Reviewerly/exHarmony huggingface dataset:
KEEFEKTIFAN MEDIA POP UP BOOK TERHADAP HASIL BELAJAR MATEMATIKA MATERI BILANGAN CACAH PADA SISWA KELAS IV SDN WONOREJO 02

Difficulty in understanding rounding material on numerical numbers, student activities in learning tend to be passive, the existence of pop up book media in the classroom but not maximized, thus causing student learning outcomes. Media in the classroom but not maximized, causing student learning outcomes to be less than optimal. Less than optimal. This type of research uses quantitative methods with Pre-experimental design with the form of one-group pretest-posttest design. Data collection techniques in this study through interviews, observations, tests, and documentation. The results showed an average pretest of 47.3 and an average posttest of 84.6 posttest average of 84.6. Completeness Individual learning is considered complete learning if it meets the Learning Completeness score of 70. Classical learning completeness is 98%. From these results it can be concluded that pop up book media is effective on learning outcomes learning outcomes in class IV SDN Wonorejo 02.
Efficient Detection of Small and Complex Objects for Autonomous Driving Using Deep Learning

The YOLOv2 is one of the most prominent model used for object detection, it works on the concept of anchor boxes. However, this model is prone to some problems like double anchor boxes, missing small objects, and high time complexity. In this paper, we aim to solve the problem of double anchor boxes and undetected small objects by tuning the parameters like intersection over union (IoU) and customizing non-max suppression thresholds. Also, to reduce the time complexity of the model, we have proposed the use of depth wise convolution (DW-Conv2D) instead of fundamental convolution (Conv2D) in this paper. Once we applied the proposed model to datasets like PASCAL VOC07 and VOC12, we observed significant improvements like reduced floating-point operations per second by 9.5% and better accuracy than the existing state-of-the-art models.
Fuzzy Prediction System of Construction Cost Based on Data Analysis Algorithm

The method based on fuzzy prediction is a commonly used and convenient fuzzy model of project cost. It can quantitatively analyze the factors involved in construction projects and achieve dynamic optimization to a certain extent. However, due to some problems and shortcomings, the final result is not ideal. In order to improve this situation and meet the practical application needs, this paper proposes to use data mining technology to build a mathematical model, use fuzzy prediction algorithm to establish a building project cost system model based on fuzzy functions, so that the project can be better managed, controlled and evaluated. Finally, the function of the system is tested. The test results show that the fuzzy prediction system of construction cost has fast prediction time, fast system response time and high prediction success rate.
The Formation and Development of the Midwife System in Colonial Korea

As the Japanese immigrated to Korea, the profession of midwife was born. In order to respond to the demand for midwives in the provinces, the colonial power established a short training course in midwifery at Jahye Hospital and created an area-restricted midwife system. Japanese immigrants were important to the colonial power. Nevertheless, the regional distribution of midwives was imbalanced. The number of pregnant women giving birth with the help of midwives in rural areas was small.BR Midwives were evaluated as pioneers who led a new civilization by overcoming the traditional ignorance of Korean society. Furthermore, the midwife was a job suited to women. It was also an attractive job because of the stable income. It was even mentioned that a midwife’s income was the same as a doctor’s one. As job stability was secured, the number of Korean midwives increased.BR To improve the skills and knowledge of midwives, education was systematized and the training period was extended. Criticism of the midwife, however, persisted. There was even criticism that midwives lacked knowledge of hygiene and cleanliness. This criticism was likely related to the short period of education. Furthermore, midwives could not provide medical treatment when they encountered difficult labor. If a pregnant woman wanted a safe birth, the midwife could be insecure. In the absence of an obstetriciangynecologist, a midwife’s chance of success was sustainable. However, the increase in doctors and hospitals was likely to change the situation.
BREAST CANCER DETECTION WITH OPTIMIZED MACHINE LEARNING AND DEEP LEARNING TECHNIQUES

The classification of breast cancer has been a subject of interest in the fields of healthcare and bioinformatics because it is the second leading cause of cancer death in women.Breast cancer can be analyzed using a biopsy, where tissue is removed and studied under a microscope.Problem identification is based on the qualifications and experience of histopathologists, who will pay attention to abnormal cells.However, if the histopathologist is not well trained or experienced, it can lead to a misdiagnosis.With a recent proposal in image processing and in the field of machine learning, there is interest in experimenting to develop strong pattern recognition a framework for improving the quality of diagnostics.In this work we will use the image function extraction approach and machine learning approach for breast cancer classification using histological images for benign and malignant.Using a histopathological image, we can preprocess it image then use feature extraction and classify the final result using CNN classification techniques.
Virtual mouse using hand gestures by skin recognition

Motion-controlled PCs and PCs have recently gained ground.This technique is known as the jump movement.Putting our hand in front of the computer and waving, we can control all of its features.Introductions made using a PC have important advantages over slides and overheads.You can use sound, video, and, surprisingly, intuitive projects to expand introductions further.Unfortunately, these techniques are more difficult to use than overheads or slides.With the new controls, the speaker should be able to manage a variety of gadgets (e.g., console, mouse, VCR controller).These devices are difficult to notice in the shadows, and using them upsets the presentation.The most common and convenient form of communication is hand signals.The camera's results will be shown on the screen.Instead of using a traditional mouse or piece of art to manage the mouse cursor, the idea is to use a straightforward camera.With the use of The Virtual Mouse, which is just a camera, establishes a foundation between the user and the system.It enables interaction between users and machines without the need of mechanical or physical mouse equipment, and even manage functions.The technique for controlling where the cursor is placed without the use of any electronics is presented in this paper.Whereas various hand gestures will be used to execute tasks like clicking and lugging stuff.The suggested design will only require a webcam as an information device.The suggested framework necessitates the use of Python, OpenCV, and other hardware.The client can further align the output from the camera by viewing it on the framework's screen.With the correct technology and programming, it is probably conceivable to create a virtual mouse using hand motions and skin detection.The main concept is to translate the movement of your hand and fingers into the equivalent movement of the pointer on the screen by using a camera to track those movements.
“In Your Face!”: Visualizing Fitness Tracker Data in Augmented Reality

The benefits of augmented reality (AR) have been demonstrated in both medicine and fitness, while its application in areas where these two fields overlap has been barely explored. We argue that AR opens up new opportunities to interact with, understand and share personal health data. To this end, we developed an app prototype that uses a Snapchat-like face filter to visualize personal health data from a fitness tracker in AR. We tested this prototype in two pilot studies and found that AR does have potential in this type of application. We suggest that AR cannot replace the current interfaces of smartwatches and mobile apps, but it can pick up where current technology falls short in creating intrinsic motivation and personal health awareness. We also provide ideas for future work in this direction.
Layout Generation for Various Scenarios in Mobile Shopping Applications

Layout is essential for the product listing pages (PLPs) in mobile shopping applications. To clearly convey the information that consumers require and to achieve specific functions, PLPs layouts often have many variations driven by scenarios. In this work, we study the PLPs layout design for different scenarios and propose a design space to guide the large-scale creation of PLPs. We propose LayoutVQ-VAE, a novel model specialized in generating layouts with internal and external constraints. LayoutVQ-VAE differs from previous methods as it learns a discrete latent representation of layout and can model the relationship between layout representation and scenarios without applying heuristics. Experiments on publicly available benchmarks for different layout types validate that our method performs comparably or favorably against the state-of-the-art methods. Case studies show that the proposed approach including the design space and model is effective in producing large-scale high-quality PLPs layouts for mobile shopping platforms.
An Effective Technique for Single Image Haze Removal using MSMO

Due to scattering of light in an atmosphere, hazy images along with noise, color distortions, block artifacts and low intensity are obtained during the image capturing process. The paper proposes a new approach to deal with the problems as mentioned to achieve a better dehazed image. The methodology involves the Dark Channel Prior (DCP) algorithm followed by multi-scale switching morphological operator (MSMO) and contrast limited adaptive histogram equalization (CLAHE). The two inputs are derived by applying MSMO and CLAHE techniques on DCP algorithm based output image and then final dehazed image is obtained through linear fusion. Extensive experiments have been done on various images collected from BeDDE dataset. Results achieved by the proposed approach demonstrate that the quality of dehazed images have significant improvements in terms of better color preservation, reduced noise and blocking artifacts.
Making with Data (and Beyond)

In this proposed panel, we will discuss the practice of Making with Data—the practice of creating physical artifacts that represent a dataset. This topic lies at the intersection of several CHI communities: data visualization, fabrication, and tangible interaction. Our goal is to discuss contemporary practices, but also to envision future ways that we might continue to make physical representations of data in the future, given emerging fabrication techniques, data representation practices, and desired interactions and experiences with data.
From exemplar to copy: the scribal appropriation of a Hadewijch manuscript computationally explored

This study is devoted to two of the oldest known manuscripts in which the oeuvre of the medieval mystical author Hadewijch has been preserved: Brussels, KBR, 2879-2880 (ms. A) and Brussels, KBR, 2877-2878 (ms. B). On the basis of codicological and contextual arguments, it is assumed that the scribe who produced B used A as an exemplar. While the similarities in both layout and content between the two manuscripts are striking, the present article seeks to identify the differences. After all, regardless of the intention to produce a copy that closely follows the exemplar, subtle linguistic variation is apparent. Divergences relate to spelling conventions, but also to the way in which words are abbreviated (and the extent to which abbreviations occur). The present study investigates the spelling profiles of the scribes who produced mss. A and B in a computational way. In the first part of this study, we will present both manuscripts in more detail, after which we will consider prior research carried out on scribal profiling. The current study both builds and expands on Kestemont (2015). Next, we outline the methodology used to analyse and measure the degree of scribal appropriation that took place when ms. B was copied off the exemplar ms. A. After this, we will discuss the results obtained, focusing on the scribal variation that can be found both at the level of individual words and n-grams. To this end, we use machine learning to identify the most distinctive features that separate manuscript A from B. Finally, we look at possible diachronic trends in the appropriation by B's scribe of his exemplar. We argue that scribal takeovers in the exemplar impacts the practice of the copying scribe, while transitions to a different content matter cause little to no effect.
Collective Healing to Support Design Futures: Building Community and Exploring Methods

This workshop explores the role of healing ourselves as a key aspect for transformative social change. It brings together social justice and community based work in HCI that engages with healing and joy to expand on current methodologies such as autoethnography, somaesthetics, and embodied design which aim to describe different ways of knowing and describing and living experiences as inputs for design futuring. Our concern of interest is the ways in which all of us have lived through continuous community grief and loss due to the ongoing COVID-19 pandemic and a continued climate crisis; and the resulting symptoms like anxiety, depression, body pain, and scattered focus. We believe that we must acknowledge these experiences and feelings about these events in order to effectively work towards more optimistic futures. This workshop takes the space and time to consider our recent collective traumas and explore how to integrate them into futures that support the development of futures that fit our emotional, ethical, social and physical needs. Our aim is to build a greater understanding of how the CHI community can integrate healing in support of social change.
Wizard of Oz Prototyping for Interactive Spatial Augmented Reality in HCI Education

In this paper we present our findings that emerged from Wizard of Oz (WOz) prototyping workshops of an undergrad course on UX design between 2017 – 2022. The purpose of these workshops is both to familiarize the students with WOz as a rapid low-complexity prototyping method and to facilitate them in designing for interactive systems beyond typical graphical user interfaces. In these workshops, students develop and test Wizard of Oz prototypes for interactive spatial augmented reality (SAR). Over the past six years, in total 93 prototypes have been created on the course. We analyzed the prototypes that emerged from the workshops based on input, purpose, play characteristics and projection surfaces. Based on the analysis and our experiences during the workshops, we conclude that the workshops stimulate creativity, enable students to rapidly create interactive SAR interfaces without being limited by their technical skills and time limitations and provide them with a tool that they apply beyond the scope of the prototyping course. We discuss advantages and limitations of the use of WOz prototyping for interactive Spatial Augmented Reality.
Case Study on the Design of High School English Writing Instruction Based on Subject Core Literacy

Since the emergence of core literacy, different disciplines have been actively exploring disciplinary core literacy. The real implementation of disciplinary core literacy needs to be deeply involved in each lesson, and different lesson types have different teaching and learning methods. This study explores how to design writing lessons based on disciplinary core literacy, mainly analyzing the teaching objectives and teaching process, forming a complete case study of writing teaching, and providing ideas and paths for the majority of high school English educators.
AUTOMATED RATION DISTRIBUTION SYSTEM USING RFID, GSM, OTP BASED REAL TIME RATION STOCK MONITORING

The current ration distribution system in India faces issues like ration hoarding and faulty weighing scales, which affects the needy population.To address these issues, we propose an automated ration distribution system that uses RFID and GSM technology.The machine is easy to use and requires the user to scan their RFID card and enter an OTP for verification.This system ensures that only authorized users receive their allotted ration and prevents illegal withdrawals.Additionally, the system sends SMS alerts to users regarding their allotted ration and real-time data to the main distributor's office for analysis.This approach improves the system's reliability, scalability, and speed.
ERROR ANALYSIS OF AFFIXATION USE IN ACADEMIC WRITING: IMPLICATIONS FOR A SECOND LANGUAGE LEARNING

Word formation through affixation is one of the most important elements in achieving the English language proficiency.The previous literature on this research area revealed that learners of English find it difficult to use affixation properly in their academic writing.Therefore, this study sought to identify the errors of affixation use committed by Saudi EFL students when they write in English.The researchers employed a descriptive qualitative approach to achieve the objective of the study.The participants comprised thirty students from the Department of the English Language at King Khalid University.The students were purposively selected from the Writing (4) course, which is a basic requirement for students to graduate from the English program.The researchers collected the data by asking the students to write an essay of no more than 200 words about online learning.The analysis of results conformed to [1] approach for analyzing errors of affixation use that includes the identification of errors, classification of errors, quantification of errors, and interpretation of errors.The findings of this study showed that students committed different types of errors such as misordering, misformation, omission, and addition.The main factors for those errors were students' limited vocabulary size and lack of grammatical knowledge.The study concludes with some pedagogical implications to students, teachers, and curriculum designers.
RESEARCH PAPER ON SPEECH EMOTION RECOGNITION (SER) ON LIVE CALLS WHILE CREATING EVENTS

Nowadays, the ambit of cybersecurity has been growing rapidly and so does its peculiarity, and one among these is "how one can recognize the emotional/mental condition of a person on live on-going calls."To address this snag, we have developed a mobile application based on 'Recurrent Neural Network' (RNN).There are several applications of our app in different fields, one of which is for police use.
Computing and evaluating saliency maps for image classification: a tutorial

Facing the black-box nature of deep learning models for image classification, a popular trend in the literature proposes methods to generate explanations in the form of heat maps indicating the areas that played an important role in the models’ decisions. Such explanations are called saliency maps and constitute an active field of research, given that many fundamental questions are yet to be answered: how to compute them efficiently? How to evaluate them? What exactly can they be used for? Given the increasing rate at which papers are produced and the vast amount of literature that is already existing, we propose our study to help newcomers become part of this community and to contribute to the research field. First, the two existing approaches to generate saliency maps are discussed, namely post-hoc methods and attention models. Post-hoc methods are generic algorithms that can be applied to any model from a given class without requiring fine-tuning. On the contrary, attention models are ad-hoc architectures that generate a saliency map during the inference phase to guide the decision. We show that both approaches can be divided into several subcategories and illustrate each of them with one important model or method. Second, we present the current methodologies used to evaluate saliency maps, including objective and subjective protocols, depending on whether or not they involve users. Among objective methods, we notably detail faithfulness metrics and propose an implementation featuring the faithfulness metrics discussed in this paper (https://github.com/TristanGomez44/metrics-saliency-maps).
Running Convolutional Neural Network on tiny devices

The introduction of deep neural network architectures has resulted in an interesting development in artificial intelligence. These architectures demonstrate how useful they are in a variety of fields, such as the classification of images, the detection of objects, and the recognition of speech. However, this efficiency is a result of sophisticated networks with very high requirements for computational power, memory space, and energy, which makes it difficult to use such models on devices with less powerful hardware. Within the scope of this study, we enable offline CNN (Convolutional Neural Network) inference on smartphones operating in real time. The following is the procedure for deployment: 1) The development of an architecture for a convolutional neural network 2) Teaching the model to perform on a reference dataset from the ground up. 3) Reduce the size of this model as well as the total amount of computation it requires while still striving to keep at least the same level of precision as the first version, since it is expected that the accuracy after optimization become lower than the baseline. 4) Deploy the improved architecture and carry out the inference process in real time on a mobile device, preferably a smartphone. The models VGG16 and ResNet that were trained on the Cat vs Dogs dataset are the ones that have been implemented.
Dealing with extremly Unbalanced Data and Detecting Insider Threats with Deep Neural Networks

The internal and external security of a company is important. External security can be secured by setting up mechanisms to monitor any external flow, while internal security is the most complex, in this case how do we monitor internal workers who have full privileges to access the resources and data of the organization? All necessary measures must be in place to avoid internal damage, which has increased considerably in last years. Since the number of harmful behaviors is very low compared to normal events, the imbalance in class scores does not allow supervised learning algorithms to provide accurate results as their learning depends on balanced categories. Therefore, it is necessary to use a model capable of distinguishing clearly the harmful category. In previous work, ML techniques were used, although they are less effective if the data used are not balanced. In this document, we propose an S-LSTM model based on the integration of sampling approach, which is the generation of synthetic samples to balance the two classes of learning by SMOTE technique and LSTM algorithm for identify abnormal behavior. To build and evaluate the model, we used the Cert v4.2 dataset, and through the experimental evaluation, which gave a high prediction accuracy of 99%, we show that the proposed model provides a better solution. to detect the insider threat.