Transparent Aspect-Level Sentiment Analysis Based on Dependency Syntax Analysis and Its Application on COVID-19

Aspect-level sentiment analysis identifies fine-grained emotion for target words. There are three major issues in current models of aspect-level sentiment analysis. First, few models consider the natural language semantic characteristics of the texts. Second, many models consider the location characteristics of the target words, but ignore the relationships among the target words and among the overall sentences. Third, many models lack transparency in data collection, data processing, and results generating in sentiment analysis. In order to resolve these issues, we propose an aspect-level sentiment analysis model that combines a bidirectional Long Short-Term Memory (LSTM) network and a Graph Convolutional Network (GCN) based on Dependency syntax analysis (Bi-LSTM-DGCN). Our model integrates the dependency syntax analysis of the texts, and explicitly considers the natural language semantic characteristics of the texts. It further fuses the target words and overall sentences. Extensive experiments are conducted on four benchmark datasets, i.e., Restaurant14, Laptop, Restaurant16, and Twitter. The experimental results demonstrate that our model outperforms other models like Target-Dependent LSTM (TD-LSTM), Attention-based LSTM with Aspect Embedding (ATAE-LSTM), LSTM+SynATT+TarRep and Convolution over a Dependency Tree (CDT). Our model is further applied to aspect-level sentiment analysis on “government” and “lockdown” of 1,658,250 tweets about “#COVID-19” that we collected from March 1, 2020 to July 1, 2020. The experimental results show that Twitter users’ positive and negative sentiments fluctuated over time. Through the transparency analysis in data collection, data processing, and results generating, we discuss the reasons for the evolution of users’ emotions over time based on the tweets and on our models.

Privacy Rating of Mobile Applications Based on Crowdsourcing and Machine Learning

With the advent of the 5G network era, the convenience of mobile smartphones has become increasingly prominent, the use of mobile applications has become wider and wider, and the number of mobile applications. However, the privacy of mobile applications and the security of users' privacy information are worrying. This article aims to study the ratings of data and machine learning on the privacy security of mobile applications, and uses the experiments in this article to conduct data collection, data analysis, and summary research. This paper experimentally establishes a machine learning model to realize the prediction of privacy scores of Android applications. The establishment of this model is based on the intent of using sensitive permissions in the application and related metadata. It is to create a regression function that can implement the mapping of applications to score . Experimental data shows that the feature vector prediction model can uniquely be used to represent the actual usage and scheme of a system's specific permissions for the application.

Privacy Rating of Mobile Applications Based on Crowdsourcing and Machine Learning

With the advent of the 5G network era, the convenience of mobile smartphones has become increasingly prominent, the use of mobile applications has become wider and wider, and the number of mobile applications. However, the privacy of mobile applications and the security of users' privacy information are worrying. This article aims to study the ratings of data and machine learning on the privacy security of mobile applications, and uses the experiments in this article to conduct data collection, data analysis, and summary research. This paper experimentally establishes a machine learning model to realize the prediction of privacy scores of Android applications. The establishment of this model is based on the intent of using sensitive permissions in the application and related metadata. It is to create a regression function that can implement the mapping of applications to score . Experimental data shows that the feature vector prediction model can uniquely be used to represent the actual usage and scheme of a system's specific permissions for the application.

PENERAPAN PRINSIP TRANSPARANSI DALAM MEWUJUDKAN PELAYANAN PRIMA PADA DINAS PENANAMAN MODAL DAN PELAYANAN TERPADU SATU PINTU KOTA KENDARI

This study aims to determine and analyze the application of the principle of transparency and the implementation of excellent service at the Kendari City Investment and One Stop Integrated Service Office. This study uses a descriptive qualitative approach, data analysis uses interactive model analysis techniques consisting of data collection, data reduction, data presentation and conclusion drawing/verification. The results showed that the application of the principle of transparency at the Kendari City Investment Office and PTSP from the informative (informative) aspect of the agency was good enough in providing convenience to the public to obtain information either directly or indirectly about services in the field of investment and licensing, in terms of openness has been good enough in conveying information to the public openly, and is easily accessible through several channels of information delivery, on the other hand, the agency still needs to be more intense in explaining all service requirements both technical and administrative in a clear and easy to understand manner using the system. and simple language to the public, from the aspect of disclosure, it is still not optimal in informing the public of financial details and annual reports regarding revenue, financial management and assets of the organization. In terms of the implementation of excellent service in terms of the dimensions of service procedures, service costs, completion time, service products, infrastructure, the overall competence of service providers is good, with the Public Service Standards that have been standardized in 2018.

OPTIMALISASI PELAYANAN PUBLIK SECARA ONLINE PADA KANTOR WILAYAH KEMENTERIAN HUKUM DAN HAM PROVINSI SULAWESI TENGGARA

This study aims to determine and describe optimizing public services online at the regional office of the Ministry of the Law and Human Rights in Southeast Sulawesi, this study uses descriptive qualitative methods to 5 informants determined by snowball sampling technique, data analysis techniques consist of data collection, data reduction, presentation data, drawing conclusions/verification, the data obtained were analyzed qualitatively and described in descriptive form. The results of this study indicate that public services carried out online at the regional office of the Ministry of Law and Human Rights in Southeast Sulawesi are very good, this can be seen from the implementation of services with standard operating procedures that apply during the pandemic and the handling that is in accordance with what has been determined at the regional office of the Ministry of Law and Human Rights by upholding the values of professionalism, accountability, synergy, transparency, and innovation. Based on the research, one form of research on optimizing public services is the existence of a digital-based service system that makes it easier for the public to receive services, supporting facilities, and infrastructure, as well as services provided quickly and responsively at the region of the Ministry of Law and Human Rights in Southeast Sulawesi.

Research Trends and Hotspots of Q Fever Research: A Bibliometric Analysis 1990-2019

Q fever is a worldwide distributed zoonosis caused by Coxiella burnetii, a Gram-negative bacterium. Despite existence of large amount of research data on the developments related to Q fever, no bibliometric analysis of this subject is available to our knowledge. Bibliometric studies are an essential resource to track scholarly trends and research output in a subject. This study is aimed at reporting a bibliometric analysis of publications related to Q fever (2,840 articles published in the period 1990-2019) retrieved from Science Citation Index Expanded, an online database of Clarivate Analytics Web of Science Core Collection. Data was retrieved using keywords “Q fever” or “Coxiella burnetii” in title, abstract, and author keywords to describe important research indicators such as the kind and language of articles, the most important publications, research journals and categories, authors, institutions, and the countries having the most significant contribution to this subject. Finally, the emerging areas in field of diagnosis, host range, and clinical presentation were identified. Word cluster analysis of research related to Q fever revealed that major focus of research has been on zoonosis, seroprevalence, laboratory diagnosis (mainly using ELISA and PCR), clinical manifestations (abortion and endocarditis), vectors (ticks), and hosts (sheep, goat, and cattle). This bibliometric study is intended to visualize the existing research landscape and future trends in Q fever to assist in future knowledge exchange and research collaborations.

PEMBINAAN TEKNOLOGI INFORMASI DAN SISTEM OPERASIONAL BANK SYARIAH

Information technology is a knowledge that is always evolving from tools or communication which is a delivery of information through rapid communications, which relates to information problems in terms of all aspects ranging from data collection, data storage, data processing from information to the process of delivering information to people who need it. The purpose of fostering information technology and healthy internet use is an educational process by providing sufficient understanding of the current latest technology and being educated with the Islamic bank operating system, the owner of the fund invests his money so he can earn interest from the bank. If the profit sharing from the customer's funds is then distributed to those in need (eg venture capital), with a profit sharing agreement according to the agreement. Guidance on the Development of Information Technology and Bank Sariah Operational Systems at the Vocational High School Nang Nadim Batam to find out how to use good information technology and know the operating system of the Sariah bank. With this coaching, it is hoped that students will be helped in understanding the service that was carried out on January 15 and 16, 2019 at SMK Hang Nadim Batu Aji, Batam City.

Characteristics Of Spiritual and Social Values in Saman Dance Culture In Gayo Community

<p>This research is going to explain the characteristics of spiritual and social values in saman dance culture in gayo community in Aceh. Saman Gayo dance is not only unique but also has original Indonesian cultural values which is one of the media for delivering da'wah messages which contains religious values, manners, togetherness, and educational values. This research on the characteristics of spiritual and social values in the Saman Dance culture in the Gayo community is a descriptive study with a qualitative approach. The data analysis technique in this paper refers to the data analysis technique of Miles and Huberman with three stages, namely data collection, data processing, and drawing conclusions. The data collection technique in this paper is to use data collection methods that are literature study to more accurately research from a scientific perspective. The results of this paper shown that the Gayo Saman Dance has various philosophies such as singing, movement, types of Saman Dance, to its function which of course must be maintained and preserved. In the other hand, Saman Dance is used to strengthen Local Identity, good values ranging from values in the field of education, unity and integrity, friendship.</p>

Knowledge of Health Care Providers of a Tertiary Hospital Regarding Post-Operative Infection

Background: Infection is a very common post-operative complication. Now a day the knowledge about infection among healthcare provider is very essential. The study was aimed to determine the knowledge health care provider regarding the management of infection in postoperative ward. Methods: The cross-sectional study was conducted among 90 respondents (60 doctors, 50 nurses & 40 supporting staff) from January 2015 to December 2015 in Dhaka Medical College and Hospital. A semi structured questionnaire was used to obtain socio-demographic data and infection management related information from the respondents through face to face interview. In-depth interviews were taken from director of DMCH, head of the department of surgery and nurse in-charge in post-operative ward for qualitative data. After collection data were complied, summarized and analyzed. The study was approved by ethical committee of National institute of Preventive and Social Medicine. Before collection of data, written permission was taken from the director of the selected hospital & take consent from the respondents. Results: Among 150 respondents, 58% were female with 26-30 years age group. Most doctors were post-graduate & nurses were diploma in nursing. Most of doctors had knowledge about infections- 83.3% told bacterial as a type, 80% told devices as a source and 94% told diabetes as a risk factor. Both doctors and nurses had knowledge about the causes and mode of transmission of infection (90% doctors & 93.2% nurses told unsterile instrument), complication due to infection (100% doctors told sepsis & 86.7% nurses told wound become red & swelling), prevention (100% doctors &92% nurses told proper sterilization of instrument). Doctors (95% and nurse (74%) known about infection control guideline and. Doctors (47.5%) and nurses (68%) received training about infection management Among 40 staff 80% were known about infection and they knew from their colleague. 82.5% staffs known about sterilization. Conclusion: This finding had great impact for management of infection and it will be beneficial for all HCW to receive formal and periodic refresher trainings. JOPSOM 2021; 40(1): 43-50

Determining Firebrand Generation Rate Using Physics-Based Modelling from Experimental Studies through Inverse Analysis

Firebrand spotting is a potential threat to people and infrastructure, which is difficult to predict and becomes more significant when the size of a fire and intensity increases. To conduct realistic physics-based modeling with firebrand transport, the firebrand generation data such as numbers, size, and shape of the firebrands are needed. Broadly, the firebrand generation depends on atmospheric conditions, wind velocity and vegetation species. However, there is no experimental study that has considered all these factors although they are available separately in some experimental studies. Moreover, the experimental studies have firebrand collection data, not generation data. In this study, we have conducted a series of physics-based simulations on a trial-and-error basis to reproduce the experimental collection data, which is called an inverse analysis. Once the generation data was determined from the simulation, we applied the interpolation technique to calibrate the effects of wind velocity, relative humidity, and vegetation species. First, we simulated Douglas-fir (Pseudotsuga menziesii) tree-burning and quantified firebrand generation against the tree burning experiment conducted at the National Institute of Standards and Technology (NIST). Then, we applied the same technique to a prescribed forest fire experiment conducted in the Pinelands National Reserve (PNR) of New Jersey, the USA. The simulations were conducted with the experimental data of fuel load, humidity, temperature, and wind velocity to ensure that the field conditions are replicated in the experiments. The firebrand generation rate was found to be 3.22 pcs/MW/s (pcs-number of firebrands pieces) from the single tree burning and 4.18 pcs/MW/s in the forest fire model. This finding was complemented with the effects of wind, vegetation type, and fuel moisture content to quantify the firebrand generation rate.