Reviewerly/exHarmony huggingface dataset:
Design and implementation of push up action evaluation system based on Kinect

The action evaluation task is to automatically analyze and evaluate the actions made by the human body. Push-up action is one of the physical fitness test items. At present, the traditional manual evaluation method is mainly used for its evaluation. This method is inefficient and the results have certain subjectivity. Based on the "National Physique Measurement Standards", this paper selects the joint points describing the push-up movement, detects the data of the push-up joint points based on Kinect, and calculates the 8 joint angles of the push-up as its action characteristics through the vector method, and then proposes based on the dynamic time warping algorithm A new evaluation method is developed, and finally a push-up evaluation system is designed and implemented.
Immersive Learning with Virtual Field Visits: Spherical Video-Based Virtual Reality of Factory Environments

The authors develop, use, and evaluate a spherical video-based virtual reality (SVVR) app for teaching operations management. The app contains a 360-degree photo-realistic copy of real factory environments with blended information and gamified exercises. To solve a learning assignment, students virtually immerse themselves in the factory using the app on their smartphones with virtual reality headsets, tablets, or computers. The authors developed the SVVR app together with an industrial partner, implemented it in a graduate-level course on production and operations management, and evaluated it by drawing on app user data, assignment grade scores, and an evaluation survey. The results show that students reported an overall positive learning experience and that students who engaged more in the SVVR app performed better on the assignment. This paper contributes to the literature on SVVR with evidence of the learning effects of SVVR in an operations management curriculum. It provides an example of how SVVR enables virtual factory visits, integrates gamification elements, and can be integrated into teaching. Funding: This work was supported by the ETH Zurich Innovedum.
Indonesian Culinary Application System Design with UML Method

Indonesia has an important role as a country that produces various high-quality culinary items. Indonesia, with its plethora of natural resources and cultural diversity, can generate a wide range of culinary goods, including traditional foods and beverages as well as modern culinary inventions. Natural and cultural potentials are the foundation for the development of exceptional food and beverages in a country manufacturing culinary products. However, numerous issues with marketing are not global enough, even though Indonesian culinary items should be able to compete with well-known food products. Information technology and the internet have altered the culinary business scene in Indonesia, making it easier for local culinary products to obtain worldwide market share. This article investigates how the existence of information technology and the internet has opened up new avenues for Indonesian culinary manufacturers to broaden their marketing reach. Indonesian culinary items can now be accessed by consumers worldwide via an online platform, removing geographical barriers and presenting substantial economic growth possibilities. This study created an application system to advertise culinary products, with the first steps focusing on designing application systems. The Unified Modeling Language technique is used to create the culinary application system. This procedure aids in visualizing the system's structure, and interactions deployment. As a result, development teams and stakeholders have a more structured design and a better grasp of the system. Unified Modeling Language is quickly becoming a valuable tool for supporting thorough collaboration and documentation in application development.
EVALUASI PEMANFAATAN KOLEKSI E-LIBRARY UIN RADEN FATAH PALEMBANG

This study aims to determine the use of e-library seen from members, visitors, readers and the amount of content read. This research was conducted using a quantitative descriptive method. The data is sourced from documentation, namely the retrieval of existing data in the e-library application. The collected data is simplified and then the data is processed and presented in graphical form so that it is easy to understand. The results of this study indicate that from the number and type of membership, the most visitors and readers are students. When viewed from the year, the highest number of visitors, readers and read content occurred in 2019 when new collections were added. So from the results of this study, it can be recommended the need to add new collections that can meet the information needs of students in supporting their lecture assignments
Word Alignment of Chinese Poetry Parallel Corpus based on Word Embedding Technology

The research of word alignment technology provides the basis for many fields in natural language processing, such as speech recognition, bilingual dictionary writing, corpus construction and information retrieval. Based on the existing research foundation of parallel corpus for poetry sentence alignment, this paper studies how to align English-Chinese words. Firstly, the parallel corpus of Chinese poetry is preprocessed and translated into words, and the English poetry word vector is trained based on word embedding method, and the optimal corresponding word is judged by similarity calculation, and finally getting word alignment from parallel corpus sentence alignment.
The Presence of Bias Based on Pre-trained Language Models

One of the most active fields of machine learning research is natural language processing (NLP). Although existing linguistic machine learning models excel numerically on a variety of linguistic comprehension tasks, they frequently lack implicit bias reduction optimization. To ensure that deep learning models can avoid the traps of implicit bias and that robots can make fair judgments, bias in NLP must be addressed adequately. The ramifications of permitting biased models to reach the actual world are serious. Thus must address this issue as quickly as feasible. This paper conducted data validation-bias experiments on several real datasets to verify the presence of gender bias in the pre-trained models, then proposed a word vector balancing algorithm to modify the actual vector representation of words by biasing the models and verifying the effectiveness of the debiasing method through debiasing experiments to mitigate the gender bias of the models while ensuring data accuracy, thereby improving the fairness of the classification results. Furthermore, this paper provides more accurate information for the future development and use of deep learning.
A Contrastive Analysis Of Passive and Active Voice In Indonesian and English

This research paper presents a contrastive analysis of passive and active sentences in Indonesian and English. The research aims to uncover the similarities, differences, and patterns underlying the use of these grammatical features in both languages. This research utilizes descriptive qualitative research. This paper explores the different forms and patterns of active and passive sentences in Indonesian and English through the analysis of spoof texts. The data analysis shows that the most dominant form of active sentences in English is the past tense (63.6%), while in Indonesian, the transitive form is the most widely used (66.7%). This contrastive analysis provides valuable insights into the linguistic structures and communicative strategies used by Indonesian and English speakers,contributing to language learning and understanding cross-linguistic differences.
The Influence of Maybrat Accents towards Eleventh Grade Students English Pronunciation

The research was applying qualitative method and instruments to collect data such as reading text which consisted of some words, phrases and sentences. Collecting data carried out by recording twenty students English reading. The students were at the eleventh grade of SMA Negeri 1 Ayamaru, Maybrat Regency, Southwest Papua Province. The student records were used for analyzing English speech sound in both aspects segmental and suprasegmental of the students’ pronunciation. The result of students record exposed the difference between the students English speech sound and the recognizable English speech sound in English standard variety. It was found that the differences were revealed in vowel sounds and was sounded by the students in English single syllable words. The suprasegmental features were different represented by the student speech sound either in words stress, phonemic stressed, length, loudness and intonation or the pitch / tone and sounds inserted and gliding into single syllable segment.
IMPLICATURE ANALYSIS ON ONLINE LECTURE COURSES OF ENGLISH EDUCATION STUDY PROGRAM

This research aims to reveal the types and implied meaning of implicature found in an online lecture courses of English education study program at one of the university in Yogyakarta. In this research using descriptive qualitative research methods by a theory from Grice (1975). The data findings consist of the three main implicatures consist of generalized conversational implicature with 14.3%, particularized conversational implicature with 8.6% and conventional implicature with 8.6%. Then, the four implicature functions are reach the intended meaning with 20%, deliver an entertainment as humor with 8.6%, boosting the power's of person's message with 14.3%, and specific information needs to be added with 25.6%. By adding up the total between main implicature and implicature functions, it turns out to be 100%. The three main implicature most widely used is generalized conversational implicature. As well as the most frequently used in the implicature function is, specific information needs to be added.
Exploring the experience of Year 10 South Korean students’ English language learning in immersive virtual reality.

A prescribed English language textbook often directs classroom teaching practices in secondary school classes in EFL contexts, such as in South Korea. The textbook is often accompanied by multimedia resources which are delivered to students as input at a regulated pace with limited opportunities for communicative interaction or spoken output. Such opportunities are further limited in the community outside of the English classroom. Immersive virtual reality (i-VR) has the potential to situate learners in a real-world context for authentic application of textbook language learning. English teachers in the formal classroom focus on linguistic competence development within time constraints by teaching new vocabulary and grammatical items in decontextualised forms. By comparison, i-VR environments focus on learning to construct meaning in communicative events in contextualised, real-world settings based on students’ existing linguistic knowledge and ability. In a small-scale pilot study, two teachers of Year 10 English classes in Seoul implemented four i-VR language learning modules in their classes: one as a self-directed learning experience that extended beyond formal classroom learning, and the other as a teacher-facilitated learning experience within the formal classroom. Both teachers were interviewed after the two-week implementation to seek their views on their perceptions of the value of such i-VR learning for their students. Beyond the motivational and entertainment value, the teachers viewed the i-VR experience as capable of incorporating pedagogical structures using the embedded multimodal resources that is not possible in other immersive forms of language learning. Moreover, the teachers believed that incorporation of authentic conversations and interactional opportunities could further enhance the learning potential.
Pengembangan Papan Konversi Satuan Menggunakan Metode Jamping Materi Satuan Berat Dan Satuan Panjang

Education is something that every human being needs to gain experience, knowledge, and skills to develop potential within him, so as to boost one's life for the better. In this study uses the type of research and development Research and Development (R&amp;D). This development model consists of five stages, namely, Analysis, Design, Development, Implementation, and Evaluation. The population and sample of this study were conducted in class III at SDN 104/II Sungai Pinang with a total of 24 students. The type of data used in research is qualitative and quantitative. This research was conducted on conversion board learning media for mathematics material units of weight and units of length in class III SD. data collection instruments using validation sheets, practicality instruments, and effectiveness instruments. Data collection techniques using observation, interviews, documentation, and learning performance tests. The data analysis technique used uses three data analysis techniques, namely validity analysis, practicality analysis, and effectiveness analysis. The results of the study obtained the validity of the unit conversion board learning media as assessed by the validator, totaling 3 people, indicating that the conversion board learning media obtained a proportion of 86% with a very valid category. Practicality was assessed from the point of view of the teacher's response to the unit conversion board learning media filled out by educators at SDN 104/II Sungai Pinang obtaining a proportion of 100%. Effectiveness comes from the results of the effectiveness sheet filled in by students on the unit conversion board learning media obtaining a proportion of 83.34% with the very effective category.
Pembuatan Media Belajar Berupa Alat Peraga untuk Meningkatkan Minat Belajar Siswa di Sekolah Dasar Negri Sukosari dan Madrasah Ibtidaiyah Sukosari Mantup Lamongan

SDN Sukosari and MI Tarbiyatul Ulum which are located in Sukosari Village, Mantup District, Lamongan Regency, where the majority of students who are still occupying the first grade are still weak in recognizing the letters of the alphabet and numbers. Thus, it is hoped that the creation of learning media in the form of praga tools can help students recognize the letters of the alphabet and numbers, moreover they will be able to read and understand more about subtraction and addition faster. This mentoring activity was carried out in Sukosari Village, Mantup District, Lamongan Regency, in less than one month starting on 03 August-27 August 2022. The objects targeted for the implementation of this program are formal educational institutions, namely SDN 1 Sukosari and MI Tarbiyatul Ulum sukosari. making learning media praga tools in the form of pictures that are used to help students to more easily understand the learning delivered. The implementation of this program is carried out on students in grades one and two for the MI Tarbiyatul Ulum institution, and grades one and six for SDN 1 Sukosari
IB-RAR: Information Bottleneck as Regularizer for Adversarial Robustness

This paper proposes a novel method, IB-RAR, which uses Information Bottleneck (IB) to strengthen adversarial robustness for both adversarial training and non-adversarial-trained methods. We first use the IB theory to build regularizers as learning objectives in the loss function. Then we filter out unnecessary features of intermediate representation according to their mutual information (MI) with labels, as the network trained with IB provides easily distinguishable MI for its features. Experimental results show that IB-RAR can be naturally combined with adversarial training and provides consistently better accuracy on new adversarial examples. The IB-RAR method improves the accuracy by an average of 2.66% against five adversarial attacks for ResNet-18, wide ResNet-28-10, and VGG-16, trained with three adversarial training benchmarks and the CFAR-10, CFAR-100, and Tiny ImageNet datasets. In addition, IB-RAR also provides good robustness for undefended methods, such as training with cross-entropy loss only. Finally, without adversarial training, the VGG-16 network trained using IB-RAR on the CFAR-10 dataset reaches an accuracy of 35.86% against PGD examples, while using all layers reaches 25.61% accuracy.
M-Rule: An Enhanced Deep Taylor Decomposition for Multi-model Interpretability

Recent intelligent decision systems based on machine learning have achieved superhuman performance by learning rich knowledge from massive amounts of data through complex deep network models. However, interpretability remains a significant challenge in artificial intelligence, as state-of-the-art decision-making frameworks still struggle with unexplained decisions. This paper conducts interpretability research from the dimension of feature contribution. The core idea is to provide a visual explanation that shows which important areas of the input guide the model to make the final decision and helps users understand the reasons for the model decision. For better redistribution and explanation of the correlation between the input and the final output, we first improve the Deep Taylor Decomposition(DTD) process and proposed an enhanced relevance propagation method called “M-Rule”. This method is suitable for use in multi-level decision-making models, including DNNs, CNNs, LSTMs, and more. Therefore, it is considered to be a multi-model interpretability approach. Then We evaluate the interpretability improvement using M-Rule on the ImageNet dataset, and address the small sample problem by replacing the region with the largest contribution in each image to achieve data enhancement and identify the regions of interest (ROIs) in COVID-19 lung CT images. The results demonstrate that the model trained in this paper using interpretation-based augmented data has a significant performance improvement compared to other classification models. With the continuous advancement of machine learning technology, we believe that interpretability will eventually produce an autonomous system capable of making human-comprehensible decisions in the future.
SNP-S<sup>3</sup>: Shared Network Pre-training and Significant Semantic Strengthening for Various Video-Text Tasks

We present a framework for learning cross-modal video representations by directly pre-training on raw data to facilitate various downstream video-text tasks. Our main contributions lie in the pre-training framework and proxy tasks. First, based on the shortcomings of two mainstream pixel-level pre-training architectures (limited applications or less efficient), we propose Shared Network Pre-training (SNP). By employing one shared BERT-type network to refine textual and cross-modal features simultaneously, SNP is lightweight and could support various downstream applications. Second, based on the intuition that people always pay attention to several "significant words" when understanding a sentence, we propose the Significant Semantic Strengthening (S3) strategy, which includes a novel masking and matching proxy task to promote the pre-training performance. Experiments conducted on three downstream video-text tasks and six datasets demonstrate that, we establish a new state-of-the-art in pixel-level video-text pre-training; we also achieve a satisfactory balance between the pre-training efficiency and the fine-tuning performance. The codebase are available at https://github.com/alipay/Ant-Multi-Modal-Framework/tree/main/prj/snps3_vtp.
Continuous-Time Distributed Filtering With Sensing and Communication Constraints

Distributed filtering is crucial in many applications such as localization, radar, autonomy, and environmental monitoring. The aim of distributed filtering is to infer time-varying unknown states using data obtained via sensing and communication in a network. This paper analyzes continuous-time distributed filtering with sensing and communication constraints. In particular, the paper considers a building-block system of two nodes, where each node is tasked with inferring a time-varying unknown state. At each time, the two nodes obtain noisy observations of the unknown states via sensing and perform communication via a Gaussian feedback channel. The distributed filter of the unknown state is computed based on both the sensor observations and the received messages. We analyze the asymptotic performance of the distributed filter by deriving a necessary and sufficient condition of the sensing and communication capabilities under which the mean-square error of the distributed filter is bounded over time. Numerical results are presented to validate the derived necessary and sufficient condition.
Strategies for Enhancing the Impact of Blended Instruction in Chinese Colleges and Universities

Due to the rapid development of information technology, traditional classroom teaching is not able to meet the needs of students. Therefore, colleges and universities are paying more and more attention to cultivating students’ individuality and comprehensive quality. The “student-centered, teacher-led” online and offline hybrid teaching mode emerged as the times require, and has become a new teaching method, which has gradually become the focus of attention of colleges and universities. In the teaching practices of colleges and universities, blended instruction has made some progress, but issues with instruction quality also arise. Upon review of the relevant literature and interviews with teachers and students, this study determined that outmoded teaching practices, lack of awareness of students’ independent learning, and absence of external conditions in colleges and universities are the most influential factors affecting the quality of mixed teaching. Based on these, corresponding measures are proposed from the perspectives of teachers, students, and schools to aid the development of blended teaching in colleges and universities and improve its effects. Teachers should continuously improve their hybrid teaching skills and ensure quality. Moreover, students should improve their sense of participation in teaching activities and develop a consciousness of independent learning. Finally, the school should improve the support structure to implement blended teaching seamlessly.
A New Stereo Matching Function by a Hybrid Convolutional Neural Network

This research introduces a novel method for creating stereo matching algorithms, combining elements of learned and custom methods. The purpose of this strategy is to provide more reliable outcomes than conventional approaches. The primary goal of the technique for stereo corresponding remains to harvest a difference map. Three-dimensional (3D) reconstruction is only one of many uses for this map. Convolutional neural network (CNN) produced raw disparity maps may still contain mistakes in low-resolution textures. The programme will utilise a hybrid CNN-based approach and a faster directional intensity computation to enhance the accuracy of the matching cost calculation phase. Use of the modified truncated value of directional intensity has the potential to greatly reduce radiometric errors. To further enhance the accuracy attained by the approach, a cost aggregation phase is used in which the bilateral filter (BF) is applied to the raw matching cost. Building a gap map with the whole cost of costs is the first step in the WTA optimisation process. The final disparity map is an improvement above the basic map thanks to several rounds of refinement. By means of the Middlebury Online Stereophonic Benchmarking Scheme, the article verified the algorithm efficacy.