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A Proposed Gamification Elements of Educational Games 6

A Proposed Gamification Elements of Educational Games

引用

6th International Conference on computer and Information Sciences, ICCOINS 2021

作者： Elaish, Monther M. Hussein, Mahmood H. Shuib, Liyana Wan Ahmad, Wan Fatimah Becker, Katrin University of Benghazi Dept. Computer Science Benghazi Libya University of Malaya Dept. Software Engineering Kuala Lumpur Malaysia Universiti Teknologi Petronas Dept. Computer and Information Sciecnes Perak Malaysia Mount Royal University Dept. Mathematics and Computing CalgaryAlberta Canada

ISBN: (纸本)9781728171517

Research interest in gamification is currently enjoying global attention. However, while game design is often the focus of these gamification systems, interface design is overlooked. This is due to the expectations of field stakeholders and experts who consider interface to be only a minor element of the gamification experience. The aim of this paper is to propose a classification method that categorizes the gamification element into tangible and intangible. This new classification would provide a clear perspective about the elements before the design and development of an educational game has even begun. The proposed new method of classification would save researcher's time and effort by assisting in the selection of the most appropriate gamification elements for the situation at hand. © 2021 IEEE.

关键词： Game design

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19

Investigating Online Searching Behavior Based on Google Tren...

引用

Frontiers in Education (FIE) Conference

作者： Abdelkader Nasreddine Belkacem Nuraini Jamil Saed Alrabaee Dept. of Computer and Network Engineering College of IT UAE University Al Ain United Arab Emirates Dept. of Computer Science and Software Engineering College of IT UAE University Al Ain United Arab Emirates Dept. of Information Systems and Security College of IT UAE University Al Ain United Arab Emirates

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning.

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Vote Bank Forecast Based on Social Media Sentiment Analysis

Vote Bank Forecast Based on Social Media Sentiment Analysis

引用

International Conference on Open Source Systems and Technologies (ICOSST)

作者： Zubair Zaland Sibghat Ullah Bazai Muhammad Imran Ghafoor Saira Akram Muhammad Waseem Muhammad Qaseem Dept. of Software Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept.of Computer Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept. of Engineering Pakistan Television Corporation Lahore Pakistan

Sentiment Analysis of human behavior from social media revolutionized many fields, such as health, business, and election prediction. Sentiment analysis is the process of analyzing an individual’s opinions, attitudes, views, sentiments, and emotions. In order to predict sentiments, predicting the polarity of words and categorizing them as positive or negative are the major goals. A quick and effective way to determine what the public thinks about one’s own political party, the opposition party, and political figures is through Twitter. prior sentiment analysis techniques have primarily struggled with classification accuracy because biases in the training data cause a sizable portion of tweets to be incorrectly classified. As a remedy, we set out to collect real-time data in order to forecast Pakistan’s next election results. Users can quickly share real-time updates on a variety of activities and events on Twitter, promoting the rapid diffusion of information and news. To do this, we accessed the Twitter API through the “Tweepy’’ Python package and retrieved real-time data from the service. Through keyword searches, we were able to find more than 5,000 tweets. We attributed feelings to particular tweets using Python’s “TextBlob’’ package. We are able to generate prediction graphics by determining the frequency of positive attitudes with respect to a particular candidate.

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The Research and Implementation of Network Slices and Resources Scheduling

The Research and Implementation of Network Slices and Resour...

引用

Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Jinfeng Wang Kun Xie Weiliang Liu Chengcheng Li Yasheng Zhang Dept. Software Engineering Beijing University of Posts and Telecommunications Beijing China Dept. Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China Dept. The 54th Research Institute of CETC China Electronics Technology Group Corporation Limited Shijiazhuang China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

This thesis advocates for the enhancement of network data throughput and service capabilities through the establishment of device identity class isolation underpinned by SDN/NFV technologies. It proposes a network slicing mechanism to segregate devices into dedicated network slices with independently allocated resources. Further, a novel resource scheduling algorithm based on Deep Deterministic Policy Gradient (DDPG) is introduced to ensure efficient resource distribution.

关键词： Scheduling algorithms Network slicing Bandwidth Throughput Deep reinforcement learning Network function virtualization Resource management software defined networking Artificial intelligence

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A Broadband Coaxial to Stripline Transition for Millimeter-wave LTCC Circuit Applications

A Broadband Coaxial to Stripline Transition for Millimeter-w...

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European Conference on Antennas and Propagation, EuCAP

作者： Maryam Sadeghi Dawood Shekari Beyragh Ammar B. Kouki Mohammad Sharawi Mohammad Helaoui Fadhel M. Ghannouchi Electrical and Software Engineering Dept. University of Calgary Calgary Canada Electrical Engineering Department École de Technologie Supérieure Montreal Canada Electrical Engineering Department École Polytechnique de Montréal Montreal Canada

This paper proposes a broadband coaxial to stripline transition for 5G millimeter-wave (mm-wave) band using low temperature co-fired ceramic (LTCC) technology. The proposed structure is specifically designed for Surface-Mount-Device (SMD) coaxial connectors. A back-to-back configuration has been designed and fabricated using a 5-layer LTCC stack and two SMD mini-SMP connectors. Measurements were carried out between 100 MHz and 34 GHz and the results show good agreement with the simulations, with a loss better than 0.7dB for a single transition over the entire range of the operating frequency.

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Mitigating Insecure Outputs in Large Language Models(LLMs): A Practical Educational Module

Mitigating Insecure Outputs in Large Language Models(LLMs): ...

引用

IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： MD Abdul Barek Md Mostafizur Rahman Shapna Akter A.B.M Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Intelligent Systems and Robotics University of West Florida USA Dept. of Cybersecurity and Information Technology University of West Florida USA Center for Cybersecurity University of West Florida USA Computer Science and Software Engineering Auburn University USA Dept. of Computer Science Tuskegee University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output.

关键词： Training Navigation Large language models Computational modeling software Security software development management

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Techno-Economic Analysis of Hydrogen for Coastal Maritime Transport Electrification

Techno-Economic Analysis of Hydrogen for Coastal Maritime Tr...

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IEEE Green Technologies Conference (IEEE-Green)

作者： Bertrand Pages Himanshu R Chavan Jeffrey Knollmeyer Saffeer Khan Dept. Electrical and Computer Engineering École Polytechnique Féminine Cachan France Energy Production & Infrastructure Center University of North Carolina at Charlotte Charlotte USA

Use of hydrogen is becoming prominent in various energy production fields as it is one of the best alternatives for fossil fuels, which cause a high amount of greenhouse gases in the atmosphere. In coastal maritime transportation, hydrogen fuel cells are being used in place of diesel operated engines, which can eliminate carbon dioxide emission entirely. This research study focuses on the techno-economic analysis of hydrogen required for the production of hydrogen that will be utilized as a fuel for the applications of maritime transportation electrification. The Levelized Cost of Hydrogen (LCOH) has been calculated using the North Carolina industrial electricity price. LCOH is a method to calculate the cost per kilogram of hydrogen based on several parameters, such as operating and capital costs. The study also focuses on the economic feasibility of the production of hydrogen for maritime transportation and power generation applications. From the study we can conclude the economic feasibility of producing hydrogen for different applications.

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

Blockchain-Enhanced Framework for Secure Third-Party Vendor ...

引用

IEEE International Conference on Big Data

作者： Deepti Gupta Lavanya Elluri Avi Jain Shafika Showkat Moni Omer Aslan Dept. of Computer Information Systems Texas A&M University - Central Texas Texas USA College of Business University of Tampa Florida USA Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida USA Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamperproof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enabl

关键词： Multi-factor authentication Smart contracts Organizations Real-time systems Regulation Blockchains Security Time factors Risk management Resilience

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RNN-based Model for an Optimal COVID-19 Cases Detection using Clinical Reports

RNN-based Model for an Optimal COVID-19 Cases Detection usin...

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International Conference on Optimization and Applications (ICOA)

作者： Akib Khanday Salah Bouktif Ali Ouni dept. Computer Science and Software Engineering CIT UAE University Al Ain UAE ETS Montreal University of Quebec Montreal QC Canada

The global impact of COVID-19 has been significant due to its rapid spread and high transmission rate. A large number of individuals have been exposed to this dangerous virus, emphasizing the importance of early detection to potentially save numerous lives. This research paper introduces an enhanced method for COVID-19 detection, utilizing a Recurrent Neural Network (RNN) and leveraging early clinical reports. Specifically, a Long Short Term Memory (LSTM) model is trained using data obtained from the metadata of the available dataset published by the World Health organisation, specifically focusing on extracting clinical reports and labels. Several pre-processing techniques and word embedding are applied to train an LSTM-based classifier that accurately identifies positive COVID-19 cases. The implementation of this proposed approach yielded superior results compared to traditional machine learning algorithms. The model achieved a testing accuracy of 87%, and future works include expanding the dataset to enhance the efficiency of the model.

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ScoutSL: An Open-Source Simulink Search Engine

ScoutSL: An Open-Source Simulink Search Engine

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Conference on Model Driven engineering Languages and Systems Companion (MODELS-C)

作者： Sohil Lal Shrestha Alexander Boll Timo Kehrer Christoph Csallner Computer Science & Eng. Dept. University of Texas at Arlington Arlington TX USA Software Engineering Group University of Bern Bern Switzerland

Simulink is one of the most widely used modelling languages in safety-critical industries. Most models created in industrial settings are valuable intellectual property to their companies and are thus often not publicly available. But to study Simulink software engineering processes or to develop new Simulink tools, access to models with relevant properties is vital for researchers. We conducted a community survey to find out what kind of models and model metrics are of interest to researchers. With these results, we implemented ScoutSL (http://***), a tool that gives researchers easy online access to over 100k open-source Simulink models from which they can select a subset according to their needs. A short video demonstration is available online at https://***/HwsHL8LrVCM

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