Reviewerly/exHarmony huggingface dataset:
Effects of Electrical Pulse Width and Output Irradiance on Intense Pulse Light Inactivation

The effects of electrical pulse width and output irradiance on the inactivation effect of intense pulse light (IPL) are studied in this paper. The measured radiant efficiency of pulsed xenon lamp can be more than 50%, and its irradiance can reach levels 100-times greater than that of a low-pressure mercury lamp. Staphylococcus aureus is used in inactivation experiments. When the irradiance and dose are both constant, there is no significant difference in inactivation efficiency when the pulse width is changed. However, a narrow pulse width corresponding to high irradiance at the same single-pulsed dose displays better inactivation effect. Experimental results are compared between the xenon lamp and low-pressure mercury lamp. The reduction factor (RF) value of the xenon lamp is more than 1.0 higher under the condition of both the same dose and irradiance. In order to achieve the same RF value, the dose of continuous-wave light must be at least three-times greater than that of pulsed light. The spectral action of the pulsed light is also studied. It is confirmed that UVC plays a major role across the whole spectrum. The experimental results show that extreme high-pulsed irradiance presents the main contributing factor behind the excellent bactericidal effect of IPL.
Pelatihan Calistung untuk Meningkatakan Budaya Literasi Pada Warga Lamakkaraseng Kecamatan Ulaweng

Literacy culture is very important to improve the quality of education in both rural and urban areas. Lamakkaraseng Village, Ulaweng District has a low literacy culture because there are residents who are not yet proficient in reading, writing or arithmetic. The Service Team conducted Calistung training activities (reading, writing, and arithmetic) for the villagers of Lamakkaraseng to improve their Literacy culture. The results obtained are that the participants' reading and writing skills as well as numeracy skills have improved, and some residents have started to get used to reading every day when they see the writings displayed around them. In addition, they are satisfied with the training activities carried out by the community service team
Half-Instance Normalization for Accurate Object Detection in Hazy Conditions

Deep learning models are designed to work in ideal image conditions. Hazy conditions are frequent in drone images. Hence, insufficient lighting and low visibility during image capture significantly degrade the performance of object detection models. Recently, Instance Normalization has been applied in low-level image restoration tasks such as de-raining, de-snowing, dehazing, etc., with significant improvement in the enhancement effect. Inspired by this, a Half-Instance Normalization network (HINet) is proposed as a preprocessing network to assist object detection models by removing hazy regions and enhancing the features of objects. The HINet is trained end-to-end using the A2I2-Haze dataset consisting of hazy and clear image pairs accompanied by bounding box annotations. Additionally, Scaled-YOLOv4 models are trained with a bag of tricks that give further improvements. Extensive experiments have demonstrated the appealing effects of image restoration in increasing the accuracy of object detection tasks.
Responsibility for Infringement of Exclusive Rights to Cumulatively Protected Results of Intellectual Activity

The paper analyzes the problems of multiplication of responsibility for infringing exclusive rights to the results of intellectual activity protected under several legal regimes. Special attention is paid to the issues of collecting compensation for the violation of exclusive rights to design solutions that can be treated as copyrighted works, industrial designs, trademarks. The author examines the stances developed by the domestic doctrine and law enforcement practice, as well as foreign approaches to solving the problem of multiplication of responsibility for violation of rights to cumulatively protected results of creative work. It is noted that in Russian practice, the prevailing approach is that the exclusive right to each ideal object is subject to independent protection. It is concluded that it is necessary to introduce the rule concerning priority of the exclusive right over an industrial design in the context of protection of rights to designs
The effect of using english blog on students’ writing ability in narrative text at tenth grade of MAN Labuhanbatu

The objective of this research is the effect of using English blog on students’ writing ability in narrative text at tenth grade of MAN Labuhanbatu in the Academic Year 2020/2021. This research is quantitative experimental research methods. The design used in this research quasi experimental and used the Nonequivalent Control Group Design model. The sample used is purposive sampling from 4 classes X MIPA. The sample was used is X MIPA 1 as experiment class and X MIPA 2 as control class which has a subject 71 students. Instrument for collecting data were the tests namely pretest and posttest. The results of this research show that used of using English blog was there is any effect on student’s writing at MAN Labuhanbatu in the academic year 2020/2021. The researcher found out that increasing of experimental class which got mean 65.8 on pretest and got mean 74.4 on posttest. Because t count value higher than t table (2.969 &gt; 1.667), means it Ha is accepted and Ho is rejected. Keywords: Using English Blog, Writing Ability
Playful Probing: Towards Understanding the Interaction with Machine Learning in the Design of Maintenance Planning Tools

In the context of understanding interaction with artificial intelligence algorithms in a decision support system, this study addresses the use of a playful probe as a potential speculative design approach. We describe the process of researching a new machine learning (ML)-based planning tool for maintenance based on aircraft conditions and the challenge of investigating how playful probes can enable end-user participation during the process of design. Using a design science research approach, we designed a playful probe protocol and materials and evaluated results by running a participatory design workshop. With this approach, participants facilitated speculative design insights into understandable interactions, especially with ML interaction. The article contributes with a design of a playful probe exercise to collaboratively study the adjustment of practices for CBM and a set of concrete insights on understandable interactions with CBM.
PENGARUH REKRUTMEN, SELEKSI, DAN PELATIHAN TERHADAP KINERJA KARYAWAN (PT. Rodateknindo Purajaya Bengkulu)

The title of this research is the effect of recruitment, selection, and training on employee performance at PT. Rodateknindo Purajaya Bengkulu. This type of research is a type of quantitative descriptive research. With the method of collecting interview data, observation, and distributing questionnaires (questionnaires). The number of respondents in this study were 40 people. By using data analysis techniques Classical Assumption Test, Multiple Linear Regression Test, Coefficient of Determination and Hypothesis Testing.&#x0D; With the results of the Multiple Linear Regression equation as follows: Y = 1.042 + 0.277 (X_1) + 0.295 (X2) + 0.456 (X3). And the coefficient of determination R2 = 0.736 or about 73.6%. The results of the tests carried out can be seen that the Recruitment variable (X1) shows t_hit&gt;t_(α/2) (2.555 &gt; 2.02809) and (sig = 0.015 &lt; 0.050), Selection variable (X2) t_hit&gt;t_(α/2) ( 3.878 &gt; 2.02809) and (sig = 0.000 &lt; 0.050), and the Training variable (X3) shows t_hit&gt;t_(α/2) (5.340 &gt; 2.02809) and (sig = 0.000 &lt; 0.050). The results of the F test show F_count &gt; F_table, namely (33.408 &gt; 2.866) and (sig = 0.000 &lt; 0.050). So it can be said that the variable Recruitment, Selection, and Training either partially or simultaneously on the variable Employee Performance (Y).
A Hybrid Approach for Web Pages Classification

Currently, the internet is growing at an exponential rate and can cover just some required data. However, the immense amount of web pages makes the discovery of the target data more difficult for the user. Therefore, an efficient method to classify this huge amount of data is essential where web pages can be exploited to their full potential. In this paper, we propose an approach to classify Web pages based on their textual content. This approach is based on an unsupervised statistical technique (TF-IDF) for keyword extraction (textual content) combined with a supervised machine learning approach, namely recurrent neural networks.
Long-Term Dynamics of the Thermal State of Technogenic Plots in Siberia Based on Satellite Data

We studied the dynamics of relative anomalies (&Delta;T/Tbg) in the ground cover thermal regime of technogenic territories in Siberia under the conditions of gold mining impact. The impact of gold deposit mining determines the change in the thermal state of the post-technogenic plots for a long time, which is an important feature of ecosystem stability monitoring. We analyzed the spectral characteristics of four technogenic sites that had gold mining quarries of different age. We evaluated the stages of technogenic plots according to the initial level of thermal anomaly, the rate of decrease in thermal anomaly, the time of stabilization recovery processes, and dispersing of the residual level of thermal anomaly.
A Study on Risk Identification and Management of Global Express Service

The growth of the e-commerce based on the development of ICT technology has grown more explosively due to the aftermath of social distancing caused by the spread of COVID-19. Because of the rapid increase of global express cargo since 2020, Global express service problems are detected from the major ports and airports in all countries. This phenomenon is expected to continue for the time being due to the proliferation of non-face-to-face transactions and an increase in diverse consumer demand. Global express service is the industry most affected by the delay of global logistics services, and various problems such as delay in delivery as well as sales of counterfeit goods, product damage, and tariffs are being pointed out. In particular, as the purchasing agency of overseas products spreads in domestic online shopping malls, not only consumers but also various entities that provide express services are faced with various problems. Accordingly, the Korean government is seeking various solutions, but there is a limitation in that it cannot take into account the characteristics of various entities participating in global express services. In this study, we define gobal express service as a standard process from the perspective of the global supply chain, and identify possible risk factors in 9 stages from the stage of ordering overseas products to receiving the goods after arriving at Incheon International Airport, the possible risk factors were identified in 9 stages to determine their relative importance. In addition, based on the survey results of those who have experienced direct purchases overseas and those involved in global express service, risk factors with high priority were compared and analyzed from the perspective of the participants. Based on this, the damage of the top 10 risk factors is divided into 4 categories : Reliability/Trust, Lead Time, Cost, and Quality/Safety. Countermeasures in terms of policy, system, and service operation were presented for each risk factor.
Analysis of COVID-19 Gov PK app user reviews to determine online privacy concerns of Pakistani citizens

Purpose The purpose of this paper is to explore the reaction of Pakistani citizens toward online privacy in the context of the Pakistan Government's COVID-19 app privacy breach. Design/methodology/approach The authors implemented supervised and unsupervised machine learning methods, that is, topic modeling and sentiment analysis on Google app store user review data. Findings There was no visible concern shown by the citizens toward the COVID-19 app privacy breach, even though it was well highlighted in the news. Other studies have also indicated that concern for online privacy remains low in developing countries, which aligns with the findings of this paper. Originality/value Globally COVID-19 apps have been a cause of concern in terms of public privacy. To the best of the authors’ knowledge, this paper is the first in the Pakistani context to show the impact of a well-document privacy breach of a government COVID-19 app.
UAV trajectory planning based on an improved sparrow optimization algorithm with multi-strategy integration

Real-time monitoring of urban high-altitude data is an important goal in the construction and development of smart cities today. However, with the development of modern cities, the monitoring space becomes complicated and narrow because of the different building heights and no-fly zones, which makes UAV trajectory planning more difficult. In this paper, a multi-strategy sparrow search algorithm (MSSA) is proposed to solve the UAV trajectory planning problem in a three-dimensional environment. The algorithm aims to minimize the flight distance and maximize the use efficiency of the UAV. First, the improved algorithm employed a reverse-learning strategy based on the law of refraction to improve the search range and enhance the optimization performance. Second, we introduced a random step size generated by Levy flight into the position update strategy of the participant. The algorithm accuracy and speed of convergence were improved by the randomness feature. Finally, the algorithm incorporated the Cauchy mutation to improve the scout position, which enhanced its ability to jump out of the local optimum of the algorithm. Sixteen benchmark test functions, Wilcoxon rank sum test, and 30 CEC2014 test function optimization results demonstrated that MSSA had better optimization accuracy, convergence speed, and robustness than the comparison algorithms. In addition, the proposed algorithm was applied to the UAV trajectory planning problem in different complex 3D environments. The results confirmed that the MSSA outperformed the other algorithms in complex 3D trajectory planning problems.
Generative Models for Fast Simulation of Electromagnetic and Hadronic Showers in Highly Granular Calorimeters

While simulation is a crucial cornerstone of modern high energy physics, it places a heavy burden on the available computing resources.These computing pressures are expected to become a major bottleneck for the upcoming high luminosity phase of the LHC and for future colliders, motivating a concerted effort to develop computationally efficient solutions.Methods based on generative machine learning models hold promise to alleviate the computational strain produced by simulation, while providing the physical accuracy required of a surrogate simulator.This contribution provides an overview of a growing body of work focused on simulating showers in highly granular calorimeters, which is making significant strides towards realising fast simulation tools based on deep generative models.Progress on the simulation of both electromagnetic and hadronic showers will be reported, with a focus on the high degree of physical fidelity achieved.Additional steps taken to address the challenges faced when broadening the scope of these simulators, such as those posed by multi-parameter conditioning, will also be discussed.
Construction and Adjustment Methods for Teacher-Student Relationship in College Student Management Based on Big Data Analysis

Building a harmonious Teacher-Student Relationship (TSR) is a necessary work for college student management, because different TSRs can result in different effects in student management and cultivation. However, in existing studies, the definitions of harmonious TSR given by different scholars are dimensionless, the understandings of the nature of TSR is not thorough enough, and the analysis of the influencing factors of TSR often lacks the support of theoretical evidences. To overcome these shortcomings, this paper aims to study the construction and adjustment methods for the TSR in college student management based on big data analysis. At first, the paper investigated the daily behavioral features of college students on online management platforms; then, with the help big data analysis and data processing techniques, this paper calculated the fitness degree of harmonious TSR between college students and the counselors and other administrators who engaged in student management works and have interacted with them. After that, based on the calculation results of the said fitness degree, this paper built a basic interactive network for college student management works, and gave the method for dividing student management groups. At last, experimental results verified the effectiveness of the proposed algorithm.
Development of 3D-based Learning Modules for University Students

Some lecturers need suitable learning media and strategies in the learning process so that learning is effective. Previously, lecturers only used book media, handouts, and whiteboards as media, and only certain materials used media. It is also known that the obstacles in using media because there are still many lecturers who do not have the expertise to develop and use media and are not even familiar with computer technology, so learning strategies are implemented conventionally. The learning system is centered on lecturers delivering learning materials with lectures and further demonstrations of lecturers giving assignments or exercises to students. This research aims to know whether the development of 3D-based learning modules with direct learning strategies in Pattern Construction courses is worth using for students in the Department of Fashion Education at Universitas Negeri Medan and to find out whether module learning based on 3D with direct learning strategies in the Pattern Construction course is effectively used by the students.
On the Role of Social Media in Spanish Language Learning in Indian Context

Research on social media in the foreign language classroom has grown significantly in the last decade. The usage of social media has increased many times during the pandemic due to known reasons. The aim of the present study is to examine the perception of the use of social media in learning Spanish as a foreign language in a non-native context. The research was carried out in Indian Higher Education Institutions (HEIs) where Spanish is taught as a foreign language at graduate and undergraduate levels. The objective was to explore the attitudes and perceptions of the students towards the use of social media platforms for learning Spanish as a foreign language and examine the differences in its use concerning gender, background and year of study. The research study was developed in the quantitative framework with the use of a comprehensive questionnaire as an instrument in this study. The results showed that the students tend to use social media for enhancing and reinforcing their communication skills and language learning capabilities including reading, writing, and learning new phrases and structures. Moreover, the study also found the benefits of collaborative learning embedded during the use of social media, which helped in improving the learning process sophistically.
Integrating a Mobile-Learning Platform for Enhancing Clinical Teaching: The Learners’ Perspective

The role of mobile technology has become an integral part of daily activity among today’s society, including medical schools and hospitals. This study aimed to establish a mobile-learning platform for providing high-quality clinical teaching in undergraduate medical education. It was a mixed-methods design of quantitative survey and qualitative focus-group discussions to analyse the learners' view for acceptance of technology-integrated learning in clinical teaching at Universiti Sultan Zainal Abidin. Forty-two undergraduate medical students from years 3 and 5 participated in this study. The vast majority found that a mobile-learning platform was a helpful medium for the integration of leaning resources and collaboration with other health-care professionals in a clinical setting (p&lt;0.001). They generally agreed that there is an improvement in clinical competence, confidence in clinical reasoning and focusing on the discussion by providing constructive feedback (p&lt;0.001). The qualitative focused group discussion’s findings indicated that most of the participants expressed their satisfaction with improving their intellectual skills and their anticipation of achieving strategic learning via integrated bedside teaching with mobile-group discussions. Overall, the participants accepted that a mobile-learning platform integrated bedside teaching is a constructive, productive approach for enhancing and facilitating their learning in a clinical setting. This study offered a unique insight into learners’ perceptions of benefits and the limitation of technology-enhanced learning in undergraduate medical training. The essential attributes of mobile technology are crucial for technology integration in high-quality clinical teaching.
GPRE: Multi-Path Guided Recommender System Based on Graph Neural Networks

Recommender systems as an effective information filtering system can be used to obtain information through the user's explicit or implicit behavior. On the one hand finding items that may be of interest to the user. On the other hand, the recommendation facilitates the interaction between the user and the item to increase the revenue. Recommender systems have been widely used in various fields, such as e-commerce, travel recommendation, online books and movies, social networks, etc, which can satisfy the intrinsic implicit needs of users through personalized services. In recent years, the development of deep learning has further improved the performance of recommendation systems. Although these methods improve the performance of the recommendation system, when the number of users and products increases, the recommendation system may face sparsity and cold start problems, and thus cannot achieve personalized recommendations. Knowledge graphs, which are structured data, have become the choice of many algorithms due to the high quality and wide scale of the data, and therefore many recommendation algorithms combined with knowledge graphs have emerged as a popular new direction in recommendation systems. These algorithms are able to preserve the rich connections between different entities. Moreover, when constructing the features of an entity, the entities that are far away from the central entity can also be utilized. Entities are no longer only directly connected to each other. To address the shortcomings of existing recommendation algorithms, this paper designs the recommendation algorithm GPRE using graph neural networks. GPRE focuses on expressing the user's features. The graph neural network provides GPRE with a strong generalization capability for modeling, which can provide long-range semantics between users and entities, as well as selective entity selection in the auxiliary graph neural network. Explicit semantic links are established between remote and central nodes to reduce the introduction of noise. In this paper, experiments are conducted on real-world datasets and the results are compared with baselines. The experimental results show that GPRE performs well on the experimental dataset.