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Correction: Humanoid robot-empowered language learning based on self-determination theory

Education and Information Technologies 2024年第5期30卷 6843-6845页

作者： Fung, Ka Yan Lee, Lik Hang Sin, Kuen Fung Song, Shenghui Qu, Huamin Interdisciplinary Programs Office (Individualized Interdisciplinary Program (IIP) Hong Kong University of Science and Technology Hong Kong China Department of Industrial and Systems Engineering (ISE) The Hong Kong Polytechnic University Hong Kong China Department of Special Education and Counselling The Education University of Hong Kong Hong Kong China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China

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Efficient Node Reduction Heuristic for GNN-Based Traffic Speed Forecasting

Efficient Node Reduction Heuristic for GNN-Based Traffic Spe...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Yuto Inokuchi Pedro Henrique González Masayoshi Aritsugi Israel Mendonça Graduate School of Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy.

关键词： Graph convolutional networks Noise Memory management Estimation Computational efficiency Forecasting Optimization Intelligent transportation systems Faces Genetic algorithms

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Glucose Sensing Utilizing Complex-Valued Neural Networks 23

Glucose Sensing Utilizing Complex-Valued Neural Networks

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Lv, Yiqi Meng, Xiang Luo, Yong Pei, Yan The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University China and Jt. International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai China Doctoral Program Grad. Sch. of Comp. Science and Engineering Graduate Department of Computer and Information Systems University of Aizu Aizuwakamatsu Japan Computer Science Division University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9798400700705

This paper proposes a Complex-Valued Neural Network (CVNN) for glucose sensing in milli-meter wave (mmWave). Based on the propagation characteristics of millimeter wave in glucose medium, we obtain the S21 parameter of glucose with the concentration range of 0-300mg/dL in the 60-80 GHz frequency band by High Frequency Structure Simulator (HFSS) simulations. Then we combine the sensing model with a neural network to detect and predict the glucose concentration relying on the learning ability of the neural network. In the prediction of the concentration of unknown samples, the absolute error between the predicted value and the true value is within 5mg/dL, which confirms the ability of the proposed CVNN model. © 2023 ACM.

关键词： Millimeter waves

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Implementation of Malaria Detection Using MobileNetV2 Pretrained Model for Improved Deep Learning Based Diagnostics

Implementation of Malaria Detection Using MobileNetV2 Pretra...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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Combining Weakly Supervised Segmentation with Multitask Learning for Improved 3D MRI Brain Tumour Classification 2nd

Combining Weakly Supervised Segmentation with Multitask Lea...

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2nd Workshop on Medical Image Learning with Noisy and Limited Data, MILLanD 2023

作者： Rajapaksa, Sajith Namdar, Khashayar Khalvati, Farzad Neurosciences and Mental Health Research Program The Hospital for Sick Children Toronto Canada Institute of Medical Science University of Toronto Toronto Canada Department of Medical Imaging University of Toronto Toronto Canada Department of Computer Science University of Toronto Toronto Canada Department of Mechanical and Industrial Engineering University of Toronto Toronto Canada Vector Institute Toronto Canada

ISBN: (纸本)9783031471964

Segmentation plays a crucial role in computer-aided medical image diagnosis, as it enables the models to focus on the region of interest (ROI) and improve classification performance. However, medical image datasets often lack segmentation masks due to cost. In this work, we have introduced a novel end-to-end pipeline that enhances classification by incorporating ROIs generated by a weakly supervised method as an auxiliary task in a deep multitask learning framework. We have demonstrated that our approach outperforms conventional methods that only rely on classification. By using weakly supervised segmentation, we are able to leverage pixel-wise information, which ultimately leads to improved classification performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image enhancement

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Lightweight Pedestrian Detection through Guided Filtering and Deep Learning 10

Lightweight Pedestrian Detection through Guided Filtering an...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

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MERNet: Multimodal Emotion Recognition in Conversation Network via Cross-Modal Knowledge Distillation and Contrastive Learning

MERNet: Multimodal Emotion Recognition in Conversation Netwo...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Sooyeon Hwang Gahgene Gweon Eunseong Kwon Interdisciplinary Program in Artificial Intelligence Seoul National University South Korea Graduate School of Convergence Science and Technology Seoul National University South Korea Dept. of Computer Science and Engineering POSTECH South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Multimodal Emotion Recognition in Conversation (ERC) is a task of predicting the emotion of each utterance in a conversation by utilizing both verbal and non-verbal modalities. However, existing approaches often struggle to bridge cross-modal gaps, resulting in misaligned features and frequent misclassification of minority emotions into semantically similar majority emotions. To address these challenges, we propose MERNet, a framework that employs cross-modal knowledge distillation and contrastive learning to align multimodal features and effectively distinguish subtle emotions in conversations. Our framework consists of two stages: 1) guiding non-verbal modalities with the text modality to transfer knowledge and align their features, and 2) applying contrastive learning with emotion labels as anchors to distinguish subtle differences between similar emotions and address the class imbalance problem. Experiments conducted on two benchmark datasets, IEMOCAP and MELD, demonstrate that our MERNet outperforms existing state-of-the-art models.

关键词： Knowledge engineering Bridges Emotion recognition Visualization Computational modeling Contrastive learning Benchmark testing Linguistics Big Data

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Comprehensive Study and Analysis for StarGAN and StarGAN-v2 in the Task of Image Generation

Comprehensive Study and Analysis for StarGAN and StarGAN-v2 ...

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作者： Yuanan Cai Yepeng Li Zhenyu Zhang Department of Computer Engineering Baskin School of EngineeringUniversity of California-Santa Cruz Department of Cognitive Science University of California–San Diego Department of information technology Monash University

With the continuous development of deep learning and artificial intelligence industry,more and more neural networks have been *** them,one typical network widely used is *** solves the problem of requiring multiple generators and discriminators for multiple domains and style *** this article,we will analyze and explore StarGAN v1 and StarGAN *** StarGAN's development history,advantages and disadvantages in the application,*** the same time,we will compare StarGAN with other neural networks such as CycleGAN and MSGAN through some discrimination criteria,and clearly demonstrate the influence of different networks on the generation of image data sets through quantitative and qualitative *** addition,we also found a representative code on some of the actual uses of StarGAN for hair replacement through online *** this code may be used to confuse the use of StarGAN v1 and v2,we will also improve this code in the article and put forward suggestions for *** this paper,we carried out the specific analysis of StarGAN's loss function based on the existing data and put forward some relevant *** taking the data we searched for StarGAN,it can be found that StarGAN could use as few resources as possible to achieve clearer and more varied results during the image generation task.

关键词： GAN StarGAN Machine learning Deep Learning Image Generation

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Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics

Novel NBA Fantasy League driven by Engineered Team Chemistry...

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IEEE International Conference on Big Data

作者： Ganesh Arkanath Nishad Gupta Hasan Kurban Parichit Sharma K R Madhavan Elham Khorasani Buxton Mehmet M. Dalkilic Computer Science Department Indiana University Bloomington IN US Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Data Science Program Indiana University Bloomington IN US Computer Science Department University of Illinois Springfield IL US

Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position; (2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA’s API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://***/fantasyleague/, and the source code can be found at https://***/gany-15/nbafan.

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High-Resolution Remote Sensing Image Captioning Based on Structured Attention and SAM Network

High-Resolution Remote Sensing Image Captioning Based on Str...

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Iranian Conference on Electrical engineering (ICEE)

作者： Yassin Riyazi Seyyed Mostafa Sadjadi Abbas Zohrevand Reshad Hosseini School of Mechanical Engineering University of Tehran Tehran Iran CIPCE School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Cognitive Sciences (SCS) Institute for Research in Fundamental Science (IPM) Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

Due to its broad applications, remote sensing image captioning (RSIC) has gained popularity in recent years. However, it poses extra challenges for containing low-resolution images with highly structured semantic content. By incorporating image labeling and segmentation, this work develops an RSIC framework using a structured attention module that highlights important semantic components to maintain a geometric and structured shape. The quality and edge emphasis of UCM-captioned photographs are improved by upsampling them to 512×512 pixels. Using the Segment Anything Model (SAM) produces better image proposals, leading to higher accuracy than traditional techniques. A balanced output of large- and small-object masks is facilitated by SAM's promptability. The decoder can more easily learn a suitable statistical model using the model's spatial structure to provide an all-encompassing attention map. This work investigates the effects of multiple hyperparameters, including teacher forcing, the number of region proposals, and the impact of DSR and AVR loss factors. Overall, by combining image labeling and segmentation, this research improves remote sensing capabilities. It also shows how well the structured attention module and SAM work together to improve accuracy and consider different hyperparameter issues.

关键词： Image segmentation Interpolation Accuracy Shape Image edge detection Semantics Sensors

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