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IEEJ Transactions on Electronics, Information and Systems 2022年第7期142卷 771-780页

作者： Furukawa, Shoya Sakuma, Takuto Kato, Shohei Creative Engineering Program Deptartment of Engineering Graduate School of Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Aichi Nagoya466-8555 Japan Computer Science Program Deptartment of Engineering Graduate School of Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Aichi Nagoya466-8555 Japan Frontier Research Institute for Information Science Nagoya Institute of Technology Gokiso-cho Showa-ku Aichi Nagoya466-8555 Japan

In recent years, making computers understand the emotions of users is necessary because emotions are an important factor in human communication. Among many methods of recognizing emotions, EEG is widely used because it has high temporal resolution and it is impossible to disguise intentionally. However, it is necessary to acquire new user data and construct the personal Emotion recognition model since EEG varies widely among individuals. The conventional methods lack practicality because it builds a different model for every new user data. Our method builds a model in a single training using new user data. To reduce the number of new user data for training and to relieve the burden of EEG measurement, we adapt existing user data. We absorb the individual differences in EEG between a new user and existing users by domain adaptation using a small number of new user data and construct a model by deep neural networks. From the experiments, we confirmed that the proposed method performs as well as the conventional methods, even though it is built with single training using new user data. © 2022 The Institute of Electrical Engineers of Japan.

关键词： Deep neural networks

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Neural Network-Based Analysis of Mammography Images for Identifying Breast Cancer Histological Subtypes

Neural Network-Based Analysis of Mammography Images for Iden...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Alqaaf, Muhammad technology, Ahmad Kamal Nasution graduate school Of science And Supriyanti, Retno Asaduzzaman, Abu Ono, Naoaki Altaf-Ul-Amin, Md. Kanaya, Shigehiko Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan Jenderal Soedirman University Department of Electrical Engineering Banyumas Indonesia Wichita State University Department of Electrical and Computer Engineering Wichita United States

ISBN: (纸本)9798350391213

Breast cancer (BC) is a complex disease with multiple histological subtypes that exhibits distinct biological and clinical characteristics. Accurate identification of these subtypes is crucial for the implementation of personalized treatment strategies and the improvement of patient outcomes. This study aimed to leverage the potential of neural networks and mammography data to identify specific histological subtypes of BC, with a focus on distinguishing between in situ and invasive carcinoma. We used the Digital Database for Screening Mammography (DDSM) and its curated subset, CBIS-DDSM, which provides mammography images of normal and malignant cases with verified pathological information. A convolutional neural network (CNN) architecture was designed to extract relevant features from mammography images. The model was trained using data augmentation techniques to enhance diversity and mitigate overfitting, resulting in training and validation accuracies ranging from 0.98 to 1.0. The features extracted by the trained model were then used for clustering analysis using Kmeans on PCA (KM-PCA), which identified nine well-separated clusters with minimal overlap. The clustering results were validated using a subset of mammography images with confirmed invasive carcinoma types from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Two validation approaches were incorporated to strengthen the hypothesis and further validate the results. Overall, this study demonstrates the potential of combining neural networks and mammography data to accurately identify BC histological subtypes, which could pave the way for personalized and effective treatment strategies. © 2024 IEEE.

关键词： Lung cancer

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Explorations of using a convolutional neural network to understand brain activations during movie watching

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Psychoradiology 2024年第1期4卷 187-195页

作者： Wonbum Sohn Xin Di Zhen Liang Zhiguo Zhang Bharat B.Biswal Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJ 07102USA Rutgers Biomedical and Health Sciences Rutgers School of Graduate StudiesNewarkNJ 07039USA School of Biomedical Engineering Health Science CenterShenzhen UniversityShenzhen51806China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518060China

Background Naturalistic stimuli,such as videos,can elicit complex brain ***,the intricate nature of these stimuli makes it challenging to attribute specific brain functions to the resulting activations,particularly for higher-level processes such as social *** We hypothesized that activations in different layers of a convolutional neural network(VGG-16)would correspond to varying levels of brain activation,reflecting the brain's visual processing ***,we aimed to explore which brain regions would be linked to the deeper layers of the *** This study analyzed functional MRI data from participants watching a cartoon *** a pre-trained VGG-16 convolutional neural network,we mapped hierarchical features of the video to different levels of brain *** maps from various kernels and layers were extracted from video frames,and the time series of average activation patterns for each kernel were used in a voxel-wise model to examine brain *** Lower layers of the network were primarily associated with activations in lower visual regions,although some kernels also unexpectedly showed associations with the posterior cingulate *** layers were linked to more anterior and lateral regions of the visual cortex,as well as the supramarginal *** This analysis demonstrated both the potential and limitations of using convolutional neural networks to connect video content with brain functions,providing valuable insights into how different brain regions respond to varying levels of visual processing.

关键词： convolutional neural network deep learning default mode network lateral occipital complex naturalistic condition supramarginal gyrus visual cortex

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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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Correlating the Ambient Conditions and Performance Indicators of the LoRaWAN via Surrogate Gaussian Process-Based Bidirectional LSTM Stacked Autoencoder

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IEEE Transactions on Network and Service Management 2023年第3期20卷 3413-3427页

作者： Bhat, Showkat Ahmad Huang, Nen-Fu Hussain, Imtiyaz Sajjad, Uzair National Tsing Hua University College of Electrical and Computer Science Hsinchu1300044 Taiwan National Tsing Hua University Department of Computer Science Hsinchu1300044 Taiwan National Taipei University of Technology Graduate Program in Energy and Opto-Electronic Materials Taipei10608 Taiwan National Taipei University of Technology Department of Energy and Refrigerating Airconditioning Engineering Taipei10608 Taiwan

LoRa's biggest advantage is its flexibility, which is the ability to increase or decrease data rate and range while decreasing or increasing sensitivity. Whenever propagation conditions change frequently, this function allows the spreading factor to be modified accordingly. Despite their efficiency and scalability, adaptive data rate algorithms ignore and fail to factor in the complex correlation between ambient weather parameters influencing the communication channel design. In this research, a Bayesian surrogate Gaussian process-based bidirectional LSTM stacked autoencoder model (BSGP-BLSTM-SAE) is proposed to estimate the channel performance indicators such as received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) and to determine the correlation between the ambient weather conditions and performance indicators for the LoRaWAN network. Bayesian optimization algorithm has been used to optimize the hyper-parameters of the developed model. A LoRaWAN experimental multivariate time series dataset has been used for the evaluation of the developed model, which upon testing and validation produces high accuracy in predicting the channel performance indicators and ambient conditions of the experimental LoRaWAN network. The mean absolute error of the developed model was around 0.45. Thus, the proposed model can predict the link performance indicators and thereby assist in real-time optimization of the transmission parameters to enhance the network performance in LoRaWAN-based systems at different ambient conditions. © 2004-2012 IEEE.

关键词： Internet of things

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Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

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computer Modeling in Engineering & sciences 2024年第7期140卷 409-430页

作者： Zhuoqun Xia Hangyu Hu Wenjing Li Qisheng Jiang Lan Pu Yicong Shu Arun Kumar Sangaiah School of Computer and Communication Engineering Changsha University of Science and TechnologyChangshaChina Hunan Province People’s Hospital(The First Affiliated Hospital of Hunan Normal University) ChangshaChina The First Affiliated Hospital of Guangxi Medical University NanningChina International Graduate Institute of AI National Yunlin University of Science and TechnologyYunlinChina Department of Electrical and Computer Engineering Lebanese American UniversityBayrutLebanon

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible *** research has failed to fully explore effective DR lesion information in fundus ***,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion ***,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis ***,the LLM generates a global attention map based on ***,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion *** paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

关键词： DDR dataset diabetic retinopathy lesion localization multi-level patch attention mechanism

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Biotech-Enhanced Online Education: Reshaping Learning and Physical Health

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Journal of Commercial Biotechnology 2023年第5期28卷 108-121页

作者： Wang, Zhaojie Saluba, Dennis J. Shi, Hong College of Teacher Education Taiyuan Normal University Jinzhong030600 China Graduate School Adamson University Manila10004 Philippines College of Computer Science and Technology Taiyuan Normal University Jinzhong030600 China

The landscape of education is undergoing a transformative shift with the increasing adoption of online learning, further accelerated by the recent global pandemic. This study explores the intersection of biotechnology and online education, particularly focusing on its implications for student learning and physical health. Amidst the COVID-19 crisis, Romanian universities rapidly transitioned to online platforms, providing a unique opportunity to assess the effectiveness of such methods over an 8-year period. This research highlights the role of biotechnological tools in enhancing online learning experiences and addressing the physical health challenges posed by the new educational environment. We conducted a web-based survey using a semi-structured questionnaire to evaluate student engagement, information assimilation, and health outcomes in the context of online learning. The study included 548 students, comprising 401 from traditional classroom settings and 147 engaged in online learning. The research aimed to compare the effectiveness of biotech-enhanced online education against conventional methods, particularly in terms of student performance and health. Our analysis extended to variations in performance across genders and class rankings, assessing if biotechnological interventions in online education had differential impacts across these groups. The findings indicate that while there was no significant difference in academic performance between online and face-to-face learners, the integration of biotechnological tools in online education platforms showed promising results in managing and improving students' physical health. This study underscores the potential of biotech-enhanced online education in not only maintaining educational standards but also in fostering better health outcomes for students in a digital learning landscape. © 2023 ThinkBiotech LLC. All rights reserved.

关键词： Students

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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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Harmonious Music-driven Group Choreography with Trajectory-Controllable Diffusion 39

Harmonious Music-driven Group Choreography with Trajectory-C...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dai, Yuqin Zhu, Wanlu Li, Ronghui Ren, Zeping Zhou, Xiangzheng Ying, Jixuan Li, Jun Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)157735897X

Creating group choreography from music is crucial in cultural entertainment and virtual reality, with a focus on generating harmonious movements. Despite growing interest, recent approaches often struggle with two major challenges: multi-dancer collisions and single-dancer foot sliding. To address these challenges, we propose a Trajectory-Controllable Diffusion (TCDiff) framework, which leverages non-overlapping trajectories to ensure coherent and aesthetically pleasing dance movements. To mitigate collisions, we introduce a Dance-Trajectory Navigator that generates collision-free trajectories for multiple dancers, utilizing a distance-consistency loss to maintain optimal spacing. Furthermore, to reduce foot sliding, we present a footwork adaptor that adjusts trajectory displacement between frames, supported by a relative forward-kinematic loss to further reinforce the correlation between movements and trajectories. Experiments demonstrate our method’s superiority. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Effects of the Attunement of an Attack Target by Whispering in AIWolf 14

Effects of the Attunement of an Attack Target by Whispering ...

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14th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2023

作者： Murakami, Tomohiro Nakao, Nozomi Miyake, Hikaru Murata, Hayate Amano, Itsuki Kojima, Taisei Ando, Keisuke Ito, Nobuhiro Aichi Institute of Technology Graduate School of Business Administration and Computer Science Toyota Japan Aichi Institute of Technology Department of Information Science Toyota Japan Aichi Institute of Technology Faculty of Information Science Toyota Japan

ISBN: (纸本)9798350324228

Recently, research on AIWolf, which is artificial intelligence that plays the Werewolf game, has attracted much attention. Werewolves in the Werewolf game should cooperate using 'whispers;' which are conversations among werewolves only. However, whispers have not been used effectively in the AIWolf Competition and little research has been conducted on this topic. Therefore, the purpose of this study is to clarify the effectiveness of strategies that use whispers in AIWolf. A wide variety of strategies use whispers, but we focus on unifying attack targets, which was shown to improve the winning rate in previous studies. In the strategy of unifying attack targets, the strategy of the guiding agents is important. Therefore, we used 13 AIWolf Competition finalists with a wide variety of strategies and added statements of whom to attack in whispers to each finalist as a guide for whom to attack. Because there are three werewolf competitions in the AIWolf Competition setting, we created two agents to be attuned to the attack target of one of the guiding agents. In an experiment, we used these agents in the same setting as in the AIWolf Competition, and analyzed the differences between when the attack targets were unified and when they were not. The results showed that when the werewolves were a combination of two attuning agents and one guiding agent, the werewolves' win rate increased by an average of 2.3 percentage points. We also found that attuning to the agent with a high winning rate or high success rate in attacks improved the winning rate. These results indicate that attuning only the attack targets of agents with high winning rates and high success rates in attacks led to higher winning rates. © 2023 IEEE.

关键词： Artificial intelligence

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