Reviewerly/exHarmony huggingface dataset:
Deep Learning Based on Attention in Semantic Segmentation: An Introductory Survey

Semantic segmentation refers to labeling each pixel in the scene to its belonging object. It is a critical task for many computer vision applications that requires scene understanding because It attempts to mimic human perceptual grouping. Despite the unremitting efforts in this field, it is still a challenge and preoccupies of researchers. Semantic segmentation performance improved using deep learning rather than traditional methods. Semantic segmentation based on deep learning models requires capturing local and global context information, where deep learning models usually can extract one of them but is challenging to integrate between them. Deep learning based on attention mechanisms can gather between the capturing of local and glopal information, so it is increasingly employed in semantic segmentation. This paper gives an introductory survey of the rising topic attention mechanisms in semantic segmentation. At first, it will discuss the concept of attention and its integration with semantic segmentation requirements. Then, it will review deep learning based on attention mechanisms in semantic segmentation. Index Terms— attention concept, computer vision, deep learning, semantic segmentation.
Impact of the Covd-19 Pandemic on the Mental Health of Nurses

The healthMental health is a state of psychological, emotional and social well-being that enables people to cope with life's challenges, realize their potential and contribute to society. The new coronavirus pandemic is triggering mental illness in health professionals, especially those in the nursing team. Faced with the pandemic scenario, nursing professionals can acquire harmful results for mental health, triggering reflexes of their hard work routines. The problematization has to do with the health sector presenting challenging situations for health professionals, as workers often have to deal with different adversities in their daily professional lives. Among the difficulties experienced were those related to the nurses' mental health. Given the above, the problem of this research is: How does mental health affect the health of nurses in times of a pandemic? This article aims to analyze the impact of the COVID-19 pandemic on the mental health of nursing teams and what are the strategies to face this challenge. The methodology used in this article is a research with a quantitative approach, with a descriptive character through a bibliographic review.
Liability for AI

The book analyses in-depth the issues raised by the European Parliament resolution of October 20, 2020 calling for an “EU Regulation on Liability for the Operation of Artificial Intelligence-Systems” which have now been followed up by the legislative proposals for an AI Liability Directive and a revised Product Liability Directive, published by the European Commission on September 28, 2022. These proposed new legal acts, which may lead to a far-reaching reshaping of liability law at European and national level, were discussed at the colloquium as the first expert event on this subject
Sports injury detection mechanism based on multi-sensor fusion

Cardiovascular disease and muscle damage caused by human sports injuries are increasing. Therefore, it is of great research significance to greatly reduce the damage to the human body through sports injury monitoring, detecting the degree of injury, and taking effective measures in time. In this paper, we propose a sports injury detection mechanism based on multi-sensor fusion. First, we determine the motion area of the human body by computing the cumulative difference between frames. Second, we utilise wavelet method and AutoEncoder model to design two feature extraction methods. Third, we combine the extracted features to detect the sports injuries. The experimental results verify the effectiveness of our proposed method.
VeriRange: A Verifiable Range Query Model on Encrypted Geographic Data for loT Environment

In the era of the Internet of Things (IoT), the rapid development of cloud computing has been advancing location-based services (LBS). To enjoy the considerable advantages of lower cost and higher performance of cloud computing, it has become the first choice for most IoT enterprises to outsource data and services to public clouds. However, the privacy protection of data and the integrity of query results cannot be effectively guaranteed since public clouds cannot be fully trusted. In this paper, we propose a lightweight verifiable range query scheme (namely VeriRange). First, a pair of mutually perpendicular locality-sensitive hashing (LSH) is adopted to divide the original geographic data into subsets. Then a pivoted k dimensional (PKD) tree and a height-balanced binomial search tree (i.e., AVL tree) are established on the client and the cloud respectively to accelerate the processes of queries. To ensure the verification of query results, a keyed hash function is utilized to generate a verification tag for each subset. Verification tags in the public cloud are encrypted and independent of queries. It ensures fast verification of query results because the verification tags are not repeatedly calculated on each query. Formal security analysis shows VeriRange ensures the privacy of data, queries, and results. Experimental studies were conducted on real and synthetic datasets and demonstrated that the query and verification time in VeriRange is almost 2-3 orders of magnitude faster than that in state-of-art schemes.
The Development of an Android-Based Educational Game “Orang Kayo Hitam” Jambi Folklore to Improve Student Motivation at Al-Falah Kindergarten Jambi

Fairy tales based on folklore are the right media to raise awareness of moral values. However, practically, it is often found that students are not interested in this kind of learning activity. The lack of interest in participating the learning activities can be caused by the learning media used by the teacher that is not attractive enough. This study developed and tested the feasibility and the effectiveness of an Android-based Orang Kayo Hitam Jambi folklore educational game. This study used the research and development method with the 4D model consisting of defining, designing, developing, and disseminating. The result of this study shows that the product feasibility assessment score through product validation from media experts was 3.8 (very feasible) and from material experts was 3.85 (very feasible). The effectiveness of this android-based Orang Kayo Hitam Jambi folklore educational game was tested through the hypothesis. The type of experimental research method used is One-Group Pretest-Postest Design in which the experiment is carried out in one group without a comparison group. The sample of this study were 30 students at Al-Falah Kindergarten. The result of the sig.(2-tailed) effectiveness test shows that the android-based Orang Kayo Hitam Jambi folklore educational game increases students' motivation in the learning process.
MEDICAL BOT USING ARTIFICIAL INTELLIGENCE

Nowadays people hardly try to consult a doctor for diagnosing disease, they instead search in internet sources, where the information is scattered and sometimes, required medical advice is not available. So, it is impractical to find the required information. People sometimes ignore the symptoms and later regret their decision. Healthcare is very important to lead a good life. However, it is very difficult to obtain the consultation with the doctor for every health problem. The idea is to create a medical chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. This will help to reduce healthcare costs and improve accessibility to medical knowledge through medical chatbot. Chatbots are computer programs that use natural language to interact with users. The chatbot stores the data in the database to identify the sentence keywords and to make a query decision and answer the question. Named Entity recognition and cosine similarity is performed to make a reliable reply to the user.
AUTOMATED COIN RECOGNITION SYSTEM USING ANN

Coins are integral part of our day to day life. We use coins everywhere like grocery store, banks, buses, trains etc. So it becomes a basic need that coins can be sorted and counted automatically. For this it is necessary that coins can be recognized automatically. In this paper we have developed an ANN (Artificial Neural Network) based Automated Coin Recognition System for the recognition of Indian Coins of denomination `1, `2, `5 and `10 with rotation invariance. We have taken images from both sides of coin. So this system is capable of recognizing coins from both sides. Features are extracted from images using techniques of Hough Transformation, Pattern Averaging etc. Then, the extracted features are passed as input to a trained Neural Network. 97.74% recognition rate has been achieved during the experiments i.e. only 2.26% miss recognition, which is quite encouraging.
Design Of The Inventory Application Using The Laravel Framework Based On PT. Nelson Mandiri Sukses Abadi

The purpose of this study is to develop an inventory application for PT. Nelson Mandiri Sukses Abadi because the warehouse division still records data recaps manually in books, which can be time consuming and prone to errors. PT. Nelson Mandiri Sukses Abadi provided the data in the form of a Microsoft Excel file. The waterfall method was used to collect the necessary data and get to the stage of making the application and the programming language used was PHP and the database was MySQL using the Laravel Framework. Based on the data analysis results, it can be concluded that PT. Nelson Mandiri Sukses Abadi requires an application as a support for recording goods every day, tracking records of goods entering and leaving the warehouse and presenting reports in order to improve performance quality.
Sistem Informasi Tugas Akhir Berbasis Web di Jurusan Teknik Informatika Universitas Trisakti

The Department of Informatics has requirements for earning a Bachelor's degree, namely the Final Project. The Final Project is a research paper that must be written for students. However, the process of implementing the final project is still managed manually so problems occur on a regular basis. To avoid further issues, the author conducted research on the Information Systems of the Final Project. This system would not be integrated into the primary system of the university. There was only the Final Project 1 process in the implementation, consisting of a proposal and results seminars. This study aimed to create an Information system of Online Final Project for students who want to take Final Project 1 or those who are taking Final Project 1.&#x0D; This study used the waterfall method which consisted of requirements, design, implementation, and evaluation. In the analysis and design, this study used PHP programming and a MySQL database server. The function test on the application was used to generate the test results. The Final Project Information System application would be used by the admins, students, lecturers (supervisors and examiners), and the final project coordinators.
The Intermedia Books: a Concept of Book And Multimedia Integration for Remedial

The research and development of the Intermedia Book are motivated by the remedial implementation conditions of Indonesian lessons that have not been optimal. The preliminary research results that students need a book that can accommodate the remedial process by the learner's era. The concept of multimedia and book integration is a good solution for Indonesian remedial learning. The purpose of this research is to develop the Intermedia Book for remedial processes of Indonesian subjects. This research used the ten steps Borg &amp; Gall model. From the ten steps Borg &amp; Gall, the researcher modifies into three main steps: (1) preparation, (2) development, and (3) validation. The product of this research is the Intermedia Book of Indonesian Subject for VII grade students. The implication of this research is to bring multimedia products closer to learners through the intermedia book. This development becomes one of the techniques to increase the interactive learning process.
Optimal solar panel tilt angle calculation and simulation in Indonesia: A Liu and Jordan sky isotropic model-based approach

The efficient utilization of solar energy is crucial for meeting the growing energy demands while reducing greenhouse gas emissions and dependence on fossil fuels. Solar panel tilt angle optimization plays a vital role in maximizing the energy output of photovoltaic (PV) systems. Determining the optimal tilt angle is essential as it directly affects the amount of sunlight captured by the solar panels. In the context of Indonesia, a country rich in solar resources and a rapidly expanding renewable energy sector, understanding the optimal tilt angle for solar panels is of paramount importance. This paper studies the optimal solar panel tilt angle calculation using Liu and Jordan Sky isotropic model-based approach. The numerical simulations were carried out for several main city locations of Indonesia. The objectives of the work are to address the suitable and optimal tilt angle of solar panels by proposing an approach based on the Liu and Jordan Sky Isotropic Model. The calculation result shows that the boundary for the tilted angle empirically satisfies all the mathematical domain of the inverse trigonometric functions. While, the optimal tilted angles will be differed for any location.
Retracted: Cleaning Quality Control Management of Medical Equipment in Hospital Disinfection Supply Room Based on Smart Medicine

[This retracts the article DOI: 10.1155/2022/4380735.].
ANALYSIS OF OIL SENTIMENT SENTIMENTS ON TWITTER USING SUPPORT VECTOR MACHINE

Twitter is one of the social media platforms used by people in Indonesia. Twitter is often used by its users to express opinions regarding a product, institution or event. From the keyword fuel, fuel subsidy is a keyword that is currently a trending topic because changes in fuel subsidies affect the prices of other staples, to find out the value of sentiment in public opinion, sentiment analysis is one of the methods used is the support vector machine and lexicon based. &#x0D; Lexicon is a labeling method by matching the words contained in the document with the words contained in the dictionary. After labeling, the data is tested using the classification method, the classification stage is carried out after going through the preprocessing phase, where the tweet classification results tend to be positive or negative, using the Support Vector Machine method and validated by K-Fold Cross Validation.This research produced 50,001 data which were divided into 21,561 positive sentiments, 9206 neutral sentiments and 19234 negative sentiments. From these results it can be concluded that the data shows public support for rising fuel prices or changing fuel subsidy prices.
Recommendation System for Mental Health Article on Circle Application

Healthy mental health is a condition when minds are in a state of peace and calm and not disturbed, thus enabling us to enjoy our daily lives with respect for others. In Indonesia, there are quite a number of sufferers of mental health disorders, approximately 19 million people over the age of 15 experience mental and emotional disorders, both at mild to severe levels. These data show that the Indonesian state has not been able to properly address mental health problems and that the existence of a pandemic tends to increase sufferers of mental health disorders, which if left unchecked will have a negative impact. Based on this problem, the Circle application, that focuses on mental health using Android technology that supports self-help with several services, one of which is an article service. The article service in the Circle application requires a recommendation system that can recommend articles according to the mental health conditions experienced by users so that the articles are able to alleviate the mental health problems currently experienced by users. Topic Modeling is an approach to analyze a collection of text documents and group them into topics. Topic Modeling has several methods that can be applied in making topics, one of which is BERTopic. It is a technique that leverages Transformer and c-TF-IDF to create dense clusters, preserving keywords in topic descriptions while making topics easier to interpret. There are 3 important components of the BERTopic algorithm namely Document Embedding, Document Clustering, Topic Representation. This study uses Topic Modeling with the BERTopic method as the baseline for the mental health article recommendation system in the circle application.
Ultrasound features of breast nonmass using a standardized lexicon: prediction of malignancy

Introduction:A breast nonmass (NM) on ultrasound (US) is associated with a wide spectrum of breast pathologies.The aims of this study were to (1) re-evaluate the ultrasonographic features of breast NM using a standardized lexicon and ( 2) identify the ultrasonographic features that predict malignancy in NM and its histopathologic confirmation.Material and Methods: Women with ultrasonographic report of breast NM with BI-RADS category 3, 4, or 5 and diagnosis by biopsy or ultrasonographic follow-up over 24 months were included.The following features of breast NM were assessed by ultrasound: echogenicity, distribution, orientation, and associated findings according to the standardized lexicon of the fifth-edition update of the ACR BI-RADS.Univariate and multivariate analyses were performed.Results: Seventy-two women with breast NM were included; 53 (73.6%) had confirmed benign NM, 3 (4.2%)had benign with upgrade potential (BWUP) NM, and 16 (22.2%)had malignant NM.Significant ultrasonographic findings predictive of malignant NM were echogenic rind (OR 12.66, 95% CI 2.08 -76.83), hypervascularity (OR 7.96, 95% CI 1.71 -37.02), and calcifications (OR 18.61, 95% CI 2.71-127.65)with sensitivity of 75.0%(95% CI, 50.5 -89.8%), specificity of 96.1% (95% CI, 86.8-98.9),PPV of 85.7% (95% CI, 60.1-96.0),and NPV of 92.5% (95% CI, 82.1-97.0).The accuracy for diagnosing malignant breast NM was 91.0% (95% CI, 81.8-95.8%). Conclusion:The ultrasonographic features of breast NM were defined using a standardized lexicon.Malignant breast NM was diagnosed in one out of four women.The predictive ultrasound findings were echogenic rind, hypervascularity, and calcifications.In breast NMs, a biopsy is recommended to confirm the diagnosis.
Multiphoton correlations between quantum images

Experimental demonstrations of entangled quantum images produced through parametric downconversion have so far been confined to studying two photon correlations. Here we show that multiphoton correlations between quantum images are accessible experimentally and exhibit many new features including being sensitive to the phase of the bi-photon wavefunction. As a concrete example, we consider a modification of existing quantum imaging experiments in which the CCD cameras are moved out of focus, provide detailed analytical predictions for the resulting 4 photon intereferences, and support these by numerical simulations. The proposed experiment can also be interpreted as entanglement swapping: Bob's photons are initially unentangled, but the joint detection of Alice's photons projects Bob's photons onto an entangled state. The general approach proposed here can be extended to other quantum optics experiments involving high dimensional entanglement.
Using Bayesian Piecewise Growth Curve Models to Handle Complex Nonlinear Trajectories

Bayesian growth curve modeling is a popular method for studying longitudinal data. In this study, we discuss a flexible extension, the Bayesian piecewise growth curve model (BPGCM), which allows the researcher to break up a trajectory into phases joined at change points called knots. By fitting BPGCMs, the researcher can specify three or more phases of growth without concern for model identification. Our goal is to provide substantive researchers with a guide for implementing this important class of models. We present a simple application of Bayesian linear BPGCMs to childrens' math achievement. Our tutorial includes Mplus code, strategies for specifying knots, and how to interpret model selection and fit indices. Extensions of the model are discussed.
Communicating the Value of Waste Management to Customers: Focus on Website Content

Understanding waste management processes and their role in positive environmental changes is significant at various levels. Among members of the public, awareness can be fostered by communicating the benefits of waste management and responding to the needs of individuals.&#x0D; The web is one of the communication channels that is easily accessible to many users and managed by waste management organisations. Therefore, this study aims to explore the combinations of content ideas, forms, and customer value dimensions in communication with society about the value of waste management through a website. Theoretical analysis showed that purposeful communication of customer value can be implemented by utilising different customer value dimensions using content marketing principles.&#x0D; Quantitative content analysis of waste management organisation websites was conducted and directed toward the current state of communication and its patterns. The results revealed that current content focuses on the repetitive communication of functional value through informative articles. Thus, a lack of more diverse content presenting the emotional and social values of waste management was identified.&#x0D; The waste management field can benefit by integrating various customer value dimensions and content marketing theory to identify new opportunities and ways to involve society and achieve the scale of impact needed.
Increasing Student Understanding and Concentration Through Virtual Reality Learning Media in Electronic Records Management Courses

Technology development today gives students many references to learn new things. The development of technology-based learning media is an alternative for teaching staff to make a breakthrough in the world of education. Virtual Reality is a technology that can make its users feel like they are in another room without having to move. This technology is supported with audiovisual features, which can make users feel isolated and focus on VR media. The purpose of developing this VR learning media is to measure students' understanding of the material presented in VR learning media and to measure student concentration when using VR learning media. This research was conducted on students from the State University of Malang who taught the Electronic Records Management course. Learning media adapts from sub-CPMK to 1.1. The development of this VR learning media uses the multimedia development method from Luther. The results of the material expert validator and media expert show that learning media is very suitable for learning media by getting a score of 94%. The results obtained from this study show that learning media influences students' understanding of the material, which is indicated by an increase in the class average at the learning stage. Post-test by 11%. This study also found that this learning media made students concentrate more because they felt more focused when using Virtual Reality.