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Flexible Capacitive Pressure Sensors and Their Applications in Human-Machine Interactions

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Flexible Capacitive Pressure Sensors and Their Applications ...

Sensor Technology and Control (ISSTC), International Symposium on

作者： Chenyu Fang Tianli Fu Yao Shi Changzhou No.1 High School Changzhou China School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan China College of Computer and Information Science College of Software Southwest University Chongqing China

Flexible capacitive pressure sensors have shown excellent application potential in human-machine interactions, wearable electronic devices, biological and medical, and electronic skin. Compared with piezoresistive and piezoelectric pressure sensors, capacitive pressure sensors with advantages such as high flexibility, high stability, low consumption, and simple construction. Because of these advantages, flexible capacitive pressure sensors have become the key research direction of pressure sensors in recent years. This paper introduces the working principle of flexible capacitive pressure sensors and discusses numerous research on improving the performance of flexible capacitive pressure sensors. Then two methods of improving the dielectric layer structures to improve the sensitivity of the sensor and some related works are reviewed. Finally, the flexible capacitive pressure sensors' huge potential for advances in human-machine interactions areas such as health care and motion monitoring is also discussed. The paper concludes with an overview of future research directions of improving the performance of flexible capacitive pressure sensors.

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CSP-RM: Reputation Management Decision Support for Crowdsourcing Service Providers

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CSP-RM: Reputation Management Decision Support for Crowdsour...

IEEE International Conference on Web Services (ICWS)

作者： Shipeng Wang Qingzhong Li Lizhen Cui Yali Jiang Zhiqi Shen Han Yu School of Software Shandong University Jinan China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Shandong University China School of Computer Science and Engineering Nanyang Technological University Singapore

The increasing popularity of crowdsourcing has resulted in the emergence of multiple crowdsourcing service providers (CSPs), such as Mechnical Turk and Crowdflower, which compete to attract crowd workers (CWs). CWs can share their experience working for various CSPs, which forms the basis of CSP reputation score. This information can be used for trust building and facilitating future CWs’ decisions on which CSP to work for. Existing reputation management research in crowdsourcing has mainly focused on controlling task quality and improving revenue from the perspective of CSPs. Little attention has been paid to helping CSPs manage their reputation to attract and retain CWs. In this paper, we propose the Crowdsourcing Service Provider Reputation Management (CSP-RM) framework to bridge this important gap. Based on the current reputation of CSPs, it dynamically balances the trade-off between the reputation maintenance cost and the long-term profit for a given CSP. It performs dynamic commission allocation for a CSP based on Lyapunov optimization to guide the recruitment of CWs, while considering the revenue and the changes in the number of CWs. Extensive experiments based on highly competitive crowdsourcing market demonstrate that CSP-RM makes the most advantageous cost-benefit trade-off compared to existing approaches, outperforming the best baseline by 23.83%, 39.21% and 3.36% in terms of average cumulative revenue, average number of CWs and public reputation, respectively. To the best of our knowledge, it is the first decision support framework for enabling CSPs to recruit more CWs in a highly competitive market, while maintaining their reputation and ensuring long-term benefit.

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Fusing Dynamics Equation: A Social Opinions Prediction Algorithm with LLM-based Agents

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arXiv 2024年

作者： Yao, Junchi Zhang, Hongjie Ou, Jie Zuo, Dingyi Yang, Zheng Dong, Zhicheng College of Computer Science Sichuan Normal University Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou China College of Information Science Technology Tibet University Lhasa China

In the context where social media is increasingly becoming a significant platform for social movements and the formation of public opinion, accurately simulating and predicting the dynamics of user opinions is of great importance for understanding social phenomena, policy making, and guiding public opinion. However, existing simulation methods face challenges in capturing the complexity and dynamics of user behavior. Addressing this issue, this paper proposes an innovative simulation method for the dynamics of social media user opinions, the FDE-LLM algorithm, which incorporates opinion dynamics and epidemic model. This effectively constrains the actions and opinion evolution process of large language models (LLM), making them more aligned with the real cyber world. In particular, the FDE-LLM categorizes users into opinion leaders and followers. Opinion leaders are based on LLM role-playing and are constrained by the CA model, while opinion followers are integrated into a dynamic system that combines the CA model with the SIR model. This innovative design significantly improves the accuracy and efficiency of the simulation. Experiments were conducted on four real Weibo datasets and validated using the open-source model ChatGLM. The results show that, compared to traditional agent-based modeling (ABM) opinion dynamics algorithms and LLM-based opinion diffusion algorithms, our FDE-LLM algorithm demonstrates higher accuracy and interpretability. Copyright © 2024, The Authors. All rights reserved.

关键词： Chatbots

A Semantic Trajectory Mining System Based on Deep Neural Network

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A Semantic Trajectory Mining System Based on Deep Neural Net...

第33届中国控制与决策会议

作者： Xinyuan Zhang Jiying Peng Xinqi Zhang School of Computer Science and Software Engineering University of Science and Technology Liaoning

At the beginning of 2020,epidemic of COrona VIrus Disease 19(COVID-19) broke *** the epidemic prevention and control,artificial intelligence,big data and other technologies have become powerful weapons against the epidemic,and have been widely used in the fields of epidemic tracing,confirming virus transmission path,resource allocation and so *** this study,BiLSTM-CRF model,Bootstrap and Tornado frameworks are used to implement a neural network-based semantic trajectory mining system for the COVID-19 *** the basis of collecting the data published by the health committees of various provinces and cities,the semantic trajectories of the patients are extracted to ensure the accuracy of the data and then establish mapping relationship between the real space and the text description of the trajectories of the patients,while taking the time and space factors into account and excavating the dynamic changes of the patients.

关键词： Bidirectional neural network Named entity recognition BiLSTM-CRF API

An efficient and secure arbitrary N-party quantum key agreement protocol using Bell states

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arXiv 2023年

作者： Liu, Wen-Jie Xu, Yong Yang, Ching-Nung Gao, Pei-Pei Yu, Wen-Bin Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China Department of Computer Science and Information Engineering National Dong Hwa University Hualien974 Taiwan

Two quantum key agreement protocols using Bell states and Bell measurement were recently proposed by Shukla et al. [Quantum Inf. Process. 13, 2391(2014)]. However, Zhu et al. pointed out that there are some security flaws and proposed an improved version[Quantum Inf. Process. 14, 4245(2015)]. In this study, we will show Zhu et al.’s improvement still exists some security problems, and its efficiency is not high enough. For solving these problems, we utilize four Pauli operations {I, Z, X, Y } to encode two bits instead of the original two operations {I, X} to encode one bit, and then propose an efficient and secure arbitrary N-party quantum key agreement protocol. In the protocol, the channel checking with decoy single photons is introduced to avoid the eavesdropper’s flip attack, and a post-measurement mechanism is used to prevent against the collusion attack. The security analysis shows the present protocol can guarantee the correctness, security, privacy and fairness of quantum key agreement. © 2023, CC BY.

关键词： Photons

Robust Heterogeneous Federated Learning under Data Corruption

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Robust Heterogeneous Federated Learning under Data Corruptio...

International Conference on computer Vision (ICCV)

作者： Xiuwen Fang Mang Ye Xiyuan Yang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Hubei Luojia Laboratory Wuhan University Wuhan China

Model heterogeneous federated learning is a realistic and challenging problem. However, due to the limitations of data collection, storage, and transmission conditions, as well as the existence of free-rider participants, the clients may suffer from data corruption. This paper starts the first attempt to investigate the problem of data corruption in the model heterogeneous federated learning framework. We design a novel method named Augmented Heterogeneous Federated Learning (AugHFL), which consists of two stages: 1) In the local update stage, a corruption-robust data augmentation strategy is adopted to minimize the adverse effects of local corruption while enabling the models to learn rich local knowledge. 2) In the collaborative update stage, we design a robust re-weighted communication approach, which implements communication between heterogeneous models while mitigating corrupted knowledge transfer from others. Extensive experiments demonstrate the effectiveness of our method in coping with various corruption patterns in the model heterogeneous federated learning setting.

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Dynamic personalized federated learning with adaptive differential privacy 23

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Dynamic personalized federated learning with adaptive differ...

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xiyuan Yang Wenke Huang Mang Ye National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Hubei Luojia Laboratory Wuhan University Wuhan China

Personalized federated learning with differential privacy has been considered a feasible solution to address non-IID distribution of data and privacy leakage risks. However, current personalized federated learning methods suffer from inflexible personalization and convergence difficulties due to two main factors: 1) Firstly, we observe that the prevailing personalization methods mainly achieve this by personalizing a fixed portion of the model, which lacks flexibility. 2) Moreover, we further demonstrate that the default gradient calculation is sensitive to the widely-used clipping operations in differential privacy, resulting in difficulties in convergence. Considering that Fisher information values can serve as an effective measure for estimating the information content of parameters by reflecting the model sensitivity to parameters, we aim to leverage this property to address the aforementioned challenges. In this paper, we propose a novel federated learning method with Dynamic Fisher Personalization and Adaptive Constraint (FedDPA) to handle these challenges. Firstly, by using layer-wise Fisher information to measure the information content of local parameters, we retain local parameters with high Fisher values during the personalization process, which are considered informative, simultaneously prevent these parameters from noise perturbation. Secondly, we introduce an adaptive approach by applying differential constraint strategies to personalized parameters and shared parameters identified in the previous for better convergence. Our method boosts performance through flexible personalization while mitigating the slow convergence caused by clipping operations. Experimental results on CIFAR-10, FEMNIST and SVHN dataset demonstrate the effectiveness of our approach in achieving better performance and robustness against clipping, under personalized federated learning with differential privacy.

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CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario

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arXiv 2023年

作者： Guo, Jingcai Guo, Song Ma, Shiheng Sun, Yuxia Xu, Yuanyuan The Department of Computing The Hong Kong Polytechnic University Hong Kong The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200030 China The Department of Computer Science Jinan University Guangzhou510632 China The School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

We study the challenging task of malware recognition on both known and novel unknown malware families, called malware open-set recognition (MOSR). Previous works usually assume the malware families are known to the classifier in a close-set scenario, i.e., testing families are the subset or at most identical to training families. However, novel unknown malware families frequently emerge in real-world applications, and as such, require to recognize malware instances in an open-set scenario, i.e., some unknown families are also included in the test-set, which has been rarely and non-thoroughly investigated in the cyber-security domain. One practical solution for MOSR may consider jointly classifying known and detecting unknown malware families by a single classifier (e.g., neural network) from the variance of the predicted probability distribution on known families. However, conventional well-trained classifiers usually tend to obtain overly high recognition probabilities in the outputs, especially when the instance feature distributions are similar to each other, e.g., unknown v.s. known malware families, and thus dramatically degrades the recognition on novel unknown malware families. To address the problem and construct an applicable MOSR system, we propose a novel model that can conservatively synthesize malware instances to mimic unknown malware families and support a more robust training of the classifier. More specifically, we build upon the generative adversarial networks (GANs) to explore and obtain marginal malware instances that are close to known families while falling into mimical unknown ones to guide the classifier to lower and flatten the recognition probabilities of unknown families and relatively raise that of known ones to rectify the performance of classification and detection. A cooperative training scheme involving the classification, synthesizing and rectification are further constructed to facilitate the training and jointly improve the model p

关键词： Malware

Dual Windows Are Significant: Learning from Mediastinal Window and Focusing on Lung Window

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arXiv 2022年

作者： Wang, Qiuli Tan, Xin Liu, Chen School of Big Data and Software Engineering Chongqing University China Department of Computer Science and Engineering Shanghai Jiao Tong University China China

Since the pandemic of COVID-19, several deep learning methods were proposed to analyze the chest Computed Tomography (CT) for diagnosis. In the current situation, the disease course classification is significant for medical personnel to decide the treatment. Most previous deep-learning-based methods extract features observed from the lung window. However, it has been proved that some appearances related to diagnosis can be observed better from the mediastinal window rather than the lung window, e.g., the pulmonary consolidation happens more in severe symptoms. In this paper, we propose a novel Dual Window RCNN Network (DWRNet), which mainly learns the distinctive features from the successive mediastinal window. Regarding the features extracted from the lung window, we introduce the Lung Window Attention Block (LWA Block) to pay additional attention to them for enhancing the mediastinal-window features. Moreover, instead of picking up specific slices from the whole CT slices, we use a Recurrent CNN and analyze successive slices as videos. Experimental results show that the fused and representative features improve the predictions of disease course by reaching the accuracy of 90.57%, against the baseline with an accuracy of 84.86%. Ablation studies demonstrate that combined dual window features are more efficient than lung-window features alone, while paying attention to lung-window features can improve the model’s stability. © 2022, CC BY.

关键词： COVID-19