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12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022

作者： Fukumoto, Makoto Miyamoto, Taichi Gan, Haoran Fukuoka Institute of Technology Dept. of Computer Science and Engineering Fukuoka Japan Fukuoka Institute of Technology Graduate School of Computer Science and Engineering Fukuoka Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

We enjoy multiple media content in our daily lives, and using congruent content composed of different media types is ideal. However, obtaining congruent media content is difficult. Interactive Evolutionary Computation (IEC) is known as an effective approach to obtain good solutions for each user. However, conventional IECs treat single media types. This study proposes new Interactive Evolutionary Computation that searches the congruent media content in terms of a good combination of different types of media content. In the proposed IEC, the solution candidate contains different media types as its variables. A system was constructed by employing a genetic algorithm to investigate the effectiveness of the IEC with experiment. The target of creation was the congruent content composed of music and scent. Twelve participants evaluated a set of musical melodies and scents repeatedly. As results, a trend of increase in the mean fitness and significant increase in maximum fitness were observed in the search experiment. However, enough increase in the fitness was not observed in the evaluation experiment. © 2022 IEEE.

关键词： Genetic algorithms

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Learned distributed image compression with decoder side information

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Digital Communications and Networks 2025年第2期11卷 349-358页

作者： Yankai Yin Zhe Sun Peiying Ruan Ruidong Li Feng Duan Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai UniversityTianjin 300350China Institute of Natural Sciences Kanazawa UniversityIshikawa 9201164Japan Faculty of Health Data Science and Graduate School of Medicine Juntendo UniversityChiba 2790013Japan NVIDIA AI Technology Center NVIDIA JapanTokyo 1070052Japan

With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data ***-view image compression aims to improve compression efficiency by leveraging correlations between ***,the requirement of synchronization and inter-image communication at the encoder side poses significant challenges,especially for constrained *** this study,we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during *** model integrates an encoder network,a quantization module,and a decoder network,to ensure both high compression performance and high-quality image *** encoder uses a deep Convolutional Neural Network(CNN)to extract high-level features from the input image,which then pass through the quantization module for further compression before undergoing lossless entropy *** decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder ***,we first introduce a channel-spatial attention module to capture and refine information within individual image feature ***,we employ a semi-coupled convolution module to extract both shared and specific information in ***,a cross-attention module is employed to fuse mutual information extracted from side *** effectiveness of our model is validated on various datasets,including KITTI Stereo and *** results highlight the superior compression capabilities of our method,surpassing state-of-the-art techniques.

关键词： Digital communication Image compression Side information Channel-spatial attention module Cross-attention module

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Multimodal Fusion with Cross-attention Transformer for HCC Early Recurrence Prediction from Multi-Phase CT and Clinical Data 24

Multimodal Fusion with Cross-attention Transformer for HCC E...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Zhang, Xianru Wang, Fang Li, Yinhao Lin, Lanfen Hu, Hongjie Chen, Yen-Wei Graduate School of Information Science and Engineering Ritsumeikan University Osaka Japan Department of Radiology Sir Run Run Shaw Hospital Zhejiang University Zhejiang China College of Computer Science and Technology Zhejiang University Zhejiang China

ISBN: (纸本)9798400717833

Hepatocellular carcinoma (HCC) stands as a primary cause of liver cancer-related diagnoses and fatalities worldwide, with patients often encountering significant risks of recurrence following resection surgeries. Accurately predicting the clinical outcomes, particularly early recurrence, is paramount for guiding clinical treatment decisions. In this study, we introduce a novel multimodal fusion approach utilizing a cross-attention transformer to forecast the prognosis of early recurrence in HCC patients. Leveraging the transformer architecture and cross-attention mechanism, we integrate multimodal CT images and clinical information to enhance diagnostic prediction performance effectively. Our experimental findings underscore the efficacy of the proposed framework, demonstrating its potential to improve prognostic accuracy in HCC management. © 2024 Copyright held by the owner/author(s).

关键词： Patient treatment

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A Private Blockchain System based on Zero Trust Architecture 26

A Private Blockchain System based on Zero Trust Architecture

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26th International Conference on Advanced Communications technology, ICACT 2024

作者： Chang, Yao-Chung Lin, Yu-Shan Sangaiahc, Arun Kumar Wu, Hsin-Te National Taitung University Department of Computer Science and Information Engineering Taitung Taiwan National Taitung University Department of Information Science and Management Systems Taitung Taiwan National Yunlin University of Science and Technology Department of International Graduate School of Artificial Intelligence Yunlin Taiwan

ISBN: (纸本)9791188428120

During the outbreak of COVID-19, many enterprises massively created Virtual Private Networks (VPNs) for companies to cooperate;however, these accounts lacked efficient management after the epidemic, leading to data leakage or the suffering of malicious attacks. Consequently, several firms have started to build private blockchains for data preservation and verification. Private blockchains are usually built internally in companies;once a firm's internal network opens for massive external logins, many servers and private blockchains cannot work properly to protect data. In general, private blockchains store critical personal information or relevant confidential data;once a private blockchain opens to external access, all the information in the network nodes will be exposed, making private them lose their protection functions. This paper proposes a security mechanism using a private blockchain system based on zero trust architecture. The zero trust architecture tracks every user's network conditions and analyses whether their behaviors are authorized. Additionally, the system utilizes micro-segmentation to divide the private blockchain, preventing the system from malicious attacks. The proposed system employs user multi-factor authentication to identify users, and the zero trust architecture tracks and analyzes if users' behaviors are reasonable. This method effectively ensures corporate networks' security and enables private blockchain to filter legal and authorized users to access and verify. © 2024 Global IT Research Institute - GIRI.

关键词： Virtual private networks

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Deep Learning Approaches for Road Damage Detection Using YOLOv8 and Custom Convolutional Neural Network 6

Deep Learning Approaches for Road Damage Detection Using YOL...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Khan, Muhammad Waseem Mahmood, Khalid Hussain, Israr Talha Qureshi, Muhammad Muhammad Sanaullah Badar, Hafiz Gao, Wu School of Computer Science Northwestern Poly technical University Center for Bio-inspired System-on-Chip Xi'an China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Taiwan Institute of Computing Mns University of Agriculture Multan Pakistan Henan University Kaifeng450046 China

ISBN: (纸本)9798331541750

The identification of road damage is deemed very important to the preservation of infrastructure. Since recent development in deep learning provide great potential approaches, in this paper, three deep learning models, namely EfficientNet, CNN and YOLOv8 are considered and their performance is evaluated on a dataset with 8,586 images with labelled road damages consisting of seven subclasses including longitudinal cracks and potholes. Besides, other issues like class imbalance and variabilities in the environment were studied and considered in its assessment based on the Models accuracy, precision, recall, and F1-score. With an accuracy of 64%, YOLOv8 is the most accurate model for identifying various types of damages, particularly in identifying multiple damages present in one or more types of damage, as was seen in the high accuracy rate of a split sample of 44 images of multiple damages. Our findings showcase YOLOv8 as a strong contender for real-time road damage detection with potential of enhancement with better augmentation and fine-tuning. © 2024 IEEE.

关键词： Convolutional neural networks

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Immersive Learning in Using the Metaverse of Satellite Communication Systems science 9

Immersive Learning in Using the Metaverse of Satellite Commu...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Akbar, Komputerio Meyliana Warnars, Harco Leslie Hendric Spits Jayadi, Riyanto Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Binus Graduate Program Bina Nusantara University Information Systems Management Department Jakarta Indonesia

ISBN: (纸本)9798350383010

Education stands out as one of the most impactful applications of the metaverse, holding immense potential for the future. Within the realm of satellite communication system science education, the integration of immersive learning through the metaverse adds a layer of interest and interactivity to the teaching and learning process. Numerous design and evaluation techniques have been employed in a growing body of research, aiming to understand how immersive learning via the metaverse can effectively contribute to specific educational objectives. Hence, this research is dedicated to delving into the development of an immersive learning metaverse model for the science of satellite communication systems. The research contributes a model that advocates for a holistic learning approach by integrating metaverse technology, VR, the 6E Instructional Process Model, and the MSLQ evaluation instrument. The objective of this model is to enhance the efficiency, effectiveness, and interactivity of learning within the field of satellite communication systems science. © 2024 IEEE.

关键词： Satellite communication systems

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Design of Wastewater Treatment Apparatus Based on Photocatalytic Materials 8th

Design of Wastewater Treatment Apparatus Based on Photocatal...

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8th International Conference on Advances in Energy Resources and Environment Engineering, ICAESEE 2022

作者： Peng, ShiJie Li, LinSen Wang, WenZhuo Ni, YueQi Zhang, ManLin Wang, JingWen Ke, CuiLing He, Kun School of Mechanical and Electrical Engineering Wuhan Institute of Technology Wuhan China School of Optical Information and Energy Engineering Wuhan Institute of Technology Wuhan China Polytechnic Institute of Zhejiang University Zhejiang Hangzhou China School of Computer Science & Engineering Wuhan Institute of Technology Wuhan China School of Chemical Engineering & Pharmacy Wuhan Institute of Technology Wuhan China Hefei Institutes of Physical Science Institute of Plasma Physics Chinese Academy of Sciences Hefei China Science Island Branch of Graduate School University of Science and Technology of China Hefei China

ISBN: (纸本)9783031425622

With the development of technology and the improvement of people’s living standards, the requirements for wastewater treatment are becoming higher and higher. In view of the current problems of environmental pollutant reduction and energy utilization, we propose to combine the semiconductor photocatalytic reaction with activated carbon membrane modules and RO membrane modules to generate strong oxidizing radicals through sunlight excitation of semiconductor photocatalytic materials at room temperature in order to achieve the treatment of harmful and refractory organic pollutants when photocatalytic materials are excited by sunlight in ordinary temperature, solving the problems of traditional wastewater treatment such as high energy consumption, incomplete treatment and easy generation of secondary pollution. The wastewater treatment apparatus designed in this paper and related technology can not only be used for the pretreatment or advanced treatment of refractory organic wastewater in pharmaceutical, breeding, and medical industries, but also for the purification of micro-polluted water sources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Wastewater treatment

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Thermal reaction characterization of nano-aluminum powder at different heating rates by synchronous thermal analysis

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Journal of Thermal Analysis and Calorimetry 2023年第11期148卷 4937-4947页

作者： Qu, Jiao Deng, Jun Luo, Zhen-Min Xiao, Yang Shu, Chi-Min School of Safety Science and Engineering Xi’an University of Science and Technology No. 58 Yanta Mid. Rd. Shaanxi Xi’an710054 China Postdoctoral Program Xi’an University of Science and Technology No. 58Yanta Mid. Rd. Shaanxi Xi’an710054 China Shaanxi Key Laboratory of Prevention and Control of Coal Fire No. 58 Yanta Mid. Rd. Shaanxi Xi’an710054 China Graduate School of Engineering Science and Technology National Yunlin University of Science & Technology 123 University Rd. Sec. 3 Yunlin Douliou64002 Taiwan

This article reports an investigation on the thermal reactivity of nano-aluminum powder (NAP) in the air. We performed tests on the metallic powder in synchronous thermal analysis at four heating rates of 5, 10, 15, and 20 °C min−1. Given the polymorphic phase change of aluminum, the oxidation process of NAP was divided into four stages. The results showed a notable effect from the heating rate on the oxidation of NAP. The mass gain decreased at stage II and increased at stage IV as the rate of heating increased. The maximum mass gain rate at 5, 10, 15, and 20 °C min−1 was 2.38, 4.70, 33.79, and 53.38% min−1, respectively, while the thermal release was 35,126, 27,955, 15,608, and 16,336 J g−1, respectively. Furthermore, the average apparent activation energy, preexponential factor, and mechanism function were obtained by the kinetic integral method. © 2023, Akadémiai Kiadó, Budapest, Hungary.

关键词： Heating rate

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Machine Learning Approach to Malware Classification Using Byte N-Grams on IoT Devices

Machine Learning Approach to Malware Classification Using By...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Yuto Yama Ryuya Uda Graduate School of Bionics Computer and Media Science Tokyo University of Technology Graduate School Hachioji Tokyo

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Malware for IoT devices has become popular in recent years. Many detection methods have been proposed to detect the malware, mainly using machine learning, but they are not designed to work on IoT devices and are often implemented using Python. Running a Python program requires the installation of a package, which is impractical given the memory and storage size of IoT devices. In addition, methods that require decompilation, static and dynamic analysis of test samples are difficult to run on IoT devices. Therefore, in this research, we propose a method implemented in C/C++ so that malware detection using byte n-grams and machine learning can be run on IoT devices, and then resource consumption can be measured. Byte n-gram methods are good approaches to detect malware without knowing the execution environment. Moreover, the Top-L approach to information gain can be effectively applied to reduce storage and memory consumption. As a result of the evaluation, when using an SVM model with Top-50000 that maintains effective classification accuracy, the memory consumption was 71.91MB and the storage consumption was 1.3GB, which can be implemented in IoT devices.

关键词： Support vector machines Radio frequency Accuracy Memory management Virtual environments Machine learning Malware Information management Internet of Things Python

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MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggregation for Lesion Detection in Multi-Phase CT Images

MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggrega...

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19th World Congress on Medical and Health Informatics, MedInfo 2023

作者： Kitrungrotsakul, Titinunt Xu, Yingying Chen, Qingqing Liu, Jing Li, Yinhao Lin, Lanfen Hu, Hongjie Tong, Ruofeng Li, Jingsong Chen, Yen-Wei Research Center for Healthcare Data Science Zhejiang Lab China Department of Radiology Sir Run Run Shaw Hospital China Graduate School of Information Science and Engineering Ritsumeikan Univ. Japan College of Computer Science and Technology Zhejiang Univ. China

ISBN: (纸本)9781643684567

Object detection using convolutional neural networks (CNNs) has achieved high performance and achieved state-of-the-art results with natural images. Compared to natural images, medical images present several challenges for lesion detection. First, the sizes of lesions vary tremendously, from several millimeters to several centimeters. Scale variations significantly affect lesion detection accuracy, especially for the detection of small lesions. Moreover, the effective extraction of temporal and spatial features from multi-phase CT images is also an important issue. In this paper, we propose a group-based deep layer aggregation method with multiphase attention for liver lesion detection in multi-phase CT images. The method, which is called MSPA-DLA++, is a backbone feature extraction network for anchor-free liver lesion detection in multi-phase CT images that addresses scale variations and extracts hidden features from such images. The effectiveness of the proposed method is demonstrated on public datasets (LiTS2017) and our private multiphase dataset. The results of the experiments show that MSPA-DLA++ can improve upon the performance of state-of-the-art networks by approximately 3.7%. © 2024 International Medical Informatics Association (IMIA) and IOS Press.

关键词： Feature extraction

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