Reviewerly/exHarmony huggingface dataset:
Are metrics measuring what they should? An evaluation of image captioning task metrics

Image Captioning is a current research task to describe the image content using the objects and their relationships in the scene. To tackle this task, two important research areas converge, artificial vision, and natural language processing. In Image Captioning, as in any computational intelligence task, the performance metrics are crucial for knowing how well (or bad) a method performs. In recent years, it has been observed that classical metrics based on n-grams are insufficient to capture the semantics and the critical meaning to describe the content in an image. Looking to measure how well or not the set of current and more recent metrics are doing, in this article, we present an evaluation of several kinds of Image Captioning metrics and a comparison between them using the well-known MS COCO dataset. The metrics were selected from the most used in prior works, they are those based on $n$-grams as BLEU, SacreBLEU, METEOR, ROGUE-L, CIDEr, SPICE, and those based on embeddings, such as BERTScore and CLIPScore. For this, we designed two scenarios; 1) a set of artificially build captions with several qualities, and 2) a comparison of some state-of-the-art Image Captioning methods. Interesting findings were found trying to answer the questions: Are the current metrics helping to produce high-quality captions? How do actual metrics compare to each other? What are the metrics really measuring?
Using Reservoir Sampling and Parallelization to Improve Dynamic Binary Instrumentation

This paper investigates two aspects of using dynamic binary instrumentation for real-time instrumentation of a distributed software systems. First, this paper investigates techniques for achieving different levels of visibility (i.e., ensuring all parts of a system are represented, or visible, in final results) into a software system without compromising software system performance. Secondly, this paper investigates how using reservoir sampling can be used to further reduce instrumentation overhead. The results of the research show that reservoir sampling can be used to reduce instrumentation overhead when compared to regular sampling methods like Constant, Percentage and Exhaustive sampling while also providing the desired system visibility.
Potential of Higher Education Institutions in Disaster Risk Reduction

Background. Indonesia is a country that has many potential disasters. Often referred to as the Supermarket Disaster state. Every individual, group and community needs to have preparedness in dealing with disasters. Preparedness can be done through training that is carried out in a planned, academic and periodic manner. So far, there is no habit of conducting disaster simulations or field rehearsals at the Health Polytechnic of the Ministry of Health in Surabaya. The identification of the potential of this higher education institution is the first step in preparing the field rehearsal module plan. The final goal is to develop a disaster preparedness module, design institutional policies and form a community disaster preparedness team. Research methods. In this study, a quantitative design was used, with a descriptive approach. Aims to identify the potential of Higher Education in disaster risk reduction efforts. The population and sample are FGD (Focus Group Discussion) participants and disaster mitigation experts/experts. The research variables were obtained from the results of literature studies and FGDs. Furthermore, the identification results are used to compile a module that can be used for Field Rehearsal guidelines. Modules are obtained in 3 ways: identification, FGD and expert consultation. The variables of this research are the existence of disaster courses, the presence of disaster course lecturers, the competence of lecturer training, the existence of training modules for lecturers, the diversity of potential sources of disaster. The results. The results of the study show as follows: 96% of campuses at the Health Polytechnic of the Ministry of Health in Surabaya have disaster courses. Lecturers of disaster courses are all from within the study program as much as 63%, a mixture of inside and outside the study program as much as 31%, and a mixture of lecturers from inside and from guest lecturers as much as 6%. Lecturers who have never attended disaster training 52%. Campuses that have never been on campus simulations are 43% and have held simulations but not regularly once a year = 31%. As many as 90% of lecturers stated that they strongly agreed to hold disaster simulation training. 34% of campuses that have the potential for 3 kinds of disasters. Conclusion: The identification results in this study indicate the importance of the existence of disaster courses, the presence of disaster course lecturers, the competence of lecturer training, the existence of training modules for lecturers, the diversity of potential sources of disaster. It is necessary to prepare a disaster preparedness training module in an effort to reduce disaster risk.
Training for Making Interactive Learning Media Based on Open Broadcaster Software (OBS) Studio for State State School Teachers Inpres Ardipura 1 Kota Jayapura

On March 2, 2020, the Unitary State of the Republic of Indonesia experienced a coronavirus disease 2019 (COVID-19) outbreak with the coronavirus disease 2019 (COVID-19) outbreak, resulting in a shift in learning practices that were previously carried out conventionally to become digital. Simultaneously, this shift from conventional to digital learning practices forces a teacher to quickly adapt and use digital technology to remain able to provide optimal educational services and not be reduced in terms of the quality of education. It is the adaptation of a teacher to digital technology which in practice in the field there are still shortcomings due to the presence of several limitations such as lack of facilities or lack of teacher competence in developing interactive learning media, which has resulted in not all teachers being able to implement online teaching and learning activities due to limitations. The. During a pandemic like this, the ability of teachers to use learning media by utilizing digital technology is essential, one of the learning applications that is currently being used as a tool for making learning materials in Indonesia is the Open Broadcaster Software (OBS) studio application which is an application video recording and live streaming that can help a teacher in making learning materials in the form of video recordings. The use of this application is very useful so that teachers can continue to provide lecture material effectively and efficiently. The implementation of the training on making interactive learning media based on Open Broadcaster Software (OBS) studio for elementary school teachers in the city of Jayapura, Inpres Ardipura 1, begins with the process of preparing for the implementation of activities after that, planning activities and training for the introduction of Open Broadcaster Software (OBS) studio in which the team will introduce the Open Broadcaster Software (OBS) studio application as a whole, starting with how to download the application, install the application, application functions to all the features contained in the application with the expected end result that all trainees can use this application. After that, then proceed to the practice of making interactive learning materials based on the Open Broadcaster Software (OBS) studio, at this stage, all participants will directly make video recordings using the Open Broadcaster Software (OBS) studio application
Assessment of Perception Factors Influencing Collaboration Between Vocational Schools and Businesses in Practical Training

As the global economy grows, the labor market's demand for qualified employees increases. However, the present situation is not able to meet labor market requirements; laborers are inexperienced, and their professional skills are inadequate. Collaboration between vocational schools and businesses is essential for educational activities. This study aimed to investigate the degree of perception, the contents, the forms, and the variables that influenced collaboration activities in practical training between vocational schools and businesses. A quantitative study method was conducted on 570 respondents (195 teachers, 125 managers, and 250 graduated students) randomly recruited from four provinces/cities in Vietnam. The results show that participants perceived the importance of practical teaching coordination activities in vocational schools and businesses. In addition, some contents and forms were applied to collaboration teaching activities between vocational schools and businesses Future studies should consider similar experimental studies to develop appropriate practical training collaboration activities models based on perception levels and factors impacting activities.
ANALISIS PENGGUNAAN APLIKASI WORDWALL PADA PEMBELAJARAN IPA KELAS IV DI SDN CIRACAS 05 PAGI

The research objective to be achieved in this writing is to improve the learning outcomes of class IV (Four) students at SDN Ciracas 05 Pagi by using the wordwall application in science subjects. This type of research uses descriptive qualitative research methods. Qualitative descriptions are activities carried out to obtain data that are what they are in accordance with the facts that occur in the field. The stages of data collection are observation and interviews. Sources of data in this study using primary data sources and secondary data. Data analysis techniques are data collection, data reduction, data presentation and conclusion drawing. The results of the research on wordwall learning media is one of the interactive media that is easy to use, and can increase student interest and motivation in learning science class IV at SDN Ciracas 05 Pagi. Wordwall learning media can be used as one of the media used in bold learning as it is today. In addition to being easy and inexpensive to use, this media also has many alternative choices in presenting material and questions. So that educators can use other alternative options as other variations in packaging other subject matter. Fun and varied media will make students more active and more interested in following the learning process boldly.
The Impact of Employee Engagement on Organizational Performance: A Study on Malaysian Pharmaceutical Organization

Organizational performance and employee engagement are vital elements for sustainability of business entities.In recent years, many organizations had suffered heightened turnover rate and pharmaceutical firms are no exceptions to it.Many pharmaceutical manufacturers have intensified their productions in achieving a faster response to the Nobel Coronavirus , where employee engagement has begun to suffer with high turnover rate.Hence, the current study investigates the impact of employee engagement on organizational performance in a local pharmaceutical manufacturing company.It is important as engaged employees cultivates good work culture, retention rate and contribute towards enhancement of business functioning, especially in pandemic-stricken environment.Online survey method was employed for data collection and it was analyzed with IBM Statistics Version 26.The study reported that working environment and training and development contribute positively towards employee engagement and thus, to organizational performance.Meanwhile, it revealed statistically non-significant relationship between rewards and recognition with employee engagement.Also, a significant partial mediation effect of employee engagement was found between factors of employee engagement and organizational performance.In future studies, other factors can be studied with respect to local and international pharmaceutical organizations with broader sample size to confirm the casual relationship found in this study.
Commentary: Tracing Management and Epidemiological Characteristics of COVID-19 Close Contacts in Cities Around Chengdu, China

GENERAL COMMENTARY article Front. Public Health, 08 July 2022Sec. Digital Public Health https://doi.org/10.3389/fpubh.2022.913189
Design of Augmented Reality as a Promotional Media at University of Raharja

Augmented Reality is a technology that combines two-dimensional and or three-dimensional virtual objects into a natural three-dimensional environment and then projects these virtual objects in real-time. Multimedia technology has increasingly been applied in various industrial sectors as an information media for various purposes and is no exception as a promotional media. The University of Raharja, as an example of an ICT college in Tangerang, is currently experiencing very rapid development. School of Management and Computer Information Raharja evidenced it into the University of Raharja. This is, of course, a very positive impact, especially for the promotion activities of the University of Raharja. In this study, an Augmented Reality design for the University of Raharja was made to be used as promotional media.
Java-Based Goods Data Recording Application System at Achfi Balamoa Store

Background: The use of information technology applied through information systems provides convenience to users, especially shop owners in doing work, not only in the recording of goods but also in the creation of goods data reports. Goods data is an important element in a store that must be managed properly and requires thoroughness so that applications are needed to minimize errors. Aim: It makes it easier the process of recording data of goods that previously still use manual systems. Method: In researching making application systems, the method used is a qualitative method, where the information obtained is sourced from various collections of data analysis and existing theories and then conclusions are drawn to obtain new theories. Findings: The result of this application system is that the collection of goods will be faster and reports of the entry and exit of goods more accurate. The research method used is a qualitative method, where the information obtained is sourced from various data analysis sets and existing theories and then concluded to get a new theory.
Proper Noun Embedding Model for the Korean Dependency Parsing

Dependency parsing is a decision problem of the syntactic relation between words in a sentence.Recently, deep learning models are used for dependency parsing based on the word representations in a continuous vector space.However, it causes a mislabeled tagging problem for the proper nouns that rarely appear in the training corpus because it is difficult to express out-of-vocabulary (OOV) words in a continuous vector space.To solve the OOV problem in dependency parsing, we explored the proper noun embedding method according to the embedding unit.Before representing words in a continuous vector space, we replace the proper nouns with a special token and train them for the contextual features by using the multi-layer bidirectional LSTM.Two models of the syllable-based and morpheme-based unit are proposed for proper noun embedding and the performance of the dependency parsing is more improved in the ensemble model than each syllable and morpheme embedding model.The experimental results showed that our ensemble model improved 1.69%p in UAS and 2.17%p in LAS than the same arc-eager approach-based Malt parser.
Open- and Closed-Loop Neural Network Verification using Polynomial Zonotopes

We present a novel approach to efficiently compute tight non-convex enclosures of the image through neural networks with ReLU, sigmoid, or hyperbolic tangent activation functions. In particular, we abstract the input-output relation of each neuron by a polynomial approximation, which is evaluated in a set-based manner using polynomial zonotopes. While our approach can also can be beneficial for open-loop neural network verification, our main application is reachability analysis of neural network controlled systems, where polynomial zonotopes are able to capture the non-convexity caused by the neural network as well as the system dynamics. This results in a superior performance compared to other methods, as we demonstrate on various benchmarks.
Generating Synthetic Monthly Net Radiation Data Conditioned on Wind Speed

Aims: This study aimed at generating synthetic monthly net radiation data conditioned on wind speed over Port Harcourt, Benue, Kano and Enugu in Nigeria.&#x0D; Duration of Study: The daily maximum and minimum Relative-Humidity, maximum and minimum air temperature, solar radiation and wind speed data was obtained from the International Institute of Tropical Agriculture (IITA) Ibadan, Nigeria for the period of thirty-four (34) years (1977-2010).&#x0D; Method: The Penman-Monteith (FAO-56) step by step method was used to compute the daily net radiation. The autocorrelation function was used to establish the fact that net radiation and wind speed exhibit the Markov property. Lastly, a two – state Markov Chain model and an indicator function of energy balance and imbalance was developed and used in the course of this work.&#x0D; Results: The finding reveals that net radiation is surplus over the study areas. The Lag one autocorrelation coefficient confirms the fact that an actual day net radiation and wind speed state depends on the previous day’s state. The monthly steady state probabilities of surplus net radiation conditioned on low wind speed is higher compared to monthly steady state probabilities of surplus net radiation conditioned on high wind speed. The indicator function of energy balance or imbalance reveals that there is energy imbalance over study areas.&#x0D; Conclusion: The generated synthetic net radiation data conditioned on wind speed preserved the characteristics of actual net radiation data when compared as observed in the study. This data is essential in the study of climate change, weather monitoring, agricultural meteorology and estimation of evapotranspiration.
Application of Motion Video Analysis System Based on SISA Neural Network in Sports Training

This paper proposes the study of motion video image classification and recognition, extracts the motion target image features, designs the image classification process, and establishes the neural network image classification model to complete the image recognition. In view of the different angles of the same element, the motion video image classification and recognition under the neural network are completed by using the error back-propagation algorithm. The performance of the proposed method is verified by simulation experiments. Experimental results show that the proposed method has a high recognition rate of moving video images, the accuracy rate is more than 98%, and the image recognition classification is comprehensive. The proposed method can classify the elements in the motion video image, which solves the technical problem that the traditional method cannot identify unclear images and has low recognition accuracy.
Analisis Sentimen dan Klasifikasi Tweet Terkait Mutasi COVID-19 menggunakan Metode Naïve Bayes Classifier

Towards the end of 2019 in Wuhan City, China, a new type of Corona Virus was discovered which has the scientific name COVID-19 and is a type of virus that causes acute disorders in the human respiratory system. The spread of this virus is very fast and causes mutations of this virus to a more lethal stage than before. Thus, sentiment analysis is expected to be able to determine the trend of public assessment of the COVID-19 mutation. Naïve Bayes Classifier is a method used in research. This method can classify data or opinions into two sentiments, namely positive and negative. The research data comes from Twitter which is taken using the Twitter API with the keyword "covid mutation", for data processing several processes are carried out, namely sentiment classification, data cleaning, and preprocessing so that the final result is obtained. The test results from this study show that the Naïve Bayes Classifier method has an accuracy of 86.67% with an f1-score of 82.00% on positive sentiment and 89.00% on negative sentiment. Based on the results of the study, it can be concluded that the Naïve Bayes Classifier method can be used to analyze sentiment data from tweets about the COVID-19 mutation with an accuracy of 86.67%.
Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA

Data Science is one of the fastest growing interdisciplinary field and has many applications in various disciplines. The actual motivation of data science came from John Tukey. In his seminal paper, in 1962, he presented the idea of data analysis which is now the field of data science. Several algorithms for data science related to statistical analysis have been developed and applied over variety of datasets since 1962. In this field, the significant development began with the aid of high performance computers that help to analyse a massive datasets. In this paper, we study the statistical analysis of the traffic stops in Nashville, Tennessee, USA for the year 2011--2021. Data is taken from the Stanford open policing project. Analysis is based on total number of 3071706 traffic stops. In this paper, we consider and investigate various aspects. This study comprises gender comparison (male vs female) and race comparison (black vs white) for different traffic offences. Complete findings and possible gaps are discussed in the conclusion.
Block Chain Based Information Storage with Concealment and Authentication in Internet of Things

Abstract: In both academics and industry, the Internet of Things (IOT) is driving a digital revolution. It makes people's life easier, but it also raises concerns about security and privacy. Blockchain, a decentralised database based on cryptographic principles, has the potential to impact a wide range of industries, including manufacturing, banking, and commerce. Though researchers offer a variety of security and data storage solutions, only a few are suitable for WSN- enabled IoTs. As a result, a blockchain- based decentralised framework with authentication and concealment- preserving techniques has been established for secure communication in WSN-enabled IoTs. Cluster heads send the collected data to the Base Station (BS) in this scheme. As a result, BS keeps track of all the key parameters on the distributed blockchain, which is quite extensive.
Face Mask Detection and Social Distance Monitoring System for COVID-19 Pandemic

&lt;p&gt;In this paper, a novel pipeline for developing an end-toend masked/non-masked face detection method is proposed&lt;br&gt; to improve the effectiveness of real-time surveillance systems&lt;br&gt; at public places.&lt;/p&gt;
The Effects of Computer-mediated communication anxiety and Empathy on English Reading Comprehension through the Mediated Effects of Self-directed Learning Ability and Instructor Credibility

This study aimed to investigate how CMCA and empathy influenced on English reading comprehension through the mediated effects of self-directed learning ability and instructor credibility. A small-scale online survey about student characteristics was carried out. Data were collected from 76 college students and 65 students’ responses were analyzed. CMCA showed a direct effect on self-directed learning ability and empathy showed a direct effect on instructor credibility. Self-directed learning ability significantly affected on English reading comprehension. Lastly, the effect of CMCA through the mediated effects of self-directed learning ability and instructor credibility was significantly effective. The results of the study suggested that researchers can take advantage of using teaching strategies to help students decrease CMCA and develop self-directed learning ability to improve English reading comprehension in online English classes.