Reviewerly/exHarmony huggingface dataset:
An Efficient Intersection Over Union Loss Function for 3D Object Detection

In the area of computer vision, object detection using convolutional neural networks (CNNs) has become quite a popular procedure because of their effectiveness and simplicity. The loss function has a great influence on the average accuracy value of the CNN model’s detector results. An improved 3D efficient intersection over union (EIoU) loss function is proposed to improve the localization accuracy. The diagonal distance between bounding boxes’ corners and centers, with the dimensional change in boxes’ geometry sides, are used for matching between the 3D predicted bounding box with the 3D ground truth bounding box. By taking the geometry sides change and diagonal distance between the 3D predicted and 3D ground truth boxes, a great influence on the localization accuracy is generated. For the network model, the localization accuracy is improved because of the strength of diagonal distance and geometry sides’ adjustment. Utilizing the one-stage object detector 3D YOLO v4 and applying the 3D EIoU experimentally on the KITTI dataset, the findings demonstrate the effectiveness of the 3D EIoU in improving the accuracy of localization for the network model. Compared with 3D GIoU, the proposed EIoU enhances the average precision (AP) value by 0.24% and AP70 by 1.179% in the car class, AP by 0.578%, and AP55 by 6.022% in cyclist class, and AP by 0.1531% and AP30 by 2.548% in pedestrian class.
AI Based Chatbot for Hospital Management System

The healthcare sector represents one of the most significant segments of the economy. A reliable healthcare system ensures a strong economy by increasing life expectancy, contributing to national growth, and reducing the burden of families. The purpose of this project is to implement a proper healthcare management system integrating all the basic functionalities powered by an Artificial Intelligent chatbot that is capable of having a very organic conversation with the user and solving their queries using knowledge base. The knowledge base is a real-time data collected in a JSON format which is pre-processed to make it ready for further processing using bag of words. The information is received and delivered in both speech and text formats. The chatbot can provide navigation links according to the requests of a user. Furthermore, it is capable of predicting the problem by performing symptom diagnosis and recommending a doctor to be consulted or any immediate measures to be taken. In addition, it also provides information regarding diagnostics beforehand.
Scrum Project Management Challenges and Solutions: Systematic Literature Review

Agile project management has been trending as one of the project management approaches, while Scrum is the most commonly used agile framework. The adoption of Scrum surely gives benefits to the companies. Yet, there are some challenges in Scrum implementation. Therefore, this paper aimed to identify the challenges and the solutions of Scrum project management by performing systematic literature review (SLR). 50 papers were found during performing SLR by using suitable query search and criteria. These papers were extracted to get the challenges and the solutions. The challenges and the solutions were mapped with to 8 project performance domains in PMBOK Guide 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> edition. The result of mapping between the challenges, the solutions, and PMBOK performance domains could give benefits to the practitioners who will implement Scrum project management The most challenges were related to team performance domain, with 23 studies, which the most challenge was trust issues in Scrum team and the solutions were transparency and easy access to information, while no studies found were related to development approach and life cycle as well as measurement.
Pengaruh Bimbingan Orang Tua melalui Aktivitas Belajar dan Motivasi terhadap Hasil Belajar Siswa

The problem in this research was the low mathematics learning outcomes of grade V SD Kecamatan Labuhan Ratu. This research aimed to 1) analyze the effect of parental guidance on students mathematics learning outcomes, 2) analyze the effect of learning motivation on students mathematics learning outcomes, and 3) analyze the effect of parental guidance and learning motivation on students mathematics learning outcomes. The data in this research was analy by using quantitative research. This type of this research was ex post facto. The population of this research was 502 students and the total sample was 84 students which waswereken from SD Negeri 2 Labuhan Ratu dan SD Negeri 2 Sepang Jaya. The sampling technique in this research was cluster sampling which was part of the probability sampling technique. Questionnaire and documentation were used as data colletechniqueshnique. Data analysis used a simple linear regression and multiple linear regression formulas. The results showed that there was a significant effect between parental guidance to learn mathematics which was indicated by tFountunt ≥ Ftable (4,55 ≥ 3,95); there was a significant effect between motivation to learn mathematics which was indicated by the Fcount ≥ Ftable (5,90 ≥ 3,95); there was a significant effect between parental guidance and motivation to learn mathematics in grade V SD Kecamatan Labuhan Ratu, which was indicated by the Fcount ≥ Ftable (4.04≥ 3.10).
RSA-based Encryption Algorithm for Digital Images

With the spread of Covid-19, secure transmission of data over the Internet has drawn prior attention. Therefore, protecting these data is a very important process. This objective can be achieved through encryption. In this study, a digital image encryption algorithm is suggested depending on the RSA cryptosystem, since the RSA is a strong and well-known asymmetric encryption system. The proposed algorithm divides the image into blocks of 2×2 size rather than encrypting one pixel at a time. This improves the encryption process and then converts each block into a single vector. The vector elements are converted to binary and then into to a single binary number. After that, the binary number is converted to decimal to be compatible with using RSA algorithm. The proposed algorithm is lossless; the original image is restored without losing any information or data. Finally, the suggested algorithm is executed in MATLAB environment and tested on various images. The results of the experiment reveal the suggested encryption algorithm is both reliable, secure and applicable to image protection.
Dynamic Multi-Threaded Path Planning Based on Grasshopper Optimization Algorithm

Over the past few years, computer hardware equipment has undergone a great deal of development, especially since multi-core processors were introduced, which increased the speed of performing tasks in various fields. Compared to single-core, multi-core chips can provide higher performance and efficiency. In this paper, a multi-core system is exploited to speed up path planning using the grasshopper optimization algorithm (GOA) as well as finding multiple paths supporting the mobile robot to navigate safely in a dynamic known environment. Then multiple paths can be practiced if the robot encounters a dynamic obstacle to fabricate a new obstacle-free path out of the planned paths. Diverse experiments have shown that the robot always lays hands on the path to the target even with multiple dynamic obstacles that block the way of the robot. Recalculating a path from the existing paths tremendously decreased the required time compared to re-planning the path.
The Evaluation of Cascade Object Detector in Recognizing Different Samples of Road Signs

In road-sign detection, several problems are involved like variations in perspective, illumination, occlusion, motion blur, and weatherworn deterioration of signs. The goal of the current work is to assess the quality of the cascade technique in detecting and recognizing road signs during the daytime in Baghdad City under different circumstances such as vehicle speed, environment, etc..., and determine the best range for the threshold value. The method consists of two main stages; labeling and training to detect and identify the true and false targets upon a set of captured images. The proposed threshold range has been implemented through cascade software and investigated for solving the problem's vision of road sign deterioration. The precision value for all used road signs obeys a polynomial relationship in precision-threshold variation with the highest value recorded for speed-40 followed by speed-80, cross, and speed-60 road sign at the final. The conducted experiments have confirmed the proposed software operability and allow recommendation for use in practice for solving the problems of road sign diagnosis and automatic classification.
Video Captioning model using a curriculum learning approach

Video captioning techniques have wide applications in a plethora of domains including surveillance, assistance of visually impaired, object tracking and much more. It is the process of generating a meaningful description about a video in a natural language without losing the semantics of the content in the video. Various works in this area have been thriving since last decade and has reached up to the usage of Transformer networks. Little significance has been made on the quality of the data used for training the model. The captions of the video may be of variable length and if not wisely trained, it can create a biased model which is noisy and irrelevant. In order to overcome this problem, we make use of an improved curriculum learning methodology in video captioning system which is very promising as per the experiments conducted so far. The Curriculum learning strategy which mimics human learning process, can rank the training data according to the difficulty measurement and hence the training model get appropriate data suitable for the training phase. The datasets and metrics used are being evaluated with some standard model and the CL approach turns to be promising.
A Proposed Class Labeling Approach: From Unsupervised to Supervised Learning

Supervised learning algorithms represent a crucial goal in data analysis fields where the data are groups according to some predefined class labels. However, many applications in real world come with no class labels in the used datasets, which in turn reduce the possibility of converting such data into knowledge. This paper presents an approach for automatic class labeling of the objects using clustering-classification approach.The approach consists of two main phases. The first one is to group the objects of the dataset into k clusters (unsupervised learning) using k mode clustering algorithm since the available data are categorical type. The second phase is to convert the clusters received in phase one into class labels with their margins (supervised learning). An accuracy of more than 85% was received using logistic regression classifier.The obtained results have been shown high range of accuracy, precision, recall, and f measure for clustering technique and high accuracy for classification.
House Price Prediction Using Linier Regression

Property entrepreneurs will compete to build properties, especially houses for investment facilities. This will make house prices increase day by day with a high purchasing power of the people. Consumers will think in buying a house whether the house they buy will have a good profit value or not. The aim of the study is to build a model that may predict house price for company and become a business decision for consumers. The methodology used seven major steps namely, business understanding, data understanding, data cleaning, data standardization, modelling, and evaluation. This study develop a linear regression model for home price prediction and tests it using data from the Maribelajar company. These are carried out on the Azure Platform by creating two pipelines. One pipeline for training and another for testing. The results are then visualized using Power Business Intelligence for providing a proper business performance analysis. From the experimental results, the model achieved RMSE= 0.0334 and R coefficient = 0.7. The data analysis and testing in this study show that the multiple linear regression model can forecast and evaluate housing prices to some extent, but the algorithm may still be improved using more advanced machine learning approaches.
An Exploratory Analysis of Head Mounted Displays for VR Applications

A Virtual Reality (VR) is a fully immersive computer-simulated environment that provides users with the feeling of being in a real world though they are in a virtual environment. It is widely used to train novices in defense, healthcare, automobiles, and other sectors. The essence of VR is in the perception of the real world. To perceive the virtual world as the real world, there are certain key factors. One of such factors is the Head Mounted Displays (HMDs). HMDs are vital for the creation of an immersive experience. One of the most important factors is the selection of an appropriate HMD, with the help of which one can experience the VR applications. Various types of HMDs are available in the market. This article provides a comparative analysis of different types of HMDs and proposes one among them as the most suitable HMDs for research work.
A Study of Smart Home User Personas Based on Context Theory

To achieve a safe, convenient, comfortable, and environmentally friendly home life experience, smart home field has reached a new stage of development, where actively providing user-centered service is crucial. Therefore, it needs to capture the real needs of users in different contexts accurately. This study proposes a theoretical method and structural model of user personas construction in the field of smart home. Firstly, we analyze the current research on smart home, and review the connotation and characteristics, construction methods, advantages and disadvantages of user personas. Then, we explain context theory and propose a method to build smart home user personas by combining context theory. The smart home product terminals and platforms collect and analyze user data in different contexts, and obtain contextual information including user, environment, time, and task, which are the elements of user behavior patterns and living habits to build a dynamically updated user persona with rich contents. This user personas model can be used in smart home platforms to provide active services and personalized recommendations for users, as well as help Eco-chain product iteration and design decisions.
sluggishQCloud: Towards the Sluggish Queueing assisted Cloud System for Efficient Resource Management

The provision of computing resources such as com- puting power, storage, networking, and IT infrastructure dynamically and virtually by service providers over the Internet on a pay-per-use basis is known as cloud computing. Customers can send a lease to the cloud service provider via the internet to request cloud-based resources. Leases are resource provisioning abstractions that include all necessary resources, the provisioning time, and the customer’s budget, among other things, in termsof the quantity, capacity, and duration of nodes. The service provider then accepts all customer leases, generates virtual machines for them, and schedules them with the assistance of a lease manager. Lease managers utilize their algorithms to handle leases, which include scheduling leases. OpenNebula uses Haizea, an open-source VM-based leasing management framework, as a resource manager. It acknowledges the user’s lease request and subsequently arranges the lease according to the physical machines’ resource availability. It enables users to submit requests as leases. In haizea, preemptible best-effort leases can be supplanted by leases with a higher priority, such as those with an advanced reservation or an immediate lease. This article models each VM as a service provider and extracts its different performance metrics, including average number of request leasesin the system/ queue, the mean waiting time of the leases in the system/queue.
Integrating Good UX Development Practices in Solo Agile

Background: Understanding customer needs with the collaboration of Agile and user collaboration has increased recently. Involving users in software development increases the value of the software product. User experience as an approach to solving user problems based on user subjectivity is able to further increase the value of the product. Moreover, startup products developed by an individual or limited team require added value to be able to compete in the market. However, lack of research articles that discuss the current issue and integration of Agile and UX. Aim: This study set out to answer the research questions. The first question is which development practices are suitable for managing UX in Solo Agile. The second question is what good practices for are integrating UX development in Solo Agile. And the last question is about which issues and challenges are associated with solo development with the user experience. Method: Systematic Mapping (SM) to represent the research trends and focus of the topic related to the field. Results: we retrieved one article from the 92 articles in the first iteration and 17 articles from the 308 articles from the second iteration. Conclusion: no article discusses the integration of solo agile and UX, however, there are few articles that talked the context of the UX that is related to the user-center design with solo agile.
Sasirangan Cloth Recognition and Shopping Experience Simulation Based on Virtual Reality Game with Non-Player Character Integration

Sasirangan cloth is one of the traditional cloths owned by Indonesia and is a typical cloth originating from the province of South Kalimantan. This Sasirangan cloth has many motifs and is unique. Sasirangan cloth is also one of the potentials that can attract local and foreign tourists to visit South Kalimantan. However, in recent years, tourist visits to South Kalimantan have decreased due to the impact of the COVID-19 pandemic. Then it also has an impact on the delay in the implementation of the Banjarmasin Sasirangan Festival which is usually held annually. The next impact is a decrease in turnover and interest in shopping for Sasirangan cloth, so that employees who work for Sasirangan cloth craftsmen are dismissed and reduce people's income. So the purpose of this study is to report the results of the development of a Virtual Reality Game application called VR-SasiranganKu by integrating the interaction of Non-Player Character (NPC) behavior with the type of narrative in the simulation genre game. VR-SasiranganKu is recommended to introduce and at the same time promote Sasirangan cloth to users, then justify the reaction to knowledge and shopping experience, and evaluate user experience using the UX Honeycomb method. Respondents involved as many as 100 people who are local and national tourists. The results show that the interaction of NPC behavior can provide good direction to users regarding the introduction of Sasirangan cloth by presenting an appropriate interaction design based on the narrative and the theme of the NPC that has been adopted, the results of justification using VR-SasiranganKu can provide knowledge reaction and very good shopping experience, and based on user experience evaluation that VR-SasiranganKu has adopted a very good UX with an average score of 4.91.
Use of Different Variants of Item Response Theory-Based Feature Selection Method for Text Categorization

In this study, we investigate the performance of the item response theory (IRT)-based feature selection (FS) approach on eight text datasets considering different feature sets and weighting schemes. We also employ its recently introduced variants in our evaluation. The results of our extensive experiments show that the IRT-based FS approach often reaches or improves the classification f-score by selecting a higher number of features compared to their well-known peers. Recently introduced variants, on the other hand, often fall behind the IRT1 and IRT2 for the task of text categorization.
Study on Smart Security Measures in Threat and Risk Assessment

SEP, more specifically Security Engineering Process, forms a part of the modern assessment and evaluation of related threats and risks, whether it is an agile form of development or SSDLC (Secure Software Development Life Cycle). While in today’s era we deal with modern concepts like cloud computing, remote processing, etc. kinds of strategies which involve data at rest and data in motion, and data computation-intensive tasks. Threat and Risk Assessment are closely related to different attack values in which an attacker can perform actions in order to gain additional privileges to your system, software, cloud etc. Threat and Risk Assessment of different projects including web applications development, cloud onboarding, requires accessing the project at the design level so that the product is more specifically secure in the terms of compliance and the coding techniques used are in accordance with the secure coding guidelines. This paper focuses on the comparison of various standardized threat and risk assessment techniques with respect to dynamic threats and possible risk mitigations.
Machine Learning for Sentiment Analysis and Classification of Restaurant Reviews

In this study, we performed sentiment analysis on restaurant reviews. To dine, we all go to restaurants with our loved ones and friends. However, we decide which restaurant to eat at before going there. Consequently, we select restaurants based on the reviews left by previous customers. A restaurant is a business, and the owner welcomes feedback from diners regarding their dining experiences so they may adjust their menu and amenities accordingly. Sentiment analysis is used to categories reviews as either good or negative, depending on whether they will have an impact on other people. Sentiment Analysis uses a machine learning algorithm and natural language processing to determine if a review is good or negative. It aids in the careful investigation of a scenario or service's good and negative effects, allowing for the suggestion of modifications. Both in terms of increasing consumer satisfaction and the caliber of the services offered, this might be advantageous. The user experience will be better as a result. This paper offers a study of key factors that distinguish between favorable and unfavorable assessments. This paper analyses various Machine Learning techniques that successfully analyze these reviews. In this study, we employed K-nearest neighbours Classifier, Logistic Regression, Support Vector Classifier (Gaussian NB and Multinomial NB), and Naive Bayes algorithm. A dataset from Kaggle was used in this study. Here, we will be able to observe which machine learning (ML) methods, can give more accurate results. We will also be able to evaluate how well an NLP can predict a sentiment from the given text. By illustrating how computers attempt to comprehend human sentiments, this article examines the possibilities. Out of everything, SVM produced an accuracy of 78 percent.