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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Chen, Yumeng Liu, Songlin Zhou, Jianhong Ling, Xiang University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications China Xihua University School of Computer and Software Engineering China

ISBN: (纸本)9798350363760

In modern real-time operating systems, complex task loads are often modeled as directed acyclic graphs (DAG) and executed in parallel on multiprocessor systems. The topological constraints present in DAG tasks prevent the DAG task allocation strategy from fully leveraging the parallel advantage of multiprocessor platforms. We analyze the parallelism of DAG tasks and propose a method to limit the number of processors that can be executed by DAG tasks. This method provides more idle processors and processor time for high parallelism DAG tasks by limiting the number of processors that can be executed by low parallelism DAG tasks. In addition, we develope optimistic parallelism scheduling algorithm (OPSA) based on the remaining utilization with low complexity. The experimental results show that our DAG task allocation strategy fully utilizes processor resources, reduces interference between DAG tasks, and achieves better worst case response time (WCRT) analysis results and shorter actual task's WCRT on real-time multiprocessor system. © 2024 IEEE.

关键词： Directed graphs

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AI-Driven Virtual Mock Interview Development 13

AI-Driven Virtual Mock Interview Development

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mishra, Prabhat Kumar Arulappan, Arun Kumar Ra, In-Ho Thanga Mariappan, L. Gina Rose, G. Lee, Young-Seok School of Computer Science Engineering and Information Systems Vit University Vellore India School of Computer and Software Kunsan National University Gunsan Korea Republic of Anna University Department of Computer Technology Chennai India

ISBN: (纸本)9798350373332

The integration of Artificial Intelligence (AI) into educational technologies marks a significant shift in learning methodologies and operational dynamics within educational institutions. At the forefront is an AI-driven virtual mock interview platform designed to address the high Customer Acquisition Costs (CAC) in the edtech sector, especially for interview preparation services. This initiative harnesses a blend of AI technologies, including ADA 2 for creating context-aware embeddings and Machine Learning (ML), to transform the traditional mock interview process into a dynamic, cost-effective system. Central to the platform is its use of advanced Natural Language Processing (NLP) techniques and GPT-4 Large Language Model (LLM), automating the process of mock interviews and providing personalized feedback, ensuring a preparation journey that meets specific candidate needs and mirrors real interview scenarios. A key evaluation among 100 students from a cohort of 1800 demonstrated a 90% cost reduction for three mock interviews, reducing expenses from 3000 to just 300 per candidate. This cost efficiency significantly enhances access to quality interview preparation, improving student satisfaction and accessibility. Moreover, the platform provides valuable insights into student performance, setting a new standard in educational technology by offering an effective, personalized interview preparation experience. This project reflects a holistic approach to student development and the critical role of technology in addressing the evolving needs of learners. © 2024 IEEE.

关键词： Cost reduction

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Research on Visual Slam Technology for UAV Positioning and Navigation 21

Research on Visual Slam Technology for UAV Positioning and N...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Wang, Zhou Zhihao, Liu Ying, Tian Yujun, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province LuZhou China College of Computer and Artificial Intelligence Software College Huaihua University Huaihua China

ISBN: (纸本)9798331519254

UAV (Unmanned Aerial Vehicle) navigation can be considered as the process of robots that determine how to successfully and quickly reach the target location. Specifically, in order to complete the scheduled task successfully, the UAV must accurately understand its status, such as position, navigation speed, heading, as well as the starting point and target position. Moreover, it mainly depends on the current environment and location. This article generally focuses on the research on visual SLAM (Simultaneous localization and mapping) for UAV positioning and navigation. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5026-5039页

作者： Li, Mengke Cheung, Yiu-Ming Lu, Yang Hu, Zhikai Lan, Weichao Huang, Hui Shenzhen518132 China Hong Kong Baptist University Department of Computer Science 999077 Hong Kong Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen361005 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tail classes correctly. In the literature, several existing methods have addressed this problem by reducing classifier bias, provided that the features obtained with long-tailed data are representative enough. However, we find that training directly on long-tailed data leads to uneven embedding space. That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning. This article therefore studies the problem of long-tailed visual recognition from the perspective of feature level. We introduce feature augmentation to balance the embedding distribution. The features of different classes are perturbed with varying amplitudes in Gaussian form. Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Subsequently, the distorted embedding spaces of all classes can be calibrated. In such balanced-distributed embedding spaces, the biased classifier can be eliminated by simply retraining the classifier with class-balanced sampling data. Extensive experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones. © 2020 IEEE.

关键词： Benchmarking

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Deep Learning-Based APT Malware and Variants Detection with Attribution Analysis 6

Deep Learning-Based APT Malware and Variants Detection with ...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Tang, Binhui Leng, Peiyun Shen, Xuxiang Wei, Yuhang Sichuan University Chengdu School of Computer and Software Chengdu Jincheng College School of Cyber Science and Engineering China Chengdu Jincheng College School of Computer and Software Chengdu China

ISBN: (纸本)9798350325485

Malware has become one of the most severe security threats in cyber security, among which APT malware attacks are more threatening than advanced sustainable threat attacks. In this paper, we perform APT malware and variant detection based on deep learning and analyze the results by detecting and attribution. The variant detection of APT malware can inhibit the spread of malicious code, which is important for network security detection and defense. This paper proposes a detection and classification method for APT malware and its variants based on deep learning. Firstly, the feature representation method of APT malware based on RGB images is proposed to solve the gradient explosion and gradient disappearance of grayscale map feature extraction and generate images with rich texture information, which can mine deeper features of APT malware attacks. Secondly, this paper also improves the convolutional neural network model by combining the Self-Attention mechanism with the spatial pyramid pool SPP-net to solve the problem of image input of different sizes, as the accuracy is above 92.14%. Then, the experiment results prove the predictive analysis of the model to understand the organization to which the APT attack belongs and the way of its specific attack, providing the possibility of tracing the source. Finally, the visual analysis of APT threat characteristics is presented. © 2023 IEEE.

关键词： Convolutional neural networks

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A Deep Understanding Video Q&A System for Film Education in Acting Department

A Deep Understanding Video Q&A System for Film Education in ...

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2023 International Conference on Intelligent Education and Intelligent Research, IEIR 2023

作者： Wu, Zhengqian Li, Ruizhe Guo, Jiahao Wang, Zhongyuan Liang, Chao Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science China National Engineering Research Center for Multimedia Software School of Computer Science China Wuhan University School of Computer Science China

ISBN: (纸本)9798350342895

Recently, advancements in artificial intelligence technology have greatly influenced the field of education, particularly in the area of intelligent homework assistance. However, current approaches are primarily designed for procedural and logical tasks and often lack comprehension abilities. This limitation is particularly evident when it comes to multi-hop and continuous tasks. To address this challenge, the integration of Large Language Model (LLM) has significantly enhanced the capability of AI systems to handle multi-hop and highly interconnected inputs. In this study, we focus on the learning needs of students in Acting Department, specifically their study of movies and the significance of classic movie videos in their learning process. However, assessing deep comprehension of classic movies poses its own challenges. To overcome these challenges, we develop a quiz system utilizing Knowledge Graphs (KG) and LLM to facilitate a deeper understanding of classic films. The generation of video quiz pairs is achieved through the use of Automatic Speech Recognition (ASR) technology, which leverages movie subtitles for question generation. For answering these questions, we employ techniques KG and LLM to process questions and retrieve corresponding answers. The proposed method achieves good performance in Deep Video Understanding (DVU) task of NIST TRECVID, demonstrating its effectiveness. © 2023 IEEE.

关键词： Motion pictures

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SCPSN: Spectral Clustering-based Pyramid Super-resolution Network for Hyperspectral Images 24

SCPSN: Spectral Clustering-based Pyramid Super-resolution Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yang, Yong Zhao, Aoqi Huang, Shuying Wang, Xiaozheng Fan, Yajing School of Computer Science and Technology Tiangong University Tianjin China School of Software Tiangong University Tianjin China School of Control Science and Engineering Tiangong University Tianjin China School of Electronics and Information Engineering Tiangong University Tianjin China

ISBN: (纸本)9798400706868

Single hyperspectral image super-resolution aims to reconstruct a high-resolution hyperspectral image (HRHSI) from an observed low resolution hyperspectral image (LRHSI). Most current methods combine CNN and Transformer structures to directly extract features of all channels in LRHSI for image reconstruction, but they do not consider the interference of redundant information in adjacent bands, resulting in spectral and spatial distortions in the reconstruction results and an increase in model computational complexity. To address this issue, this paper proposes a spectral clustering-based pyramid super-resolution network (SCPSN) to progressively reconstruct HRHSI at different scales. In each image reconstruction layer, a clustering super-resolution block (CSRB) consisting of spectral clustering block (SCB), patch non local attention block (PNAB), and dynamic fusion block (DFB) is designed to achieve the reconstruction of detail features. Specifically, for the high correlation between adjacent spectral bands in LRHSI, a SCB is first constructed to achieve clustering of spectral channels and filtering of hyperchannels. This can reduce the interference of redundant spectral information and the computational complexity of the model. Then, by utilizing the non-local similarity of features within the channel, a patch non-local attention block (PNAB) is constructed to enhance the features of hyperchannels. Next, a dynamic fusion block (DFB) is designed to reconstruct the features of all channels in LRHSI by establishing correlations between enhanced hyperchannels and other channels. Finally, the reconstructed channels are upsampled and added to the corresponding channels to obtain the reconstructed HRHSI. Extensive experiments validate that the performance of SCPSN is superior to that of some other state-of-the-art (SOTA) HSSR methods in terms of visual effects and quantitative metrics. In addition, our model does not require training on large-scale datasets compared to oth

关键词： Image reconstruction

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Ensemble deep learning for blood pressure estimation using facial videos 3

Ensemble deep learning for blood pressure estimation using f...

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3rd Vision-Based Remote Physiological Signal Sensing Challenge and Workshop, RePSS 2024

作者： Liu, Wei Wu, Bingjie Zhou, Menghan Zheng, Xingjian Wang, Xingyao Xie, Yiping Luo, Chaoqi Zhen, Liangli Singapore Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Electrical Engineering Southwest Jiaotong University Chengdu China

Blood pressure (BP) estimation is a standard and critical component of routine health assessment, especially for cardiac disease patients. Traditional methods typically require direct contact with the patient, which can cause discomfort and inconvenience. Remote photoplethysmography (rPPG) that enables non-contact measurement of the blood volume pulse using trivial cues from facial videos has drawn attention to measure vital signs. This paper presents an ensemble deep learning approach for estimating BP remotely using facial videos. Specifically, to address the vulnerabilities and biases in deep learning models for BP measurement, we emphasize both the accuracy of individual models and the diversity within the ensemble. We utilize advanced deep learning architectures to construct several regression models incorporating convolutional neural networks and transformer blocks, which learn the spatiotemporal relationships between different frames and locations. These trained models are then combined to measure BP readings. Additionally, to enhance the system's robustness under varying lighting conditions, data augmentation techniques are employed to generate more training data. The proposed method is tested on an unseen dataset and the average root of mean squared error (RMSE) is 12.95 mmHg, ranking 1st in the 3rd Vision-based Remote Physiological Signal Sensing (RePSS) Challenge. © 2024 Copyright for this paper by its authors.

关键词： Blood pressure measurement deep learning ensemble learning remote photoplethysmography

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Thermal Simulation Computing Network Based on Deep Learning

Thermal Simulation Computing Network Based on Deep Learning

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2024 International Symposium on Digital Home, ISDH 2024

作者： Wang, Ruomei Teng, Yi Zhang, Yaoquan Zhou, Fan School of Software and Engineering Guangzhou510303 China School of Computer Science Guangdong University of Education Guangzhou510303 China School of Computer Science Sun Yat-sen University National Engineering Research Center of Digital Life Guangzhou510006 China

ISBN: (纸本)9798331509873

The heat and moisture transfer simulation about human body and clothing is a method to simulate the regulation mechanism of human body, and heat and moisture performance of clothing in thermal environment by using computer technology. According to the simulation results, it can be used to the monitor and predicate the key physiological data of human body, and the functional clothing design. Some mathematical models about this area have been built. However, a large number of differential equations are involved to be used to the numerical computation in the simulation process, which leads to high computational complexity, large memo-ry consumption and long computing time. With the expansion of computing scale, these problems will become more obvious and directly affect the practical application of simulation models. The main work of this paper is to study the efficient solution method based on neural network, and proposed a deep learning-based differential equation computing network. The network uses differential equation as the constraint of the loss function of neural network, and integrates the differential equation into the training and prediction process of the network. The experimental results show that the proposed network can reduce the computational complexity of the simulation model and improve the computational efficiency. © 2024 IEEE.

关键词： Physiological models

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On the Adversarial Robustness of Hierarchical Classification

On the Adversarial Robustness of Hierarchical Classification

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Ran Zeng, Simeng Wu, Wenhui Jia, Yuheng Ng, Wing W. Y. Wang, Xizhao School of Mathematical Sciences Shenzhen University Shenzhen518060 China College of Electronic and Information Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Engineering Southeast University Nanjing211189 China School of Computer Science and Engineering South China University of Technology Guangzhou510006 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781665410205

Deep neural networks (DNNs) have demonstrated remarkable success on various learning problems, but they face a formidable challenge in the form of adversarial attacks. Especially, when dealing with complex classification tasks for numerous classes with a hierarchical structure, the adversarial robustness of a DNN model may drop seriously. In this paper, we investigate the adversarial robustness of DNN models on such complex classification tasks. In response, we propose a two-stage hierarchical classification framework, which is composed of a coarse-grained classifier and a series of fine-grained classifiers. A data correction sampling module is designed between the two stages, in order to mitigate the influence of misclassification caused by the coarse-grained classifier;and a discriminative filter learning module is employed in the fine-grained classification, in order to gain better distinguish abilities among fine-grained categories. Experiments on the well-known dataset CIFAR-100 and a newly-constructed hierarchical dataset mini-ImageNet76 demonstrate that employing a hierarchical framework can effectively improve the model robustness on such complex classification tasks. © 2024 IEEE.

关键词： Generative adversarial networks

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