Reviewerly/exHarmony huggingface dataset:
Analisis Hubungan Antara Intensitas Membaca, Isi Buku Panduan Dan Relasi Pembaca Buku Panduan Terhadap Pemenuhan Kebutuhan Informasi Bagi Pegawai Museum Kepresidenan RI Balai Kirti

In an agency, institution or organization is very closely related to a media. Media published in an agency, institution or organization aims to be used as internal media that can be used by employees as a fulfillment of information needs. This study aims to examine employee exposure/reading intensity with the fulfillment of employee information needs at the Indonesian Presidential Museum Balai Kirti, to determine the contents of the museum manual with the fulfillment of employee information needs at the Indonesian Presidential Museum Balai Kirti, and to determine the relationship between museum manual readers and fulfillment of employee information needs at the RI Presidential Museum Balai Kirti. This study uses descriptive analysis techniques, validity tests, reliability tests and Rank Spearman correlation tests, with a census sample technique, the number of employees is divided into 19 civil servants, 8 PPNPN staff, and 4 apprentice students as educators at the Indonesian Presidential Museum Balai Kirti . The method of collecting data is through questionnaires (google form), interviews, field observations, and the theory of uses and gratification. The results of this study in the validity test showed valid results from each indicator contained in variable X and Y. In the reliability test, variable X and Y showed RELIABLE results. In the Spearman Rank correlation test, it shows a SIGNIFICANT result value and the correlation number has a positive relationship with a strong relationship. In the t test hypothesis test there is one result showing the independent variable (X2) which has no effect on the dependent variable (Y). Then the F test shows significant results simultaneously between the three independent variables (X1), (X2), and (X3) on the dependent variable (Y). The conclusion of this research is that the existence of this museum manual has an influence on fulfilling the information needs of employees at the Indonesian Presidential Museum, Balai Kirti. Keywords: Museum, Guidebook, Information
Insurance Request and Approval Process for Proton Beam Therapy in Patients with Esophageal Cancer

Randomized data suggests proton beam therapy (PBT) reduces the risk and severity of toxicities when compared to IMRT in the neoadjuvant or definitive setting for esophageal cancer. As a critical barrier to access to PBT, we investigate our institutional experience with the prior authorization (PA) process for patients (pts) with esophageal cancer.One hundred and four (104) consecutive pts with esophageal cancer from 2016-2020 at one institution for whom PBT was recommended were analyzed. Data was collected from the customer relations management (CRM) database and electronic health record. Patient characteristics, tumor characteristics, treatment parameters, types of insurance, and clinical outcomes (OS, LF, DF) were recorded. Timepoints of the steps of the PA process included the PA team's initial inquiry, the initial decision, first through third appeals, and outcomes of each appeal (the third of which was regarded as the final decision). Rates of approval and successful appeal were calculated. Logistic and Cox regression models were used to evaluate whether insurance decisions were associated with clinical factors or clinical outcomes.Approval rates by Medicare (n = 68) and private insurance (n = 36) were 100 and 42% on initial request, at a median (range) 0 and 3 (0-15) days from inquiry to determination, respectively. All 21 pts initially denied coverage appealed the decision. Overall, denial was overturned in 48% of pts (median [range] time, 14 [7-36] days from initial inquiry [FIQ]), i.e., PBT was eventually approved for them. The remaining pts proceeded with self-pay or photon therapy. Upon first appeal, 14 pts were denied (median [range] time, 7 [1-26] days FIQ). Upon second appeal, 6 pts were denied (median [range] time, 18 [7-34] days FIQ). Finally, upon third appeal, 2 pts were denied and 1 patient was approved (median [range] time, 2 [20-39] days). Medicare insurance (P < .01) and possessing secondary insurance (P = .02) were found to be associated with increased likelihood of initial approval. Notably, a trend was found between clinical trial enrollment and decreased likelihood of initial approval. Neither initial nor final insurance decisions were found to be associated with OS, LF, or DF.Despite randomized data and policy efforts supporting the use of PBT in esophageal cancer, pts with private payers experience treatment delays and often eventual denial after appeal.
Naive Bayes Classification

The data mining methods we have been learning all favor numerical data: linear regression, LDA, k-means clustering, logistic regression, K-NN, and hierarchical clustering. Naïve Bayes favors categorical data, however. Because of its simplicity, Naïve Bayes data mining method is much more efficient compared to many other data mining methods, while its performance can still match most other data mining methods.
Decision Trees

Decision tree is probably the most intuitive data classification and prediction method. It is also used frequently. While most of the data mining methods we have learned are parametric, decision tree is a rule-based method. The most critical concept in understanding decision trees is entropy which will be explained soon. A tree is composed of nodes and the leaves are the bottom nodes. At each node except for the leaf nodes, a decision must be made to split the node into at least two branches. Figure 10-1 depicts a sample decision tree structure.
K-Means Clustering

Please download the sample Excel files from . Double-click Chapter3-1a.xlsx to open it.
Hierarchical Clustering and Dendrogram

You may wonder why I do not explain hierarchical clustering right after k-means clustering. Well, there are some reasons. The first one is that Excel is not as suitable for hierarchical clustering or dendrogram as it is for k-means clustering. I would like to introduce a few more other popular data mining methods before diving into this challenging topic. The second reason is that I consider it suitable to explain hierarchical clustering right after K-NN. The last reason is because of dendrogram. To draw a dendrogram in Excel requires quite some manual work, which could be boring to some learners.
Artificial Neural Network

A data mining book without artificial neural network is not complete. By its name, we can tell that artificial neural network mimics the architecture and biological process of human neurons. In our brain, millions of neurons work together to generate corresponding outputs from various inputs. This conceptual understanding is taken to develop artificial neural network method, though how neurons work together biologically still needs much research to elucidate.
Robot Path Planning Algorithm Based on Improved Q-Learning in Dynamic Environment

A Q-learning path planning algorithm is proposed for the path planning problem based on the artificial potential field method in an unknown environment. First of all, an algorithm that the value of exploration coefficient decreases with exploration step increasing is put forward. In the process of Q value initialization, the artificial potential field method is introduced. Besides, obstacle area potential energy value is zero. Secondly, after adding new obstacles in the original environment, the mobile robot uses the combination of the trained Q table and the repulsive field thinking to avoid obstacles in the changed environment. The simulation results show that the improved algorithm is more able to learn adaptation in the path planning than the traditional Q-learning algorithm, and the optimal path step convergence speed is faster, and at the same time, it also has a good obstacle avoidance ability for dynamic obstacles, which verifies the feasibility and efficiency of the method.
Adapting Faster R-CNN and Video Inpainting for Badminton Player Detection

In badminton matches, player recognition and tracking can give crucial information to help coaches make better decisions in future matches. Consequently, it is also challenging to manually observe player movement in badminton video matches. Several visual tracking techniques have previously been adopted in sports to improve the analyzing process. In the current study, the authors focused on identifying badminton players that use Faster R-CNN method (Faster Region Convolutional Neural Network) in the video of the badminton game. To speed up the preparation of the dataset, Video Inpainting method is applied. Video Inpainting helps to eliminate the area that interrupts the detection process and also turns badminton video match into 540 image frames for training, evaluating, and testing the model. The dataset was collected from three badminton video matches with 4, 6, and 8 seconds long and 29,7 FPS, respectively. As a result, Faster R-CNN can recognize the player with impressive. The average accuracy value was obtained by up to 0.8048.
Video Injection Attack Detection on CCTV Using Ensemble Learning with Random Forest Classification

CCTV cameras, often known as surveillance cameras, are among the most sophisticated security systems now available. Even though surveillance cameras (CCTV) are a security tool, it is common for them to be targeted to conceal a crime caught on camera. Video injection is one of the methods used to compromise surveillance cameras (CCTV). Video injection attacks insert live video feeds, resulting in a loss of data integrity that can impede or even alter the absolute truth. This paper employs the ensemble learning approach to recognize video injection attempts on security cameras. Ensemble learning utilized here is random forest and support vector machine (SVM) estimators. The Random Forest estimator-based model yields a f1-score value of 91% and an accuracy of 93% with a total dataset of 600 data, while the Support Vector Machine (SVM) estimator yields a f1-score value of 84% and an accuracy of 87% with a total dataset of 12000 data. The accuracy of Random Forest is fairly high and may be used to identify video injection attacks.
Design of Mobile Geographic Information System Based on Android

In the era of rapid development of mobile internet, geographic information systems based on mobile technology are deeply integrating into our lives and changing the traditional GIS work mode. With the transformation of GIS in various fields, it is completely feasible to apply the latest GIS technologies such as map application programming interface to mobile geographic information systems. This paper analyzes the system requirements, overall design, and detailed design of a city's mobile geographic information system development. Using ESRI's ArcGIS Runtime mobile development package and ArcGIS API for Android-based mobile GIS development technology, functions such as online tile service and feature service download, local feature service editing and uploading, map zooming and panning, GPS positioning, layer control, map annotation, nearby query, data query, distance and area measurement, statistical analysis, data comparison, and data management were implemented, ultimately achieving an Android-based mobile geographic information system.
MSViT: A video anomaly detection for real time unconstrained environment

Most of the previously proposed approaches are either based on CNN or transformer. CNN due to its spatial inductive bias can only learn the local representation whereas due to the absence of this property, the transformer-based approaches can learn only the global representation. Thus, the approaches based on these techniques are generally heavy as they required deeper and wider layers to learn visual representation. In addition, these approaches are mainly designed for the constrained environment. In this paper, we proposed the novel approach MSViT using MobileNet and a transformer that can extract both the local and global features from the incoming frames. Our approach is light, and shallow and can detect the video abnormal activities in both the constrained and unconstrained environment in real-time with high accuracy. The abnormal object recognized by our approach is further detected and tracked using the YOLO and DeepSORT algorithms. For the evaluation, we used our custom unconstrained environment dataset GEU and for the comparison, we used constrained environment dataset Ped1, Ped2, CUHK Avenue, and ShanghaiTech for which we obtain AUC of 97.58%, 97.21%, 94.32%, and 95.56% and EER of 3.25%, 3.49%, 6.8%, and 8.25% respectively. Our approach has an average running time of 0.00785 (127 FPS).
A Number Recognition Method Based on Improving Lightweight ResNet

This paper proposes a lightweight ResNet for highly accurate recognition of the numbers of wheel dials. The proposed residual network is based on an optimized ResNet-18 network with the depthwise separable convolution and Dropout function. In addition, a case study on automatic water meter readings based on captured images is presented. Compared to existing methods, the results demonstrate the improved recognition accuracy of this method is up to 99.8% and increased computation speeds with reduced model parameter size.
QASports: A Question Answering Dataset about Sports

Sport is one of the most popular and revenue-generating forms of entertainment. Therefore, analyzing data related to this domain introduces several opportunities for Question Answering (QA) systems, such as supporting tactical decision-making. But, to develop and evaluate QA systems, researchers and developers need datasets that contain questions and their corresponding answers. In this paper, we focus on this issue. We propose QASports, the first large sports question answering dataset for extractive answer questions. QASports contains more than 1.5 million triples of questions, answers, and context about three popular sports: soccer, American football, and basketball. We describe the QASports processes of data collection and questions and answers generation. We also describe the characteristics of the QASports data. Furthermore, we analyze the sources used to obtain raw data and investigate the usability of QASports by issuing "wh-queries". Moreover, we describe scenarios for using QASports, highlighting its importance for training and evaluating QA systems.
Increasing the accuracy of handwriting text recognition in medical prescriptions with generative artificial intelligence

The object of the research is a system for recognizing handwritten text in medical prescriptions. The peculiarities of handwriting, the variety of calligraphy styles, as well as the specificity of medical prescriptions, create many problems and challenges for recognition algorithms, causing errors and reducing recognition accuracy. The work presents a new system with additional components of post-processing the recognition results to increase the accuracy of the final results. An algorithm for combining words into lines and blocks is proposed, which makes it possible to group words while preserving contextual connections between them. Also, a generative neural network with a large language model is used to analyze the recognition result and correct possible errors. The results of the testing show an improvement in recognition accuracy by 0.13 %. Successful cases of generative artificial intelligence usage are analyzed, as well as examples of the results deterioration, that are related to grammatical errors in the initial input data. The obtained results show the use of generative artificial intelligence as an additional step for processing the recognition results really can improve the accuracy of text recognition systems. The results of the study can be used for further experiments to improve recognition results in other tasks related to text recognition and in related fields.
THE NOUN PHRASE AND ITS QUALITY OF CATEGORY SHIFT USED IN THE NOVEL THE FAULT IN OUR STARS BY JOHN GREEN

This research aimed to find out what types of category shifts were used in noun phrases and to find out quality assessments in the novel The Fault in Our Stars by John Green. The researcher used a descriptive qualitative method to analyze the noun phrases of category shift used in the novel. Data collection techniques used reading the novels, identifying the noun phrase found in both novels, finding the meaning of the words and phrases, and classifying them. Data analysis techniques used data reduction, data display, and drawing conclusions. The results showed that 102 data of category shifts were found in this research. There were 47 data for structure shift as the highest frequency and 6 data for class shift as the lowest frequency. Unit shifts found in this research were 38 data, and intra-system shifts occurred with 11 data. In the quality assessment of the category shift aspects, there were 78 data (76,5%) categorized as accurate. 20 data (19,65%) were less accurate, and 4 data (3,9%) were inaccurate. Moreover, in the acceptable aspect, the researcher found 92 data (90,2%) were acceptable, 9 data (8,8%) were less acceptable, and 1 data (0,98%) was unacceptable. The last aspect was readable; 90 data (88,2%) were readable, 9 data (8,8%) were less readable, and 3 data (2,9% ) were not readable. The conclusion of this research was the highest frequency of category shift was structure shift, and the lowest frequency was class shift. For quality assessment, the acceptable translation gained the biggest frequency, while the unacceptable got the lowest frequency in this research.
Deep Sentiment Classification using Topic Modeling for Covid-19

Objectives: To extract various topics related to Covid-19 from Twitter API using LDA topic modelling technique and to analyse the sentiment of the people about the extracted topics. An interactive Q/A system with both voice and text interface is also proposed to guide COVID-19 related decision-making. And also to summarize the tweets containing a query and to suggest suitable solutions. Method: The proposed extracts Covid-19 related tweets from twitter API and uses Natural Language Process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. The training dataset consists of 3,38,666 COVID 19 related comments and the testing dataset consists of 1,12,888 comments. LSTM recurrent neural network is used for sentiment analysis of the extracted tweets and to produce summary for each topic identified through topic modelling. Findings: The accuracy comparison has been done for the existing system against the proposed model with respect various machine learning classifiers. The findings are- LSTM gives an accuracy of 79.5%, the Naïve Bayes classifier gives the accuracy of 74%, the Multinomial Naïve Bayes gives an accuracy of 74.5%, whereas the linear regression classifier achieves an accuracy of 76%, KNN classifier achieves an accuracy of 74.5% and the random forest with an accuracy of 75.5%. Novelty: The proposal of interactive Question Answering system is first of its kind. This work sheds light on the importance of using public opinions and suitable computational techniques to understand issues surrounding Covid 19 and to guide related decision-making. Keywords: COVID19; LDA Topic Modeling; LSTM; Sentiment Analysis; Q/A System
ANALYSIS OF FACTORS AFFECTING THE ADHERENCE OF TAKING ANTITUBERCULOSIS DRUGS AT PUSKESMAS KOTA MALANG

Tuberculosis (TBC) is an infectious disease caused by Mycobacterium tuberculosis. The high mortality rate from TBC is associated with cases of resistance due to low adherence to medication. This study aimed to determine the effect of age, number of family members, and family support on adherence to antituberculosis drugs. The study was observational using a cross-sectional design. The subjects were 52 outpatients with tuberculosis who met the inclusion and exclusion criteria during the July-August 2022 period at Puskesmas Malang. The results of the study of the most age were 26-35 years as much as 26.92%. The highest number of family members was 3-4 people at 61.54%. Most family support was in the strong category at 86.54%. The results of the ordinal regression test showed that age had a p value of 0.045. The number of family members had a p value of 0.127. Family support had a p-value of 0.040. The conclusion was that age and family support affected medication adce with (p &lt; 0.05). The number of family members did not affect medication adherence (p &gt; 0.05). Age, number of family members, and family support simultaneously influenced medication adherence by 23.7%, while 76.3% were influenced by other factors that were not studied. Keywords: Adherence to taking medication, Family motivation, Tuberculosis
Multi-dimensional fusion: transformer and GANs-based multimodal audiovisual perception robot for musical performance art

In the context of evolving societal preferences for deeper emotional connections in art, this paper explores the emergence of multimodal robot music performance art. It investigates the fusion of music and motion in robot performances to enhance expressiveness and emotional impact. The study employs Transformer models to combine audio and video signals, enabling robots to better understand music's rhythm, melody, and emotional content. Generative Adversarial Networks (GANs) are utilized to create lifelike visual performances synchronized with music, bridging auditory and visual perception. Multimodal reinforcement learning is employed to achieve harmonious alignment between sound and motion.The study leverages Transformer models to process audio and video signals in robot performances. Generative Adversarial Networks are employed to generate visually appealing performances that align with the musical input. Multimodal reinforcement learning is used to synchronize robot actions with music. Diverse music styles and emotions are considered in the experiments. Performance evaluation metrics include accuracy, recall rate, and F1 score.The proposed approach yields promising results across various music styles and emotional contexts. Performance smoothness scores exceed 94 points, demonstrating the fluidity of robot actions. An accuracy rate of 95% highlights the precision of the system in aligning robot actions with music. Notably, there is a substantial 33% enhancement in performance recall rate compared to baseline modules. The collective improvement in F1 score emphasizes the advantages of the proposed approach in the realm of robot music performance art.The study's findings demonstrate the potential of multimodal robot music performance art in achieving heightened emotional impact. By combining audio and visual cues, robots can better interpret and respond to music, resulting in smoother and more precise performances. The substantial improvement in recall rate suggests that the proposed approach enhances the robots' ability to accurately mirror the emotional nuances of the music. These results signify the potential of this approach to transform the landscape of artistic expression through robotics, opening new avenues for emotionally resonant performances.