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IEEE Conference on Global Communications (GLOBECOM)

作者： Jingwei Liu Zihan Zhou Rong Sun Xiaojiang Du Mohsen Guizani Shaanxi Key Laboratory of Blockchain and Secure Computing Xidian University Xi'an China State Key Laboratory of ISN Xidian University Xi'an China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken USA Department of Computer Science and Engineering Qatar University Doha Qatar

With the advancement of cloud technology, the storage and computing overhead in large-scale biometric authentication is mitigated by outsourcing data to the cloud. Since biometric features serve as a unique identifier bound to each individual, transmitting them directly to the cloud may bring about serious privacy disclosure risks. To guarantee users' biometric features, there are many solutions have been proposed. However, most of them neglect to protect identity security. In light of the challenges, this paper proposes a strong privacy-preserving fingerprint authentication via clustering. Besides safeguarding fingerprint features, the scheme also blurs the identities of users to enable anonymity of identity. Meanwhile, the authentication efficiency of the proposed scheme is improved by vector processing of fingerprints and fast retrieval of clustered identities. Furthermore, a dual-server matching architecture effectively reduces the communication overhead of the service provider. The security analysis and experimental results indicate that the proposed scheme provides strong privacy preservation while maintaining high efficiency.

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Enabling All-to-Circular Polarization Up-Conversion by Nonlinear Chiral Metasurfaces with Rotational Symmetry

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Physical Review Letters 2025年第2期134卷 023804-023804页

作者： Dmitrii Gromyko Jun Siang Loh Jiangang Feng Cheng-Wei Qiu Lin Wu Science Mathematics and Technology (SMT) Singapore University of Technology and Design (SUTD) 8 Somapah Road 487372 Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 117583 Singapore Singapore Institute of High Performance Computing ASTAR (Agency for Science Technology and Research) 1 Fusionopolis Way No. 16-16 Connexis 138632 Singapore Singapore

We introduce a stacking strategy to design nonlinear chiral metasurfaces with high rotational symmetry, enabling degenerate quasi-bound-in-the-continuum resonances of absolute chirality. This symmetry allows for converting a circularly polarized pump into a circularly polarized nonlinear signal. Consequently, our rotation-symmetric bilayered metasurface design, tailored to respond solely to one specific circular polarization, can up-convert a linear or arbitrary polarized pump into circularly polarized nonlinear signals. The intensity ratios of these signals scale as the fourth (second) power of the chiral resonance amplitude for the second (third) harmonic.

关键词： Circular polarization

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Audio-Visual Target Speaker Extraction with Reverse Selective Auditory Attention

arXiv

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arXiv 2024年

作者： Tao, Ruijie Qian, Xinyuan Jiang, Yidi Li, Junjie Wang, Jiadong Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computer Science and Technology University of Science and Technology Beijing Beijing100083 China Chinese University of Hong Kong Shenzhen518172 China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore University of Bremen 28359 Germany

Audio-visual target speaker extraction (AV-TSE) aims to extract the specific person’s speech from the audio mixture given auxiliary visual cues. Previous methods usually search for the target voice through speech-lip synchronization. However, this strategy mainly focuses on the existence of target speech, while ignoring the variations of the noise characteristics, i.e., interference speaker and the background noise. That may result in extracting noisy signals from the incorrect sound source in challenging acoustic situations. To this end, we propose a novel selective auditory attention mechanism, which can suppress interference speakers and non-speech signals to avoid incorrect speaker extraction. By estimating and utilizing the undesired noisy signal through this mechanism, we design an AV-TSE framework named Subtraction-and-ExtrAction network (SEANet) to suppress the noisy signals. We conduct abundant experiments by re-implementing three popular AV-TSE methods as the baselines and involving nine metrics for evaluation. The experimental results show that our proposed SEANet achieves state-of-the-art results and performs well for all five datasets. The code can be found in: https://***/TaoRuijie/*** © 2024, CC BY.

关键词： Background noise

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Efficient Module-Lattice-based Certificateless Online/Offline Signcryption Scheme for Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Bai, Ye He, Debiao Yang, Zhichao Luo, Min Peng, Cong Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Naval University of Engineering Department of Information Security Wuhan430000 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China

The Internet of Medical Things (IoMT) has achieved remote diagnosis and real-time health monitoring through intelligent sensor devices and Internet of Things (IoT) technology, providing great convenience for analyzing medical conditions between doctors and patients. However, sensitive information such as patient medical data may face security challenges such as data leakage and abuse during transmission in the IoMT. To ensure the confidentiality and unforgeability of medical data transmission, scholars have proposed many cryptographic schemes. With the development of quantum computers, schemes based on traditional cryptographic primitives have become insecure. Existing cryptographic schemes for IoMT cannot simultaneously meet the security requirements of high communication performance, low computational overhead, and resistance to quantum attacks. Therefore, we propose an efficient module-lattice-based certificateless online/offline signcryption (MLCLOOSC) scheme resistant to quantum attacks while meeting the confidentiality and unforgeability requirements under Type I and Type II attacks. Compared with five recent CLOOSC schemes, theoretical analysis, and experimental test results show that the proposed scheme outperforms the other five schemes regarding computational and communication costs and security. Therefore, our scheme is more suitable for application in IoMT scenarios. © 2014 IEEE.

关键词： Quantum computers

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Optimizing Resource Allocation in Mobile Edge computing for IIoT through Reinforcement Learning and Multiobjective Particle Swarm Optimization

Optimizing Resource Allocation in Mobile Edge Computing for ...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sudhakar AVV Mohan M K. Maharajan Mohammed I. Habelalmateen N. Naga Saranya Department of Electrical and Electronics Engineering SR University Telangana India Department of ISE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Bangalore Krishnankoil India Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johor Malaysia Department of Computer Technical Engineering College of Technical Engineering the Islamic University Najaf Iraq Department of Computer Applications Saveetha College of Liberal Arts and Science SIMATS Chennai India

Mobile networks are flexible enough to support a range of resource allocation models and service-based choices in computing domains, which affects both virtual reality and the Industrial Internet of Things (IIOT). Virtual resource management is made easier by the Mobile Edge computing (MEC) paradigm, which also makes edge connectivity between data terminals and execution in the core network under heavy load possible. Meeting customer requirements efficiently is achieved through thoughtful planning, aided by cognitive agents. User data, incorporating behavioral patterns, is amalgamated to cater to IIOT service types. Neural caching for memory during task execution is made easier by the use of swarm intelligence and reinforcement learning techniques. Prediction strategies optimize business operations and caching to reduce execution delays. Predictive models evaluate performance by taking into account variables like nearby user equipment and mobile edge computing resources. This method's efficacy is demonstrated by a cognitive agent model that manages resource distribution and creates networks of communication to improve service quality. For accurate resource distribution among end users, reinforcement learning techniques—more especially, Multi-Objective Particle Swarm Optimization (MOPSO) algorithms—are used. This includes building cost mapping tables and optimizing allocation in MEC. The proposed method outperforms existing algorithms like Task-Offloading and Resource Allocation Strategy and achieves a better throughput value of 785.32.

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CNN-Based Emotional Stress Classification using Smart Learning Dataset 15

CNN-Based Emotional Stress Classification using Smart Learni...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Andreas, Andreou Mavromoustakis, Constandinos X. Song, Houbing Batalla, Jordi Mongay Department of Computer Science University of Nicosia University of Nicosia Research Foundation Nicosia Cyprus Security and Optimization for Networked Globe Laboratory Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach FL32114-3900 United States Warsaw University of Technology National Institute of Telecommunications Warsaw Poland

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Smart learning analytics aims to support researchers investigating mental health by improving the interpretation of the datasets acquired from physiological biomarkers. The key enabler for emotional stress classification are Machine Learning (ML) methods in conjunction with Online Transfer Learning (OTL). The knowledge of high-level characteristics at the top layers is obtained through an optimized Convolutional Neural Network (CNN)-based on emotional stress datasets. Nevertheless, the lack of performance in a real-time environment and the temporal patterns of data acquisition complications and their interpretation motivated us to contribute by tackling these concerns. Therefore, we propose an innovative procedure based on the aforementioned orientation through our research work. Considering mining data streams with concept drifts, we enable the ensemble classifiers. For evaluation, we compare the proposed classification, the LIBrary for Large LINEAR classification (LIBLINEAR) and the Deep Belief Network with Transfer Learning (DBNTL) model. Furthermore, we utilized a multimodal dataset of physical and biological characteristics obtained by fifteen individuals during a lab study. Finally, our framework based on the extracted results has presented more accuracy in classifying an individual's sense of stress. Hence, the proposed method achieves higher efficiency than the state-of-the-art models. © 2022 IEEE.

关键词： Classification (of information)

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks

Agricultural Water Conservation through AI-Enhanced Solar Io...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： S. P. Maniraj Monisha J R. Amutha R. Kavitha P. J. Shriidhar C. Srinivasan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha Universityi Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future.

关键词： Irrigation Accuracy Food security Artificial neural networks Water conservation Real-time systems Internet of Things Sustainable development Water resources Intelligent sensors

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Improving Quantum Circuit Synthesis with Machine Learning

arXiv

引用

arXiv 2023年

作者： Weiden, Mathias Younis, Ed Kalloor, Justin Kubiatowicz, John Iancu, Costin Department of Electrical Engineering and Computer Science University of California Berkeley United States Computational Research Division Lawrence Berkeley National Laboratory United States

In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations of quantum algorithms that minimize the number of expensive and error prone multi-qubit gates is vital to ensure computations produce meaningful outputs. Unitary synthesis, the process of finding a quantum circuit that implements some target unitary matrix, is able to solve this problem optimally in many cases. However, current bottom-up unitary synthesis algorithms are limited by their exponentially growing run times. We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms. This paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries. QSeed maintains low gate counts and offers a speedup of 3.7× in synthesis time over the state of the art for a 64 qubit modular exponentiation circuit, a core component in Shor's factoring algorithm. QSeed's performance improvements also generalize to families of circuits not seen during the training process. © 2023, CC BY.

关键词： Machine learning

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Dielectric strength characterization in BSZT ceramic capacitors

Dielectric strength characterization in BSZT ceramic capacit...

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2023 IEEE Pulsed Power Conference, PPC 2023

作者： Neto, Lauro P. Silva Aredes, Rangel G. Rossi, José O. Antonelli, Eduardo Lima, Gustavo N. Schamiloglu, Edl. Barroso, Joaquim J. Federal University of São Paulo Dept.Science and Technology São José dos Campos Brazil Associated Plasma Laboratory National Institute for Space Research São José dos Campos Brazil University of New Mexico Electrical and Computer Engineering Department Albuquerque United States

ISBN: (纸本)9798350332339

Ceramic capacitors are used in electronic devices that need a specific behavior or some project requirements such as temperature range, maximum voltage, dielectric strength, low loss, and others. In this work we synthesized ceramic capacitors using BSZT (Barium, Strontium, Zirconium, and Titanium) to be applied in Nonlinear Transmission Lines - NLTLs to produce high-power oscillations. In addition to sintering, we performed a dielectric strength characterization using ten samples and the Weibull distribution to determine the average dielectric strength and standard deviation. The results showed a ceramic material with dielectric strength of the order of 7.0 kV/mm and a standard deviation of +/- 1.2 kV/mm. © 2023 IEEE.

关键词： Weibull distribution

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