Reviewerly/exHarmony huggingface dataset:
The topological space generated from the graphs by use of some relation

In this article, we study the topology and its properties generated from a simple undirected graph with no isolated vertices. We use a simple undirected graph to generate the topology, which connects event edges with the neighboring relationship between vertices, edges, and studies some properties of the graph.
IMPROVING VOCABULARY MASTERY THROUGH SEGONG GAME FOR FIRST-GRADE STUDENTS AT ALOK PUBLIC JUNIOR HIGH SCHOOL

Vocabulary proficiency plays a vital role in English language acquisition, enabling individuals to comprehend written texts and effectively articulate their ideas in oral and written communication. This article presents an academic study that focuses on enhancing the vocabulary competency of first-grade students through the implementation of traditional Segong activities. The research was conducted in the context of Alok Public Junior High School during the academic year 2022/2023. This study reexamines the efficacy of employing specific Segong traditional games as a strategy to facilitate the development of English vocabulary among students in an educational setting. The Segong game format was utilized to enhance students' vocabulary knowledge while harnessing the teacher's instructional prowess. The research employed a two-cycle classroom action research methodology, incorporating assessments, questionnaires, and observations to gather data. The findings reveal a substantial improvement in vocabulary mastery, as evidenced by an initial data analysis showing a percentage increase of 18% in the pre-cycles. Notably, the first cycle demonstrated a growth rate of 63%, while the second cycle witnessed a remarkable increase of 85.72%. These outcomes affirm the effectiveness of the Segong game in enhancing English vocabulary acquisition
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize the novel classes which cannot be collected for training a prediction model. Accordingly, generative models (e.g., generative adversarial network (GAN)) are typically used to synthesize the visual samples conditioned by the class semantic vectors and achieve remarkable progress for ZSL. However, existing GAN-based generative ZSL methods are based on hand-crafted models, which cannot adapt to various datasets/scenarios and fails to model instability. To alleviate these challenges, we propose evolutionary generative adversarial network search (termed EGANS) to automatically design the generative network with good adaptation and stability, enabling reliable visual feature sample synthesis for advancing ZSL. Specifically, we adopt cooperative dual evolution to conduct a neural architecture search for both generator and discriminator under a unified evolutionary adversarial framework. EGANS is learned by two stages: evolution generator architecture search and evolution discriminator architecture search. During the evolution generator architecture search, we adopt a many-to-one adversarial training strategy to evolutionarily search for the optimal generator. Then the optimal generator is further applied to search for the optimal discriminator in the evolution discriminator architecture search with a similar evolution search algorithm. Once the optimal generator and discriminator are searched, we entail them into various generative ZSL baselines for ZSL classification. Extensive experiments show that EGANS consistently improve existing generative ZSL methods on the standard CUB, SUN, AWA2 and FLO datasets. The significant performance gains indicate that the evolutionary neural architecture search explores a virgin field in ZSL.
Review Study: Blockchain Application in Payroll System

In this era, where business execution is primarily reliant on information, the sooner and more precisely it is received, the better. Blockchain is great for information sharing because it provides instant, shareable, and entirely transparent data kept in an immutable ledger that only network users with permission may access. On the blockchain network, virtually any asset may be recorded and exchanged, reducing risk and expense for all parties involved. This research focuses on blockchain payroll implementation. It is critical to employ the proper tactics to preserve and improve payroll, as blockchain technology is critical for securing the payroll system because it can move both value and information. This review paper's main point is that blockchain technology can be used to reduce overhead and administrative burden, reduce tax spending, and promote openness and accountability in a variety of situations. Blockchain challenges to the payroll system and implementation issues have been highlighted. Given the quantity and complexity of blockchain issues, many of the most significant blockchain roadblocks are characteristic of any new technology's growing pains. The benefits and challenges of using blockchain technology in payroll procedures were discussed after the study. It was determined that doing so would make all employee records and employer payments matching instantly available at the minute level to various governmental organizations. At a fraction of the cost of current payroll compliance utilizing a fiat cryptocurrency, a blockchain payroll application will provide quick payroll compliance. A blockchain framework that is approved for use with payroll systems and is encrypted using the high-efficiency encryption algorithm to ensure its high security will need to be designed and evaluated.
KeyRecs: A keystroke dynamics and typing pattern recognition dataset

Keystroke dynamics can valuably contribute to the development of intelligent authentication systems by enabling a single and continuous authentication process in a passive and non-intrusive manner by continuously verifying a user's identity. This work describes the KeyRecs dataset, which contains fixed-text and free-text samples of user typing behavior and demographic information of the participants age, gender, handedness, and nationality. The keystroke data was obtained from 99 participants of various nationalities who completed password retype and transcription exercises. The recorded samples consist of inter-key latencies computed in a digraph fashion measuring the time between each key press and release during an exercise. KeyRecs can be leveraged to improve the recognition of authorized users and prevent unauthorized access in biometric authentication software.
A Comparative Study on the Physical Activity Promotion System(PAPS) Measurement Results of Middle School Students Before and After COVID-19

Purpose: This study aims to analyze the effect of COVID-19 on the meuarsement results of the Physical Activity Promotion System (PAPS) for middle school students.BRMethod: Data from the national and municipal offices of education weer obtained through the school notification system, which was based on the measurement results of the PAPS conducted in 2019 and 2020. National and Office of Education data were acquired throguh the School Info System based on the results of the PAPS conducted in the 2019 and 2020. For scho-ollevel data, Gong-ju National University sent an official letter to each school to obtain data on which student personal information was deleted. In this study, the number of people by PAPS grade was analyzed using the chi-square test. The measurement results were calculated as average (M) and standard deviation S(D) using the t-test. The significance level for hypothesis verification was α=.05.BRResults: The results of this study are as follows. 1) The health-related physical fitness of middle school students across the country decreased after COVID-19 compared ot before COVID-19 (p.001). 2) In middle school, both boys and girls experienced a decrease in cardiopulmonary extension (p.001). 3) Male middle school students showed increased flexibility p(.01). 4) Muscular strength increased in male middle school students (p.001), while female middle school students experienced a decrease in muscular extension (p.05). 5) Male middle school students increased their quickness (p.01), while female middle school students decreased in quickness (p.01).BRConclusion: The results of this study indicate that the health-related pyhsical fitness of middle school students has been decreased by COVID-19. Specifically, among male middle school students, the cardiopulmonary endurance decreased, while flexibility, muscular strength, and quickness increased. On the other hand, among female middle school students, there were decreses in cardiopulmonary endurance, muscular endurance sue to the effects of COVID-19.
KARA GOLD ORE CLUSTER (EASTERN TRANSBAIKALIA): GEOCHEMICAL FEATURES OF ORES, THE RELATIONSHIP OF MAGMATISM WITH MINERALIZATION

Dykes of the Amudzhikano-Sretenskiy complex (J3–K1) including hybrid porphyries are widely developed in the area of the Kara gold ore cluster. The parameters of the lanthanides distribution in them are close to those of ores of the quartz-tourmaline-pyrite association, indicating probable single sources of their formation. In hybrid porphyries, ΣTR is 52.24–123.35; in ores of the quartz-tourmaline-pyrite association ΣTR is 52.82–133.78. In hybrid porphyries, Eu/Sm is 0.18–0.65; in ores of the quartz-tourmaline-pyrite association Eu/Sm is 0.18–0.47. The degree of differentiation of magmatic chambers according to Eu/Eu* is 0.61-2.26 in dikes of hybrid porphyries; Eu/Eu* is 0.78–1.49 in ores of quartz-tourmaline-pyrite association. The represented data indicate that their magmatic chambers of dikes of hybrid porphyries and quartz-tourmaline-pyrite ores were not differentiated and, consequently, had a deep source. According to the geochemical composition, part of the Amudzhikano-Sretenskiy complex granite values corresponds to ore-bearing granites with goldmolybdenum mineralization. Ores of the actinolite-magnetite association are characterized by the highest gold contents among productive stages of gold mineralization. Close correlation relationships of Au with Zn, W, Mo were established in ores of the actinolite-magnetite association; in ores of the quartz-tourmaline-pyrite association – with Mo, Pb; in ores of the quartz-arsenopyrite association – with Pb, Mo, As, Ag.
Analisis Tarif Air PDAM Untuk Kelayakan Penerapan Sistem Pemanenan Air Hujan pada Skala Rumah Tangga

The limited availability of clean water is a problem that often occurs lately. Water use continues to increase due to population growth and technological developments. The availability of abundant rainwater and the application of a rainwater harvesting system are alternatives for water fulfillment as water supplies are limited. Rainwater harvesting system performance simulation modeling is essential in deciding the suitability of rainwater harvesting systems. In this study, the rainwater harvesting system simulation modeling was carried out using the Yield Before Spillage (YBS) algorithm in 3 cities in Indonesia (Palembang City, Bogor City, and Mataram City). This assessment considers 5 combinations of demand, 5 combinations of catchment area, 5 tank capacities, and 3 applicable water rates, resulting in 1,875 different value configurations. The performance and potential assessment of the rainwater harvesting system are determined by Water Saving Efficiency and Timetric Reliability, while the financial assessment is determined using the payback period and benefit-cost ratio. Benefits are derived from the potential for water savings with a rainwater harvesting system which is converted into financial savings according to the prevailing water rates and costs are determined based on the initial installation costs of the rainwater harvesting system. The results of this study indicate that the city of Bogor with an average rainfall of 3805.95 mm/year has the highest WSE rate of 100% and the fastest payback period is 4 years. Palembang City with an average rainfall of 2551.57 mm has the highest WSE rate of 99,273% and the fastest payback period is 5.42 years. Mataram, with an average rainfall of 1661.79 mm, has the highest WSE rate of 91.752% and the fastest payback period is 8.17 years. Topographical conditions and rainfall greatly affect the performance of the rainwater harvesting system.
Image Captioning Network for Disaster Dataset Based on Transfer Learning and Disaster Convolution Block

Image captioning has been widely studied using various deep learning models on extensive and well-prepared datasets. Accurately captioning images of serious and sudden disasters is important; however, the study of disaster image captioning is yet to be thoroughly investigated compared to natural image captioning. Furthermore, existing image captioning models may need to perform better in generating captions for disaster images because there are fewer disaster images in popular datasets than non-disaster images. To address these problems, we refine and propose a cleaned disaster dataset and an image captioning model optimized for the dataset. Experimental results showed that our proposed model outperformed the existing model in terms of generating accurate captions for disaster images.
Bitcoin Unveiled: A Rational Exploration for Skeptical Minds

&lt;p&gt;From its inception in 2008 as an idea, Bitcoin has evolved into a functional system by 2009 and found its initial real-world usage in 2010, priced at less than $0.01 per coin. Over time, it has transformed into a globally recognized currency, with each coin valued at over $8,000 and the total market value exceeding $150 billion as of May 2020. Despite its impressive performance as one of the most lucrative investments in the last decade, Bitcoin remains a subject of controversy. Its nature as either a novel form of currency, a speculative bubble, or a combination of both is still debated.&lt;/p&gt; &lt;p&gt;While conventional investment frameworks are well-established for assets like stocks, bonds, and real estate, Bitcoin's unique status as a monetary asset presents challenges in evaluation due to its rarity and infrequent appearance. This research paper introduces a straightforward and easy-to-understand framework to assess Bitcoin as a novel monetary asset.&lt;/p&gt;
An Action Research on Music and Movement Activities for Enhancing Language Abilities of Multicultural Children

Objectives The purpose of this study is to examine the process of implementing music and movement activities for enhancing the language ability of multicultural children, and to investigate the operational strategies for the activities for multicultural children and their responses to language-enhancing the activities.&#x0D; Methods To achieve this, four multicultural children over the age of three from a daycare center located in Gyeonggi-do were selected as the target children, and a self-reflective action research cycle model was applied, progressing through the stages of planning, execution, observation, and reflection.&#x0D; Results The study was conducted from September 2022 to February 2023, and in September 2022, the possibility of using music and movement activities through action research to enhance the language ability of multicultural children was confirmed through preliminary research and literature review. In September 2022, research consent was obtained from the parents of the observed children and the teachers who were observers during the preparation stage. The operational strategies and responses of multicultural children for enhancing their language ability were derived from the execution and observation process of music and movement activities.&#x0D; Conclusions First, the operational strategies for music and movement activities for multicultural children were effective in enhancing their language ability. Second, music and movement activities for enhancing the language ability of multicultural children had a positive effect on their various responses.
HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation

Current semantic segmentation models have achieved great success under the independent and identically distributed (i.i.d.) condition. However, in real-world applications, test data might come from a different domain than training data. Therefore, it is important to improve model robustness against domain differences. This work studies semantic segmentation under the domain generalization setting, where a model is trained only on the source domain and tested on the unseen target domain. Existing works show that Vision Transformers are more robust than CNNs and show that this is related to the visual grouping property of self-attention. In this work, we propose a novel hierarchical grouping transformer (HGFormer) to explicitly group pixels to form part-level masks and then whole-level masks. The masks at different scales aim to segment out both parts and a whole of classes. HGFormer combines mask classification results at both scales for class label prediction. We assemble multiple interesting cross-domain settings by using seven public semantic segmentation datasets. Experiments show that HGFormer yields more robust semantic segmentation results than per-pixel classification methods and flat-grouping transformers, and outperforms previous methods significantly. Code will be available at https://github.com/dingjianswl0l/HGFormer.
Research on Trajectory Planning Algorithm for Intelligence Robot Cutting-Grinding

Under the guidance of the concept of “machine intelligence”, in order to complete the robot intelligent cutting and polishing process while increasing its teaching-free performance, this article is based on the computer.Net framework and according to the actual cutting and grinding needs of the processed parts, constructs an intelligent robot cutting and grinding system, completing a series of functions such as system construction, graphic control, virtual controller interaction, online monitoring, etc; A-manually selectable cutting and grinding method, with the system automatically generating the cutting and polishing trajectory. This paper studies the construction framework, trajectory algorithm and kinematics analysis, realizes the automatic generation and optimization of intelligent cutting and grinding trajectory in the virtual workstation, and automatically adjusts the robot motion posture, speed and acceleration under different trajectories. The experimental simulation results show that the intelligent robot cutting and polishing system can complete the required functions in a teaching free environment, reducing the difficulty of operator development and debugging, while greatly increasing the universality of cutting and polishing parts and the autonomy of trajectory selection.
Application of the Interoperability Score in the home and building domain

The home and building domain is an area were different ages of buildings and technologies come together. In this paper, we analyse the interoperability of our home and building domain located project with a metric called Interoperability Score and make interoperability between systems measurable. In order to fit in the home and building domain, we propose adaptions of the metric. Thus, the ability to prioritise ICT interfaces in the home and building domain is given.
Recent Trends in Sensor-based Activity Recognition

This seminar introduces recent trends in sensor-based activity recognition technology. Technology to recognize human activities using sensors has been a hot topic in the field of mobile and ubiquitous computing for many years. Recent developments in deep learning and sensor technology have expanded the application of activity recognition to various domains such as industrial and natural science fields. However, because activity recognition in the new domains suffers from various real problems such as the lack of sufficient training data and complexity of target activities, new solutions have been proposed for the practical problems in applying activity recognition to real-world applications in the new domains. In this seminar, we introduce recent topics in activity recognition from the viewpoints of (1) recent trends in state-of-the-art machine learning methods for practical activity recognition, (2) recently focused domains for human activity recognition such as industrial and medical domains and their public datasets, and (3) applications of activity recognition to the natural science field, especially in animal behavior understanding.
High-Dimensional Smoothed Entropy Estimation via Dimensionality Reduction

We study the problem of overcoming exponential sample complexity in differential entropy estimation under Gaussian convolutions. Specifically, we consider the estimation of the differential entropy h(X + Z) via n independently and identically distributed samples of X, where X and Z are independent D-dimensional random variables with X sub-Gaussian with bounded second moment and $Z\sim {\mathcal{N}}\left({0,{\sigma ^2}{I_D}}\right)$. Under the absolute-error loss, the above problem has a parametric estimation rate of $\frac{{{c^D}}}{{\sqrt n }}$, which is exponential in data dimension D and often problematic for applications. We overcome this exponential sample complexity by projecting X to a low-dimensional space via principal component analysis (PCA) before the entropy estimation, and show that the asymptotic error overhead vanishes as the unexplained variance of the PCA vanishes. This implies near-optimal performance for inherently low-dimensional structures embedded in high-dimensional spaces, including hidden-layer outputs of deep neural networks (DNN), which can be used to estimate mutual information (MI) in DNNs. We provide numerical results verifying the performance of our PCA approach on Gaussian and spiral data. We also apply our method to analysis of information flow through neural network layers (c.f. information bottleneck), with results measuring mutual information in a noisy fully connected network and a noisy convolutional neural network (CNN) for MNIST classification. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>
Generalization of pixel-wise phase estimation by CNN and improvement of phase-unwrapping by MRF optimization for one-shot 3D scan

Active stereo technique using single pattern projection, a.k.a. one-shot 3D scan, have drawn a wide attention from industry, medical purposes, etc. One severe drawback of one-shot 3D scan is sparse reconstruction. In addition, since spatial pattern becomes complicated for the purpose of efficient embedding, it is easily affected by noise, which results in unstable decoding. To solve the problems, we propose a pixel-wise interpolation technique for one-shot scan, which is applicable to any types of static pattern if the pattern is regular and periodic. This is achieved by U-net which is pre-trained by CG with efficient data augmentation algorithm. In the paper, to further overcome the decoding instability, we propose a robust correspondence finding algorithm based on Markov random field (MRF) optimization. We also propose a shape refinement algorithm based on b-spline and Gaussian kernel interpolation using explicitly detected laser curves. Experiments are conducted to show the effectiveness of the proposed method using real data with strong noises and textures.
Joint Learning with Group Relation and Individual Action

This paper proposes a method for group relation learning. Different from related work in which the manual annotation of group activities is required for supervised learning, we propose group relation learning without group activity annotation through recognition of individual action that can be more easily annotated than group activities defined with complex inter-people relationships. Our method extracts features informative for recognizing the action of each person by conditioning the group relation with the location of this person. A variety of experimental results demonstrate that our method outperforms SOTA methods quantitatively and qualitatively on two public datasets.