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Understanding User Perception of Biometric Privacy in the Era of Generative AI 4

Understanding User Perception of Biometric Privacy in the Er...

引用

4th international conference on Communication, computing and Industry 6.0, C216 2023

作者： Srinivasan, Srihari Cambridge International AS & A level GEMS New Millennium School - Al Khail Dubai United Arab Emirates

ISBN: (纸本)9798350327328

In the dynamic world of technology, the fusion of biometrics and artificial intelligence has ushered in an unprecedented era marked by convenience, security, and cutting-edge innovation. Biometrics, the science of using unique physical or behavioral attributes for identification and authentication, has seamlessly merged into daily routines. From unlocking smartphones with a fingerprint to clearing airport security with facial recognition, biometrics has transformed the way people interact with technology and the world. Simultaneously, Generative AI, powered by machine learning models, has unlocked new frontiers in generating remarkably realistic synthetic data, including human faces, voices, and fingerprints. This convergence of biometrics and Generative AI lies at the heart of a complex and rapidly evolving landscape, giving rise to profound questions concerning individual privacy and security. This study examines the correlation between demographic factors like age, gender, educational background, technological competence, and the regularity of employing biometric authentication, and their awareness about biometric technologies. Additionally, this research explores concerns regarding the potential misuse of biometric data and the notion that organizations should seek explicit consent before collecting such data. Lastly, it assesses the awareness of potential privacy risks and the belief that individuals should receive education regarding the utilization of their biometric data in AI systems. Descriptive research design has been used in this study. The first section of the questionnaire using Microsoft Forms covers the demographic factors, technological proficiency and frequency of using biometric authentication (e.g., fingerprint, facial recognition). The next section focuses on awareness and usage of biometric technologies, biometric privacy, trust, education, awareness of biometrics, generative AI and deepfakes using the Likert Scale. 53 samples were obtained th

关键词： Biometrics

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Analysis and Enhancement Strategies of Private Undergraduate Students' Learning Effect of Principle of Singlechip Based on Online and Offline Mixed-mode Teaching: K-Fold Cross-Validation Method 3

Analysis and Enhancement Strategies of Private Undergraduate...

引用

3rd international conference on Intelligent computing, Communication and convergence, ICI3C 2023

作者： Ren, Yan Wang, Yunjiang Shenyang Institute of Science and Technology Department of Information and Control Engineering Shenyang China Shenyang Aerospace University School of Mechatronics Engineering Shenyang China

ISBN: (纸本)9798350343922

The principle of single chip microcomputer is a professional basic course with "program design"as the main line, and it is also a very practical course, involving a lot of practical engineering applications. For private undergraduate colleges, there are some difficulties and challenges in the teaching process. In this paper, k-fold cross-verification method is adopted to analyze and improve the learning effect of singlechip microcomputer based on the mixed mode of online and offline teaching. This is a student-centered, teacher-guided, student-independent learning as the purpose, fully mobilize students' subjective initiative and enthusiasm, improve learning efficiency and effect of teaching methods. The experimental results show that this method can improve the students' motivation score to 85 points, which was 12 points higher than the previous maximum score of 73 points. At the same time, innovation ability, practical ability and sense of achievement have also improved. It can be seen that the method adopted in the article has a good effect in improving the learning effect. © 2023 IEEE.

关键词： Microcomputers

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Photonics technology for a Sustainable and Smart Society 27

Photonics Technology for a Sustainable and Smart Society

引用

27th OptoElectronics and Communications conference/international conference on Photonics in Switching and computing, OECC/PSC 2022

作者： Okada, Akira Ntt Device Technology Labs Ntt Corporation 3-1 Morinosato-wakamiya Atsugi-shi Kanagawa243-0198 Japan

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

We at NTT Device technology Laboratories are working on several photonics technologies for a sustainable smart society. These include wavelength-tunable semiconductor lasers for gas sensing, ultra-compact RGB couplers for vision devices, photonics-electronics convergence devices for highly energy-efficient information processing, and quantum light sources (squeezed light sources) for optical quantum computers. © 2022 IEICE.

关键词： Light sources

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Tourism Route Recommendation Using Reinforcement Learning 8

Tourism Route Recommendation Using Reinforcement Learning

引用

8th IEEE international conference for convergence in technology, I2CT 2023

作者： Mubarak, Muhammad Ilham Baizal, Z.K.A. Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350334012

Tourism is a rapidly growing sector that has a significant impact on increasing a country's national income. Indonesia's GDP is expected to grow in the coming years, and tourism is a major contributor to this growth. To address the high demand for tourism, we propose a personalized tourism route recommendation system that can assist tourists in planning their itineraries. This problem can be modeled as a Traveling Salesman Problem, which can be approached using Markov Decision Processes and reinforcement learning. In this paper, we proposed a method for generating N-days tourism routes in the Special Region of Yogyakarta that involves using Q-learning to recommend routes. We have included time constraints in our approach to fit the tour into a specific time frame and adhere to the operating hours of tourist attractions. Additionally, our method uses the Multi-Attribute Utility Theory to consider various attributes, such as rating, travel time, and cost, as cost functions to satisfy the user's custom desired needs and preferences. The proposed method was compared to the Firefly algorithm in multiple experiments to assess its performance and determine its optimality. The experiment results showed that the proposed method is 42.89% more optimal for generating the tour than the Firefly algorithm. © 2023 IEEE.

关键词： Reinforcement learning

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HNSW-based DBSCAN for Large-scale Point Cloud computing 8

HNSW-based DBSCAN for Large-scale Point Cloud Computing

引用

8th IEEE/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2023

作者： Park, Jiwan Oh, Seongjin Kim, Tae-Yong Lee, Jieun Lee, Jaebin Jeong, Jongpil Sungkyunkwan University Dept. of Smart Factory Convergence Suwon Korea Republic of Sungkyunkwan University School of Civil Architecture Engineering Suwon Korea Republic of

ISBN: (纸本)9798350373615

This research aims to introduce an innovative algorithm for processing point cloud data, a key technology in the Fourth Industrial Revolution. Point cloud data, which is used to accurately represent real-world objects and environments, is often noisy due to environmental factors, which affects the accuracy and reliability of the data. The proposed algorithm effectively solves the noise problem to achieve high accuracy in data analysis and information extraction. The framework also combines the HNSW (Hierarchical Navigable Small World) index and the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm for efficient data retrieval and management in large data sets. These tools can be applied to various domains and have demonstrated robust performance in experiments. Therefore, this research has the potential to drive innovation in the Fourth Industrial Revolution and is expected to serve as a practical tool for accurate data analysis and information extraction from large data sets. © 2023 IEEE.

关键词： Clustering algorithms

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Bamboo Forest Growth Optimization Algorithm for Night Image Enhancement 15th

Bamboo Forest Growth Optimization Algorithm for Night Image...

引用

15th international conference on Genetic and Evolutionary computing, ICGEC 2023

作者： Shi, Hao-Jie Pan, Jeng-Shyang Chu, Shu-Chuan Kong, Lingping Snášel, Václav College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9789819700677

The Bamboo Forest Growth Optimization(BFGO) algorithm is an algorithm proposed by the growth law of bamboo forest, which has the advantages of fast convergence and not easily falling into local optimum. In this paper we have experimented the BFGO with several other population intelligence optimization algorithms on 23 benchmark test functions and proved its superiority. Also we apply the bamboo forest optimization algorithm to nighttime image enhancement in this paper, and the algorithm achieves better results on this application. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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An Image Feature Extraction Algorithm Based on Tissue P System

An Image Feature Extraction Algorithm Based on Tissue P Syst...

引用

2023 international conference on Applied Mathematics and Digital Simulation, AMDS 2023

作者： Huang, Yourui Song, Hongping Han, Tao Xu, Shanyong Jiang, Lingya Anhui University of Science and Technology Huainan232001 China West Anhui University Lu'an237012 China

As digital images continue to generate an increasing amount of data, image feature extraction has become a crucial component of image recognition. This paper proposes an image feature extraction method based on membrane computing to extract image features. The author first uses the rotation invariant local phase quantization (RILPQ) to extract image features and combines the tissue P system with the binary particle swarm optimization (MBPSO) to select the best image features and maximize the classification accuracy. Based on 4 public datasets, 28 datasets are newly constructed, and the proposed method is verified on 28 datasets. Specifically, firstly, local binary pattern (LBP) algorithm and RILPQ are used to extract image features, and then MBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA) and membrane genetic algorithm (MGA) are used to select the optimal features. The experimental results demonstrate that our proposed image feature extraction method achieves high classification accuracy, stability, and convergence. © Published under licence by IOP Publishing Ltd.

关键词： Local binary pattern

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Reliability Optimization Design of Computer Networks Based on Improved Ant Colony Algorithm 11th

Reliability Optimization Design of Computer Networks Based o...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Yao, Yuwei School of Computing Guangdong Business and Technology University Guangdong Zhaoqing526020 China

In this paper, we adopted an improved ant algorithm to conduct reliability optimization research on existing computer systems. On this basis, a novel adaptive search method was proposed and a new search strategy was adopted to improve search speed. Through experiments, it has been shown that this method has good network reliability, convergence speed, global optimization ability, and efficiency in using paths. In practical applications, the average reliability of this method is 92.3%, and its effectiveness has been verified through experiments. Based on the above research results, the improved ant algorithm proposed in this paper improves the reliability of the network and optimizes the overall performance of the network. © 2024 The Authors. Published by Elsevier B.V.

关键词： Ant colony optimization

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Prediction of Neuro Cognitive Disorders using Supervised Comparative Machine Learning Model & Scanpath Representations 8

Prediction of Neuro Cognitive Disorders using Supervised Com...

引用

8th IEEE international conference for convergence in technology, I2CT 2023

作者： Vinayak, Vineet Paliwal, Mohan Amudha, J. Jyotsna, C. Amrita School of Computing Bengaluru Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham India Amrita School of Computing Bengaluru Department of Computer Science Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350334012

Dementia has become a pressing public health issue worldwide, with the number of affected individuals steadily increasing. As a syndrome, it is characterized by a decline in cognitive performance that extends beyond normal biological aging, caused by a diverse range of brain disorders and diseases. Alzheimer's disease is the most prevalent form of dementia, and it constitutes the majority of dementia cases. In addition to its physical and psychological impacts, dementia is also a significant economic burden on families and society at large, given the extensive care required. One potential approach to understanding the cognitive performance of individuals with dementia is the use of scan path representations. A scan path is a visual representation of eye movements and is created by an ordered set of fixations connected by saccades. By analyzing these patterns, researchers aim to better understand the visual behaviors of people with dementia and potentially develop more effective treatment options. To achieve this goal, the proposed supervised comparative machine learning model utilizes scan path representations to provide a more comprehensive understanding of dementia. By exploring the visual behaviors of individuals with the condition, the model aims to provide insights into the use of supervised machine learning algorithms in trail making tests to better classify the dementia patients using their scanpath representations. This research paper aims to contribute to the ongoing efforts to combat the global challenge of dementia and provide a more nuanced understanding of the condition. © 2023 IEEE.

关键词： Eye movements

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Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge computing

Personalized Federated Deep Reinforcement Learning-based Tra...

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2023 IEEE/CIC international conference on Communications in China, ICCC 2023

作者： Song, Zhengrong Ma, Chuan Ding, Ming Yang, Howard H. Qian, Yuwen Zhou, Xiangwei Nanjing University of Science and Technology Nanjing China Zhejiang Lab Hangzhou China Zhejiang University ZJU-UIUC Institute Haining314400 China Data61 Csiro Sydney Australia Louisiana State University Baton RougeLA70803 United States

ISBN: (纸本)9798350345384

In the era of 5G mobile communication, there has been a significant surge in research focused on unmanned aerial vehicles (UAVs) and mobile edge computing technology. UAVs can serve as intelligent servers in edge computing environments, optimizing their flight trajectories to maximize communication system throughput. Deep reinforcement learning (DRL)-based trajectory optimization algorithms may suffer from poor training performance due to intricate terrain features and inadequate training data. To overcome this limitation, some studies have proposed leveraging federated learning (FL) to mitigate the data isolation problem and expedite convergence. Nevertheless, the efficacy of global FL models can be negatively impacted by the high heterogeneity of local data, which could potentially impede the training process and even compromise the performance of local agents. This work proposes a novel solution to address these challenges, namely personalized federated deep reinforcement learning (PF-DRL), for multi-UAV trajectory optimization. PF-DRL aims to develop individualized models for each agent to address the data scarcity issue and mitigate the negative impact of data heterogeneity. Simulation results demonstrate that the proposed algorithm achieves superior training performance with faster convergence rates, and improves service quality compared to other DRL-based approaches. © 2023 IEEE.

关键词： Reinforcement learning

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