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Organic-inorganic hybrid piezotronic bipolar junction transistor for pressure sensing

Microsystems & Nanoengineering 2024年第3期10卷 229-244页

作者： Emad Iranmanesh Zihao Liang Weiwei Li Congwei Liao Shunyu Jin Chuan Liu Kai Wang Shengdong Zhang Charalampos Doumanidis Gehan A.J.Amaratunga Hang Zhou Guangdong Provincial Key Laboratory of In-Memory Computing Chips School of Electronic and Computer EngineeringPeking University Shenzhen Graduate SchoolShenzhen 518055P.R.China School of Mechanical Engineering Guangdong Technion-Israel Institute of TechnologyShantou 515063P.R.China State Key Laboratory of Microelectronics Device and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029P.R.China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 23000PR China School of Electronics and Information Technology Sun Yat-sen UniversityNo.132 East Waihuan RoadGuangzhou 510006P.R.China Department of Mechanical Aerospaceand Biomedical EngineeringUniversity of South AlabamaShelby Hall3128MobileAL 36688USA Electrical Engineering Division Department of EngineeringUniversity of Cambridge9 JJ Thomson AvenueCambridge CB30FAUnited Kingdom Zhejiang University International CampusHainingChina

With the rapid development of the Internet of Things(IoTs),wearable sensors are playing an increasingly important role in daily monitoring of personal health and *** signal-to-noise-ratio has become the most critical performance factor to *** enhance it,on the one hand,good sensing materials/devices have been employed;on the other hand,signal amplification and noise reduction circuits have been ***,most of these devices and circuits work in an active sampling mode,requiring frequent data acquisition and hence,entailing high-power *** this scenario,a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great ***,we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor(PBJT)that acts as an autonomous and highly sensitive,current-and/or voltage-mediated pressure *** PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collectorbase *** force exertion on the emitter side,as a result of the piezoelectric effect,the emitter-base diode is forward biased while the collector-base diode is reverse *** to the inherent BJT amplification effect,the PBJT achieves record-high sensitivities of 139.7 kPa^(-1)(current-based)and 88.66 kPa^(-1)(voltage-based)in sensing *** PBJT also has a fast response time of<110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger ***,the PBJT advances the state of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous,making it promising for pressure sensing and other IoT applications.

关键词： bipolar transistor IoT

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A Topic-Aware Graph-Based Neural Network for User Interest Summarization and Item Recommendation in Social Media 28th

A Topic-Aware Graph-Based Neural Network for User Interest ...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Chen, Junyang Fan, Ge Gong, Zhiguo Li, Xueliang Leung, Victor C. M. Wang, Mengzhu Yang, Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China Tencent Inc. Shenzhen China State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science University of Macau China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9783031306716

User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a topic-aware graph-based neural interest summarization method (UGraphNet), enhancing user semantic mining by unearthing potential user relations and jointly learning the latent topic representations of posts that facilitates user interest learning. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations are conductive to the user automatic language understanding. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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Noise-Augmented Contrastive Learning for Sequential Recommendation 1

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24th International Conference on Web Information Systems engineering, WISE 2023

作者： He, Kun Meng, Shunmei Li, Qianmu Liu, Xiao Beheshti, Amin Chi, Xiaoxiao Zhang, Xuyun School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computing Macquarie University Sydney Australia

ISBN: (数字)9789819972548

ISBN: (纸本)9789819972531

Recently, contrastive learning has been widely used in the field of sequential recommendation to solve the data sparsity problem. CL4Rec augments data through simple random crop, mask, and reorder, while DuoRec proposes a model-level data augmentation method. However, these methods do not take into account the issue of noisy data in sequential recommendation, such as false clicks during browsing. The noise may lead to poor representations of learned sequences and negatively affect the augmented data. Current sequential recommendation methods tend to learn the user’s intention from their original sequences, but these methods have certain limitations as the user’s intention for the next interaction may change. Based on the above observations, we propose Noise-augmented Contrastive Learning for Sequential Recommendation (NCL4Rec). Our NCL4Rec proposes sequential noise probability-guided data augmentation. We introduce supervised noise recognition during training instead of obtaining it from original sequences. Moreover, we design positive and negative augmentations of the sequence and design unique noise loss function to train them. Through experiments, it is verified that our NCL4Rec consistently outperforms the current state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Contrastive Learning Sequential Recommendation

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Expert with Outlier Exposure for Continual Learning of New Diseases

Expert with Outlier Exposure for Continual Learning of New D...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Xu, Zhengjing Chen, Kanghao Zheng, Wei-Shi Tan, Zhijun Yang, Xiaobo Wang, Ruixuan Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE China State Key Laboratory of Dampness Syndrome of Chinese Medicine Guangzhou China Peng Cheng Laboratory Department of Network Intelligence Shenzhen China

ISBN: (纸本)9781665468190

Current intelligent diagnosis systems struggle to continually learn to diagnose more and more diseases due to catastrophic forgetting of old knowledge when learning new knowledge. Although storing small old data for subsequent continual learning can effectively help alleviate the forgetting issue, the heavy data imbalance between old classes and to-be-learned new classes in classifier training often causes biased prediction towards the new classes just learned by the updated classifier. In this study, an outlier detection technique is novelly applied to train an additional expert classifier for new classes to help alleviate the class imbalance issue and discriminate the learned new classes from old classes during inference (instead of the training phase). Specially, the stored small data of old classes are considered as outliers during training the expert classifier, such that the output probability distributions from the expert classifier are expected to be obviously different between test data of the old classes and those of the new classes. Such difference between old classes and new classes can be used to fine-tune the original output from the updated classifier which is responsible for prediction of all learned (old and new) classes. During inference, a novel ensemble strategy is proposed to combine the predictions from the updated classifier, the expert classifier, and the previously learned old classifier. The proposed learning and inference framework can be easily combined with existing continual learning strategies. Empirical evaluations on three medical image datasets and one natural image dataset show that the proposed framework can effectively improve continual learning performance. © 2022 IEEE.

关键词： Forecasting

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Adversarial Example Attacks Against Intelligent Malware Detection: A Survey

Adversarial Example Attacks Against Intelligent Malware Dete...

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International Conference on Applied Machine Learning (ICAML)

作者： Xuyan Qi Yonghe Tang Huanwei Wang Tieming Liu Jing Jing State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou China

With the advancement of information technology and the increasing prevalence of the internet, the number of malware has experienced an exponential, and the security threat to users' property and privacy is becoming more serious. With the great success of artificial intelligence especially deep learning technology in fields such as computer vision and speech processing, deep learning are gradually introduced into the field of malware detection, to process large amounts of malware more efficiently. However, in recent years, researchers have discovered that the deep learning model itself also has security risks. While malware detection based on deep learning brings convenience and technical progress, it also introduces some new threats, such as adversarial examples. This paper investigates adversarial example attacks against deep learning-based malware detectors. First, We reviewed research on malware detection techniques. Then we analyze the works related to adversarial example attacks against intelligent malware detectors. Finally, we discuss the challenges and prospects in future research.

关键词： Deep learning Computer vision Technological innovation Privacy Perturbation methods Detectors Predictive models

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Traffic Modeling and Rescheduling for High-speed Train Based on Block Sections 4

Traffic Modeling and Rescheduling for High-speed Train Based...

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4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

作者： Yue, Peng Jin, Yaochu Dai, Xuewu Cui, Dongliang Shi, Qi Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Bielefeld Nature Inspired Computing and Engineering Group BielefeldD-33619 Germany

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Affected by unexpected events, the nominal operation of high-speed trains will become invalid. To maintain the efficiency of trains, train dispatchers need to reschedule the train timetable, which is a challenging task. On the one hand, the dispatchers need to take into account complex conflicts between trains on the track;on the other hand, the rescheduled timetable should be efficient to reduce operating costs. To address the above issues, this study proposes a traffic modeling method for high-speed trains based on a block section to describe in detail the operation conflicts between trains. A train rescheduling approach combining reinforcement learning and model predictive control is proposed to accomplish train rescheduling efficiently. The experiments show the effectiveness of the proposed method. © 2022 IEEE.

关键词： Reinforcement learning

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A novel hyperchaotic multistable heterogeneous neural network with hidden attractors and its application in image encryption

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Chinese Journal of Physics 2025年 96卷 851-874页

作者： Zhi Huang Zhen Li Qiao Wang Weijie Tan Xianming Wu College of Big Data and Information Engineering Guizhou University Guiyang 550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang 550025 China Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing College of Mathematics and Big Data Guizhou Education University Guiyang 550018 China State Key Laboratory of Public Big Data Key Laboratory of Advanced Manufacturing Technology of Ministry of Education Guizhou University Guiyang 550025 China School of Mechanical and Electrical Engineering Guizhou Normal University Guiyang 550025 China

By simulating the unique properties of different types of neurons and their interconnecting patterns, heterogeneous neural networks can more accurately reflect the structural and functional characteristics of biological neural systems. Therefore, a novel hyperbolic non-volatile locally active memristor is proposed, enabling the construction of a heterogeneous Hindmarsh–Rose neuron memristive synapse-coupled Hopfield neural network (HR-M-HNN) via synaptic characteristic emulation. Dynamic analysis reveals that the HR-M-HNN exhibits hidden attractor characteristics due to the absence of equilibrium points and can generate complex dynamics behaviors, including hyperchaos, chaos, periodic and quasi-periodic, and multistability. Further research reveals a strong correlation between the chaotic state of HR-M-HNN and the locally active control parameter of the memristor. To verify the accuracy of the dynamic analysis, the corresponding analog circuit is designed. Furthermore, based on the HR-M-HNN hyperchaotic system, a novel image encryption scheme is proposed, which utilizes the position index of the Latin square to perform synchronous permutation and diffusion on the plain image, reducing redundant operations and effectively enhancing the encryption speed. Security analyses demonstrate that the proposed image encryption scheme offers excellent performance and robustness.

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MDFA: A Malware Detection Framework Based on Attention Mechanism with Bi-LSTM

MDFA: A Malware Detection Framework Based on Attention Mecha...

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International Conference on Applied Machine Learning (ICAML)

作者： Xuyan Qi Huanwei Wang Yonghe Tang Wei Liu Jiangtao Xie State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou China

As the existing malware intelligent detection methods have shortcomings and low accuracy of manual feature extraction and feature processing, a Malware Detection Framework with Attention mechanism based on Bi-directional Long Short-Term Memory (MDFA) was proposed. To training this method, We have collected 15, 893 malware and 10, 959 benign program samples and constructed a dataset. Then, the sample data was labeled and sanitized by determining the results based on VirusTotal analysis. Finally, the Bi-directional Long Short-Term Memory model injected into the attention mechanism was built and trained to complete the malware detection. Experimental results show that the accuracy of MDFA was improved by at least 2. 3%, and the recall rate was improved significantly compared with the existing methods, verifying the effectiveness of MDFA.

关键词： Training Computer viruses Bidirectional control Manuals Machine learning Feature extraction Transformers

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A Neighbor Immunization Strategy to Block Virus Spreading on Networks 2

A Neighbor Immunization Strategy to Block Virus Spreading on...

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2nd Information Communication Technologies Conference, ICTC 2021

作者： Yaohui, Hao Yingze, Liu Baole, Zhao Qingxian, Wang Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Engineering University Zhengzhou China

ISBN: (纸本)9780738142876

In order to achieve better effect on blocking virus propagation, a neighbor immunization strategy was proposed and verified in this paper. On email-EU real networks and on scale-free model network, the efficiencies of the neighbor immunization strategy and the random immunization strategy in SIR and SEIR virus spreading models were compared and analyzed by simulation experiment. The result indicates that the neighbor immunization strategy is a feasible and effective. This phenomenon shows that neighbor immunization strategy can improve to the future virus and effectively prevent the virus spreading. © 2021 IEEE.

关键词： Immunization

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Pruned-F1DCN: A lightweight network model for traffic classification 22

Pruned-F1DCN: A lightweight network model for traffic classi...

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Proceedings of the 2022 International Conference on Cyber Security

作者： Ruo nan Wang Jin long Fei Rong kai Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou China China

ISBN: (纸本)9798400700132

With the continuous development of deep learning, deep neural networks are gradually applied to traffic classification problems. However, the large network structure and parameter number of deep neural networks hinder the application on edge computing devices. Reducing network scale helps relieve computational pressure, this paper proposes a lightweight traffic classification model to provide reliable accuracy and reduce the consumption of computing resources. In this work, we design an F1DCN network, which takes full advantage of the convolution layer parameters and the convolution kernel field of view. The lightweight approach effectively improves the classification performance and saves massive parameters. The model pruning method is applied to find the optimal structure of the network. Experiments on two public datasets show that the proposed model reduce more than 80 % parameters and 45 % FLOPS compared with traditional traffic classification methods, and maintaining more than 95 % classification accuracy.

关键词： Convolutional neural network Lightweight Model pruning Traffic classification

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