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Cyber science and Technology Congress (CyberSciTech), IEEE

作者： Jianhua Ma Qun Jin Hui-Huang Hsu John Paul C. Vergara Antonio Guerrieri Claudio Miceli Ao Guo Faculty of Computer and Information Sciences Hosei University Japan Department of Human Informatics and Cognitive Sciences Waseda University Japan Department of Computer Science and Information Engineering Tamkang University Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Philippines ICAR-CNR Italy Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Graduate School of Informatics Nagoya University Japan

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Generative Artificial Intelligence (GenAI) represents a significant milestone in the development of artificial intelligence, bringing sophisticated AI capabilities into daily life and work. As we approach the era of Hyper Intelligence (Hyper-I), a variety of critical challenges and emerging issues have come to light, ranging from computational complexity to ethical concerns. This paper explores the evolution of AI from the perspective of human learning, comparing machine and human intelligence, and identifying key considerations for the development of future AI systems. It also highlights the growing importance of regulating advanced AI models, such as Reinforcement Learning-based Long-Term Planning Agents, to ensure that Hyper-I remains under human control. Additionally, the paper discusses the computational complexity of transformer-based models, their applicability to intractable problems, and their role in cognitive building systems and resource-constrained environments through TinyML. By analyzing these pressing challenges, this work provides insights into the future of AI and the path toward responsible innovation in generative and hyper-intelligent systems.

关键词： Hyper-intelligent systems Ethics Technological innovation Generative AI Computational modeling Tiny machine learning Pressing Transformers Regulation Computational complexity

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Medical CT Image Super-resolution Algorithm based on GAN

Medical CT Image Super-resolution Algorithm based on GAN

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computer, Vision and Intelligent Technology (ICCVIT), International Conference on

作者： Saihu Tian Taile Peng Zhongan Wang Zhengfeng Li School of Computer Science and Technology Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei Normal University Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Computed Tomography (CT) images are essential for visualizing the internal structures of the human body and are widely applied in the diagnosis of various diseases. However, during the processes of generating, storing, and transmitting medical CT images, the clarity of these images can be degraded by non-human factors, resulting in issues such as reduced resolution and blurred texture details, thereby compromising image fidelity. In this study, we present an approach utilizing Generative Adversarial Networks (GANs). that incorporates a grayscale value loss as an additional constraint while maintaining the original loss function constraints. We conduct experiments for 4x super-resolution reconstruction. To evaluate the reconstruction performance, we compare the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) of the reconstructed images. The experimental results demonstrate ,which the proposed method exhibits promising performance in both quantitative metrics and visual quality.

关键词： Visualization PSNR Computed tomography Computational modeling Superresolution Noise Gray-scale Generative adversarial networks Image reconstruction Medical diagnostic imaging

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Prediction of autism spectrum disorder using epigenetic, brain, and sensory behavioral factors

Prediction of autism spectrum disorder using epigenetic, bra...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Yong Jeon Cheong Jihyun Bae Seonkyoung Lee Ro Ji Hyeong Hirotaka Kosaka Ilwoo Lyu Minyoung Jung Cognitive Science Research Group Korea Brain Research Institute Daegu Republic of Korea Department of Neuropsychiatry University of Fukui Fukui Japan Computer Science and Engineering Ulsan National Institute of Science and Technology Ulsan Republic of Korea

Autism spectrum disorder (ASD) is characterized by clinical and etiological heterogeneity. Although there is broad agreement on the necessity of integrating reliable biomarkers of ASD, little is known about how epigenetic and/or brain factors contribute to ASD. To probe epigenetic factors, we obtained the participants’ saliva samples to obtain DNA methylation values of the oxytocin receptor and arginine vasopressin genes. Given the atypical sensory processing associated with ASD and the role of thalamus as the sensory relay station, we acquired neuroimaging data to create a thalamocortical resting state functional connectivity map of ASD. Integrating features derived from epigenetic, brain, and sensory behavior factors, the current study aims to develop a model that predicts ASD using an eXtreme Gradient Boosting algorithm with ensemble feature selection procedure based on feature occurrence frequency and automatically optimized hyperparameters. We found the full model with all three factors (83.95%) to be superior for ASD classification, compared to models with epigenetic and behavioral factors (76.89%) or with brain and behavioral factors (81.19%).

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Unified Approach to Video-Based AI Inference Tasks in Augmented Reality Systems Assisted by Mobile Edge Computing 31

Unified Approach to Video-Based AI Inference Tasks in Augmen...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： Sousa, Alexandre Ladeira De Okey, Ogobuchi Daniel Rosa, Renata Lopes Saadi, Muhammad Rodriguez, Demostenes Zegarra Federal University of Lavras Department of Computer Science Minas Gerais Lavras Brazil Federal University of ABC Graduate Program in Information Engineering Sao Paulo Santo Andre Brazil University of Central Punjab Department of Electrical Enginnering Punjab Pakistan Federal University of Lavras Computer Science Department Minas Gerais Lavras Brazil

ISBN: (纸本)9798350301076

In the rapidly evolving field of Augmented Reality (AR), delivering real-time, immersive experiences places a significant demand on computational resources, particularly in the context of video-based Artificial Intelligence (AI) inference tasks. Some solutions explorate optimize the tasks using the potential of Mobile Edge Computing (MEC), in which MEC brings the advantages of reduced latency and network congestion, which are critical for AR systems. In this work, we designed a novel algorithm named AR-MEC Optimization for the distribution of AI inference tasks among edge servers while considering several factors including task complexity, server capacity, and network conditions. Our approach not only ensures low-latency responses but also improves overall system efficiency and the user's Quality of Experience (QoE). Experimental results demonstrate that our unified approach significantly outperforms traditional cloud-based methods in terms of response time and computational efficiency, thereby highlighting the potential of MEC-assisted AR systems for real-world applications. © 2023 University of Split, FESB.

关键词： Augmented reality

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The Effect of Major Depressive Disorder on Producing Intelligible Speech

SSRN

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SSRN 2024年

作者： Seneviratne, Nadee Li, Leslie Espy-Wilson, Carol Department of Electrical and Computer Engineering University of Maryland College ParkMD20740 United States Program in Neuroscience and Cognitive Science Department of Linguistics University of Maryland College ParkMD20740 United States

Understanding the characteristics of depressed speech can provide insights on aspects to consider when developing biomarkers for automatic depression detection systems. We present the results of a speech intelligibility study that was conducted to assess the intelligibility of the speech of a female and a male subject when they were severely depressed and when they were in remission. Our analysis shows that, in general, the depressed speech is at least as intelligible as in-remission speech. This is further supported by the underlying "simpler" articulatory coordination of depressed speech as derived from the Articulatory Coordination Features. Acoustically, the simpler articulatory coordination means that there is less variability due to coarticulation in the speech signal. © 2024, The Authors. All rights reserved.

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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A Novel Resource Allocation in Software-Defined Networks for IoT Application 31

A Novel Resource Allocation in Software-Defined Networks for...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： De Sousa, Alexandre Ladeira Okey, Ogobuchi Daniel Rosa, Renata Lopes Saadi, Muhammad Rodriguez, Demostenes Zegarra Federal University of Lavras Department of Computer Science Minas Gerais Lavras Brazil Federal University of ABC Santo Andre Graduate Program in Information Engineering Sao Paulo Brazil University of Central Punjab Department of Electrical Enginnering Punjab Pakistan Federal University of Lavras Computer Science Department Minas Gerais Lavras Brazil

ISBN: (纸本)9798350301076

The rapid proliferation of Internet of Things (IoT) devices has posed significant challenges for network resource allocation and management. In this paper, we propose a novel methodology for efficient resource allocation in Software-Defined Networks (SDNs) to meet the unique requirements of IoT applications. Our approach leverages the flexibility and programmability of SDN to dynamically allocate network resources based on the specific needs of IoT devices. Our proposal combines reinforcement learning with dynamic resource allocation, demonstrating its effectiveness in optimizing resource allocation and enhancing network performance. The results highlight the potential of our approach to addressing the resource challenges in SDN-based IoT environments. The proposed method obtains results superior to 30% of throughput in comparison to a traditional resource allocation in SDN to IoT applications. © 2023 University of Split, FESB.

关键词： Internet of things

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IoT for Water Quality Categorization 5

IoT for Water Quality Categorization

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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Understanding the stability of advanced inorganic-organic hybrid thin films for advanced resist applications 41

Understanding the stability of advanced inorganic-organic hy...

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Advances in Patterning Materials and Processes XLI 2024

作者： Le, Dan N. Chu, Thi Thu Huong Kim, Jin-Hyun Veyan, Jean-Francois Lee, Won-Il Tiwale, Nikhil Lee, Minjong Choi, Seungsoo Woo, Jihoon Nam, Chang-Yong Choi, Rino Kim, Jiyoung Department of Materials Science and Engineering University of Texas at Dallas RichardsonTX75080 United States Department of Materials Science and Chemical Engineering Stony Brook University Stony BrookNY11794 United States Center for Functional Nanomaterials Brookhaven National Laboratory UptonNY11973 United States Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX75080 United States Program in Enviromental and Polymer Engineering Inha University Incheon22212 Korea Republic of Department of Materials Science and Engineering Inha University Incheon22212 Korea Republic of

ISBN: (纸本)9781510672208

In this study, we focus on examining the stability of Al-based inorganic-organic hybrid thin films deposited through the molecular atomic layer deposition (MALD) process in ambient environment. Our observations reveal an initial reduction in material thickness within the first 3 days, followed by a period of stability. XPS analysis is employed to further investigate the chemical alternations in the aged Al-based hybrid thin films, revealing an increase in C=O species as well as overall oxygen content in the material. We also evaluate the effects of atmospheric exposure on the sensitivity of the Al-based hybrid thin films using electron flood exposure. This study aims to enhance the understanding of the stability of a vapor-phase synthesized hybrid thin film system for advanced resist applications. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Atomic layer deposition

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Learning Force Distribution Estimation for the GelSight Mini Optical Tactile Sensor Based on Finite Element Analysis

arXiv

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arXiv 2024年

作者： Helmut, Erik Dziarski, Luca Funk, Niklas Belousov, Boris Peters, Jan Department of Computational Engineering Technical University of Darmstadt Germany Department of Computer Science Technical University of Darmstadt Germany Germany Centre for Cognitive Science Technical University of Darmstadt Germany Darmstadt Germany

Contact-rich manipulation remains a major challenge in robotics. Optical tactile sensors like GelSight Mini offer a low-cost solution for contact sensing by capturing soft-body deformations of the silicone gel. However, accurately inferring shear and normal force distributions from these gel deformations has yet to be fully addressed. In this work, we propose a machine learning approach using a U-net architecture to predict force distributions directly from the sensor’s raw images. Our model, trained on force distributions inferred from Finite Element Analysis (FEA), demonstrates promising accuracy in predicting normal and shear force distributions for the commercially available GelSight Mini sensor. It also shows potential for generalization across indenters, sensors of the same type, and for enabling real-time application. The codebase, dataset and models are open-sourced and available at https://***. Copyright © 2024, The Authors. All rights reserved.

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