bluuebunny/crossref_metadata_2025 huggingface dataset:
Forecasting Students' Employment Rate Under the OBE Model
Authors: Deng Xiaoke , Hou Huakun , Jin Meiyu , Zhai Linbo
Published: September 2023

With the rapid development of information technology and its penetration into the field of education, it has become a worthwhile research topic to explore how to combine Outcome Based Education(OBE) model with data mining techniques to obtain predictions of secondary school students' employment rates. Therefore, this paper first collected attribution data affecting the employment rate of secondary school students, and then used three models to predict the highly correlated attribution data. Finally, based on the analysis and prediction results, the OBE model was implemented for the corresponding classes. The experimental results found that the combination of machine learning algorithms and educational practices facilitated the development of secondary education and significantly increased the employment rate of secondary school students.
Optimised Association Rule Mining for Health Data
Authors: Das Purnima , F. Roddick John , A. H. Williams Patricia , Nasim Mehwish
Published: September 2023

Association Rule Mining (ARM) has been recognised as a valuable and easy-to-interpret data mining technique in response to the exponential growth of big data. However, research on ARM techniques has mainly focused on enhancing computational efficiency while neglecting the automatic determination of threshold values for measuring the "interestingness" of items. Selecting appropriate threshold values (such as support, confidence, etc.) significantly affects the quality of the association rule mining outcomes. This study proposes an algorithm that utilises Particle Swarm Optimization (PSO) and ARM techniques to determine optimised threshold values in the health domain automatically. The algorithm was evaluated using the UCI machine learning medical database for heart disease. Results show that the proposed algorithm is capable of generating frequent itemsets and rules in an efficient manner and can detect the optimum threshold values. This research has practical implications for the health domains, as it can extract valuable results.
CNN's Resnet, YOLO, and Faster R-CNN Architectures on the Disease and Pest Classification of Local Agricultural Vegetables Towards Sustainable Production
Authors: S Echalar Loyd , C. Fajardo Arnel
Published: September 2023

The Philippines is known to be a country that values the agricultural sector. Agriculture is the backbone of the Philippine economy, contributing around 9% to its gross domestic product (GDP) and providing livelihood to millions of Filipinos. Local vegetables such as pechay, mustasa, sitaw, talong, and ampalaya are some of these essential agricultural crops, used in different famous dishes in the country. The emergence of technology helps individual and community improve their way of administering and managing crops, which is why it is very important to develop an innovative way to produce sustainable vegetable crops. The focus of this paper is on the creation of an application that can effectively categorize the ailments, pests, and nutrient deficiencies found in vegetable crops. This application uses different Convolutional Neural Networks architectures such as ResNet, YOLO, and Faster R-CNN to dissect information from digital photographs. By offering diverse insights into diseases, pests, and deficiencies, this application equips users with the knowledge to effectively handle and nurture crops, ensuring sustainable production. The mobile application helps many vegetable growers identify the problems and challenges of their crops. The used CNN architecture provides accurate detection, analysis, and interpretation of the content of digital photographs, and served as a way to provide information on the solutions. ResNet architecture provides a high accuracy rate among YOLO and Faster R-CNN in the detection and classification of diagnosis.
A Dual Band Printed F-Antenna using a Trap With Small Band Separation
Authors: Samudrala Prasad , Jose Justin , Kulkarni Amit
Published: September 2023

Trap antenna is well known method and has many applications. With this method, trap(s) are used on antenna to block currents of some frequencies and so electrically divide the antenna into multiple segments and thus one antenna can work on multiple frequencies. In this paper, trap antenna method is used to design a dual band Sub GHz printed F-antenna. The antenna is printed on FR4 board to achieve low cost solution. The two bands are 865- 870 MHz and 902-928 MHz. The challenge of this design is that the frequency separation of the two bands is very small. In this case, and also the extra section for low frequency band is too small. Then, the influence of trap LC component variation due to tolerance to the two resonant frequencies is big, and so it is difficult to achieve good in band return loss within the LC tolerance. This is the main difficulty of this design. The problem is solved by placing the low band section away from the end of the antenna.
Towards Streamlined Club Management: Integrating Communication and Management in a Centralized Application
Authors: Rao Zhongshun , Phan Victor
Published: September 2023

This project explored a solution to help with club management [1]. Club management has been a complex task for many people [2]. The solution to this problem is to create a centralized management application that allows all management and communication to be done on it [3]. The application is made with FlutterFlow and google firestore. It consists of two main parts, the communication and the management. Possible challenges are the users’ experiences in features like chat and task management, but these will be improved in the future through updates. The application can be applied to all kinds of clubs like sport club or school clubs, providing communication and management for members. It is hoped that this application could provide users a convenient and effective way to organize their clubs
Exploring the Cultural Significance of Ecommerce Websites Through Semiotic Analysis: A Literature Review
Authors: Hujran Omar
Published: September 2023

Semiotic analysis serves as a tool for comprehending the ways in which individuals interpret and comprehend their surroundings by scrutinizing the interplay between signs, symbols, and their connotations. The efficacy of digital business platforms is heavily reliant on the creation of well-crafted, meticulously structured websites that facilitate effective communication with targeted customers. E-commerce websites extensively employ images and texts to enhance product awareness and cultural significance. Scholars contend that the utilization of e-commerce websites will expand if designers integrate the cultural norms of different user subsets. Thus, this study aimed to examine this issue through a literature review of semiotic analysis applied to e-commerce websites.
Sequential Recommendation Model for Next Purchase Prediction
Authors: Chen Xin , Reibman Alex , Arora Sanjay
Published: September 2023

Timeliness and contextual accuracy of recommendations are increasingly important when delivering contemporary digital marketing experiences. Conventional recommender systems (RS) suggest relevant but time-invariant items to users by accounting for their past purchases. These recommendations only map to customers’ general preferences rather than a customer’s specific needs immediately preceding a purchase. In contrast, RSs that consider the order of transactions, purchases, or experiences to measure evolving preferences can offer more salient and effective recommendations to customers: Sequential RSs not only benefit from a better behavioral understanding of a user’s current needs but also provide better predictive power. In this paper, we demonstrate and rank the effectiveness of a sequential recommendation system by utilizing a production dataset of over 2.7 million credit card transactions for 46K cardholders. The methodfirst employs an autoencoder on raw transaction data and submits observed transaction encodings to a GRU-based sequential model. The sequential model produces a MAP@1 metric of 47% on the out-of-sample test set, in line with existing research. We also discuss implications for embedding real-timepredictions using the sequential RS into Nexus, a scalable, lowlatency, event-based digital experience architecture.
HealthPet: Harnessing the Power of Virtual Pets to Motivate Exercise and Promote Active Lifestyles
Authors: Chen Long , Park Andrew
Published: September 2023

In light of the recent pandemic and the increasingly online lifestyles of general people, the need to maintain an active lifestyle is becoming more important to consider as “more than a quarter of all adults [are] not getting enough physical activity. This puts more than 1•4 billion adults at risk of developing or exacerbating diseases linked to inactivity”[1]. HealthPet seeks to help tackle the issue by providing a place where users can earn and interact with virtual pets based on how active they are throughout the day. By using Unity and Firebase alongside native code, we are able to put together a package that provides a goal and incentive while also being able to preserve and maintain a user's progress on their path toward a healthier day.
PlantAssist: A Smart Solution for Convenient and Automated Indoor Plant Watering using AI Prediction
Authors: Liu Xichen , Zhang Yujia
Published: September 2023

This paper introduces PlantAssist, a system designed to address the challenges faced by residents in maintaining indoor vegetation [4]. With an increasing number of people leaving their homes for extended periods, the need for a convenient and reliable solution to water indoor plants has become crucial. Existing options such as hiring caretakers or relying on friends pose security risks and inconvenience. PlantAssist presents a hardware and software solution that emulates regular outside irrigation systems [6]. The hardware is seamlessly integrated into the plant's pot, allowing users to choose their preferred style without compromising the room's aesthetic [5]. The system utilizes smart AI prediction to determine when watering is necessary, ensuring plants receive optimal care [7]. Through a user-friendly app, residents can effortlessly access information about water levels and schedule watering sessions. PlantAssist offers users a hassle-free lifestyle, ensuring their plants are watered without any effort required [8].
Subverting Two Character Stereotypes at Once: Exploring AI's Role in Subverting Stereotypes
Authors: Feng Xiaohan , Murakami Makoto
Published: September 2023

The Aim of this paper is to explore different ways of using AI to subvert stereotypes more efficiently and effectively. It will also enumerate the advantages and disadvantages of each approach, helping creators select the most appropriate method for their specific situations. AI opens up new possibilities, enabling anyone to effortlessly generate visually stunning images without the need for artistic skills. However, it also leads to the creation of more stereotypes when using large amounts of data. Consequently, stereotypes are becoming more prevalent and serious than ever before. Our belief is that we can use this situation in reverse, aiming to summarize stereotypes with AI and then subvert them through elemental exchange. In this study, we have attempted to develop a less time-consuming method to challenge character stereotypes while embracing the concept of "exchange." We selected two character archetypes, namely the "tyrant" and the "mad scientist," and summarized their stereotypes by generating AI images or asking ChatGPT questions. Additionally, we conducted a survey of real historical tyrants to gain insights into their behavior and characteristics. This step helped us comprehend the reasons behind stereotyping in artwork depicting tyrants. Based on this understanding, we made choices about which stereotypes to retain. The intention was to empower the audience to better evaluate the identity of the character. Finally, the two remaining character stereotypes were exchanged, and the design was completed. This paper documents the last and most time-consuming method. By examining a large number of sources and examining what stereotypical influences were used, we were able to achieve a greater effect of subverting stereotypes. The other method is much less time-consuming but somewhat more random. Whether one chooses by subjective experience or by the most frequent choices, there is no guarantee of the best outcome. In other words, it is the one that best guarantees that the audience will be able to quickly identify the original character and at the same time move the two characters the furthest away from the original stereotypical image of the original. In conclusion, if the designer has sufficient time, ai portrait + research or chatGPT + research can be chosen. If there is not enough time, the remaining methods can be chosen. The remaining methods take less time and the designer can try them all to get the desired result.
Evaluating The Performance of Feature Extraction Techniques Using Classification Techniques
Authors: Mittal Harshit
Published: September 2023

Dimensionality reduction techniques are widely used in machine learning to reduce the computational complexity of the model and improve its performance by identifying the most relevant features. In this research paper, we compare various dimensionality reduction techniques, including Principal Component Analysis(PCA), Independent Component Analysis(ICA), Local Linear Embedding(LLE), Local Binary Patterns(LBP), and Simple Autoencoder, on the Olivetti dataset, which is a popular benchmark dataset in the field of face recognition. We evaluate the performance of these dimensionality reduction techniques using various classification algorithms, including Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The goal of this research is to determine which combination of dimensionality reduction technique and classification algorithm is the most effective for the Olivetti dataset. Our research provides insights into the performance of various dimensionality reduction techniques and classification algorithms on the Olivetti dataset. These results can be useful in improving the performance of face recognition systems and other applications that deal with high-dimensional data.
Drift Detection in Models Applied to the Recognition of Intentions in Short Sentences Using Convolutional Neural Networks for Classification
Authors: R. Junior Jairo , A Silva Leandro
Published: September 2023

Significant advancements have been achieved in natural language processing models for text classification with the emergence of pre-trained transformers and deep learning. Despite promising results, deploying these models in production environments still faces challenges. Classification models are continuously evolving, adapting to new data and predictions. However, changes in data distribution over time can lead to a decline in performance, indicating that the model is outdated. This article aims to analyze the lifecycle of a natural language processing model by employing multivariate statistical methods capable of detecting model drift over time. These methods can be integrated into the training and workflow management of machine learning models. Preliminary results show that the statistical method Maximum Mean Discrepancy performs better in detecting drift in models trained with data from multiple domains through high-dimensional vector spaces after being subjected to an untrained auto-encoder. The classifier model achieved an accuracy rate of 93% in predicting intentions, using accuracy as the evaluation metric.
Text Similarity From Image Contentsusing Statistical and Semantic Analysis Techniques
Authors: Kulkarni Sagar , Govilkar Sharvari , Amin Dhiraj
Published: September 2023

Plagiarism detection is one of the most researched areas among the Natural Language Processing(NLP) community. A good plagiarism detection covers all the NLP methods including semantics, named entities, paraphrases etc. and produces detailed plagiarism reports. Detection of Cross Lingual Plagiarism requires deep knowledge of various advanced methods and algorithms to perform effective text similarity checking. Nowadays the plagiarists are also advancing themselves from hiding the identity from being catch in such offense. The plagiarists are bypassed from being detected with techniques like paraphrasing, synonym replacement, mismatching citations, translating one language to another. Image Content Plagiarism Detection (ICPD) has gained importance, utilizing advanced image content processing to identify instances of plagiarism to ensure the integrity of image content. The issue of plagiarism extends beyond textual content, as images such as figures, graphs, and tables also have the potential to be plagiarized. However, image content plagiarism detection remains an unaddressed challenge. Therefore, there is a critical need to develop methods and systems for detecting plagiarism in image content. In this paper, the system has been implemented to detect plagiarism form contents of Images such as Figures, Graphs, Tables etc. Along with statistical algorithms such as Jaccard and Cosine, introducing semantic algorithms such as LSA, BERT, WordNet outperformed in detecting efficient and accurate plagiarism.
Chunker Based Sentiment Analysis for Nepali Text
Authors: Yajnik Archit , Tamang Sabu Lama
Published: September 2023

The article represents the Sentiment Analysis (SA) of a Nepali sentence. Skip-gram model is used for the word to vector encoding. In the first experiment the vector representation of each sentence is generated by using Skip-gram model followed by the Multi-Layer Perceptron (MLP) classification and it is observed that the F1 score of 0.6486 is achieved for positive-negative classification with overall accuracy of 68%. Whereas in the second experiment the verb chunks are extracted using Nepali parser and carried out the similar experiment on the verb chunks. F1 score of 0.6779 is observedfor positive -negative classification with overall accuracy of 85%. Hence, Chunker based sentiment analysis is proven to be better than sentiment analysis using sentences.
Unveiling the Power of TAG Using Statistical Parsing for Natural Languages
Authors: Kurariya Pavan , Chaudhary Prashant , Bodhankar Jahnavi , Singh Lenali , Kumar Ajai
Published: September 2023

The Revolution of the Artificial Intelligence (AI) has started when machines could decipher enigmatic symbols concealed within messages. Subsequently, with the progress of Natural Language Processing (NLP), machines attained the capacity to understand and comprehend human language. Tree Adjoining Grammar (TAG) has become powerful grammatical formalism for processing Large-scale Grammar. However, TAG mostly rely on Grammar which is created by Languages expert and due to structural ambiguity in Natural Languages computation complexity of TAG is very high o(n^6). We observed that rules-based approach has many serious flaws, firstly, language evolves with time and it is impossible to create grammar which is extensive enough to represent every structure of language in real world. Secondly, it takes too much time and language resources to develop a practical solution. These difficulties motivated us to explore an alternative approach instead of completely rely on the rule-based method. In this paper, we proposed a Statistical Parsing algorithm for Natural Languages (NL) using TAG formalism where Parser makes crucial use of data driven model for identifying Syntactic dependencies of complex structure. We observed that using probabilistic model along with limited training data can significantly improve both the quality and performance of TAG Parser. We also demonstrate that the newer parser outperforms previous rule-based parser on given sample corpus. Our experiment for many Indian Languages, also provides further support for the claim that above mentioned approach might be an awaiting solution for problem that require rich structural analysis of corpus and constructing syntactic dependencies of any Natural Language without much depending on manual process of creating grammar for same. Finally, we present result of our on-going research where probability model will be applying to appropriate selection of adjunction of any given node of elementary trees and state chart representations are shared across derivation.
What's in a Domain? Anaylsis of URL Features
Authors: Hawkins John
Published: September 2023

Many data science problems require processing log data derived from web pages, apis or other internet traffic sources. URLs are one of the few ubiquitous data fields that describe internet activity, hence they require effective processing for a wide variety of machine learning applications. While URLs are structurally rich, the structure can be both domain specific and subject to change over time, making feature engineering for URLs an ongoing challenge. In this research we outline the key structural components of URLs and discuss the information available within each. We describe methods for generating features on these URL components and share an open source implementation of these ideas. In addition, we describe a method for exploring URL feature importance that allows for comparison and analysis of the information available inside URLs. We experiment with a collection of URL classification datasets and demonstrate the utility of these tools. Package and source code is open on https://pypi.org/project/url2features.
Brands, Verticals and Contexts: Coherence Patterns in Consumer Attention
Authors: Hawkins John
Published: September 2023

Consumers are expected to partially reveal their preferences and interests through the media they consume. The development of visual attention measurement with eye tracking technologies allows us to investigate the consistency of these preferences across the creative executions of a given brand and over all brands within a given vertical. In this study we use a large-scale attention measurement dataset to analyse a collection of digital display advertising impressions across a variety of industry verti- cals. We evaluate the extent to which the high attention contexts for a given brand’s ads remain consistent for that brand, and the extent to which those contexts remain consistent across many brands within an industry vertical. The results illustrate that consumer attention on advertising can vary significantly across creatives for a specific brand, and across a vertical. Nevertheless, there are coherence effects across campaigns that are stronger than random, and that contain actionable information at the level of industry vertical categorisation.
Exploring End-Users’ Engagement with Security Threats: A Survey-Based Investigation from Academic and Security Experts’ Perspective
Authors: Jari Mousa , Coopamootoo Kovila , Ibrahim Rasha
Published: September 2023

Amid growing concerns about security and privacy, and their impact on decision-making, researchers have sought to understand the reasons behind users’ seemingly risky behaviour in disregarding security advice. In this study, we delve into the perceptions of security experts on end users’ threat models and their cybersecurity practices and habits. This research explores the perceptions of security and privacy experts regarding end users’ threat models and their behaviours in relation to cybersecurity. A survey was conducted with 55 experts, including 27 females and 28 males, to gain insights into end users’ habits, practices, and feelings from the perspective of security experts. The study reveals that end-users express moderate concern about privacy and security while carrying out their daily tasks. However, security experts believe that end-users tend to be passive towards organisational security policies, and their lack of knowledge about these policies which may lead to negative feelings. Additionally, experts perceive that end-users may be unaware of security measures, have difficulties understanding security concepts, and are at high risk of falling victim to phishing attacks by opening attachments and clicking on unknown links.
Factorial!: A Google Chrome Extension to Analyze and Rate News Articles using Machine Learning
Authors: Miao Daniel , Liu Tyler , Park Andrew
Published: September 2023

The advent and spread of the internet has caused many users to favor the convenience and breadth of reporting that online news offers, whether it be from media companies or social platforms, which in turn has led to the monetization and corruption of said stories [4]. Large, company-owned news sites each try to appeal to only a few groups across the political spectrum, oftentimes sacrificing the indifference and integrity which serve as the tenets of honest journalism. We propose to aid in solving this problem a Chrome extension which serves to provide metrics, information, and line-by-line analysis of article text in order to help readers stay aware and healthily skeptical [5][6]. Using machine learning (ML) as well as traditional algorithms, we aim to provide key info on the article’s truthfulness as well as the source’s bias and ownership [7]. In this project, we used 3 main models, each to detect fake news, political leaning and sentiment, in addition to traditional criteria such as readability, # of words, and time to read. All of our models performed well both theoretically and practically, giving above 80% accuracy on all occasions.
Buy When? Survival Machine Learning Model Comparison for Purchase Timing
Authors: Vallarino Diego
Published: September 2023

The value of raw data is unlocked by converting it into information and knowledge that drives decision-making. Machine Learning (ML) algorithms are capable of analysing large datasets and making accurate predictions. Market segmentation, client lifetime value, and marketing techniques have all made use of machine learning. This article examines marketing machine learning techniques such as Support Vector Machines, Genetic Algorithms, Deep Learning, and K-Means. ML is used to analyse consumer behaviour, propose items, and make other customer choices about whether or not to purchase a product or service, but it is seldom used to predict when a person will buy a product or a basket of products. In this paper, the survival models Kernel SVM, DeepSurv, Survival Random Forest, and MTLR are examined to predict tine-purchase individual decisions. Gender, Income, Location, PurchaseHistory, OnlineBehavior, Interests, PromotionsDiscounts and CustomerExperience all have an influence on purchasing time, according to the analysis. The study shows that the DeepSurv model predicted purchase completion the best. These insights assist marketers in increasing conversion rates.
Video Classification-Based Action Recognition with Enhanced Convolutional Neural Networks
Authors: Mei Bo
Published: September 2023

The classification of videos has become increasingly important in the field of data science research, as it has numerous practical applications in modern society. Compared to image classification, video classification poses a significantly greater challenge. One of the most obvious difficulties is that video classification tasks require more powerful computers due to the large number of features that need to be computed. Additionally, conventional 2D Convolutional Neural Networks (2D CNNs) are not effective in handling such tasks. This paper proposes a novel 2-layer Convolutional Neural Network (CNN) architecture for action recognition that addresses these challenges. The proposed architecture achieved a high test accuracy of 79.66% for classifying large video clips. The results indicate the effectiveness of the proposed approach for video classification tasks.
An Efficient Program to Detect DDoS Attacks using Machine Learning Algorithms
Authors: Bao Kaige , Li Ang
Published: September 2023

This paper investigates the efficacy of machine learning algorithms for the detection of Distributed Denial of Service (DDoS) attacks [4][5]. The study explores different approaches, including Support Vector Machines (SVM), logistic regression, and decision trees, and evaluates their performance using metrics such as accuracy, precision, recall, and F1-score [6]. The results demonstrate the effectiveness of SVM models with polynomial or radial basis function (RBF) kernels, logistic regression models with a polynomial degree of 4, and decision tree models with depths exceeding 10 [7][8]. These algorithm configurations exhibit promising potential in mitigating DDoS attacks and safeguarding network infrastructures [9]. However, limitations such as dataset availability, imbalanced data, and the focus on offline detection warrant further research. Enhancements in these areas can lead to more robust and efficient DDoS detection systems. The findings of this study contribute to the advancement of network security and offer insights for organizations aiming to counter the growing threat of DDoS attacks.
A New Measurement Called RoA for Estimating the Audience Rate of a Family of Web Radio Stations
Authors: DJARA Tahirou
Published: September 2023

In this paper, we have developed a new measure called (Radio Online Audience) of the audience rate of a web radio station. The is a method that uses real-time audience tracking and measurement technologies to provide a more accurate estimate of the audience rate of web radios. It is a composite formula that incorporates the radio broadcast frequency, the number of radio listeners and the average listening time of all radio listeners. In order to implement the new measure, an experiment is being conducted on a family of twelve radios from the “AfricaWebRadio” ecosystem. We have also extended the experiment on the results of the ranking of radio stations (March 2023) carried out by the ACPM (Alliance for Press and Media Figures). The experimental results are promising.
An Effective Sensor Network System for Early Wildfire Detection: Empowering Communities using Advanced Technologies
Authors: Liang William , Sahagun Jonathan
Published: September 2023

This project focuses on the development of an affordable and accessible solution for early wildfire detection in order to help empower individuals and communities with the ability to proactively detect fire sources[1]. By leveraging a network of sensors spread across a wide area, wildfires are able to be easily discovered by the network and relevant stakeholders are alerted to the threat[2]. Advanced algorithms analyze environmental factors such as temperature, humidity, and wind speed to enhance the accuracy and timeliness of wildfire detection [3]. By addressing the limitations of existing methodologies, this project contributes to improving wildfire prevention and mitigation efforts [4]. Through cost-effective technology and widespread implementation, it aims to create safer and more resilient communities in the face of the increasing threat of wildfires [5].
Survival Quest: Raising Awareness for Endangered Species Through the Panda Game Created with Unity
Authors: Zhang Zeyu , Morris John
Published: September 2023

The goal of this project is to bring up awareness on the safety of endangered species by creating an educational game called "Panda." [4]. By selecting a panda as the main character, which symbolizes endangered species, the game aims to engage players in learning about the challenges these animals face [5]. In the game, players manipulate a panda and strive to survive as long as possible while encountering various threats such as car accidents, predators, and hunters. To enhance the educational aspect, a fact sheet pops up after each panda death, informing the player about the human causes behind the panda's demise and suggesting preventive measures. For instance, if a panda dies in a car accident, the fact sheet would highlight the staggering statistic of 1 to 2 million wildlife deaths caused by car accidents annually in the U.S. It would also recommend supporting the Roadless Rule as a means to reduce such accidents [6]. Although the proposed solution is promising, its effectiveness still requires validation as the game is relatively unknown and has been played by only a few individuals. Future plans involve conducting surveys among a larger player base to assess the game's impact and ascertain whether the desired awareness and behavioral changes regarding endangered species conservation are achieved.
Exploring Large-Scale 3d Donation Networks: A Visualization System for Immersive and Playful Philanthropic Experiences
Authors: Liu Jialiang , Andaya Moddwyn
Published: September 2023

Currently, there is a significant rise in the popularity of 3D donation programs, wherein numerous participants actively engage in mutual acts of charitable giving. Drawing upon extensive research conducted within these virtual philanthropic communities, we have developed and implemented a visualization system aimed at providing users with an immersive and playful experience while exploring and navigating large-scale 3D donation networks. Our design leverages familiar three-dimensional representations to introduce novel techniques for comprehending the interconnectedness of complex donation structures, supporting visual analysis and search capabilities, as well as automatically identifying and visualizing philanthropic clusters. Through public installations and controlled studies, our system has demonstrated its usability, ability to facilitate discovery, and potential for fostering enjoyable and socially engaging philanthropic activities.
A User-Friendly Mobile Application to Accelerate the Attainment of the First Class Rank in the Boy Scouts of America using the Unity Engine
Authors: Jia Yuanxin , Morris John
Published: September 2023

The process of Scouts within the Boy Scouts of America ranking up to First Class, and eventually Eagle, can be quite gruesome and challenging [1]. Though there are various resources online ranging from articles and videos that explain how to potentially speed up the ranking process, none of them actually give an effective solution to the problem. Scouts nowadays lack things like attention span because of the harmful side of social media, especially TikTok [2]. They don’t want tips and tricks on how to rank faster, and they also don’t want to flip through massive textbooks to find the answers to requirements. They just want straightforward and quick answers to their problems without any stalling. This is exactly what the Trail to First Class app is. It gives them quick answers to their problems without any ties. Though, the app is not perfect. There is a severe limitation that if some requirements require scouts to, for example, identify things around their geological area, the app cannot help them because providing the answers to every single possible geological area would be quite inefficient. Though something like this might be possible in the future, it definitely cannot come now. Despite the limitations, the app is more than enough to provide scouts with all the resources they need in order to accelerate their ranking process.
Leader’s Legacy: An RPG Game to Bring Out the Leadership of its Players
Authors: Zhang Chunxuan , Park Andrew
Published: September 2023

There has not been a sustainable amount of leaders matching up to the demand for such skill by 2030. Leader’s Legacy has been created to help alleviate the issue by presenting educational questions on leadership principles within an interactive game [1]. Our solution is built with a combination of quiz-style questions and scenarios that are tied to player progression for the combat system [2]. We also integrated a cloud-based backend via Firebase to allow users to access their information from anywhere for greater accessibility.
Pengembangan Jaringan Untuk Memperbaharui Sistem NComputing Versi Lawas ke Versi Terbaru Dengan Metode SDLC
Authors: Kristiyanto Yogi , Sugiyono Sugiyono
Published: September 2023

NComputing is the world's fastest growing desktop virtualization company, with more than 70,000 customers and 20 million daily users in 140 countries. The current obstacle is that the average user of NComputing products in Indonesia is still using the old version of the product which has been declared an end-of-life (EOL) and end-of-support (EOS) product by NComputing. Older NComputing products that are still commonly used today are vSpace 4 or 6 on host servers and L230 or U170 devices on clients. For current operating systems it is also not recommended to use Windows Server 2008 or other older versions because it is already EOS which is vulnerable to security issues At the moment. This study aims to provide information or guidance for developing information technology infrastructure built using NComputing products from older versions to the latest versions using the System Development Life Cycle (SDLC). There are new features in the new version of NComputing that make it easier for administrators to manage the NComputing network, supported by the latest Windows Server operating system which still gets support from Microsoft, however, judging from the details of the costs incurred for this development, it is quite large using products from Microsoft .
Hospedantes en frutales de importancia económica asociados a patógenos foliares del duraznero (Prunus persica L.)
Authors: Ceballos-Chávez Ángel R. , Lizárraga-Sánchez Glenda J. , Leyva-Madrigal Karla Y. , Mc Caughey-Espinoza Diana M. , Chávez-García Leidy E. , Valenzuela- Escoboza Fernando A. , López-Valenzuela Blanca E.
Published: September 2023

En México, la presencia de hongos fitopatógenos ocasiona severos daños en frutos de durazno (Prunus persica L.), después de la cosecha, causando disminución en la exportación de productos procesados y en vida de anaquel. El objetivo de este trabajo fue describir el rango de hospedantes de hongos foliares en frutos de manzano (Malus domestica Borkh), fresa (Fragaria spp.), ciruela (Prunus domestica L.), pera (Pyrus communis L.) y membrillo (Cydonia oblonga Mill.), y en hojas de variedades de durazno. Para la prueba de patogenicidad se utilizó la técnica de aspersión y las enfermedades se valoraron mediante la escala de severidad de 5 grados de 0 a ≥75 % de presencia de síntomas sobre el fruto y del 10 a ≥60% para hoja desprendida y porcentaje de incidencia. Se inocularon conidios de Fusarium spp, Alternaria spp, Epicoccum spp, Collectotrichum spp y Monilinia spp., en todos los frutos y las hojas de las variedades de durazno amarillo, prisco y blanco, con siete repeticiones, dejando un testigo con agua destilada estéril para cada uno de los tratamientos; se colocaron en cámara húmeda en condiciones asépticas, a 27°C ± 1 y se sometieron a regímenes de 14 horas oscuridad y 10 horas luz, por 4, 7, 10 y 12 días. Los datos se sometieron a un análisis de varianza utilizando la comparación de medias de Tukey (P≤0.05). La variedad amarillo de durazno presentó mayor susceptibilidad a todos los hongos, los cuales fueron patogénicos a los hospedantes con severidad de 0 a 97,14 %.
SISTEM INFORMASI MONITORING PERKEMBANGAN PEKERJAAN PROYEK
Authors: Dedy Irawan Joseph , Munasih Munasih , P. P. Renaldi
Published: September 2023

Sebuah perusahaan yang bergerak dalam bidang pekerjaan konstruksi, seperti pembangunan gedung, jembatan, jalan raya, dan pekerjaan-pekerjaan yang lain, memerlukan sistem pengawasan yang baik terhadap perkembangan proyeknya, karena keterlambatan pengerjaan suatu pekerjaan akan berakibat kerugian pada pelaksana pekerjaan maupun pemberi pekerjaan, oleh sebab itu, dibutukan sebuah sistem yang dapat melakukan monitoring dari pekerjaan tersebut, setiap pekerjaan pasti memiliki perencanaan, dimana salah satunya adalah masalah waktu pengejaan, sehingga dengan membuat sebuah sistem informasi yang dapat melakukan monitoring progres dari semua pekerjaan-pekerjaan yang sedang berjalan, akan menghasilkan sebuah perusahaan yang dapat melaksanakan semua pekerjaannya tepat waktu, hasil akhir dari penelitian ini adalah sebuah sistem informasi yang dapat memonitor progres dari semua pekerjaan proyek, sehingga dapat dengan cepat diketahui jika terjadi resiko keterlambatan sebuah proyek.
ANALISIS MODEL BISNIS STARTUP KEENANSUPERCAR DENGAN METODE VALUE PROPOSITION DESIGN
Authors: Syuja Naufal Dzaki Surya  , Putra I Gede Juliana Eka , Tiawan Tiawan
Published: September 2023

Keenansupercar startup is a luxury car rental service platform in Bali. At this time, startup keenansupercar wants to innovate its business model because there is no customer profile and proper product development that matches the wishes of the market/customers. The goal of business model innovation is to create a value proposition for startups, customers, and society. There are various methods that are often used by startup founders in analyzing business models, one of the methods that can be used to analyze business models is value proposition design. The value proposition design method directs startup founders how to use the value proposition canvas in designing and testing a large value proposition on a continuous search for what customers want which will result in a value proposition canvas.
APPLICATION OF DATA MINING USING NAIVE BAYES ALGORITHM FOR CLASSIFICATION OF EMPLOYEE CONTRACT EXTENSION AT PT TORINI JAYA ABADI
Authors: Winda Yulianti  , Wiyanto Wiyanto , Nisa Nurhidayanti
Published: September 2023

An employment contract is an agreement made verbally or in writing between an employee and an employer, the working status of contract employees in a company is an important matter and needs to be considered in order to achieve company goals. PT Torini Jaya Abadi is a company that uses a work contract system. Every year there must be employees who are protected or their work contracts are terminated. In terms of determining whether an employee is strained or terminates his work contract, there are often difficulties in determining this. This is done to create a system that can help determine the extension of an employee's work contract by classifying it into the labels Extended and Disconnected which have 18 Variables for the process of assessing employees who will have problems or termination of employment contract. This system uses the Naïve Bayes method and is built using the Bootstrap programming language and MySQL database. The results of this study are a system that helps PT Torini Jaya Abadi in extending employee work contracts.

 

Keywords : Employment Contract, Naïve Bayes algoritm, PHP MySQL
Especialización de enfermería en anestesiología y reanimación como necesidad formativa en Cuba
Authors: Sarduy-Lugo Mirelys , Fernández-Rodriguez Yuleidy , Sarduy-Lugo Anabel
Published: September 2023

Introducción: Las especialidades de posgrado constituyen la formación académica que le proporciona al profesional la actualización, profundización, perfeccionamiento o ampliación de las competencias laborales para el desempeño asistencial que requiere.Objetivo: Explorar la percepción de los enfermeros certificados para la práctica de la anestesiología sobre la necesidad de la formación de enfermeros especialistas en anestesiología y reanimación.Métodos: Se realizó un estudio cualitativo, de tipo Investigación Convergente Asistencial en el Departamento de Docencia e Investigación del Hospital Provincial Pediátrico Universitario de Villa Clara, Cuba, entre los meses de enero y marzo del año 2022. El universo estuvo compuesto por 100 profesionales de enfermería certificados en anestesiología, y la muestra seleccionada a través de un muestreo teórico quedó constituida por 80. Se realizaron entrevistas informales y ocho grupos de discusión en el proceso investigativo.Resultados: Los participantes perciben como necesaria la formación de enfermeros especialistas en anestesiología, reanimación y alivio del dolor. Las estrategias para lograr esta formación deben estar dirigidas a su aprobación por parte de las instancias de posgrado del Ministerio de Salud Pública y el Ministerio de Educación Superior.Conclusiones: Perciben como perentorio la formación de enfermeros especialistas en anestesiología y reanimación, ya que permitirá garantizar la adecuada renovación de este recurso humano, contar con enfermeras capacitadas y capaces de brindar atención anestesiológica óptima, alcanzar un nivel profesional superior que los coloque paralelos a otras áreas del cuidado y formar recursos humanos más jóvenes que tengan un nivel de preparación superior en esta especialidad.
Apuntes sobre la educación del cuerpo: Discusiones en torno del saber, la formación y el currículum
Authors: Escudero Carolina
Published: September 2023

Este libro es el producto de una serie de discusiones que se vienen desarrollando en el Centro Interdisciplinario Cuerpo, Educación y Sociedad (CICES, FaHCE, IdIHCS) y que dan cuenta del estado actual de las investigaciones y estudios que sostienen desde su constitución en el año 2013.

La obra está organizada en cuatro partes. Danza, gimnasia y educación,recupera discusiones en torno a la danza y a la gimnasia como prácticas corporales y se incluyen debates sobre la cuestión de las relaciones entre su enseñanza y la educación. Tiempo libre, recoge discusiones teóricas y reflexiones acerca de experiencias de trabajo concretas sobre las relaciones entre tiempo libre, ocio, juego y prácticas corporales. Saber y currículum, reúne trabajos que ponen en juego las dimensiones epistemológica e institucional con las que se vincula el currículum como realidad de discurso y como objeto de análisis. Por último, Actualidad del cuerpo cierra con dos reflexiones en las que se ponen en escena los embates de la pandemia sobre el cuerpo.
Recension systématique de l'efficacité des interventions ciblant le passé traumatique et l'attachement de l'enfant offertes aux parents d'accueil
Authors: Fortin Frédérique , Beaupré Juliette
Published: September 2023

L'exposition à la maltraitance et à d'autres formes de traumatisme chez l’enfant peut avoir un impact délétère sur son développement et son bien-être (Carrion et coll., 2007). L’enfant faisant l'objet d'un placement extrafamilial est souvent confronté à de multiples adversités, comme des gestes de maltraitance, une séparation avec sa figure d'attachement primaire (Murray et coll., 2019) et des problèmes comportementaux et émotionnels (Hussey et coll., 2012). De leur côté, les parents d’accueil peuvent être mal informés quant à l'impact des expériences traumatiques chez l’enfant (Konjin et coll., 2020). De ce fait, certaines études suggèrent que les parents d'accueil réclament une formation spécifique pour accomplir adéquatement leur rôle (Cooley et Petren, 2011; Murray et coll., 2011). Il est donc important de se demander si des interventions auprès des parents d’accueil font une différence sur le développement des enfants placés et sur les pratiques parentales. Afin de répondre à cette question, une recension des écrits centrée sur l’efficacité des interventions aux parents d’accueil est effectuée. À cette fin, les bases de données Érudit, PycNet, Social Work Abstracts et Medline et la plateforme Covidence sont utilisées. Les résultats obtenus suggèrent que des interventions de formation des parents d’accueil ciblant la sensibilisation aux traumatismes ou visant un attachement sécure chez l’enfant sont pertinentes au bon développement de celui-ci. En effet, des programmes tels que l’Incredible Years, le Fostering Connection ainsi que le programme bref sur la sensibilité parentale montrent des retombées positives sur les pratiques des parents d’accueil et sur le développement de l’enfant.