Reviewerly/exHarmony huggingface dataset:
Application of Discovery Learning Learning Model to Improve Learning Outcomes of Fourth Grade Learners

The purpose of this research is to improve learners' learning outcomes through the application of Discovery Learning models. The type of research used is Class Action Research (PTK). The subject of the study was a class IV student of SDN Talagasari 3 Karawang City. Data collection techniques use tests, observation data, and rubric activities of Teachers and Learners. Data analysis techniques are used in the form of quantitative and qualitative descriptive. The learning outcomes of 28 learners in the first cycle study of IPA subject content with KKM 67 showed that as many as 19 learners had reached KKM if it was percentage to 68%. While 11 students have not reached KKM if the percentage becomes 32%. Then the results of the Second Cycle study increased when compared to cycle I which was (93%) or as many as 26 completed learners. Based on these data, it can be concluded that learning outcomes in cycle II have reached the target of 85%. Thus thr application of the Discovery Learning learning model can improve the learning outcomes of class IV learners of SDN Talagasari 3 Karawang City.
Rancang Bangun Sistem Informasi Penjualan “Pasar Kita” Di Pasar Tradisional Dringu Berbasis Web

As technology advances and people's needs for shopping, considering that currently shopping can be accessed through applications. The Probolinggo District Office of Industry and Trade facilitates buying and selling without entering the market, shopping online through the WhattsApp and Instagram applications with the "Mak Blonjo" icon, the Dringu traditional market is the program's main pilot project, the lack of information about products is an obstacle in marketing. The aim of this research is to produce an application in the form of a website with the "Our Market" icon which is used as a sales information system in the Dringu traditional market so that it can make it easier for various parties to carry out fast and precise interactions and transactions. The software development method uses the waterfall method. The system created uses the PHP programming language with the CodeIgniter framework and MYSQL database. System testing is carried out using the black box testing method. While testing the satisfaction of the application uses the UAT (user acceptance test) test in the form of a questionnaire and performs a Likert scale calculation. It can be concluded that the level of satisfaction with users of the web-based "Pasar Kita" sales application reached 84.1% with 15 consumer respondents, 73.1% with 3 admin/market clerk respondents and 76.1% with 15 merchant respondents. The final result of this research is that the website-based “Pasar Kita” sales information system design can be implemented in Dringu traditional markets as a form of ordering process service.
PROBLEMATICS OF THE IMPLEMENTATION OF THE EMANCIPATED CURRICULUM (IKM) IN INDONESIAN LANGUAGE OF CLASS X

This study aims to describe the problems and solutions to problems when implementing IKM, especially in the Indonesian language subject, which is one of the mandatory subjects that must be mastered by students in Indonesia. This research is in the form of descriptive qualitative research with data collection methods in the form of interviews, documentation, and simple random sampling techniques. From the research that has been done, it concludes that the problems experienced are related to technical problems in implementing the curriculum due to a lack of preparation with this new curriculum change, such as the absence of definite textbooks, being stuck with the previous curriculum, and the lack of facilities and infrastructure such as Wi-Fi networks that is not the stable and inadequate number of projectors. While the solutions that can be implemented are less effective and only temporary solutions if applied in the long term, so they require more mature preparation for the future.
Person Detection and Tracking Using UAV and Neural Networks

With the recent advancements in drone technology, there has been an increase in the development of human detection and tracking techniques for various applications, especially near borders. In this research, we propose methods to enhance people detection performance in diverse outdoor scenarios. Our dataset design includes a wide range of lighting and color changes, different target distances, angles, and postures. The experimental data consists of images taken in various environmental situations, such as changing the drone’s flight height and capturing pictures in intensive light. To evaluate the performance of our proposed method, we enhanced the generic YOLOv5 model using the gathered data, and calculated key performance indicators, including loss functions, recall, accuracy, and mAP50. We compared the performance of our enhanced model against the standard YOLOv5 model and its versions on the same testing set.
A Compact Version of EfficientNet

EfficientNet is a convolutional neural network architecture that was created by doing a neural architecture search with the AutoML MNAS framework, which optimized both accuracy and efficiency. It is based on MobileNetV2’s inverted bottleneck residual blocks, as well as squeeze-and-excite blocks. With far lower parameter computation burdens on the ImageNet challenge, EfficientNet may compete with the best. This paper provides a mobile version of EfficientNet that has accuracy similar to the ImageNet dataset but runs nearly twice as fast.
Toward an Integrated and Multi-scale Ontology for Knowledge Sharing and Integration for Migration Pattern Analysis in West-Africa

This study proposes an ontology on migration in West-Africa. The main aim of this study is to provide a common framework for knowledge integration and sharing on migration in West-Africa. The resulting ontology – MigraOnto – is generic as it can be applied in any country of West-Africa. In addition it provides a multi spatial and temporal scales description of migration while taking account the social, economic, environmental and political dimensions of migration. As perspectives, MigraOnto will be transformed into an integrated database for knowledge sharing and integration.
APLIKASI PENCARIAN PROPERTI DI JAKARTA DENGAN METODE ANALYTIC NETWORK PROCESS DAN TOPSIS

There have been several buying and selling applications that are intended to help bring together buyers, sellers, or agents in finding the house criteria needed. But there are still conditions where buyers find it difficult to find the house or property of their dreams. At the same time sellers are also difficult to find the right buyer. For that we need an application that can realize a search application with property recommendation features that can be used to find the best unit that suits their wishes. The methods used are the Analytic Network Process and TOPSIS methods. The ANP method is used because of the consideration of the interrelationships between elements at different levels. TOPSIS method is also used to be able to display more accurate results in terms of ranking. From the recommendation test, 90% of the 10 tests were obtained and the percentage result was 94.1% from 17 respondent tests. Based on these tests, it is known that the error occurred because the data was not found. The absence of property data in accordance with the Criteria can be caused by filling out invalid criteria and can also be caused by a lack of variation in property data.
Hybrid recommendation system combining collaborative filtering and content-based recommendation with keyword extraction

With the development of recommendation systems, large amount of information collected from e-commerce could help customers to find the potential interesting products. Collaborative filtering and content-based recommendation systems are two common recommendation systems. While collaborative filtering has the problem of cold-start, content-based recommendation system could not explore the potential interests of users. Hybrid system combining these two techniques could achieve better results. This paper applies hybrid recommendation methods to the Amazon food reviews and evaluate the results in the aspects of precision, recall, diversity and novelty. It is found that the weighted hybrid recommendation system combing 0.95 weight of collaborative filtering and 0.05 content-based recommendation system achieves a good precision and diversity.
The modal logic of abelian groups

Abstract We prove that the modal logic of abelian groups with the accessibility relation of being isomorphic to a subgroup is $$\mathsf {S4.2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>S</mml:mi> <mml:mn>4.2</mml:mn> </mml:mrow> </mml:math> .
Writing anxiety among international students in a Thai university

Literature on writing anxiety among undergraduate students in international universities situated in English as a foreign language context remains scarce. In this study, a survey questionnaire was distributed to know the writing anxiety level of students and anxiety level differences based on nationality and locality. A total of 138 students participated using convenience sampling. Afterward, five students, based on their writing anxiety level, were purposely chosen to attend two sorting activities of statements regarding the change in their writing anxiety. Then, individual semi-structured interviews were conducted to know the factors of writing anxiety change. Descriptive results from the survey have shown that students in an international university experienced high anxiety in specific situations in writing. Also, varying writing anxiety levels were found based on nationality and locality. Findings from the sorting activity indicated that some writing anxiety-provoking situations have changed over a 2-week time frame due to some factors. Some implications for teaching were recommended to reduce writing anxiety in the classroom.
Video Super-Resolution Reconstruction Based on Deep Learning and Spatio-Temporal Feature Self-similarity (Extended abstract)

Video super-resolution (SR) reconstruction technology aims at obtaining high quality reconstruction of high-resolution (HR) video sequences by inferring the lost detailed information from their low-resolution (LR) counterparts. However, this technology is an ill-posed problem because significant detailed information is lost in the process of video degrading. The existing learning-based SR reconstruction methods can be adapted to a larger super-resolution factor, but it cannot be guaranteed that any low-resolution image block can find its corresponding high-resolution block matching in a limited-scale training set. Some noise and over smooth phenomenon usually exist while dealing with some unique features that rarely appear in a given training data set. The self-similarity based SR methods do not rely on accurate sub-pixel motion estimation and thus can be adapted to complex motion patterns. However, under conditions of insufficient internal similar blocks, some visual flaws are usually produced due to the mismatched internal instances.
Unsupervised Local Discrimination for Medical Images

Contrastive learning, which aims to capture general representation from unlabeled images to initialize the medical analysis models, has been proven effective in alleviating the high demand for expensive annotations. Current methods mainly focus on instance-wise comparisons to learn the global discriminative features, however, pretermitting the local details to distinguish tiny anatomical structures, lesions, and tissues. To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images. Specifically, this model is equipped with an embedding module for pixel-wise embedding and a clustering module for generating segmentation. And these two modules are unified by optimizing our novel region discrimination loss function in a mutually beneficial mechanism, which enables our model to reflect structure information as well as measure pixel-wise and region-wise similarity. Furthermore, based on LD, we propose a center-sensitive one-shot landmark localization algorithm and a shape-guided cross-modality segmentation model to foster the generalizability of our model. When transferred to downstream tasks, the learned representation by our method shows a better generalization, outperforming representation from 18 state-of-the-art (SOTA) methods and winning 9 out of all 12 downstream tasks. Especially for the challenging lesion segmentation tasks, the proposed method achieves significantly better performance.
Demystifying Scholarly Metrics: A Practical Guide

"Demystifying Scholarly Metrics: A Practical Guide." Technical Services Quarterly, 40(3), pp. 243–244
Vehicle Counting And Traffic Congestion Detection Using YOLOv3

The purpose of this project is to utilize Python modules and a sophisticated deep learning algorithm to identify, categorize, track, and tally moving vehicles from highway CCTV footage. Additionally, the system predicts traffic congestion by analyzing the number of vehicles in consecutive video frames. When congestion is detected, the application automatically notifies the traffic police who receive a message on their mobile, prompting them to address the existing traffic jam in the area. The project's core involves a vision-based vehicle detection and counting system that relies on the YOLOv3 model and openCv-python library to achieve its objectives.
The Prediction of The Drought Index in The Indragiri Watershed Using SARIMA and SPI Methods

Farmers are concerned about the problem of drought in agriculture because it will disrupt the production system and cause losses. In 2015, the Indragiri Hulu area experienced a drought on 273 hectares of agricultural land. This area is part of the Indragiri River Watershed in Riau Province. To prevent the impact of future droughts, forecasting is required. The level of drought is influenced by rainfall, so the Seasonal Auto Regressive Integrated Moving Average (SARIMA) method is used to model rainfall data. In addition, the Standardized Precipitation Index (SPI) is also used to analyze the drought index. After the analysis, it was found that the best model in forecasting is SARIMA model (1,0,1)(2,1,1)12 with an average residual square of 18.0175, and the drought index in the Indragiri watershed is currently included in the "Normal" category. The results of this forecasting are expected to assist the Riau Province National Disaster Management Agency in anticipating and mitigating the impact of future drought disasters.
High Layer Coding for Video Transmission

In this paper we consider improving the effectiveness of video transmission with the help of high layer coding. We show that the unequal error protection of parts of video stream of different importance with the help of high layer coding could be more beneficial under hard erasure channel conditions. We also show that applying even short codes with low complexity decoding at high network layers can provide significant improvements in the received video quality.
Interaction-aware Predictive Collision Detector for Human-aware Collision Avoidance

With their progressive deployment in increasingly complex environments, autonomous vehicles will more often interact with humans in shared spaces. However proactive planners, the most effective for human-aware navigation, are rarely applicable with real-world constraints because of their inherent complexity. Meanwhile classical approaches fail to navigate in cooperation with humans in complex or crowded scenarios. Therefore we propose to extend a global kinodynamic predictive collision avoidance approach with an interaction-aware behavioral prediction model for human-vehicle interactions. Thanks to a grid based Bayesian perception, our approach is versatile in modeling uncertainty and complex scenes. We deploy this solution on a robotic car and show that it can be used in real-world applications. With a qualitative and quantitative validation, we show that this interaction-aware collision avoidance solution is safe and performs well in crowded scenarios. Less computationally demanding and more versatile than proactive planners but still able to benefit from cooperation with humans, this interaction-aware approach offers a compromise between predictive and proactive planners.
Highly secure intelligent computer data detection of anomalies

The latest technology is focusing on the detection of different attacks occurring in the cloud and IoT data centers and services. So, there is a need of providing high dimensional security for the data. The appraisal issue prompts the suggestion of a coordinated methodology for sifting the unusual data of interest and perspectives for seeing anticipated exceptions in the new youngster around subspaces of high-layered enormous information. High-layered immense information requires significant computational memory and brings a tremendous computational weight, no matter what the way that continuous figuring sources solid areas are. Thus, the fundamental examination objective of this assessment is to develop an adaptable system for inconsistency recognizing confirmation that arrangements with the issue of the scourge of dimensionality in goliath instructive combinations. The issue of irregularity range has different highlights, and how we depict irregularities, the sort of information, and anticipated surrender can have a critical effect on recognizable confirmation techniques. As a result of these contrasts, issue plans shift enormously, requiring the utilize of different shrewd frameworks to address them.
Distributed entanglement and teleportation on a quantum network

We report on the realization of a multi-node quantum network. Using the network, we have demonstrated three protocols; generation of a entangled state shared by all nodes, entanglement swapping and quantum teleportation between non-neighboring nodes.
Real-time hyperspectral video understanding via universal metasurface encoders

We here present Hyplex™, an artificial i ntelligent h ardware a nd software optoelectronic platform for acquiring and processing high-resolution hyperspectral videos at 30 frames per second. We will illustrate the implementation and various applications in the field.
Machine-Learning Enabled Optimization for Robust Soliton Crystal Generation in Microring Resonators

We illustrate a novel strategy to robustly generate soliton states in Hydex microring resonators pumped with a continuous-wave laser source, by employing genetic algorithms to optimize the parameters required for coherent state generation.
Benchmarking Machine Learning-Derived W State Witnesses on NISQ Hardware

We find that our W state witnesses derived with a Support Vector Machine have comparable noise tolerance while requiring fewer measurements than the fidelity method; this result is physically verified on an IBM Quantum Processor.
Engineering quantum states of light using superradiance

We present a protocol for the generation of many-photon quantum states such as NOON and Schrodinger-cat states. We rely on a novel superradiance-type phenomenon existing in ensembles of quantum emitters with degenerate ground states.
Aesthetic education of personality development in the field of education

The article highlights the essence of aesthetic education, which in the modern field of education is represented by a purposeful process that innovatively develops a modern, creative, socially active person who has a conscious life position formed on the examples of human values that exist forever and on high culture. The purpose of the article is to single out the main tasks of aesthetic education, analyze its content, show ways of implementing aesthetic education of students, reveal the meaning of self-education. In the article, the methodological concept reveals a complex of approaches: competence, personal-activity, interdisciplinary, systemic, acmeological, prognostic. The article reveals the content and shows the ways of realizing the aesthetic education of the students of education. The task of aesthetic education is defined (the formation of the ability of the individual to relate to the world aesthetically). Four historical periods of the development of aesthetic education of the young generation are distinguished. The system of innovative aesthetic education of youth, which should exist in the interrelationship of all its components, which are disclosed in the article, is substantiated. The structure of aesthetic consciousness (aesthetic perceptions, aesthetic judgments, aesthetic feelings, aesthetic ideal, aesthetic tastes) was studied.
Analisis Pengaruh Faktor-Faktor Building Information Modeling terhadap Tingkat Penerapan dan Kinerja Proyek pada Proyek Design and Build

This study aims to determine the effect of the level of BIM application on project performance (cost, quality, time). Building Information Modeling (BIM) is a collaborative approach in construction project management that uses digital information models to integrate all aspects of a project, from planning to execution. The research method carried out in this study is using quantitative methods by observation using questionnaires. The results of the analysis show that the factors that influence the application of BIM have a significant influence on the level of BIM application in Design and Build projects. In addition, the level of BIM implementation also positively impacts overall project performance, with increased time efficiency, reduced costs, and improved quality of project results. This research provides a better understanding of the importance of implementing Building Information Modeling (BIM) in Design and Build projects, as well as identifying factors that can effectively improve the level of project implementation and performance. Thus, this research is expected to be a guideline for stakeholders in the construction industry to optimize the benefits of using BIM in future projects. &#x0D;
Movement Optimization of Robotic Arms for Energy and Time Reduction using Evolutionary Algorithms

Trajectory optimization of a robot manipulator consists of both optimization of the robot movement as well as optimization of the robot end-effector path. This paper aims to find optimum movement parameters including movement type, speed, and acceleration to minimize robot energy. Trajectory optimization by minimizing the energy would increase longevity of robotic manipulators. We utilized particle swarm optimization method to find the movement parameters leading to minimum energy consumption. The effectiveness of the proposed method is demonstrated on different trajectories. Experimental results show that 49% efficiency was obtained using a UR5 robotic arm.
Canonical Gradings of Monads

We define a notion of grading of a monoid T in a monoidal category C, relative to a class of morphisms M (which provide a notion of M-subobject). We show that, under reasonable conditions (including that M forms a factorization system), there is a canonical grading of T. Our application is to graded monads and models of computational effects. We demonstrate our results by characterizing the canonical gradings of a number of monads, for which C is endofunctors with composition. We also show that we can obtain canonical grades for algebraic operations.