Accepted Papers

Heart Disease Early Prevention By Entropy Triangle-based Non-sinus Rhythms Prediction

Arman Daliri1, Mahmood Alimoradi2, Mahdieh Zabihimayvan3 and Reza Sadeghi4*, 1Department of Computer Engineering, Karaj Branch, Islamic Azad University, 2Independent Researcher, 3Department of Computer Science, Central Connecticut State University, New Britain, CT,USA and 4*School of Computer Science and Mathematics, Marist College, Poughkeepsie, NY, USA

ABSTRACT

One of the most important problems in medical science is to make the prediction easier. This research proposes a new framework, Entropy Triangle-based Oversampling, for predicting non-sinus rhythms using a new machine learning method. This framework contains three steps: feature engineering, entropy triangle oversampling, and predicting the disease. The data set used in this research is a 12-lead electrocardiogram (ECG) database of arrhythmia research for 10,646 patients. This data set has 11 different types of heart rhythm: five sinus, and six non-sinus rhythms. In this article, we present two novelties in machine learning and medical science, which result in a prediction of non-sinus rhythms with an accuracy of higher 85%. Our experimental result, among other findings, reports that the most accurate classifier and the most useful oversampling based on Entropy Triangle are supported vector classifier and shark smell oversampling method.

KEYWORDS

Classification, Prediction, Entropy Triangle, Sinus and Non-sinus Rhythm, ECG.

Different Methods of Authentication for Mobile Banking Nikita

Leili Nosrati1 and Laleh Nosratri2, 1Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran and 2Industrial Engineering, Tarbiat Modares University, Tehran, Iran

ABSTRACT

Online banking authentication has been recognized as a key factor in the security of online banking. nowadays, different methods have been developed for online banking validation which cause problems from hacker attacks and Internet theft. Our research showed that biometrics is appropriate options for dealing with these issues. In this article, different authentication protocols for online banking have been compered.

KEYWORDS

Face Authentication, Mobile Banking, Artificial Neural Network, Face Detection, Machine Learning.

Design and Develop Mass Surveillance System Using Machine Learning Approach

Jemal Abate1*, Ashenafi Tulu2, Tamrat Delessa2, Matiyos Alemayehu2, College of Computing and Informatics, Haramaya University, Dire Dawa, Ethiopia

ABSTRACT

The pandemic of the novel coronavirus disease 2019 (COVID-19) is a public health emergency, with epidemiologic models forecasting grave implications, including high death rates, if the virus is allowed to run its course without intervention. Technology-assisted contact tracking is a useful tool for limiting disease spread during an epidemic or pandemic. Because resources for mass testing and significant amounts of vaccines are unlikely to be available for rapid use, contact tracing is considered the first and most effective approach in limiting an outbreak. Even before vaccines are available, effective contact tracking can allow societies to reopen from lockdown. The goal of contact tracing is to reduce the time it takes to contain an outbreak by automating the traditional interview-based contact tracing process. In essence have proposed a framework for contact tracing solutions to identify the contact history of an infected person.

KEYWORDS

COVID-19, Contact Tracing, Mass Surveillance, Face Recognition, Machine Learning.

Development and Evaluation of an AI-Enabled Nutrient Intake Monitoring App for Obesity andDiabetes Prevention in Young People: A Comparative Study with Live Scanning and Photo-Uploading Methods

Jiheng Yuan1, Victor Phan2, 1Santa Margarita Catholic High School, 22062 Antonio Pkwy, Rancho Santa Margarita, CA 92688, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768

ABSTRACT

Obesity and diabetes are prevalent health issues worldwide, especially among young people. To address this, an app was proposed to help users monitor their daily nutrient intake and prevent obesity and diabetes [1]. The app uses AI scanning to analyze the nutrient level of food and suggests a suitable daily nutrient intake for the user based on their age and gender. Data storage allows users to track their meal history and create a personalized diet plan [2]. The app was compared to similar systems, and it was found that live scanning is more intuitive and convenient than photo uploading. Additionally, the proposed app was tested in two experiments and was found to be effective in identifying food items and received generally positive feedback from users, but further improvements are necessary to enhance accuracy and user experience. In the first experiment, the accuracy of the AI model for predicting food items was tested using a combination of existing and customized datasets [3]. A total of 227 food items were tested, including bananas, watermelons, peaches, tomatoes, pineapples, rice, fries, hamburgers, eggs, noodles, and other items. The results showed an overall accuracy rate of 82% for all food items tested, with pineapple having the highest accuracy at 100% and peaches having the lowest accuracy at 60%. In the second experiment, 15 participants tested the applications features and provided feedback through a survey. The results showed that the application was successful in its implementation of features and received generally positive feedback, with an average functionality rating of 8.13 and an average convenience rating of 7.67.

KEYWORDS

Obesity, Diabetes, Nutrient Intake Monitoring, AI Scanning.

An Explanatory Research of English Writing Teaching With Internet Plus Tools in the Perspective of Memetics

Wang Qian and Hanipah Hussin, Faculty of Education and Liberal Studies, City University Malaysia, 46100 Petaling, Jaya, Selangor, Malaysia

ABSTRACT

This dissertation explores the application of memetic approach in teaching English writing through the internet plus tools. The study aims to provide an in-depth understanding of the effectiveness of this approach on students writing performance, their attitudes towards writing, and their motivation to learn English. The study is conducted through explanatory research, which combines both qualitative and quantitative research methods. The data is collected through surveys, interviews, and observations of students writing performances before and after the intervention. The results of the study suggest that the use of a memetic approach in English writing teaching with internet plus tools had a positive impact on EFL students writing proficiency. The students reported that the use of internet plus tools made writing more engaging, and the memetic approach helped them understand the cultural and social contexts of writing. The findings also indicate that the students writing skills significantly improved after the intervention. This research provides valuable insights into the use of a memetic approach in teaching English writing and highlights the potential benefits of internet plus tools in enhancing the students learning experiences.

KEYWORDS

Memetics; English Writing Teaching; College English; Internet Plus Tools; Exploratory Research.

An Survival Knowledge Training Platform to Self-rescue From Campus Shooting Using 3D Modeling and Machine Learning

Congyu Zhao1, Suraj Singh2, 1Northwood High School, 4515 Portola Pkwy, Irvine, CA 92620, 2Computer Science Department, California State Polytechnic University, Pomona, CA91768

ABSTRACT

School accidents have still been a major problem that happens frequently over decades [1]. Safety hazards are alsothe problem that parents and teachers were worried about, especially campus fire [2]. Schools are the main placeswhere we study and live, and are also special places where a large number of minors gather. How to do a goodjobin campus fire safety is not only related to the normal order of education and teaching on campus, but also tothesafety of teachers and students. the vital interests of households and the future of the motherland. Accordingtostatistics, more than 80% of school fires are caused by human factors, and classrooms, student dormitories, restaurants and other living places are more prone to fires. Habitual violations of regulations have become one of the main factors that cause fires [3]. However, base on all those type of question, this paper designs an game forpreventing campus fire and other types of safety hazards might appearances at school. Our design builds upononunity engine and C# script [4].

KEYWORDS

Settings, Menu, Opinions, FPS, TPS.

A Procedural Generation Platform to Create Randomized Gaming Maps Using 2D Model and Machine Learning

Nathan Lee1, John Morris2, 1Northwood High School, 4515 Portola Pkwy, Irvine, CA 92620, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768

ABSTRACT

As a video game developer, the most difficult problem I ran into was creating a map for the game, as it was difficult to create non repetitive and original gameplay [1]. My project proposes a solution to this problem as I use Answer Set Programming to create a program to procedurally generate maps for a video game [2]. In order to test its reliability, I allowed it to generate around 10,000 maps, stored the data of each of the maps, and used the common trends I find in the data to find problems with the program and fix it in the future. In developing a video game, level creation consumes a major portion of the development total time and level procedural generation techniques can potentially mitigate this problem. This research focused on developing a VVVVVV style level generator using Answer set programming for the game Mem.experiment which was developed at the same time. VVVVVV is a 2D puzzle platformer that uses changes in direction of gravity instead of jumping for the player’s vertical movement [3]. During the development of the level generator, 10,000 levels were created. I found out that the average total time it took between generations is 45 seconds, and the average time for ASP to generate a map is 12 seconds [4]. This means that the process of displaying the generation took between 2x - 3x longer than generating the ASP solution.

KEYWORDS

ASP, Procedural Generation, 2D Game.

Guidelines and Best Practices for Serious Game Development

Besma Ben Amara1, 2, Hedia Mhiri Sallami2 , and Lamjed Ben Said2, 1University of Tunis El Manar, Faculté des Sciences Economiques et de Gestion de Tunis, B.P 248 2092 Tunis, Tunisia, besma.benamara@fsegt.utm.tn, 2University of Tunis, Institut Supérieur de Gestion de Tunis, SmartLab, Av de la Liberte, Bouchoucha, Tunisia

ABSTRACT

As multidisciplinary software, Serious Games (SG) are invaluable tools for learning, training, and improving skills in many domains, owing to their ability to engage and motivate players toward achieving planned processes and objectives. Multiple studies have proposed methods, models, and frameworks to facilitate the development of SG. However, designers, developers, teachers, and researchers still face difficulties in SG design, resulting in many developed games falling short of their intended objectives. Toward an approach for SG design development, this paper proposes a set of guidelines and best practices from the software engineering and video games industry, along with SG design success factors. The proposed approach outlines essential recommendations that designers must consider from the beginning of the design process to game testing to ensure the creation of high-quality SG.

KEYWORDS

Serious Game Development, Software Engineering Practices, Video Game Industry, Serious Game Design Guidelines, Serious Game Design Approach.

A Customer Service Text Label Recognition Method Based on Sentence-level Pretraining Technology

Xiaoyu Qi1,2, Bo Cheng1,2, Kang Yang3, Lili Zhong3 and Yan Tang3, 11Shenzhen Audencia Business School, WeBank Institute of Fintech, Shenzhen University, 2State Key Laboratory of Network and Switching Technology of Beijing University of Posts and Telecommunications, 3Ping An Bank Co.. Ltd.

ABSTRACT

Customer service text data is known as the dialogue text data between users and customer service provider, and it contains a large amount of user information. The effective use of customer service text content can bring great business plan optimization to the service provider. Based on the traditional machine reading comprehension model, this paper builds a customer service text user’s attribute label recognition model, and proposes a model pre-training method based on sentence-level pre-training technology: aiming at the background of poor performance of the model in answering comprehensive full-text content analysis questions such as user intent and text sentiment analysis. This paper extracts text summaries based on the T5-pegasus model, constructing a text summaries dataset for model pre-training. Then build a text summarization model including a ERNIE pre-training model, train the models ability to understand the full text, and improve the models ability to answer questions that need to be combined with full-text content understanding, such as user intent and sentiment analysis. Use the pre-trained model to solve customer service text label recognition tasks based on machine reading comprehension tasks. The test results on the data set show that the improved model has a significant performance improvement in the task of customer service text label recognition.

KEYWORDS

Machine Reading Comprehension, Pre-trained model, Customer Service Text Analysis, Natural Language Generation, Attention Mechanism

A Topic Model Based on Weighted Word Co-occurrence Matrix and User Topic Relationships

Ziqi Xu1,2, Bo Cheng1,2, Kang Yang3, Lili Zhong3 and Yan Tang3, 11Shenzhen Audencia Business School, WeBank Institute of Fintech, Shenzhen University, 2State Key Laboratory of Network and Switching Technology of Beijing University of Posts and Telecommunications, 3Ping An Bank Co.. Ltd.

ABSTRACT

Various industries have widespread adopted the intelligent customer service, thus how to understand customer intent more accurately and extract key information has become a current research hotspot. However, the features that customer service dialog texts are short length, specialization and sparse lead to the poor performance of traditional topic extraction. Based on the above background and characteristics, this paper proposes a topic model WCMUT-HDP, which is based on a weighted word co-occurrence matrix and user topic relations. In the WCMUT-HDP model, this paper introduces a semantically weighted word co-occurrence matrix to mine the statistical and semantic features of customer service texts and optimize the effect of clustering. For the structure of customer service dialogues, this paper introduces temporal and author attributes of customer service dialog into the topic recognition of customer service texts. This method helps us to accurately extracts the users intention. WCMUT-HDP is based on the Dirichlet process, and does not need to specify the number of topics in advance, which saving the time overhead of parameter experiments and evaluation. In the end of this paper, the experimental results show that the WCMUT-HDP model can effectively identify the topics of customer service conversations, and the extracted topics can accurately reflect the users conversational intent.

KEYWORDS

Customer Service Text,Word Co-occurrence Matrix,Hierarchical Dirichlet Process,Topic Model

Enhancing Camera Model Identification Using Resnet and Weight Fusion of Global Information

Boru Chen and Waleed Abdulla, Department of Electrical, Computer, and Software Engineering, University of Auckland,Auckland, New Zealand

ABSTRACT

Camera Model Identification (CMI) is a crucial aspect of digital image forensics that significantly prevents image-related crimes. Currently, the most widely used CMI techniques are based on deep learning, particularly Convolutional Neural Networks (CNNs), which have greatly improved the efficiency and accuracy. However, CNN-based approaches can be affected by the structural characteristics of the image content, which may negatively impact their performance. To overcome this problem, we propose a new approach based on the Residual Neural Network (ResNet), Adaptive Preprocessing Module (APM), and Weight Fusion of Global Information (WFGI). The proposed method was evaluated using the Vision and Dresden databases, and its robustness was tested under various attack scenarios. The experimental results demonstrate that the proposed method achieves more than 97.5% accuracy in large-scale datasets containing 45 camera models. Moreover, the method’s robustness is verified under different attack scenarios, demonstrating its potential for practical use in image forensics applications.

KEYWORDS

Camera Model Identification, Convolutional Neural Network, Model Fusion, Image Forensics.

Research On Image Reconstruction Algorithm For Positron Emission Tomography Based On Thin Plate Prior

Min Yao and XinRan Zhang, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

ABSTRACT

Image reconstruction is an important part of positron emission tomography. Maximum Likelihood-Expectation Maximization (MLEM) is a classic algorithm in medical image reconstruction. The MLEM algorithm is a maximum likelihood algorithm based on the Poisson model. The algorithm performs maximum likelihood estimation on the data of the reconstructed image by solving the maximum expectation, and uses the pixel as the parameter to be sought, and sets the corresponding number of iterations. The likelihood function is approximated to an extreme value to obtain a maximum likelihood estimate. However, the maximum likelihood maximum algorithm has instability. With the increasing iterations, random factors such as noise in the image are amplified in the reconstruction process and the local signal is excessively increased, the noisy influence of reconstruction image increases. To solve this problem, this paper introduces the Thin Plate prior distribution on the basis of MLEM, and combines various filtering to achieve the purpose of suppressing noise and protecting edges.

KEYWORDS

Positron Emission Tomography, Maximum a Posteriori, Thin Plate Prior, Anisotropic Filtering .

Framework on Enhancing Security of Transformation Based Biometric Template Protection Schemes Using Residue Number System

OmotoshoF.S and Gbolagade K. A, Department of Computer Science, Faculty of Information and Communication Technology, Kwara State University, Malete. Nigeria

ABSTRACT

We integrate biometric technology in our applications to provide a dependable approach to the problem of user authentication.However,study as proven that there are susceptible points in typical biometric systems which are prone to manipulation by hackers. The most concerned is biometric template attacks, reason for various biometric template techniques proposed by researchers in the literature to secure biometric raw template. The main challenge of most biometric template protection techniques is decrease in recognition performance when compare to unprotected system. Also, most of them required Auxiliary Data (AD) for verification.This study will leverage on features ofResidue Number System (RNS) by integratingit withBiometric Template Protection (BTP) Transformation technique. Since, RNS is carry-free arithmetic; BTP technique based on RNS system will achieve high speed of computation because of its parallel computing nature. It will also, eliminate the need to remember auxiliary data during verification.

KEYWORDS

Biometric, Traits, Template, Transformed, Residue Number System (RNS), Parallel, Authentication, Biometric Template Protection (BTP).

A Mobile Application for Creating Dance Choreography According to Musicality of Inputted Audio Using Machine Learning

Keren Brown1, David Wei2, 1Woodbridge High School, 2 Meadowbrook, Irvine, CA 92604, 2Computer Science Department, California State Polytechnic University, Pomona, CA91768

ABSTRACT

Even the best writers in history were not blessed enough to have a constant surge of inspiration, a never-endingflowof ink on paper, or fingers flying across keyboards. These blocks in creativity are commonly known as writer’s block, and many people experience this, since communication is important in every subject. Less commonly knownisdancer’s block, which is when a choreographer experiences a stop in inspiration while choreographing [1]. Theseblocks can continue for days or even weeks, before an idea strikes the dancer, setting back the ef iciency of choreographing. When due dates come into play, such as choreographing for a production, show, or assignment, adancer cannot put forth their best choreography, and will be left feeling unsatisfied with the quality of their work. When a choreographer faces a challenge towards creating original choreography, how can one gain inspirationtoovercome this block [2]? How can we ensure that the inspiration given is appropriate for the song and style whichthe choreographer is designing the dance to? This paper develops an application to choreograph visuals inacreative manner, while assessing the musicality of the audio in order to reflect the same emotion in the movement. We applied our application to a jazz piece to be performed as part of a local high school dance show and conducteda qualitative evaluation of the approach. The results show that with the software application, dancers will be abletofind inspiration to continue choreographing, pushing them past a barrier of creativity, and allowing themto finishtheir dance with a quality of choreography that they can be proud to present [3].

KEYWORDS

Dance, Choreography, Machine Learning, Audio Analysis.

Smoothing Parameter Selection and Alpha-stable P-ADIC Time Signals

Rachid SABRE1, and Walid HORRIGUE2, 1Biogeosciences (UMR CNRS/uB 6282) University of Burgundy,/Agrosup, 26, Bd Docteur Petitjean Dijon, France and 2UMR Agroécologie Equipe Biocom, INRA Dijon, Agrosup, 26 Av Docteur Petitjean 21000 Dijon, France

ABSTRACT

This work is a tentative method to obtain the optimal smoothing parameters of the spectral windows used in the estimation of the spectral density for alpha-stable random fields (two-dimensional signals) with p-adic time. We are inspired from the cross-validation method minimizing the integrated square error estimate using the “Leave-out-I” technique.

KEYWORDS

Spectral density, p-adic processes, stable random field.

Character Archetypes in an Online Creative Course Setting: The Case of Femme Fatale

Xiaohan Feng1 and Makoto Murakami 2, 1Graduate School of Information Sciences and Arts, Toyo University, Kawagoe, Saitama, Japan, 2Dept. of Information Sciences and Arts, Toyo University, Kawagoe, Saitama, Japan

ABSTRACT

Creative writing courses have flourished in recent years. However, the accuracy of the content of some courses has yet to be examined. Character archetypes are an unavoidable topic in most courses requiring character creation. We found that some online creative courses include characters whose character archetypes are debatable. For example, the femme fatale is often considered a character archetype, but some scholars believe it is a stereotype bias. While the use of character archetypes and stereotypes in creative work is a matter of personal freedom, it is inappropriate to educationally disseminate content that is still controversial in the academic community. This study explores the history and reasons for the generalization of femme fatale, the differences between femme fatale and other character archetypes, and derives three reasons why femme fatale is inappropriate as a character archetype in creative work. This study is intended to call attention to the rigor of the content of online creative writing courses.

KEYWORDS

Online Education, Creative Course, Femme Fatal, Archetype, stereotype.

Intention to use e-learning system through TAM: Study applied in Lebanese private universities

Soumaya Kaakour, Marketing, Lebanon

ABSTRACT

The purpose of this paper is to examine the aspects affecting intention to use e-learning by extending the Technology acceptance model (TAM) with the following external variables (computer self-efficacy, perceived enjoyment and personnel innovativeness). The model is applied on a sample of students from Lebaneseprivate universities. Findings indicated that all hypotheses are acceptedexcept the relationship between computer self-efficacy(CSE) and perceived usefulness (PU) and the relationship between perceived enjoyment (PE)and perceived ease of use (PEOU). Thus, this studyrevalidate the TAM theory. Results give opportunities to more investigations determining factors affecting the adoption of e-learning.

KEYWORDS

Technology acceptance model, computer self-efficacy, perceived enjoyment, personnel innovativeness, perceived usefulness, perceived ease of use, attitude, intention to use e-learning.

The State of Virtualisation at a Selected University in the Eastern Cape Province of South Africa

Nteboheng Patricia Mefi and Samson Nambei Asoba, Department of Public Management and Administration, Walter Sisulu University

ABSTRACT

Virtualisation in higher education emerged as a new imperative after the covid-19 pandemic as well as the technological revolution. Technological revolution and virtualisation have, however, put attention once more on socioeconomic inequalities in South Africa. Inequalities in readiness and adoption of digital systems have been observed along geographical as well as socioeconomic dimensions in resulting in a need to inquire on the state of virtualisation in different contexts. The study sought to describe the state of virtualisation at a selected university in the Eastern Cape Province which is considered one of the poorest in South Africa. Specifically, the state of virtualisation was described in terms of three dimensions, namely; (1) perception of academics on virtualisation, (1) attitudes of academics on virtualisation and (2) university initiatives in promoting virtualisation. The study adopted a qualitative design based on interviews with nine (9) academics from a selected university in the Eastern Cape. The perceptions of virtualisation were found to be consistent with those of the literature where virtualisation was considered to imply remote teaching and learning, digitalisation as were as non-physical educational strategies. Virtualisation was found to be associated with both negative and positive attitudes. Positive attitudes were related to views that virtualisation was flexible, associated with learning everyone and increased access to education. Negative attitudes were found to stem from information overload as well as sentiments that virtualisation needed specialised resources and training. It was found that University has done considerably better in training academics as well as in providing them with relevant tools for virtualisation. The study recommends strengthening virtualisation to ensure every academic benefits from using it.

KEYWORDS

Virtualisation, Higher Education, Remote Learning, University, Technology.

Effectiveness of Organisational Support for Job Satisfaction of Academics in Avirtualcontexts at a Selected Institution of Higher Learning in the Eastern Cape Province of South Africa

1Nteboheng Patricia Mefi and 2Emmanuel I Edoun, 1Department of Public Management and Administration, Walter Sisulu University, 2Department of Operations Management, University of Johannesburg

ABSTRACT

With increased virtualization of high education given the Fourth Industrial Revolution (4IR) as well as in response to the Covid-19 pandemic concern has been raised over the implication of this on the job satisfaction of academics. The aim of this study was to determine the effectiveness of the support given to academics in ensuring job satisfaction of the academics working online. A quantitative methodology which was based on the collection of data using a questionnaire given to academics was adopted. The results indicated that sixty percent (60.4%) of the respondents had received some support by university management. Most support for the virtual environment was provided through workshops and training. However, the support given was not consistent with the other finding that eighty six percent (86%) of the academics had indicated that the major factor affecting their job satisfaction was connectivity and lack of cooperation of students (42.6%). It is encouraged that HEIs should strengthen their relationships and cooperation with supportive institutions for virtualisation such as ESKOM as well as the private and public community.

KEYWORDS

Virtualisation, organisational support, covid-19, technology, innovation.

Accessible Information Communication Technology for Mainstreaming Persons With Visual Disabilities in Development Process in Nepal

Birendra Raj Sharma Pokharel, Action on Disability Rights and Development-Nepal

ABSTRACT

Persons With Disabilities face disadvantage resulting from impairment and disability associated with barriers that limit their participation. ICT particularly offers enormous opportunities to Persons with visual disabilities if this is designed in a way to meet universal design to improve the quality of millions of their life. Lack of accessible ICT creates barriers in education, employment, online services and social participation. There are widespread barriers for promoting accessible ICT in Nepal that is observed in three major components such as accessibility, adoptability and affordability which was examined by the personal experiences of 100 key informants. It found that the ICT should reflect the goal of fostering Education, greater participation and inclusion of Persons with visual disabilities. It is concluded that, Dignity, Efficacy, Non-discrimination, Inclusion, Autonomy and Livelihood are the six indicators of meaningful inclusion, hence, the indicator of digital inclusion is "Accessible ICT mitigating DENIAL of Persons with visual disabilities".

KEYWORDS

Visual, Disability, Accessibility, Adoptability, Affordability.

Action Learning: a Practice-based Approach for Leveraging Entrepreneurs’ Potential, Which Aim to Launch Startups in Sustainable Development

Dr. Mohamed Yacine, Action Learning Institute, Algeria

ABSTRACT

In incubators and pre-incubation in universities, theoretical perspectives, such as social learning theory and action learning theory, are used to evaluate the influence of action learning in the development of entrepreneur’s potential, which aim to launch start-ups in sustainable development; from four factors, naming, (1) self-efficacy; (2) thinking and describing precisely what their MVP will look like; (3) dealing with various challenges; and (4) demonstrating Proof Of Concept. For this purpose, the researcher conducted a research through 2 case studies with 71 incubated entrepreneurs using a research methodology that combined several qualitative techniques. Participatory observation, semi-directive interviews, and analysis of learning deliverables were utilized to examine differences in entrepreneurs’ potential. The study provides evidence that entrepreneurship education based on action learning methods may positively influence the entrepreneurial potential of entrepreneurs.

KEYWORDS

Action Learning, sustainable development entrepreneurship, incubation, entrepreneurs’ potential.

Designing and Testing User-club Systems to Improve Club Organization and Participation in High Schools and Colleges

Hank Cao1, Marisabel Chang2, 1West High School, 20401 Victor St Torrance CA, 90503, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768

ABSTRACT

Contemporary high schools have seen a significant rise in student-led clubs, whether they are academic, sports, or special-interest related. In them, students are able to collaborate with other like-minded peers and develop their unique hobbies and interests. However, in many of these high schools and especially those of ours, we have observed a lack of motivation and participation associated with poor club organization and communication. Inspired by existing software utilized by these clubs and building upon their features, we have designed and implemented a user-club system intended to help a myriad of clubs in high schools and colleges to manage their club events and membership [4]. Two experiments were conducted to test the effectiveness of two different applications designed to help high school clubs manage their events and membership. For the first experiment, 10 participants tested a user-club system, and for the second experiment, another 10 participants tested an application. Both experiments showed positive results, with participants providing feedback on the applications functionality and convenience. However, a few participants reported issues, indicating that refinement may be necessary for optimal usability. In the first experiment, most participants reported improvement in their club s participation and member interest, but a few reported little to no improvement, suggesting that the system may not be effective for all types of clubs. Further testing and refinement are necessary for both applications to determine their effectiveness for different types of clubs and user populations.

KEYWORDS

Mobile Development, Social, School

Enhancing Public Safety Awareness: the Role of a 2d Top-down Game in Training Individuals to Recognize Potential Threats While Outdoors

Billy Hsu1, Marisabel Chan2, 1Crean Lutheran High School, 12500 Sand Canyon Ave. Irvine, CA 92618-110, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768

ABSTRACT

When engaging in outdoor activities like walking in a park or having a picnic, it is crucial to keep in mind the potential dangers that may exist in certain areas, such as the presence of thieves or criminals. Recent public shootings underscore the importance of being mindful of one s surroundings and taking necessary precautions to ensure a safe and enjoyable experience. Therefore, it is crucial to educate people on the significance of staying vigilant, particularly in unfamiliar environments. Simple measures like staying in well-lit areas, avoiding isolated places, and being alert to suspicious people can significantly reduce the risk of falling prey to these dangers. By promoting safety measures and increasing awareness, individuals can fully appreciate the natural beauty of their surroundings without compromising their safety. To solve this issue we developed a game involving zombies that spawn in the darkness and chase the player, who can use a map to check for zombies beyond their radius. The goal is to escape in a vehicle or kill all the zombies, but escaping is the better option as it is nearly impossible to beat all the zombies. The game encourages the player to run and pay attention to their surroundings to avoid getting attacked by zombies. It also emphasizes the importance of cooperation and teamwork, as being a lone wolf is not a viable option in real life situations [4]. The objective of the game is survival, not victory. Understanding player preferences is essential to creating an enjoyable gaming experience. Two experiments demonstrate the importance of collaboration and communication in gaming. The first study found that survival and health-related features were rated higher than action-packed features, suggesting that players value these aspects more [5]. The second study found that incorporating voice communication in cooperative games can enhance teamwork and improve overall performance. Trust, shared goals, support, and communication were crucial to teamwork, and players provided positive feedback on their experience. Further research with larger samples is necessary to confirm the findings.

KEYWORDS

Public safety awareness, Outdoor safety, 2D top-down game.

Higher Education Migration and Indispensable Amenities for Onboarding in Uganda

Kanyesigye Rullonga Monicah1 & Kirumira Fred2, 1Academic Department, Ernest Cook Research and Education Institute (ECUREI), 2Administration department, Ernest Cook Research and Education Institute (ECUREI)

ABSTRACT

Globalization has led to a substantial surge of international student enrollments in higher education in Uganda.Migrating students require an understanding of their new environment and the social-culturalpolitics in order to smoothly on-board. A study among 120 International students in Uganda revealed that the social culture environment in Uganda gave limited cultural shocks, because the environment was similar to where they were coming from. The study noted a number of challenges international students meet while on boarding and therefore makes recommendations for institutions of higher learning to adopt for smooth student migration and on-boarding.

KEYWORDS

International Students’ Migration, Higher Education Migration.

Smart Phone Use Pattern of College Students in Relation to Gender, Residential Locality and Academic Stream

Partha Sarathi Mallik and Suruchi Sahoo, School of Education, Gangadhar Meher University, Sambalpur, Odisha, India

ABSTRACT

Research literature is enriched by findings of individual psychological attributes as predictor of smart phone use time and purpose of smart phone use but it is less explicit, how group identity affect smart phone use behaviour. The present study aims at finding out purpose wise smart phone use pattern of college students in relation to their gender, residential locality and academic stream. Primary data about purpose wise time spent on smart phone was collected from 180 college students of Odisha, India by ‘Stay Free’ application software and analysed by mean, S.D. and MANOVA. Results show that college students from India use smart phone on average 4 hours 34 minutes and 53 seconds per day and highest time is spent for entertainment purpose, followed by social media , followed by making voice call, internet surfacing and e-commerce respectively. Student’s residential locality has significant effect on time spent for social media and entertainment; gender has significant effect only on time spent for entertainment and student’s academic stream has significant effect on social media and making voice call. Effect of these socio-academic predictors on smart phone use time has been explained from psycho-structural perspectives. The results of this study will be helpful for educational policy framers for re-directing and modelling smart phones use behaviour of college students for better academic outcomes by initiating intervention measures.

KEYWORDS

Smartphone usage time, college students, purpose of smart phone use.

Enhancing Collaborative Skills of Learners Through Steam

Mohini Tripathi, Research Scholar, Department of Teacher Education, Central University of South Bihar

ABSTRACT

STEAM (Science, Technology, Engineering, Arts, and Mathematics) is an emerging educational approach focusing on the development of the creative thinking, problem solving, collaborative, and other life skills of the learners. To foster and strengthen national development and innovations, STEAM-based schools and curricula are showing up. This is an approach to integrating Art into STEM. In spite of the increasing interest in STEAM universally, research on the approaches to evaluate collaborative skills is still lacking. The proposed study is an effort to know the effect of the STEAM Learning Approach on the Collaborative Skills of the learners. Quasi-Experimental Design has been used on two intact sections of 40 learners of class VII. Mean, Standard Deviation, and Critical ratio (t) were calculated. It can be concluded that STEAM Learning Approach does enhance the Collaborative Skills of learners.

KEYWORDS

STEM, STEAM, Collaborative Skills.

A Structured Note-taking Program to Aid in Data Retention and Promote Organization Using Mobile Devices as a Mediu

Mingyuan Gao1, Armando Contreras2, 1Oaks Christian school, 31749 La Tienda Rd, Westlake Village, CA 913, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768

ABSTRACT

In recent years, note-taking applications have become a common tool due to the simple organizational features it can provide. A key aspect of note-taking applications is the ability to ef iciently record and edit large amounts of data. SQL databases are well suited to this task because they provide a reliable and scalable way to manage structured data, allowing note-taking applications to more ef iciently store notes, tags and metadata associated with each note. They also support search and retrieval operations, enabling users to quickly find the information they have recorded. In addition, SQL databases provide features such as data stabilization, alternation and access, which help ensure the integrity and security of user-entered data. By utilizing SQL database replication and sharing features, note-taking applications can distribute their data across multiple servers, improving performance and reliability. In our experiments, the app demonstrated its reliability and memory capabilities. With a mean load time of 3 seconds. In summary, note-taking applications benefit from using SQL databases due to their ef iciency, scalability, and data management features. These advantages help users maintain integrity, find information quickly, and keep their notes secure.

KEYWORDS

Note-Taking, Application, Flutter, SQ.

A Community Based Mobile Application to Reduce Waste From Un-used Bikes Using Social Media

Alexander Junwen Tan1, Jonathan Sahagun1, 1Northwood HighSchool, 4515 Portola Parkway. Irvine, CA 92620, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768

ABSTRACT

Around 15 million bikes are discarded annually, which poses an environmental risk [1]. The rubber from bike tires takes a long time to decompose, and toxic chemicals are released into the soil during this process [2]. Additionally, the popularity of e-bikes is increasing, and the lithium batteries they use harm the environment during extraction. To address this problem, a bike donation app is proposed, which reduces the number of bikes produced, minimizes waste, and benefits those in need [3]. By operating online, the cost of running the operation is minimal, and the project can reach and help anyone with internet access. However, the app s success relies on a user base, which may be a significant challenge. Furthermore, the app s design may need improvement to attract users. Blind spots in the program may include inaccurate bike donation recommendations and a lack of proper verification for donated bikes safety and condition. An A/B test shows that personalized recommendations through the app increased the conversion rate for successful bike donations. The verification process for donated bikes was effective in ensuring the bikes safety and quality. By developing a mobile app that provides personalized recommendations and addresses bike waste, the project contributes to sustainable transportation and reduces environmental harm [4].

KEYWORDS

Environment, Application, Donation, Bikes.

Artificial Intelligence, Robotics and Human Computer Interaction in Education

Aicha Adoui1,2, 1Laboratory of Communication, Education, Digital Usage and Creativity, Mohammed First University, Oujda, Morocco, 2Centre For Social Justice Research Teaching and Service, Georgetown University, Washington DC, USA

ABSTRACT

The rapid advancement of technology has introduced a path for the integration of artificial intelligence (AI), robotics, and human-computer interaction (HCI) in the field of education. These technologies have generated fresh avenues for personalized and adaptive learning, innovative teaching techniques, and heightened student participation. The purpose of this paper is to provide an overview of the present status of AI, robotics, and HCI in education, and to delve into their conceivable influence on the pedagogical processes. By delving deeper into the potential applications of these cutting-edge technologies, the paper elucidates how they can be leveraged to advance and enrich the learning experience. In addition, the paper comprehensively examines the myriad benefits of integrating AI, robotics, and HCI in education, such as customized learning, adaptive learning, and increased student engagement. However, the paper also acknowledges the challenges that come with the integration of these technologies in education, including concerns about data privacy, cybersecurity, and ethical considerations. The paper also provides several examples of how AI, robotics, and HCI are currently being used in education, such as intelligent tutoring systems, robot-assisted learning, and gamification. The paper discusses the potential future developments in AI, robotics, and HCI in education, such as the use of virtual and augmented reality in the classroom. Thus, the paper highlights the potential of AI, robotics, and HCI in education and emphasizes the need for a careful and responsible integration of these technologies in the teaching and learning processes.

KEYWORDS

Artificial Intelligence, Robotics, Human-Computer Interaction, Education, Intelligent Tutoring Systems, Robot-Assisted Learning, Virtual Reality.

Call in Efl Situations at Higher Secondary Schools in Bangladesh

Md Nazrul Islam, Assistant Professor & Director, Quality Education College, Dhaka

ABSTRACT

CALL is the most crucial tool and technique to help improve the students language competencies. The new technology in language education has amplified learner autonomy, creativity, productivity, and group work. However, CALL becomes successful if the students, teachers, and administrators comprehend the importance of it and the teachers execute the system properly. This study explores the schools existing facilities, hurdles, and possibilities to implement CALL. The study accompanies to find answers to three research questions with observations and interviews. The theory of behaviorism and social constructivism establishes the research findings. The analysis finds that the students are eager to use technology in language learning. However, the teachers must be knowledgeable about CALL-related e-tools. Finally, the heads of schools react positively to the future development of teachers through training. This study suggests that trained teachers, the assistance of institutions, and available funds are essential to implement CALL in Bangladesh

KEYWORDS

Technology, observations, behaviorism, social constructivism, e-tools.

Adventure You in “parcours Avenir” How to Develop Guidance Skills Through a Serious Game? Vocational Guidance by the Prism of Cyber Psychology.

Hassina BOUCHEMLA, D.U en Cyberpsychologie - Université de Paris - 2020 – 2021

ABSTRACT

Young daily immersion in cyberspace and increasing use of video games raises questions about their effects on the construction of their identity and their orientation path. The psychology of lifelong orientation is a field of study making us understand what is at stake at the identity, societal and economic levels and in different contexts (school, professional, cyberspace, etc.). We thought it would be interesting to study how a "serious game" can contribute to the development of youngs guidance skills. Between reality and virtuality, what happens in terms of guidance education?

KEYWORDS

guidance education, serious game, guidance skills, lifelong guidance psychology.

Question-type Identification for Academic Questions in Online Learning Platform

Azam Rabiee, Alok Goel, Johnson D’Souza, Saurabh Khanwalkar, Course Hero, Inc.

ABSTRACT

Online learning platforms provide learning materials and answers to students’ academic questions by experts, peers, or systems. This paper explores question-type identification as a step in content understanding for an online learning platform. The aim of the question-type identifier is to categorize question types based on their structure and complexity, using the question text, subject, and structural features. We have defined twelve question-type classes, including Multiple-Choice Question (MCQ), essay, and others. We have compiled an internal dataset of students’ questions and used a combination of weak-supervision techniques and manual annotation. We then trained a BERT-based ensemble model on this dataset and evaluated this model on a separate human-labeled test set. Our experiments yielded an F1-score of 0.94 for MCQ binary classification and promising results for 12-class multilabel classification. We deployed the model in our online learning platform as a crucial enabler for content understanding to enhance the student learning experience.

KEYWORDS

Question-Type Identification, Content Understanding, Learning Platform, BERT, Education.

Text Summarization and Keyword Extraction

Pallavi Sharma and Min Chen, Computing and Software Systems, University of Washington Bothell, Bothell, USA

ABSTRACT

With the explosion of data in the digital age, it is an important yet challenging task to extract meaningful information from long texts. In this paper, a novel framework is presented to facilitate users in extracting summaries and keywords from long texts at real-time. It uses a hybrid approach based on feature extraction and unsupervised learning to generate quality summaries. In addition, it integrates machine learning with semantic methods to extract keywords and phrases from the source text. The framework is deployed as a mobile app that allows users to manage, share and listen to the extracted information to improve user experience. To test the effectiveness of the work, experimental and research evaluations are carried out on DUC 2002 dataset using ROGUE parameters. Results demonstrate a higher F1-score than the state-of-theart methods used for extractive summarization on the same dataset. Experiment also shows an accuracy of 70% for the keyword extraction method, which is in par with other work in the field.

KEYWORDS

Keyword Extraction, Summarization, Text Synthesis, Unsupervised Learning.

Modelling Uncertainty With Ontologies

Amelia Kahn, Department of Philosophy, University at Buffalo

ABSTRACT

Words like “likely”, “highly probable”, and “high confidences”, or terms of estimative probability, are widely used in intelligence, medicine, scientific research, and many other fields. These natural language terms are indispensable, but they can pose problems when used in data-tagging and semantic web applications, because their meaning can vary from community to community, and even between contexts within the same community. I use the use of terms of estimative probability within the intelligence community as a case study to examine what issues arise when trying to manage data with these terms. I then propose a system for modelling TEPs using OWL ontologies in a way that minimizes ambiguity and conforms with the ISO standard top-level ontology, Basic Formal Ontology (BFO), along with several mid-level ontologies in the field.

KEYWORDS

Ontology, Estimative Probability, Confidence, Risk, Subjective Credence, Semantic Web

An Intelligent Mobile Application to Assist in Taking Mathematical Notes Using Speech Recognition and Natural Language Processing

Casson Qin1, Jack Wagner2, 1Diamond Bar High School, 21400 Pathfinder Rd, Diamond Bar, CA 91765, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768

ABSTRACT

This study evaluated the accuracy and reliability of Voice Note Taking, a technology designed to transcribe spoken language and support note-taking. The experiment analyzed the transcription accuracy and word definition selection feature of Voice Note Taking using a series of audio files featuring individuals speaking in English in different settings. The results showed that Voice Note Taking is reliable and accurate, with an overall transcription accuracy rate of 87.81%. However, the study identified room for improvement, particularly in improving accuracy in noisy environments and developing more sophisticated algorithms for word definition selection. Future research could explore the integration of advanced natural language processing techniques to improve the accuracy of word definition selection, including leveraging machine learning algorithms to recognize the specific context and meaning of words. Several previous studies have shown the potential of mobile note-taking apps to enhance student achievement, satisfaction, and accessibility, suggesting further research in this area. Overall, this study highlights the strengths and limitations of Voice Note Taking and provides insight into potential areas for future development.

KEYWORDS

Natural Language Processing , Speech Recognition, Note Taking, Mathematics.

The Identification of the Cognitive Units in Cognitive Analysis of KOSs

Maziar Amirhosseini, Faculty Member, Assistant Professor in Information and Knowledge Management, Academic Relations and International Affairs (ARIA), Agricultural Research, Education & Extension Organization (AREEO), Tehran, Iran

ABSTRACT

Knowledge organization systems (KOSs), from the simplest to the most complex systems, are responsible for two fundamental tasks. Firstly, KOS reflects the social and cognitive organization of knowledge based on a broader perspective. Secondly, in the narrower sense of the word, KOSs play essential roles in the knowledge organizing processes (KOP). In both tasks, semantic structures are the infrastructure for creating the cognitive organization of knowledge and establishing a knowledge organization for content storage and retrieval purposes. Accordingly, the structure of KOSs, particularly modern KOSs such as thesauri and ontologies, cannot be distinguished from the structure of the semantic systems. In the cognitive analysis of KOSs structure, which includes the semantic networks between concepts, identifying the cognitive units is considered the primary step. The present article identifies and explains the cognitive units or the building blocks of the cognitive structure of KOSs to provide a platform for developing cognitive studies and analyses of the semantic structure of KOSs. The mentioned analysis adopts a documentary method in studying literature, theories, and mechanisms related to cognitive sciences to clarify the building blocks of the cognitive structures of KOSs. In this context, the theory of concept is utilized to represent the building blocks of the cognitive root of knowledge in the cognitive structure of KOSs.

KEYWORDS

Knowledge Organization Systems (KOSs); Cognitive Structure; Semantic Structures; Cognitive Units; Cognitive Analysis.

Identifying Depressive using Natural Language Processing (NLP) Frameworks

Damilola Oladimeji, Laura Garland and Qingzhong Liu, Department of Computer Science, Sam Houston State University, Texas University

ABSTRACT

The number of patients diagnosed with depression yearly is a growing concern among mental health advocates. Consequently, the effect of this ailment is detrimental to not only the patient but also family members, as well as their jobs or school. Many factors, ranging from hereditary conditions to life-altering experiences, can trigger depression, and symptoms vary between individuals. Hence, the disparity of symptoms in diagnosing depression makes it difficult to identify early on. Fortunately, the prevalence of social media platforms has led to individuals posting updates on various aspects of their lives, particularly their mental health. These platforms now provide valuable data sources for mental health researchers, aiding in the timely diagnosis of depression. In this research, we use sentiment analysis to identify depressed tweets from random tweets. We used six natural language processing frameworks for our classification. They are BERT, XLNet, ALBERT, DeBERTa, RoBERTa, and ELECTRA. Our results show that BERT performs best with an accuracy of 99%, while ALBERT is the model with the lowest accuracy rate at 87%. This research shows that by leveraging NLP frameworks, we can successfully utilize machine learning for the early detection of depression and help diagnose individuals struggling with this ailment.

KEYWORDS

sentiment analysis, depressed tweets identification, BERT, NLP

The Effects of Mobile Phones in Mitigatingcultural Shock Amongst Refugees: Case of South Africa

Sarah Vuningoma1 Maria Rosa Lorini2 and Wallace Chigona3, 1Department of Information Systems, University of Cape Town, Cape Town, South Africa, 2School of Management, University of London, London, UK, 3Department of Information Systems, University of Cape Town, Cape Town, South Africa

ABSTRACT

Mobile phones have the potential to contribute towards reducing isolation and loneliness, and to assist in improving interpersonal relations and fostering assimilation processes. Mobile phones may facilitate the incorporation of refugees into a new place. The purpose of this study is to assess how the use of mobile phones helps refugees to mitigate the effects of culture in the host country. Data for the study were collected using semi-structured interviews. The sample consisted of 27 participants. The participants were refugees living in South Africa. Data were analysed using thematic analysis. The study demonstrate that the refuges face a myriad challenges in their host country, including lack of local culture skills, separation of family and friends from their home countries, obstacle in obtaining the legal documents, and the difficulties of becoming integrated into the new environment. The use of mobile phones offers refugees several benefits, such as developing language and culture knowledge, integrated in the host country, facilitating communication, and finding many opportunities. At the same time, mobile phones enable refugees in South Africa to navigate culture shock.

KEYWORDS

Mobile phones, culture shock, refugees, South Africa.

Creating an Equitable Option for High School Internship Opportunities: an Online Platform for Efficiently Connecting Students and Employers

Eiffel Vuong1, Kevin Jones2, 1Portola High School, 1001 Cadence, Irvine, CA 92618, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768

ABSTRACT

Internships are nearly impossible to find as a highschooler outside of paying for one or having familial connections [1]. A reason for this is that there are only substandard public resources for finding these internships as a highschooler [2]. So, by creating an equitable option through an online website, highschoolers would be able to find internships much better. Within the proposed website would include ways to search/filter for internships, have employers create accounts and create internship posts. Students would also need to quickly be able to browse internship options, which was fixed through adding a list of random internships of different varieties that can be scrolled through on the main page [3]. With this ease of access to obtaining an internship, many highschoolers would be able to find what interests them easily and help them find experiences that are more worthwhile beyond just the skills they learn such as helping with college applications or getting a job [4].

KEYWORDS

Highschool internships, internship platform, Firebase database.

Digital Era: Navigating VMI and Supply Chain for Sustainable Success

Odunayo J. Akindote College of Technology, Wilmington University, Delaware, United States.

ABSTRACT

This study looks at how digital transformation affects supply chain management and vendor management inventory (VMI), highlighting the benefits and problems of the digital era. The research investigates how information technology (IT) may improve operational effectiveness, foster innovation, and optimize operations. It also addresses the importance of integrating sustainability into VMI and supply chain management practices and highlights how IT can support these efforts. The paper discusses various strategies for achieving sustainable VMI and supply chain management, such as green procurement, resource efficiency, and closed-loop supply chain management. This paper intends to offer insights for businesses looking to enhance their VMI and supply chain management processes and assure long-term success in a fast-shifting business environment by evaluating the role of IT in tackling the complexity created by digital transformation.

KEYWORDS

Digital transformation, vendor management, supply chain, innovation

Abstractive Text Summarization using machine learning

Dr Parkavi K, Ratnesh Kumar Maurya, School of Computing Science and Engineering, VIT University, Chennai, India

ABSTRACT

Abstractive text generation is the task of creating a summary or paraphrase of a given text document or piece of text. It is a challenging task as it requires the model to understand and accurately represent the original text or any document, as well as to generate new text that is coherent and grammatically correct. In this report, we explore the use of longshort-term memory (LSTM) and neural networks for abstractive text generation. LSTM are a kind of recurrent neural network (RNN) that are effective in learning enduring dependencies in data and have been successful in a variety of NLP tasks. We describe the steps involved in training an LSTM model for abstractive text generation, including datapreprocessing, model design, and optimization. We also discuss the challenges and limitations of using LSTM models for this task, and suggest areas for future research.

KEYWORDS

LSTM, Abstractive text summarization, RNN, NLP

Development of an Ai-robotics 3d Printed Circle of Command for Enhancing Accessibility and Mobility in Individuals With Mobility Issues

Chia Sheng Shih1, Jonathan Sahagun2, 1Pacific Academy Irvine, 4947 Alton Parkway, Irvine, CA 92604, 2Computer Science Department, California State Polytechnic University, Pomona, CA91768

ABSTRACT

According to the CDC, 13.7% of adults in the United States have mobility issues, which is about 42 million peoplewho have that issue. The purpose of this paper is to utilize artificial intelligence and robotics packaged in a3Dprinted Circle of Command to help make the lives of those 42 million people easier [5]. A raspberry pi is usedtopower and process the voice control commands and turn the voice commands into actions that respond tothecommands [6]. The voice control is achieved through three microphones that are embedded from the inside of theCircle of Command using holes to listen to any potential voice commands, and as the raspberry pi can’t input analog output which is outputted from the microphones, the microphones and the raspberry pi are connected toananalog to digital converter that allows the raspberry pi and the microphones to exchange information smoothlyRobotics is used in the stepper motor and the gear that is used to turn the top of the lazy susan, which is 3Dprintedso that there are gears on the inside of the top. There is also a home switch that will home the lazy susan. Thereisno practical application of this AI Robotic Circle of Command as it is just a prototype, and so there are no results, but it is hoped that it will be able to change some of the 42 million lives once the production of the production model begins, the production model will be larger than the prototype as the prototype serves as only a proof of concept andshows that this idea is actually doable [7].

KEYWORDS

Artificial Intelligance, Circle of Command, Mobility Issues, Voice Control/Command

Analysis of Cricket Shots and Dismissal of Batsmen

Sai Ramya Paturi, Sejal Maurya, Shreyas Bhaktharam, Abhijith S and Dr.Mamatha H R, Department of Computer Science and Engineering, PES University, Karnataka, India

ABSTRACT

The field of computer vision has seen a significant advancement in recent years, largely due to the intersection of deep learning and computer hardware. Convolutional Neural Networks (CNNs) have been at the forefront of this revolution and have been widely used for various computer vision tasks, including image classification, object detection, and image segmentation. The use of CNNs in the field of sports, particularly cricket, has also seen a significant increase. Hence in this paper, the CNN architecture is used to classify the type of batting shot played by a batsman in cricket. The method involves extracting frames from a video of the batsman playing and feeding them into a pre-trained CNN model. Transfer learning is used to fine-tune the pre-trained model to improve its accuracy in the specific task of shot classification. The paper also explores the use of computer vision for dismissal prediction in cricket. The predictions are made based on the length of the ball bowled and the position of the batsman at the time of delivery. The model compares and analyses the prediction model accuracy with specific batsmen to understand its effectiveness and give a comprehensive analysis. The combination of deep learning and computer vision has the potential to greatly enhance the analysis of cricket. The results of this paper have the potential to be applied to other sports and further improve the analysis of the game.

KEYWORDS

CNN, Transfer Learning , Logistic regression, Dismissal Prediction.

ROTATIONAL AUGMENTATION TECHNIQUES: A NEW PERSPECTIVE ON ENSEMBLE LEARNING FOR IMAGE CLASSIFICATION

Unai Muñoz-Aseguinolaza, Basilio Sierra and Naiara Aginako, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastián, 20018, Gipuzkoa, Spain

ABSTRACT

The popularity of data augmentation techniques in machine learning has increased in recent years, as they enable the creation of new samples from existing datasets. Rotational augmentation, in particular, has shown great promise by revolving images and utilising them as additional data points for training. The research in this study aimed to evaluate the effectiveness of rotational augmentation techniques and different voting systems in improving image classification accuracy. To accomplish this, several image datasets were evaluated using various augmentation methods, which were employed to generate testing sets. Subsequently, voting systems were used to determine the most reliable outcome for each original data. The findings of this study suggest that rotational augmentation techniques can significantly enhance the accuracy of classification models. Additionally, the selection of a voting scheme can considerably impact the model’s performance. Overall, the study found that using an ensemble-based voting system produced more accurate results than simple voting.

KEYWORDS

Machine Learning, Image classification, Data augmentation, Rotational augmentation techniques, Ensemble learning, Voting schemes

Beyond the Coach: Exploring the Efficacy of a Machine Learning Application for Improving Tennis Players Performance

Jiawen Hao1, Huijun Hu2, 1Rye High School, 1 Parsons Street Hudson Valley Rye, Westchester, New York 1058, 2Computer Science Department, California State Polytechnic University, Pomona, CA 917

ABSTRACT

At some point in their lives, most tennis players are likely to encounter the dilemma in which they can’t constantly receive guidance from a coach. Are there any alternatives that can provide players with the means to improve their game? For a tennis player, it is dif icult to always rely on a coach for tips and suggestions [4]. Mundane constraints often prevent players from consistently improving their techniques and strokes through the guidance of a professional figure [5]. In this paper, we discuss the prospect of using an application to act in the stead of a coach to help aspiring tennis players improve. Using machine learning, the application analyzes and compares two videos of corresponding strokes inputted by users [6]. The AI aligns the frames by clustering the motions of the strokes and then outputs appropriate tips according to the results [7]. This application will allow tennis players to work on and improve their performance on occasions where their coaches aren’t available, improving both ef iciency and consistency.

KEYWORDS

Tennis players, Coach alternatives, Machine learning

Compromising Insecure Crypto Implementations: a Deep-learning Based Cryptosystem-agnostic Testing Framework

Leonard Hill1, Florian Schimanke1, Stefan Schiffner2 and 1, 2Robert Mertens, 1HSW University of Applied Sciences, Hameln, Germany, 2Berufliche Hochschule Hamburg, Hamburg, Germany

ABSTRACT

The use of insecure implementations of cryptographic systems makes encrypted communications vulnerable to practical attacks. Today, attacking, i.e. testing implementations require human labour and an understanding of the cryptographic system. Automated systematic testing can reduce the insight needed to discover faulty implementations. The approach presented in this paper employs neural networks as the core of a universal framework for cryptographic attacks on arbitrary black-box encryption schemes. The framework trains a neuronal network to automatically perform decryption of ciphertext without knowing the corresponding decryption key. The network approximates the decryption function by encrypting randomly generated plaintext using an arbitrary encryption function and attempting to learn the relationship between plain- and ciphertext. If the decryption function for a certain key is successfully approximated by the framework, the plaintext of any message encrypted with this key can be restored.

KEYWORDS

fuzzing, neuronal networks, machine learning, cryptanalysis

Intrusion Detection in Computer Networks Using a Hybrid Model of Firefly and Differential Evolution Algorithms

Mohammad Besharatloo1 and Atiye Rahimizadeh2, 1Artificial Intelligence and robotics, VGTU University, Vilnius, Lithuania, 2Artificial Intelligence and robotics, Aryan university, Babol, Iran

ABSTRACT

Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the users access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

KEYWORDS

Intrusion detection system, Differential evolution,Firefly Algorithm, Support vector machine, Decision tree.

Deuterium: A Secure Protocol for Group Messaging with Rotating Keys and Identity Verification

Xiyou Jin1, Jonathan Sahagun2, 1Northwood High School, 4515 Portola Pkwy, Irvine, CA 92620, 2Computer Science Department, California State Polytechnic University, Pomona, CA91768

ABSTRACT

Deuterium is a protocol for instant messaging that allows users to join a channel, securely exchange messages, androtate the group key for security purposes [1]. When a user wants to join a channel, they must first send their publickey, wallet address, and a digital signature to verify their identity. If the users identity is successfully verified, thechannels creator will perform an elliptic Dif ie-Hellman key exchange with the user using curve25519, generatingagroup key for encrypting messages in the channel [2]. The group key is periodically rotated for security purposes. Users can send messages to the channel by encrypting them with the X25519-XSalsa-Poly1305 algorithm, includinga Galois Message Authentication Code (MAC) instead of an index after keys are exchanged, and attaching a digital signature to verify the authenticity of the message [3]. The protocol also includes a "Termination event" forhandling errors or exceptions that may occur during key exchange or message exchange [4].

KEYWORDS

MetaMask, Ethereum, PubSub, Protocol