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Personality and demographic correlates of support for regulating artificial intelligence

AI and Ethics 2023年第2期4卷 419-426页

作者： Bartneck, Christoph Yogeeswaran, Kumar Sibley, Chris G. Computer Science and Software Engineering University of Canterbury Christchurch New Zealand Department of Psychology University of Canterbury Christchurch New Zealand School of Psychology University of Auckland Auckland New Zealand

The arrival of artificial intelligence (AI) in our society has sparked many hopes and fears, with people having diverging views on the need to strictly regulate AI. The current study investigates how demographic and personality traits are associated with a desire to strictly regulate AI using a representative sample of adults from New Zealand (N = 47,951 participants). Data revealed that support for strict regulation of AI is positively related with agreeableness, neuroticism, and honesty–humility. However, it is negatively related to openness to experiences. A wide range of demographic factors including gender, age, ethnicity, religiosity, neighbourhood level economic deprivation, living rural, relationship status, and parental status were additionally related to support for regulation of AI. However, all these effects were fairly small suggesting that both personality and socio-demographic factors contribute to support for regulating AI, but other factors beyond these characteristics should also be considered for understanding people’s support for regulating AI.

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Ensemble Online Sequential Extreme Learning Machine for Air Quality Prediction 7

Ensemble Online Sequential Extreme Learning Machine for Air ...

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7th International Conference on Control Science and Systems engineering, ICCSSE 2021

作者： Liu, Ye Cao, Weipeng Liu, Yiwen Li, Dachuan Wang, Qiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University College of International Exchange Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9781665444057

Online Sequential Extreme Learning Machine (OS-ELM) has been confirmed by numerous studies to be an effective algorithm for online learning scenarios. However, we found that some parameters of OS-ELM are randomly assigned and remain unchanged in the subsequent learning process, which leads to great instability in the model performance in practice. To alleviate this problem, we propose a novel ensemble OS-ELM algorithm (EOS-ELM-R) for solving air quality prediction problems. EOS-ELM-R uses multiple distribution functions to initialize the random parameters of the base OS-ELM models and its final output is the average of the predictions of these base models. Extensive experimental results on two real-world air quality prediction problems show that EOS-ELM-R is effective, and it can achieve better generalization capabilities than similar algorithms. © 2021 IEEE.

关键词： Distribution functions

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Reinforcement Learning for Testing Android Applications: A Review

Reinforcement Learning for Testing Android Applications: A R...

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Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Asmau Usman Moussa Mahamat Boukar Muhammed Aliyu Suleiman Ibrahim Anka Salihu Ndukwe-Oke Eke Department of Computer Science Abdu Gusau Polytechnic Talata Mafara Nigeria Department of Computer Science Nile University of Nigeria Abuja Nigeria Department of Software Engineering Nile University of Nigeria Abuja Nigeria

ISBN: (数字)9798350358834

ISBN: (纸本)9798350358841

This paper offers a review of current research studies that use reinforcement learning (RL) to test Android applications. The primary purpose of this study is to simplify future research by collecting and investigating the current state of Android app testing approaches using the RL technique. We provide a well-defined criterion comprising of seven key points. The key points are: addressed problems, reasons for using the RL technique, RL algorithms, supported events, testing techniques, validation, and evaluation methods. In the literature, we have analyzed various techniques to evaluate their efficiency. This study showed that model-based testing is the most commonly used testing technique. Q-learning is the best algorithm in terms of predictive accuracy. We identified that code coverage is the most widely used evaluation metric and comparison with other tools and techniques is the preferred validation approach.

关键词： Measurement Q-learning Codes Prediction algorithms Testing Graphical user interfaces

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Decoding Sentiments: An Efficient Trans-Long Short Term Memory to Understand Viewer Reactions on Ad-Supported Video Contents

Decoding Sentiments: An Efficient Trans-Long Short Term Memo...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Shashishekhar Ramagundam Niharika Karne Department of Computer Science Software Development & Engineering in Content Discovery & AI COMCAST USA Troy University AL Department of Electrical Engineering Sr. Principal Systems Engineer in C & RS Platform Services DT Amtrak USA University of South Alabama

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Viewer reaction analysis in ad-supported video content focuses on the use of advanced techniques to understand and interpret the emotional responses expressed by viewers during ad viewing experiences. This analysis goes beyond simple positive or negative sentiment and delves into the nuances of viewer reactions, allowing for a more comprehensive understanding of the emotional impact of ads. With this information, advertisers can make data-driven decisions to optimize their advertising strategies, create more engaging content, and ultimately enhance the viewer experience. One of the challenges in sentiment analysis for viewer reaction analysis is the inherent subjectivity and variability of human emotions. Emotions can be complex and subjective, making it difficult to accurately capture and interpret them solely based on text or viewer feedback. Different individuals may perceive and express emotions differently, adding another layer of complexity to the analysis process. Addressing these challenges requires a combination of advanced deep learning model refinement. Overcoming these challenges can provide valuable insights into viewer reactions, enabling advertisers and content creators to create more engaging and impactful ad-supported video content. In this paper, an Artificial Intelligence (AI)-based sentiment analysis is introduced to analyze the viewer's reaction while watching the ad in the video content. Here, an efficient model named Trans-Long Short-Term Memory (Trans-LSTM) is developed to perform the sentiment analysis. The developed Trans-LSTM is helpful in supervising the emotional data in the online video content and identifying the popularity of the corresponding video. The suggested model effectively analyzes the reviewer’s actions while watching all types of video content. Finally, the experimental analysis is performed to find the effectiveness of the developed sentiment analysis model via various metrics.

关键词： Measurement Deep learning Analytical models Sentiment analysis Logistic regression Memory management Emotional responses Decoding Security Artificial intelligence

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Generative AI-Driven Incentive Mechanism for Semantic Communications in RSMA Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Liu, Dongqing Liu, Yongwen Zhang, Lei Hafid, Abdelhakim Khoukhi, Lyes Li, Mingyuan Liu, Yutao Dong, Yadong The First Affiliated Hospital of Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou China Zhengzhou University of Light Industry Software Engineering College Zhengzhou China Henan University of Technology College of Electrical Engineering Zhengzhou China University of Montreal Department of Computer Science and Operational Research Canada GREYC Laboratory ENSICAEN Normandie University France

This paper proposes a framework integrating Rate Splitting Multiple Access (RSMA), semantic communications, and generative AI for optimizing next-generation wireless networks. We present a unified model that combines RSMA with semantic communications to enhance both spectral efficiency and semantic fidelity. Our system model focuses on a downlink semantic communication system with a multi-antenna base station serving multiple single-antenna users. A dynamic contract-based incentive mechanism is developed to address user heterogeneity and information asymmetry in semantic RSMA scenarios. We introduce a diffusion model-based approach for joint optimization of resource allocation and contract design in RSMA systems. The semantic encoding process extracts key information, i.e., free-space detection, interest points, object attributes, and spatial relationships, from image data. A loss function is designed to train the semantic encoder and RSMA scheme, incorporating semantic extraction, partitioning, reconstruction, and task specific components. Our framework includes a multi-objective performance evaluation that considers both conventional metrics and semantic accuracy in a multi-user RSMA environment. We also define a multi-component semantic accuracy metric to assess the quality and utility of the extracted semantic information. Extensive simulation results demonstrate the superiority of our proposed framework over existing approaches in terms of system throughput, energy efficiency, and semantic fidelity across various network scenarios and user distributions. © 2015 IEEE.

关键词： Resource allocation

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CodeSafari: A Customized Mobile Solution for Introducing Programming Concepts to Children

CodeSafari: A Customized Mobile Solution for Introducing Pro...

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International Conference on Information Technology Research (ICITR)

作者： Fahmi M.F.A. Sanjeevan M.C.M.A. Samanthi E.R. Siriwardana Mihiri Samaraweera Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

Following the COVID-19 pandemic, Sri Lanka's economy faced significant setbacks, particularly in key sectors such as tea exports, garment manufacturing, and tourism, which experienced severe downturns. However, the IT sector showed resilience amid these challenges, highlighting its potential to drive economic growth. Despite this potential, Sri Lanka has struggled to capitalize fully on global demand for IT services due to a skills gap among Sri Lankan youth compared to their Indian counterparts. India's IT outsourcing industry has thrived partly because of the early involvement of young professionals in open-source projects such as the Android Open Source Project (AOSP), Linux Kernel and React. This trend underscores the importance of foundational programming education to develop competitive technical skills from a young age. To address this gap, authors developed a mobile learning solution CodeSafari, designed to introduce core programming concepts data types, conditional statements, and loops to young Sri Lankan learners. The platform uses Personalized Storytelling and a Customized Kids Assisting System in students' native language, making programming more accessible. Programming training with the app improved test scores for 58 percent of participants. By addressing the skills gap early, this research aims to build a strong IT foundation for Sri Lankan students, positioning them to compete more effectively in the global outsourcing market. These findings underscore the essential role of programming education in fostering economic resilience and preparing a workforce capable of thriving in an evolving technology landscape.

关键词： Training Economics Pandemics Prototypes software Outsourcing Usability Programming profession Testing Resilience

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Tailin Liang Zhaolin Yuan State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Department of Industrial and Systems Engineering Research Institute of Advanced Manufacturing The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications.

关键词： Meters Deep learning Accuracy Ultrasonic variables measurement Transportation Manuals Feature extraction Surface texture Convolutional neural networks Surface treatment

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Beam divergence and operating wavelength bands effects on free space optics communication channels in local access networks

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Journal of Optical Communications 2023年第s1期44卷 S823-S831页

作者： Kumar Paul, Bikash Alatwi, Aadel M. Rashed, Ahmed Nabih Zaki Shahriar Parvez, Ahm Ahmed, Kawsar Department of Software Engineering Daffodil International University Sukrabad Dhanmondi Dhaka1207 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Santosh1902 Bangladesh Electrical Engineering Department Faculty of Engineering University of Tabuk Tabuk Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of Computer Science and Engineering Bangladesh University 15/1 Iqbal Road Mohammadpur Dhaka1207 Bangladesh Department of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh Group of Bio-photomati Mawlana Bhashani Science and Technology University Tangail Santosh1902 Bangladesh

The paper outlines the divergence of beam and operating wavelength bands effects on free-space optics communication channels in local access networks. The max Q factor and min bit error rate are measured based on the divergence of beam and operating wavelength bands variations. The signal is tested also at the receiver side by using an optical power meter visualizer at various operating signal wavelength bands and under the optimum values of the divergence of the beam. The study has presented the optimum operating conditions for upgrading network operation efficiency. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Bit error rate

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A Novel Rigorous Measurement Model for Big Data Quality Characteristics

A Novel Rigorous Measurement Model for Big Data Quality Char...

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IEEE International Conference on Big Data

作者： Haochen Zou Kun Xiang Department of Computer Science and Software Engineering Concordia University Montreal Quebec Canada Department of Science and Engineering Hosei University Tokyo Japan

ISBN: (纸本)9781665480468

Satisfiable data quality is the basic guarantee for data-based research, decision-making, and service. Today, new trends in the creation, collection, and utilization of data are constantly emerging. With the usage of massive data, the problem of data quality is highlighted. Several studies on the measurement, evaluation, and management of big data quality have been proposed, and the data quality problem in the big data environment has received attention. The big data characteristics Vs model describes the dimensions and attributes information of data sources in detail, which can be implemented in big data quality measurement. In this paper, a novel rigorous big data quality measurement architecture is proposed for automatically and parallelly quantifying the value of six big data Vs, which are Volume, Variety, Velocity, Veracity, Validity, and Vincularity according to the developed algorithms in every big data process step and time phase of the big data pipeline. Thresholds for the six big data Vs are provided correspondingly for analyzing the result values. The hierarchical measurement model is constructed with multiple-based measures, derived measures, and indicators. The model is verified by comparative experiments and experiments results indicate that the designed architecture can improve the outcomes of data source implementation.

关键词： Soft sensors Data integrity Volume measurement Pipelines Big Data Data models Mathematical models

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Adaptive Learning and Virtual Reality based Conceptual Framework for Dental Training

Adaptive Learning and Virtual Reality based Conceptual Frame...

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International Conference on computer and Applications (ICCA)

作者： Meriam Sherif Nahla Barakat Abeer Hamdy Department of Software Engineering Faculty of Informatics and Computer Sceince The British University in Egypt Cairo Egypt Department of Artifical Intellegence Faculty of Informatics and Computer Sceince The British University in Egypt Cairo Egypt Department of Software Enginerring Faculty of Informatics and Computer Sceince The British University in Egypt Cairo Egypt

Recently, there has been increasing interest in utilizing virtual reality (VR) technology in dental education and student training. VR based training can provide students with sufficient training and hands-on experience before entering the clinical setting, which in turn helps avoid risks ranging from small errors to severe consequences. However, previous research in this area is limited and none of the previous work proposed a complete, scalable framework for dental students’ education and training. This paper proposes an improved virtual environment for dental training, through an adaptive learning approach, integrated with the VR environment, which is - to the best of our knowledge - the first framework proposed for this purpose. The framework consists of eight modules that aim to provide students with a holistic dental training experience, equipping them with the essential skills and confidence required for their future clinical practice. Furthermore, it aims to improve patient care and safety. In order to guarantee the effectiveness and usability of the proposed framework, a thorough evaluation will be conducted, encompassing both a system perspective, which involves assessing software metrics, as well as evaluating its usability.

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