Reviewerly/exHarmony huggingface dataset:
Automatic Discovery of Multiword Nouns Based on Syntactic-Semantic Representations

This research aims to explore a deeper representation of the internal structure and semantic relationship of multiword nouns (MWNs) for improving MWN discovery. This representation focuses on MWN formations, which follow a series of categorical and semantic constraints. Linguistically motivated semantic features are defined by computing the internal semantic relations of MWNs. The internal structures are represented by describing categorical combinations in a hierarchy, and the internal semantic relations are represented with the help of semantic combinations of constituents. The results show that combining linguistically motivated semantic features with statistically motivated semantic features improves MWN discovery.
Android-based Video Sharing Application Design

The impact of the COVID-19 virus pandemic that has hit the whole world has more or less affected the way people move. Many activities have to be centered at home, from working at home to studying at home. On the other hand, students at school still need tutoring. However, the signs of returning to school amid the rules of Large-Scale Social Restrictions (PSBB) and Adaptation of New Habits (IMR) still appear gray. The final solution is to keep children learning online. Online learning is learning that is done online, using learning applications and social networks. Online learning is learning that is done without face-to-face, but through an available platform. For example in this mobile application which aims to assist the learning process for students and teachers. This learning video application is made for learning and teaching tools. The process of designing this educational video sharing information system begins with analyzing the learning system, designing UML diagrams, and designing mock ups as an interface for an overview of the application. The result of this final project is as an encouragement for students who are expected to be better by using a system that has not been computerized
Knowledge Sharing: Bersama MGMP Bahasa Inggris SMK dalam Mengkonstruksi Soal Reading Comprehension Berorientasi HOTS

Test construction and its implementation become an important aspect of assessment that need to be understood for English teachers, especially in teaching reading comprehension. The activity of sharing knowledge with teachers in the Musyawarah Guru Mata Pelajaran (MGMP) community aimed at sharing experiences and knowledge on how to construct and apply assessments, especially on reading comprehension skills according to the characteristics of English subjects and the target skills to be achieved. The mechanism of knowledge sharing activities is carried out with several steps of assistance, including: determining the competencies to be measured, materials, formulating indicators, determining items, cognitive level and form of questions. As an evaluation and reflection step, it ends with a Focus Group Discussion (FGD). This activity has a positive impact on the quality of learning, this can be proven by the achievement of outcomes; implementation of HOTS-based test construction. It can also be seen the enthusiasm and motivation of teachers in following each stages of knowledge sharing
Improving English Communication for Specific Purposes Using Role-Play and Learning by Doing Techniques for the staff At The Samaya Resort Seminyak, Badung

When the COVID-19 pandemic hit the tourism sector in Indonesia, especially in Bali, it experienced a quite drastic decline, so many workers were laid off. Not only decreased of tourist arrivals, but also decreased knowledge of English communication among workers, due to a lack of communication with guests staying at the hotel. Seminyak is a fashionable destination in Bali, especially its beaches and home to famous resorts, restaurants and boutiques. One of the most luxurious resorts in the Seminyak area is The Samaya Resort Seminyak, which is located on Jalan Laksmana Seminyak. English communication improvement training is a very important point in everyday life, especially in the world of hospitality work in improving speaking skills especially to guests staying at The Samaya Resort Seminyak. This can improve the quality of staff service to guests, and minimize miscommunication. Community Partnership Program activities in the form of teaching English using role-play techniques and learning English while practicing it directly (Learning by Doing) are appropriate activities for staff at The Samaya Resort Seminyak in developing and improving their communication skills using the language English with good and correct English grammar and pronunciation. The method applied in teaching English communication uses the role play and learning by doing methods. This English teaching activity is held for 1 (one) month for purposes, starting from 6 to 27 June 2023. From the teaching provided, the materials needed for staff at The Samaya Resort Seminyak are English material for specific related to service and services in the hotel according to the department of each staff. Based on the background that has been described, the main problems of partners are: 1). How do the staff at The Samaya Resort Seminyak master the grammar and pronunciation of English for Specific Purposes? 2). What are the results of the application of English learning techniques by Role-Play and Learning by Doing for Specific Purposes to the staff at the resort? To solve the problems faced by partners, an English teaching program was carried out using the role-play method and learning English while practicing it directly (Learning by Doing). The participants in this English training were 28 staff of The Samaya Resort Seminyak from all departments. For this reason, English will be taught at an intermediate level, with specific vocabulary and grammar according to their respective departments. This training will be held with meetings once a week for each department.&#x0D;
The visual complexity of bike maps

Abstract. More and more cities try to encourage residents to cycle more. Therefore, governments are developing comprehensive bike maps to facilitate trip planning and increase the popularity of cycling. However, research on the topic of bike maps is rare and the versatility of possible features shown on a bike map makes these visually more complex than others. To understand how base maps and the display of cycling related features affect the visual complexity of bike maps and thus their effectiveness, we used different metrics (GMLMT, Subband Entropy, Edge Density, Feature Congestion, and Distinct Object-Type Counts) on four bike maps with four different visual complexity levels. We ran an eye-tracking experiment with 35 participants solving four different everyday tasks with these four bike maps. The findings suggest that adding more detail to base maps and displaying more cycling related features on a map resulted in a visually more complex bike map. Size, shape, and colour were found to have the biggest influence on the applied metrics. The analysis of eye-tracking data revealed that the display of cycling related features can affect the time needed for successfully completing a task. To deepen the gained understanding, further research should in more detail investigate how base maps influence bike maps efficiency.
A Static Letter Sign Language Recognition Algorithm Based on Improved YOLOv5

This article presents an improved static sign language recognition algorithm based on the YOLOv5 network model. The algorithm utilizes SimAM attention module and ASFF adaptive feature fusion module to optimize the original model and enhance its capacity for both feature extraction and target recognition. Experimental outcomes demonstrate that the enhanced model achieves a remarkable mAP increase of 3.7 percentage points while maintaining the same level of inference speed, manifesting an impressive level of effectiveness.
Method of Signaling Analysis Including Jitter Effect for Large-Scale Nonlinear Channel and Links

Signaling analysis of nonlinear channel and links is challenging because it cannot rely on linear time-invariant principles. To find the bit error rat (BER), an exhaustive approach requires performing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{m}j^{m}$</tex> nonlinear simulations for a nonlinear channel of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> -bit memory and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$j$</tex> -jitter states, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> can be larger than 100 in today's high-speed and high-performance design. In this work, we develop a fast and accurate method for signaling analysis of large-scale nonlinear circuits while considering jitter effect. The proposed method only requires <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(k)$</tex> nonlinear simulations to find the characteristic responses of the nonlinear circuit, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> is much less than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{m}j^{m}$</tex> and independent of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{m}j^{m}$</tex> . From the characteristic responses, the worst-case eye and BER can be obtained efficiently. Simulations of a real-world nonlinear circuits with jitter effect demonstrate the excellent accuracy and efficiency of the proposed method.
Analysis and verification of code-based key encapsulation mechanism BIKE in Maude

Information technologies are everywhere. From mobile devices to the simplest components, all of them share information over the network. This network might prove to be insecure in the near future with the introduction of quantum cryptography. This concern is addressed by the National Institute for Standards and Technologies with the Post Quantum Cryptography project. In this paper, we select from round 4 of this project the code-based Key Encapsulation Mechanism BIKE and analyze it in Maude. We use a previously defined framework on which we can specify the symbolic model and perform two kinds of formal verification. The reachability analysis shows that a Man-In-The-Middle attack is present. Also, a design vulnerability in one cryptography primitive makes the scheme insecure. For both cases, we provide solutions.
Breast Cancer Image Classification Using Deep Learning Methods

Breast cancer is a type of cancer that begins in the breast cell. The only options to lessen the damage are early discovery and appropriate treatment. People willfully disregard physical problems in their bodies due to ignorance or a lack of detection technology. Deep learning is being used more frequently in the field of medical science, and it is good at a variety of tasks, including segmentation, detection, and classification. This article focus on the breast cancer images classification using VGG-16 and VGG-19. Analyze their models, precision, and a number of other aspects. The accuracy of image classification in VGG-16 and VGG-19 is 91% and 92%, respectively.
Perbandingan Keberhasilan Intubasi pada Upaya Pertama antara Video Laryngoscope dengan Direct Laryngoscope

This study aims to determine differences in the success of intubation on the first attempt between video laryngoscopes and direct laryngoscopes. The method used is an experimental quasi-experimental type with a Two Group Post Test Design. The sampling technique was carried out by consecutive sampling with the research instrument, namely the observation sheet. Data were collected in the operating room of Prof. Dr. R. D. Kandou Manado with a sample size of 40 respondents, with 20 respondents intubating with a direct laryngoscope and 20 respondents intubating with a video laryngoscope using an unpaired t-test. The results showed that the probability value obtained was 0.035. In conclusion, the success rate of intubation on the first attempt with a video laryngoscope is higher than that of a direct laryngoscope.&#x0D; &#x0D; Keywords: Direct Laryngoscope, Intubation, Video Laryngoscope
DBVC: An End-to-End 3-D Deep Biomedical Video Coding Framework

Biomedical videos require tremendous storage space and transmission bandwidth, so efficient coding methods are urgently required. Existing methods can be roughly divided into motion-based methods and wavelet-based methods. Motion-based methods use motion estimation designed for natural videos and independently optimize prediction, transform, and entropy coding modules. Wavelet-based methods treat the more redundant time dimension exactly the same as other spatial dimensions. They are both unable to completely remove the redundant spatial-temporal information in biomedical videos. In this paper, to address these problems, we build an end-to-end framework named DBVC with 3-D motion estimation, MV coding, 3-D motion compensation, and residual coding networks for efficient 3-D biomedical video coding. First, we propose a simple yet efficient 3-D motion estimation network to extract motion information. Specifically, we obtain the region with the most intense motion by a segmentation network and then perform unsupervised motion estimation exclusively on this region. After that, to encode and decode the estimated motion vectors, we apply a 3-D autoencoder-based MV coding network. Moreover, we use a lossless learnable wavelet transform for residual coding, which makes lossless coding possible. To the best of our knowledge, this is the first end-to-end video coding framework that supports both lossy and lossless coding, thus meeting the requirements of 3-D biomedical video coding. Extensive experiments demonstrate that our framework achieves state-of-the-art performance on both 3-D biological videos and 3-D medical videos.
IMPROVING STUDENTS’ VOCABULARY MASTERY BY USING THE RUMAH BELAJAR APPLICATION AT ONE OF JUNIOR HIGH SCHOOLS

ABSTRACT&#x0D; Vocabulary is an important factor in learning a language. People cannot hear, speak, read, and write without knowing vocabulary. Likewise in learning a foreign language such as English, vocabulary plays a very important role. However, not all junior high school students learn vocabulary in English easily. This is caused by several factors including learning media that are less attractive so that children get bored quickly, teaching methods or techniques of teachers who are not creative and monotonous, and teaching and learning activities that are more dominated by teachers without involving or involving students to participate. Rumah Belajar application is one of application used in learning and found a new understanding of the new media used by the teacher in media online learning. In this research, the researcher used Classroom Action Research as the design method. The data was collected from the seventh grade which has been learned using Rumah Belajar. The sample was 24 students selected by purposive sampling. The research found that the majority of students had a positive perception of Rumah Belajar in learning based on the results of their interviews. They improved their vocabulary learning through the Rumah Belajar application in learning and found a new understanding of the new media used by the teacher in media online learning. On the other hand, some students gave the perception that online learning has obstacles affected by a poor network, the quality of the student's mobile phone, and the capacity of the online learning application. &#x0D; Keywords: Vocabulary, Rumah Belajar, Application.
RAINFALL PREDICTION USING MULTIPLE LINEAR REGRESSION ALGORITHM

Indonesia is a tropical region with ever-changing weather changes. It is necessary to conduct a research on weather prediction as a decision making regarding weather information that will occur in the future. Rainfall is one of the factors that cause changes in weather in an area. This research was conducted on the climate in the Yogyakarta region in the form of mountains and lowlands causing differences in rainfall. The variables that are used to make predictions are several parameters that affect rainfall, namely temperature, humidity, wind speed and duration of solar radiation. These 5 variables are processed through the data obtained then carried out research and comparisons with the previous data. Multiple linear regression is the algorithm used. This algorithm is one of the machine learning techniques by making rainfall data as the dependent variable and other parameters as independent variables. This study uses Yogyakarta City, Central Java climate data for 2010-2020. The results obtained are an R2 score of 12.99%. Prediction of rainfall is obtained at 14.41778516. Then the RMSE evaluation resulted in a deviation between predicted rainfall and actual rainfall of 14.78316110508722. Based on these results, it shows that there is light rain because it is in the intensity category of 5 mm – 20 mm/day.
Perspectives on Digital Competencies in University: What’s Ahead for Education?

Digital competencies were part of online education and now when returning to the classroom what will happen or what we expect for the future in education. The objective is to analyze and describe the perspectives of the level of digital competence, the non-experimental descriptive quantitative methodology is used with 1987 university students. Resulting that the instrument adapted to the Peruvian version is adequate according to statistics such as Cronbach's Alpha. Concluding that the digital competences improved the academic quality and have a good level. Society is waiting for the next step to know what is presented to us in education.
Action Recognition using Spatial and Temporal Features with Kernel SVM

A new low-level visual feature, called Spatio-temporal context distribution feature of interest points is used to describe human actions. Each action video is expressed as a set of relative XYT coordinates between interest points listed pair wise in a local region. From the input image frames the Locally Weighted Word Context (LWWC ) descriptor encodes the spatial context interest points rather than being limited to a single interest point and the Graph Regularized Nonnegative Matrix Factorization (GNMF) is used to encode the geometrical information by constructing a nearest neighbour graph. By extracting the kernel weights of the obtained feature variables , the kernel weighted SVM is modelled to jointly capture the compatibility between multilevel action features and action classes and the compatibility between multilevel scene features and scene classes. The contextual relationship between action classes and scene classes is derived using the kernel weight as a variable.
Comparative Analysis of Machine Learning and Deep Learning Models for Sentiment Analysis in Somali Language

Understanding and analysing sentiment in user-generated content has become crucial with the increasing use of social media and online platforms.However, sentiment analysis in less-resourced languages like Somali poses unique challenges.This paper presents the performance of three ML algorithms (DTC, RFC, XGB) and two DL models (CNN, LSTM) in accurately classifying sentiment in Somali text.The CC100-Somali dataset, comprising 78M monolingual Somali texts from the Common crawl snapshots, is utilized for training and evaluation.The study employed rigorous evaluation techniques, including train-test splits and cross-validation, to assess classification accuracy and performance metrics.The results demonstrated that DTC achieved the highest accuracy among ML algorithms, 87.94%, while LSTM achieved the highest accuracy among DL models, 88.58%.This study's findings contribute to sentiment analysis in less-resourced languages, specifically Somali, and provide valuable insights into the performance of ML and DL techniques.Moreover, the study highlights the potential of leveraging both ML and DL approaches to analyze sentiment in Somali text effectively.The results and evaluation metrics benchmark future research in sentiment analysis for Somali and other low-resource languages.
Research Progress of Robot Path Planning Algorithm

Path planning refers to the problem of making the robot move along a safe path and reach the end point through the corresponding planning algorithm from the starting point to the end point under the condition of multiple constraints. Firstly, the research content of path planning is briefly described. Then, the research results of path planning at home and abroad are summarized. According to the different planning methods, it is divided into traditional path planning algorithm, sampling-based path planning algorithm and intelligent algorithm-based path planning algorithm. Introduce its development status, implementation principles, applicable occasions, and existing experts and scholars ' optimization results. Finally, according to the existing path planning research results, the future improvement direction and development trend of the robot are prospected.
Penerapan Model Pembelajaran Project Based Learning untuk Meningkatkan Keterampilan Menulis Pantun Siswa Sekolah Dasar

Pantun writing skills are needed to train students' literary writing skills from an early age. Pantun writing skills require good vocabulary processing, especially when making sampiran and the content must pay attention to rhyme (sound equations). PjBL is one of the innovative learning models, where learning is more student-centered, using projects as learning media.. Several studies have shown that PjBL can improve the poetry writing skills of elementary school students. The method of writing this scientific paper is to use a literature study (library research). The results of the research are expected to provide an overview to develop further research on the project based learning model and be integrated into other learning.
COMPARISON OF IMAGE SEGMENTATION METHOD IN IMAGE CHARACTER EXTRACTION PREPROCESSING USING OPTICAL CHARACTER RECOGINITON

Today, there are many documents in the form of digital images obtained from various sources which must be able to be processed by a computer automatically. One of the document image processing is text feature extraction using OCR (Optical Character Recognition) technology. However, in many cases OCR technology are unable to read text characters in digital images accurately. This could be due to several factor such as poor image quality or noise. In order to get accurate result, the image must be in a good quality, so that digital image need to be preprocessed. The image preprocessing method used in this study are Otsu Thressholding Binarization, Niblack, and Sauvola methods. While the OCR technology used to extract the character is Tesseract library in Python. The test results show that direct text extraction from the original image gives better results with a character match rate average of 77.27%. Meanwhile, the match rate using the Otsu Thressholding method was 70.27%, the Sauvola method was 69.67%, and the Niblack method was only 35.72%. However, in some cases in this research the Sauvola and Otsu methods give better results.