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IEEE International Conference on Communications (ICC)

作者： Ping Yin Wei Chen Jiaxi Zheng Yiting Zhang Lifa Wu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Online image sharing on social media platforms faces information leakage due to deep learning-aided privacy attacks. To avoid these attacks, this paper proposes a privacy protection mechanism for image sharing without changing the visual effect, which is based on reversible adversarial examples. Specifically, social media platform users can change the class activation feature to convert the original image into an adversarial image before sharing. When users want to restore the adversarial image to the original image, they can use an improved generative adversarial network model to restore it. The experimental results prove that the conversion model in this paper can effectively prevent privacy attacks from analyzing and stealing users' private information while having no visual impact. At the same time, the proposed restoration model can restore the adversarial examples with high accuracy.

关键词： Training Privacy Image recognition Social networking (online) Perturbation methods Visual effects Information leakage

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Inverse Quadratic Transform for Minimizing A Sum of Ratios

Inverse Quadratic Transform for Minimizing A Sum of Ratios

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yannan Chen Licheng Zhao Yaowen Zhang Kaiming Shen School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) China Shenzhen Research Institute of Big Data China

A major challenge with the multi-ratio Fractional Program (FP) is that the existing methods for the maximization problem typically do not work for the minimization case. We propose a novel technique called inverse quadratic transform for the sum-of-ratios minimization problem. Its main idea is to reformulate the min-FP problem in a form amenable to efficient iterative optimization. Furthermore, this transform can be readily extended to a general cost-function-of-multiple-ratios minimization problem. We also give a Majorization-Minimization (MM) interpretation of the inverse quadratic transform, showing that all those desirable properties of MM can be carried over to the new technique. Moreover, we demonstrate the application of inverse quadratic transform in minimizing the Age-of-Information (AoI) of data networks.

关键词： Transforms Signal processing Minimization Information age Cost function Acoustics Iterative methods

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Healthcare gamification: science mapping the body of literature 22

Healthcare gamification: Science mapping the body of literat...

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22nd International Conference on Electronic Business, ICEB 2022

作者： Machado, Franklin Jean Sanchez, Otávio Próspero Department of Technology and Data Science Fundação Getulio Vargas's Sao Paulo School of Business Administration Sao Paulo Brazil

Healthcare gamification is a research topic being investigated in numerous contexts. As it is an interdisciplinary subject, it is hard for researchers to keep up with the research published in these venues. This paper aims to unveil the intellectual structure of the healthcare gamification research field through a systematic literature review. We apply a science map strategy to a 520-document database. This database was analyzed to build an evolutionary map revealing the most co-cited authors, their publications, and the theories used. Results evidence that referenced research can be classified into six research traditions. Finally, research opportunities are presented. © 2022 International Consortium for Electronic Business. All rights reserved.

关键词： Health care

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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A Novel Design of Fatigue Driving Detector with mmWave Sensor AWR1642boost 23

A Novel Design of Fatigue Driving Detector with mmWave Senso...

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2023 International Conference on Intelligent Computing and Its Emerging Applications, ICEA 2023

作者： Tseng, Shih-Pang Hsiao, I-Hui Wang, Jhing-Fa School of Software and Big Data Changzhou College of Information Technology Changzhou China Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9798400709050

According to statistics from the Ministry of Transportation, distracted driving is one of the main causes of traffic accidents. In Taiwan, approximately 20% of total accidents each year are attributed to distracted or fatigued driving, ranking it as the second leading cause of accidents. Typically, drivers start to feel fatigued and experience decreased reaction and decision-making abilities after driving continuously for 2-3 hours. Post-meal and midnight driving also tend to induce drowsiness more than other times. Additionally, alcohol consumption, drug use, and the use of hypnotic medication can lead to loss of consciousness. Fatigued driving poses a serious public safety issue, endangering both drivers and other road users. Therefore, developing effective methods for detecting driver fatigue is crucial. Based on existing research, employing facial monitoring systems can reduce accident rates by 10% to 20%. However, these systems are limited by lighting conditions. Thus, this paper proposes the use of millimeter-wave technology to realize a fatigue detection system, effectively addressing the high reliance of facial monitoring systems on lighting conditions. In this study, data collection was performed using the AWR1642boost, a highly integrated millimeter-wave radar development board developed by Texas Instruments. Subsequently, a Multi-experts system was developed to integrate and analyze four features: nodding, yawning, breathing, and heart rate. The system employed a Support Vector Machine (SVM) model to improve the accuracy of fatigue detection in driving scenarios. Experimental results demonstrated that the system achieved an accuracy rate of 90%, effectively discerning whether the driver was experiencing fatigue. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Highway accidents

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Scribble-Supervised RGB-T Salient Object Detection

arXiv

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arXiv 2023年

作者： Liu, Zhengyi Huang, Xiaoshen Zhang, Guanghui Fang, Xianyong Wang, Linbo Tang, Bin Computer Science and Technology Anhui University Hefei China Artificial Intelligence and Big Data Hefei University Hefei China

Salient object detection segments attractive objects in scenes. RGB and thermal modalities provide complementary information and scribble annotations alleviate large amounts of human labor. Based on the above facts, we propose a scribble-supervised RGB-T salient object detection model. By a four-step solution (expansion, prediction, aggregation, and supervision), label-sparse challenge of scribble-supervised method is solved. To expand scribble annotations, we collect the superpixels that foreground scribbles pass through in RGB and thermal images, respectively. The expanded multi-modal labels provide the coarse object boundary. To further polish the expanded labels, we propose a prediction module to alleviate the sharpness of boundary. To play the complementary roles of two modalities, we combine the two into aggregated pseudo labels. Supervised by scribble annotations and pseudo labels, our model achieves the state-of-the-art performance on the relabeled RGBT-S dataset. Furthermore, the model is applied to RGB-D and video scribble-supervised applications, achieving consistently excellent ***://***/liuzywen/RGBTScribble-ICME2023. Copyright © 2023, The Authors. All rights reserved.

关键词： Superpixels

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Efficient Feature Selection Techniques for Accurate Cancer Analysis Using Krill Heard Optimization 4

Efficient Feature Selection Techniques for Accurate Cancer A...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Singh, Shubham Narendra Mishra, Shruti Satapathy, Sandeep Kumar Cho, Sung-Bae Mallick, Pradeep Kumar Vellore Institute of Technology Computer Science and Engg Tamil Nadu Chennai India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai India Yonsei University Department of Computer Science 50 Yonsei-ro Sudaemoon-gu Seoul03722 Korea Republic of Kiit University School of Computer Engineeing Odisha India

ISBN: (纸本)9798350349856

In this research, we introduce a novel biologically-inspired optimization algorithm known as Krill Herd (KH) to address feature selection challenges in cancer analysis. Inspired by the collective behaviour of krill individuals, the KH algorithm optimizes feature subsets by minimizing individual krill distances from food sources and herd density. We model the dynamic positions of krill individuals considering factors such as interactions with peers, foraging behaviour, and random diffusion. To enhance the algorithm's performance, we incorporate adaptive genetic operators. Our method's efficiency is evaluated across various benchmark optimization problems, demonstrating its capability to outperform eight established algorithms. In parallel, we delve into the critical domain of Cancer Feature Selection and classification in computational molecular biology. Identifying cancer-causing genes is pivotal for precise treatments and drug discovery. To address these challenges, we propose an enhanced Krill Herd Optimization (KHO) technique for gene selection, combined with Random Forest (RF) for classification. RF is chosen for its high classification accuracy. This research advances the field of cancer analysis by offering efficient and accurate feature selection techniques, providing insights into cancer-related genes, and facilitating more precise treatments and drug discoveries. © 2024 IEEE.

关键词： Feature Selection

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MEEL: Multi-Modal Event Evolution Learning

arXiv

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arXiv 2024年

作者： Tao, Zhengwei Jin, Zhi Huang, Junqiang Chen, Xiancai Bai, Xiaoying Zhao, Haiyan Zhang, Yifan Tao, Chongyang MOE China School of Computer Science Peking University China Peking University China Advanced Institute of Big Data China Beihang University China

Multi-modal Event Reasoning (MMER) endeavors to endow machines with the ability to comprehend intricate event relations across diverse data modalities. MMER is fundamental and underlies a wide broad of applications. Despite extensive instruction fine-tuning, current multi-modal large language models still fall short in such ability. The disparity stems from that existing models are insufficient to capture underlying principles governing event evolution in various scenarios. In this paper, we introduce Multi-Modal Event Evolution Learning (MEEL) to enable the model to grasp the event evolution mechanism yielding advanced MMER ability. Specifically, we commence with the design of event diversification to gather seed events from a rich spectrum of scenarios. Subsequently, we employ ChatGPT to generate evolving graphs for these seed events. We propose an instruction encapsulation process that formulates the evolving graphs into instruction-tuning data, aligning the comprehension of event reasoning to humans. Finally, we observe that models trained in this way are still struggling to fully comprehend event evolution. In such a case, we propose the guiding discrimination strategy, in which models are trained to discriminate the improper evolution direction. We collect and curate a benchmark M-EV2 for MMER. Extensive experiments on M-EV2 validate the effectiveness of our approach, showcasing competitive performance in open-source multi-modal LLMs. Code and dataset are available on https://***/TZWwww/MEEL. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Adaptive Graph Attention Network with Temporal Fusion for Micro-Expressions Recognition

Adaptive Graph Attention Network with Temporal Fusion for Mi...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yiming Zhang Hao Wang Yifan Xu Xinglong Mao Tong Xu Sirui Zhao Enhong Chen School of Data Science University of Science and Technology of China Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

Automatic micro-expression recognition (MER) has essential applications in the psychological field. Graph-based models, due to their advantages in analyzing regionalized faces, have become a powerful method for MER. However, how to construct a graph from ME videos remains to be studied. To solve this problem, we design an adaptive graph attention network with temporal fusion to model the dynamic relationships between facial regions of interest (ROIs). Specifically, we first propose adaptive graph attention to establish learnable spatial graphs from ME videos. Then, we adopt an optical-flow-based feature as the suitable input for the graph network. In addition, an implicit semantic data augmentation algorithm is employed and improved as a data-driven weighted loss for better performance. Extensive experiments on SMIC-HS, CASME II and SAMM datasets have demonstrated the effectiveness of the proposed method, and it achieves to be the first graph-based model where UF1 and UAR both exceed 0.90 for 3-classes MER on CASME II. Code will be available at https://***/MEA-LAB-421/ICME2023-Recognition.

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Event-triggered Disturbance Observer-based Control for Trajectory Tracking of Ships with Actuator Saturation

Event-triggered Disturbance Observer-based Control for Traje...

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第42届中国控制会议

作者： Shuwen Feng Xin Hu Xinjiang Wei Huifeng Zhang Shaoyan Cui School of Mathematics and Statistics Science Ludong University Shandong Provincial Big Data Development and Innovation Laboratory of Public Resources

This paper presents an event-triggered anti-disturbance control scheme for the trajectory tracking of ships with timevarying ocean disturbances and actuator *** combining the disturbance observer with the auxiliary dynamic system,a robust disturbance observer-based control is designed with the backstepping technique and event-triggered *** disturbance observer provides the on-line time-varying disturbance *** auxiliary dynamic system helps to reduce the actuator saturation *** event-triggered mechanism avoids the wear and tear of actuators through implementing the control signals on triggering *** is theoretically proven that the event-triggered anti-disturbance controller achieves the trajectory tracking and all signals in the closed-loop tracking control systems are uniformly ultimately *** simulations on a 1:70 scaled model ship confirm the event-triggered anti-disturbance tracking control.

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