Reviewerly/exHarmony huggingface dataset:
Content Analysis on Career Education Included in Family Life Area of Middle School ｢Technology⋅Home Economics｣ Textbooks

이 연구는 학교 진로교육 목표 및 성취기준 개정안을 바탕으로 2015 개정 가정과 교육과정이 반영된 중학교 기술⋅가정교과서 가정생활 분야 내용을 분석하여 중학교 기술⋅가정 교과서 가정생활 분야에 포함된 진로교육 내용을 분석하는 것을목적으로 하였다. 이를 위하여 2015 교육과정이 적용된 중⋅고등학교 기술⋅가정 교과서 5종을 선정하여 한국직업능력연구원(2021)에서 개발한 학교 진로교육 목표 및 성취기준 개정안을 교과서 분석을 위한 틀로 사용하였다. 분석결과 2015 개정 중학교기술⋅가정 교과서 가정생활 분야에서 진로교육 내용 반영 수준은 생애 설계와 진로 선택 단원이 87개로 가장 많았고, 청소년의발달 특성 단원이 36개, 저출산⋅고령사회와 일⋅가정양립 단원이 31개 순으로 반영되어 있었다. 가정 교과서에 나타난 진로교육내용으로는 다양한 직업인의 탐색(24.30%)이 가장 많았고, 미래사회의 변화 탐색(22.74%), 직업세계의 변화 탐색(18.66%) 순으로많이 반영되고 있었다. 연구 결과 중학교 기술⋅가정 교과서(가정생활 분야)에 반영된 진로교육의 수준이 높음을 확인하였고이는 가정교과연계 진로교육으로의 적용가능성이 높음을 확인하였다.The purpose of this study was to analyze the contents of middle school technology and home economics textbooks(family life area) that reflect the school career education goals and achievement standards in the 2015 revision of the Technology and Home Economics curriculum. Five different textbooks on middle school technology and home economics written based on the 2015 curriculum were selected and the school career education goals and achievement standards developed by the Korea Research Institute for Vocational Education and Training (2021) were used as a framework for textbook analysis. The results are as follows. First, the units that cover career education contents the most were ‘life design and career choice(n=87)’, ‘developmental characteristics of adolescents(n=36)’, and ‘low birth rate, aging society, and work and family balance(n=31)’. The major contents of career education covered in technology and home economics textbooks(family life area) were ‘search for various professionals(24.30%)’, ‘exploration of the changes in the future society(22.74%)’, and ‘exploration of the changes in the job world(18.66%)’. Thus it was found that the goals and achievement standards of school career education are evenly reflected in the middle school technology and home economics textbooks(family life area) based on the 2015 curriculum.
Web-Based Public Complaint Information System Design Using the Laravel Framework

In this day and age technology is growing rapidly and technology has an important role in all aspects of human life, especially websites which are currently becoming media information that is very easy to achieve using only the internet. Website is a web page that can be accessed by the public using the internet, and the website also has a domain name that is owned by each website. In addition, the website can also be opened via computers, laptops, smartphones, tablets, and so on. The purpose of this research is to design a system that has the Create and Read feature which will later be used by the public in reporting, to design a website public complaint system so that information sent by the public to the government is fast and accurate, based on a website-based public complaint system located on the website. In it there is a feature that can send an email to notify that the information provided by the public has been received, or it is resolved with the SMTP feature which later on the public can find out the complaint process. The system made is web-based using the Laravel framework, the system is made based on the public complaint system, consisting of people who can upload and send complaints to the government as well as people who see the status of their complaints, besides that there are admins who can view and process complaints, can add officers, and can print report data in the form of pdf. Officers can only view and process complaints and can print report data in the form of pdf, where users of this system are admins and officers from government and the community are users.
Self-Supervised Classification Using a Language Model for Functional Data

This study analyzes functional gait data to evaluate the normality of rehabilitation after treating cerebral palsy patients. We propose applying a large language model with a highly comprehensive text ability in natural language processing for analyzing the functional gait data. A large language model is an effective way to learn a sequence of tokens, but it requires many sentences and highlevel computational resources. Hence, we categorize functional data, define a token as an input of a language model, and learn the distribution of functional data through self-supervised learning. In addition, we devise a data augmentation method through trend-filtering to secure the data necessary for a large language model. Also, we develop a pre-training method that can increase estimation efficiency by introducing noise contrastive estimation, which contrasts observed data from augmented data. From the gait data analysis, we find that the proposed method is robust to the alignment of data measurement points and improves cerebral palsy patients’ normal/abnormal classification performance.
Search with Two Stages of Information Acquisition

This paper presents a methodology for estimating a sequential model of online consumer search. The literature on such structural econometric models typically assumes that, for each alternative, there is only one stage of optimal information acquisition. For many e-commerce websites, however, there are two stages: obtaining information from (1) the search results page and (2) clicking on an alternative. We develop a methodology for estimating a model with two stages, in which the consumer makes optimal decisions in each stage. The search problem is viewed as a variant of a multi-armed bandit problem. We estimate this model using a dataset of clicks and purchases on the website expedia.com. In contrast to models with one stage of optimal information acquisition, our model can be used to analyse not only the clicking and purchasing behaviour of consumers but also the extent to which they browse alternatives on the search results page.
Pengembangan Aplikasi Game Edukasi Matematika SD 02 Pagi Pondok Labu Berbasis Android dengan Metode Rapid Application Development Menggunakan Adobe Flash CS6

This research was conducted at SD 02 Pagi Pondok Labu, Cilandak, South Jakarta, with a background of the problem, namely the learning process used in education today where computer-based learning media is only limited to transferring paper media to digital media with the same appearance. This is what causes the learning media to be less than optimal as is the case in the mathematics learning process where the media used in learning material about mathematics in elementary schools is less attractive because of the learning process that still uses classical methods or conventional methods in the teaching and learning process. This research method uses the Rapid Application Development (RAD) method while the data collection methods used are observation, interviews, and literature study. This study aims to design and build learning media applications in the form of Android-based math educational games. The results of the research are in the form of a mathematics educational game application at Pondok Labu 02 Pagi Elementary School that can be played with a smartphone or laptop as a medium to increase interest in learning for students and media for teachers in providing learning about mathematics.
Digital Archive System Development in Improving Public Services Using Extreme Programming

The process of filing a letter is usually done by recording it in an archive book, then the letter is stored in a place or filing cabinet that has been provided. This is considered not optimal, in providing correspondence services that are needed by the community. Due to the limited staff working in the village office, the process takes a long time. This research discussed how to create and design digital archive applications and correspondence starting from data collection methods (interviews, observations and documentation) using extreme programming development methods, system design using UML with Use case Diagram design model, and CRC Card. TRITAM model test results that have been conducted involving 16 respondents showed results of 82.56% with good criteria. From the results of these tests showed good results for prototype users for the mail filing system.
Web Based Ordering and Inventory Information System for Online Printing Services with TAM Method

In this digital era, it is easier for people to carry out all their activities with the help of technology, one of which is ordering activities. PT Media Langit Persada is a company engaged in digital printing where the ordering process is still done manually. For example customer must come to the printing house and make direct order transactions even the management division still doing data collection manually. This is considered less effective because it may causes many errors while storing data and the information obtained by the customer. This study aims to create a web-based ordering system that is expected to make it easier for customers to make ordering transactions while the company easier in managing other business processes. This system will later be tested using the Technology Acceptance Model (TAM) method which will determine whether this system is suitable for use and can be accepted by the community or not. This task can be done by distributing questionnaires to several prospective respondents who are considered to be contributing to this system.
Bayesian Active Summarization

Bayesian Active Learning has had significant impact to various NLP problems, but nevertheless it's application to text summarization has been explored very little. We introduce Bayesian Active Summarization (BAS), as a method of combining active learning methods with state-of-the-art summarization models. Our findings suggest that BAS achieves better and more robust performance, compared to random selection, particularly for small and very small data annotation budgets. Using BAS we showcase it is possible to leverage large summarization models to effectively solve real-world problems with very limited annotated data.
KEMAMPUAN BERPIKIR KRITIS SISWA DALAM MENULIS TEKS DESKRIPSI MENGGUNAKAN MEDIA VIDEO PENGETAHUAN ALAM KELAS VII SMP

This research was conducted to find out the influence of natural knowledge video media on students' critical thinking skills in writing the text of the description of class VII junior high school student Siman Jaya Lamongan. This type of research uses Quasi experiment (pseudo experiment), pretest- posttest control group design design. Data collection techniques in the form of giving assignments to write description text to students. Data analysis techniques are carried out using prerequisite tests in the form of normality tests and homogenite tests, after which hypothesis tests are conducted using t-tests with a significant level of 5% (0.05). The results found in this study were that students' critical thinking ability to write text descriptions in control classes had an average score of 69.65 while in the experimental class had an average score of 79.81. Based on the hypothesis test using the t-test shows that the value of thitung = 5.40, thus it is known if thitung &gt; ttabel i.e. (5.40 &gt; 2.004) then Ho is rejected and Ha is accepted. Based on this, it can be concluded that there is a significant influence on students' critical thinking skills in writing description texts using media in the form of natural knowledge videos in class VII siman jaya lamongan junior high school.
An Automatic Environment Monitoring System Using a MobileNet Transfer Learning

The universities have an important role to encourage and support the Sustainable Development Goals (SDGs). UIN Alauddin Makassar as one of the universities in Indonesia establishes a green campus program to support it. An automatic environment system was built to ensure the cleanliness of the university environment. A comfortable and healthy work environment is expected can improve the productivity and motivation of the academic civities. The system was built using a MobileNet architecture that using a transfer learning approach. It can detect the cleanliness level of the environment which consists of three classes: â€œCleanâ€, â€œLess_Cleanâ€, and â€œDirtyâ€ in real-time. The dataset used to train the model was obtained by capturing images of the environment around the university. The best result of the model was achieved by using an Adam optimizer with applying a dropout in the last layer of the network. The total accuracy of the model is about 83%.
Aplikasi Perpustakaan di SMA YAPEMRI Depok Timur Berbasis Web

The library is part of a separate building or structure that contains a collection of books, which are arranged and arranged in such a way that it is easy to find and use them at any time when needed by readers. Along with the development of the times, many libraries have abandoned the manual system and switched to a digital system. However, the Yapemri library still uses a manual system from borrowing to freezing, so it is not uncommon for opening files to be lost. This causes losses to the library because many loan reports are often lost. Therefore, by utilizing technology the research team developed the Yapemri library application so that the library no longer uses a manual system. The Yapemri Library application was created using the PHP programming language and MySQL as the Xampp database as a web server. This application has gone through two stages of testing, namely user testing and black-box testing. The test results with black-box show that all the features in the application run well. The results of the responses from users stated that they were quite good for this application. Thus, the authors can conclude that the website-based Yapemri library application is successful.
Burns Optimized Friendly Theorem

Consider the question: What is the smallest x such that you can arrange the numbers 1 through x in an order such that any two next to each other add up to a perfect square? The answer in this example is 15. The Burns-Optimized Friendly Theorem is a generalization which states that in order to arrange the numbers 1 through x so that any two next to each other add up to a perfect nth power, x has to be at least (2n + floor((n+23)/13))^n - (2n + floor((n-3)/13))^n -1. This formula works as a helpful lower bound to help calculate previously unknown examples n>3.
Smooth Unsupervised Domain Adaptation Considering Uncertainties

Collecting sufficient labeled data is time-consuming, and even infeasible in some applications, such as streaming data. Unsupervised domain adaptation is a reasonable solution that adapts a model from a labeled source domain to an unlabeled target domain. However, since these domains arise from different distributions, the adaptation is associated with uncertainty. Most studies on domain adaptation have been done in the context of stationary environments and have ignored the uncertainty between domains. Regarding these issues, the proposed method addresses the classification problem of the concept drifting data stream under an unsupervised domain adaptation. Thus, a fuzzy rough set-based approach has been presented to reduce uncertainties' impact on feature alignment in non-stationary environments. Also, an incremental classifier over the non-stationary data stream of the target domain is introduced to provide a fast way of adapting to change. The advantages of the proposed method are excessive alignment avoidant, training cost reduction, and gradual reduction of dependency on the source domain for domain adaptation or classification. The effectiveness of the proposed method has been compared to the state-of-the-art methods on fifteen tasks taken from two benchmark datasets. The experimental results show its superiority in terms of accuracy and computational time.
Charging Out Rectangle: Quadrangle Iou for Scene Text Detection Under Balanced Supervision

In the past few years, text detection approaches have been able to cope with more and more challenging complex scenes. In this field, the arbitrary shape and various scale of text instances are the two most frequently mentioned topics. Among the existing methods, the bounding box prediction methods based on Intersection over Union (IoU) are efficient, but can only handle multi-oriented rectangles. Although arbitrary shape text detection methods have been proposed, these methods are often aimed at curved text. Besides, the post-processing is usually cumbersome. Considering that the affine transformation phenomenon caused by out-plane rotation is more frequent than curved text in real scenario, we design a quadrangle IoU (QIoU) loss to efficiently predict arbitrary convex quadrangle text. Furthermore, we analyze the challenge of variable scale in text detection from the perspective of unbalanced supervision information, and propose a cross-scale knowledge distillation (CSKD) method to improve the detection accuracy of large-scale text. An extensive ablation study demonstrates the effectiveness and superiority of the proposed strategies. During inferring, our method does not consume extra time. Using the basic feature pyramid network for feature extraction, our method achieves state-of-the-art results on several popular datasets for scene text detection.
An Efficient Multimodal Representation and Fusion Method for Multimodal Intent Recognition

Intent recognition is a crucial task in natural language understanding. Current research mainly focuses on task-specific unimodal intent recognition. However, in real-world scenes, human intentions are complex and need to be judged by integrating information such as speech, tone, expression, and action. Therefore, this paper proposes an effective multimodal representation and fusion method (EMRFM) for intent recognition in real-world multimodal scenes. First, text, audio, and vision features are extracted based on pre-trained BERT, Wav2vec 2.0, and Faster R-CNN. Then, considering the complementarity and consistency among the modalities, the modality-shared and the modality-specific encoders are constructed to learn shared and specific feature representations of the modalities. Finally,Â an adaptive multimodal fusion method based on an attention-based gated neural network is designed to eliminate noise features. Comprehensive experiments are conducted on the multimodal intent recognition MIntRec benchmark dataset. Our proposed model achieves higher accuracy, precision, recall, and F1-score than state-of-the-art multimodal learning methods. In addition, the experiment demonstrates that the model's multimodal representation well learned the modality's shared and specific features. The multimodal fusion of the model achieves adaptive fusion and effectively reduces possible noise interference.
Implementasi Data Mining Untuk Mengetahui Pola Pembelian Pelanggan Pada Produk Vinâ€™s Cafe Dengan Algoritma Apriori Dan Pengujian Kualitas Melalui Metode ISO 9126

Vin's Cafe is a bistro that sells food and drinks whose customers are generally children. The presence of deals consistently at Vin's Bistro, makes the information exchange deals continue to grow and makes the information capacity increase. The motivation behind this review is to decide on an example of a client's purchase based on the consequences of handling the inferred calculations and to test and implement the side effects of various information in the web structure. The technique used is the calculation of the deduction, the calculation of the deduction is a calculation that works by looking for affiliation rules without stopping among the advertised items. The affiliate rules in question are brought out through the system to work out help and trust relationships something. Affiliate rules should be attractive assuming the price of favor is more prominent than the base favor and the price of certainty is more important than the basic certainty. The result of this review is that Vin's Bistro can monitor the future offering system with 4 standards obtained from the exchange of information using inferred calculations and experimental results using ISO 9126 can be considered in the Generally very good Model with the side effect of the Usability variable being 85%, the result of Dependency angle 74.5%, Consequence of Ease of use section 88%, Consequence of Proficiency section 76%, Side effect of Practicality section of 75% and Side effect of Compactness section of 85%.
Patterns for All-Digital Quantum Ghost Imaging Generated by the Ising Model

We present new illumination patterns for imaging in all-digital quantum ghost imaging (GI) systems. The digital patterns, written as computer generated holograms on spatial light modulators (SLM), are generated by the Ising model, a well-known ferromagnetic model. In general, Ising patterns are affected by two parameters: temperature (T) and magnetic field (H). Here, we set H to 0 and offer a method for applying Ising patterns made with various T values to reconstruct high quality ghost images with very little background noise.
Multi-Granularity Temporal Complementary Feature Aggregation for Video-Based Person Re-Identification

Video-based person re-identification (reID) aims at matching the same person across different video clips. However, there are severe spatial and temporal misalignment for video tracklets due to the imperfect pedestrian detections. As the final retrieval accuracy is limited by the detection accuracy, existing methods usually focus on the most representative local region. Due to the diversity of characters in the image sequence, it is easy to miss fine-grained clues. In order to solve the above problems, a Multi-Granularity Temporal Complementary Feature Aggregation (MGTCFA) network is proposed in this paper. In this way, the rich spatiotemporal clues in videos can be fully utilized. Furthermore, a simple Re-Detect and Crop (DC) module is proposed against the spatiotemporal dislocation problem. The interference of background can be effectively eliminated by DC module through applying the deep learning-based detection to the tracklets. MGTCFA network is composed of Global and Local Branches. The complementary scales are modeled by the Global Branch for different frame feature maps to extract multi-scale features. The Global Branch is a bilateral structure including spatial and detail branches. The frame feature maps are processed at the original resolution in spatial branch. By down-sampling input frames to small size, larger receptive field can be provided to encode long-range spatial contexts in detail branch. MGTCFA appends a Temporal Saliency Erasing (TSE) module to each branch in the Global Branch in order to extract complementary features of the consecutive frames in a video. So that an integral characteristic of target person can be obtained. The key local information is extracted by uniformly cutting features on Local Branch and concatenating them into a single feature. Finally, the features of the Global and the Local Branches are fused to obtain a more comprehensive representation of pedestrian features.