a shift from a researcher’s perspective to that of the participants, offering a holistic understanding of their experi-ences within the convergence major. Data collection involved semi-structured, in-depth interview with nine par-ticipants who had undertaken this interdisciplinary major.&#x0D; Results The interview revealed participants’ experiences categorized into three primary themes: ‘Experiential Characteristics of Participation within the Eco-Friendly Smart Ship Convergence Major’, ‘Unique Elements of Classes within the Eco-Friendly Smart Ship Convergence Major,’ and ‘Insights Derived from Engagement in the Eco-Friendly Smart Ship Convergence Major.’ The central founding was that the program significantly contributed to individual growth through diverse experiences.&#x0D; Conclusions To achieve educational objectives, it’s imperative to present students with a comprehensive spec-trum of opportunities. Beyond the core curriculum, this encompass overseas programs, internships, and various educational initiatives that assist in career exploration. This research emphasizes the potential and significance of such collaborative convergence programs, suggesting they could represent an innovative blueprint for future edu-cational collaborations, as experienced from a learner’s perspective.
Linear codes and cyclic codes over finite rings and their generalizations: a survey

We survey a recent progress of cyclic codes over finite rings and their generalization to skew cyclic as well as skew cyclic codes with derivation over finite rings, focusing on structural properties of the codes. We also report recent developments on the construction methods of linear codes from graphs, in particular strongly regular as well as distance regular graphs.
Implementation Deep Learning Method On Decision Support System For Smart Indonesia Card Scholarship At University Of KH. Bahaudin Mudhary Madura

Scholarships are financial aid or funds awarded to individuals or groups to support education, research, or other academic development. There are many types of scholarships such as academic achievement scholarships, non-academic achievement scholarships, financial scholarships, and others. College scholarships are financial aid or funds provided to students to assist them in their pursuit of higher education. Scholarships in tertiary institutions can be full such as smart card scholarships or partial such as free tuition scholarships, and the aim is to encourage wider access to education and help students who are talented but face financial constraints in pursuing their education. At the University of KH. Bahaudin Mudhary Madura for scholarship applicants there are very many, there are hundreds or even nearly thousands of students who register, especially on the smart Indonesian card scholarship and this also requires very fast time and a good level of accuracy in decision making. Therefore, it requires a decision support system. In this study, the deep learning method was applied to a decision support system with a loss of 1.43%, an accuracy rate of 98.57% and a system runtime of 77ms/step.
Sentiment Analysis for Indonesian Salt Policy uses Naïve Bayes and Information Gain Methods

Salt production is one of the concerns of the Indonesian government. Several government policies such as salt imports have had a major impact on local salt farmers. Other factors are due to the increased demand for salt, decreased domestic salt production which is unfavorable due to weather factors, and the price of imported salt is lower than that of local salt. Many people express their opinions regarding the salt import policy, via Twitter social media. Sentiment analysis can be applied to analyze tweets or writings by the public regarding salt import policies and classify the data. This study uses the naïve Bayes classifier algorithm model as a sentiment classification algorithm on Twitter social media tweets. The classification process uses the Naïve Bayes algorithm. The feature extraction and weighting method is the TF-IDF method. Not all of the features resulting from the TF-IDF process are used, so feature selection is carried out using the information gain method. Model testing was carried out 5 times with 500 data, using feature selection and without feature selection. Without feature selection, the highest accuracy result is 84% at K=4, while without feature selection it produces an accuracy of 71% at K=3, so there is an increase of 13%.
Exploring students’ speaking anxiety in an EFL classroom

This research aimed to find out the types of students’ speaking anxiety and students’ strategies to cope with their speaking anxiety in an EFL classroom. This research was conducted using descriptive qualitative method. The data collected by using observation checklist, in-depth interview, and online questionnaire. The participants of this research were students of first, third, and fifth semesters in Business English Communication program, Universitas Negeri Makassar. Based on the results of this research, it was found that there are more students who experience state anxiety and situation-specific anxiety than trait anxiety, as the type of anxiety that occurs when speaking English as a foreign language. Moreover, it was found that most students in the first, third, and fifth semesters used various techniques in doing preparation, relaxation, positive thinking, and seeking a peer as strategies to cope with speaking anxiety.
Comparison of Forecasting Accuracy Using the Short Moving Average (SMA) Method Using Boxplot Outlier Filtering and Not Using Outlier Filtering for Data that has a high level of variation

The Short Moving Average (SMA) forecasting method is one of the most widely used forecasting methods, especially for processing data with a high level of variation and is not linear with time. However, opportunities to develop and improve forecasting performance using the SMA method are still wide open. The performance of a forecasting method can be seen from the distribution of errors. SMA does not see and does not sort the type of input data that will be processed into a forecast value, whether the input data has small or large variations, or has outlier data. If the input data has an outlier, then that outlier can make the forecasting performance not good. One of the efforts to improve SMA forecasting performance is by filtering outlier data. In this study, a comparison was made of the forecasting results for SMA using outlier filtering with the forecasting results for SMA not using outlier filtering. The next step is to compare the error values, namely those that produce the smallest Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values. From the results of the study it can be seen that the performance of SMA using the Boxplot filtering method gives better forecasting results than those without using outlier filtering.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS APLIKASI GOOGLE SLIDES MATERI SISTEM PENCERNAAN MAKANAN PADA MANUSIA BAGI SISWA KELAS VIII SMP

In this study, the aim was to produce learning media based on the Google Slides application for natural science subjects on the human digestive system at Fatumfaun Public Middle School and then to find out the feasibility of Google Slides-based learning media for natural science subjects on the food digestive system for humans at public junior high schools. Fatumfaun based on the results of expert reviews and trials. Research and development (Research and Development) is a method used in this investigation. Both qualitative and quantitative data kinds are employed. The results of the assessment of media experts have an average value of 92.30% and are stated to be very good for testing. The results show that the material expert's assessment obtained an average value of 96.67% and was stated to be very good for testing. Based on Table 5, the trial of learning media products based on the Google Slides application is included in the very good category to be tested at Fatumfaun Public Middle School with an average score of 89.25% of student assessment results. The development of Google Slides application-based learning media for the digestive system material in humans that was developed was declared feasible to be tested.
サイバーフィジカルシステムを基盤とするデジタルツインプロトタイプ構築実習型教育法

In order to realize a digital twin, in addition to future prediction technology using AI, modeling technology for virtualization specialized for physical systems and know-how for identifying data used in modeling are required. However, there are many cases in which the know-how specialized in these physical systems is not sufficient in educational settings. For this reason, we set assumptions about modeling techniques specific to physical systems and data to be collected, and prepare prototypes that realize digital twins based on those assumptions. This prototype provides a foundation for machine learning, AI model tuning, and implementation of trained AI models into digital twins. By experiencing the construction of this prototype, we will cultivate the ability to construct future prediction systems using AI in digital twins. In addition, by setting various hypotheses and carrying out realization and verification with prototypes, the educational effect will be enhanced. This paper describes the proposal of this educational method and its practice and evaluation.
A Lightweight Reinforcement Learning Based Packet Routing Method Using Online Sequential Learning

Existing simple routing protocols (e.g., OSPF, RIP) have some disadvantages of being inflexible and prone to congestion due to the concentration of packets on particular routers. To address these issues, packet routing methods using machine learning have been proposed recently. Compared to these algorithms, machine learning based methods can choose a routing path intelligently by learning efficient routes. However, machine learning based methods have a disadvantage of training time overhead. We thus focus on a lightweight machine learning algorithm, OS-ELM (Online Sequential Extreme Learning Machine), to reduce the training time. Although previous work on reinforcement learning using OS-ELM exists, it has a problem of low learning accuracy. In this paper, we propose OS-ELM QN (Q-Network) with a prioritized experience replay buffer to improve the learning performance. It is compared to a deep reinforcement learning based packet routing method using a network simulator. Experimental results show that introducing the experience replay buffer improves the learning performance. OS-ELM QN achieves a 2.33 times speedup than a DQN (Deep Q-Network) in terms of learning speed. Regarding the packet transfer latency, OS-ELM QN is comparable or slightly inferior to the DQN while they are better than OSPF in most cases since they can distribute congestions.
The Effects of Genre Based Approach and Process Approach on EFL Writing Class

Indonesian students learn English as a Foreign Language (EFL). Indonesian students who are second language users (L2 learners) have limited knowledge of frequently used words compared to first language users (L1 learners) and native English speakers. EFL students made more mistakes and errors in EFL writing classes due to limited vocabulary. A frequent word is a single word in the first three frequency levels of the 1000 words. To fill this gap, this study aimed to examine the effectiveness of a genre-based approach (GBA) and a process approach to writing (PAW) on student writing skills. In this study, a quasi-experimental study design was applied in the form of a pre-test and post-test control group design. As a sample for this study, two classes of 3rd semester undergraduate students from the Faculty of Economics and Business in Sumenep, East Java, Indonesia were randomly selected. Seventy students participated in the study, of which 35 students in the experimental group received the GBA and another 35 students in the control group received the process approach to writing. Data were analysed using independent and dependent (paired) sample t-tests. The resulting findings showed that: 1) There was a significant difference in post-test scores between students taught using a genre-based approach and those taught using a process approach to writing. 2) Adopting a genre-based writing approach or a process approach has had a big impact. This study shows that the genre-based approach contributes more than the writing approach. In conclusion, the GBA or PAW was effective in teaching English writing skills.