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Stress Severity Detection in College Students Using Emotional Pulse Signals and Deep Learning

IEEE Transactions on Affective computing 2025年

作者： Li, Mi Li, Junzhe Chen, Yanbo Hu, Bin School of Information Science and Technology Beijing University of Technology Beijing International Collaboration Base on Brain Informatics and Wisdom Services Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education China Engineering Research Center of Digital Community Ministry of Education Beijing China Beijing University of Technology School of Information Science and Technology Beijing China Beijing Institute of Technology Institute of Engineering Medicine Beijing China Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou China

College students face increasing stress from difficulties with studies, employment, and social interactions, which, if left unaddressed, may lead to depression and physical illnesses. Currently, the detection of stress severity relies on self-assessment scales, while machine learning or deep learning-based approaches primarily focus on classification. This study proposes an approach using pulse signals containing emotional cues and deep learning to automatically detect the severity of stress in college students. Firstly, pulse signals of 177 college students were collected using photoplethysmography (PPG) during they watched five virtual reality (VR) emotional scenes, including calm, sadness, happiness, fear, and tension. Pulse rate variability (PRV) and discrete PPG (dPPG) were extracted from these signals as input for detecting stress severity. Then, the proposed stress detection framework, 1DCNN-BiLSTM + Cross-Attention + XGBoost, was employed to detect stress severity, incorporating an emotional Cross-Attention mechanism. The impact of induced emotions on stress severity detection performance was examined. The results indicated that stress severity detection in emotional scenes outperformed in calm. Furthermore, the detection performance that integrates multiple emotions surpassed single emotions. The fusion of PRV and dPPG signals yielded the best detection performance. This study provides an end-to-end automated approach for detecting stress severity in college students. © 2010-2012 IEEE.

关键词： Photoplethysmography

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D3Net: An Effective Method Toward Degradation Deepfake Detection Via Feature Restoration

SSRN

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SSRN 2022年

作者： Ke, Jianpeng Wang, Lina Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China

The abuse of deepfakes, a rising face swap technique, causes severe concerns about the authenticity of visual content and the dissemination of misinformation. To alleviate the threats imposed by deepfakes, a vast body of data-centric detectors have been deployed. However, the performance of these methods can be easily defected by degradations on deepfakes. To improve the performance of Degradation Deepfake Detection, we creatively explore the recovery method in the feature space to preserve the artifacts for detection instead of directly in the image domain. In this paper, we propose a method, namely D3Net, against degradation deepfakes by modeling the degraded images and transforming the extracted features to a high-quality level. To be specific, the whole model consists of three key components: an image feature extractor to capture image features, a feature transforming module to map the degradation features into a higher quality, and a discriminator to determine whether the feature map is of high quality enough. Extensive experiments on multiple video datasets show that our proposed model performs comparably or even better than state-of-the-art counterparts. Moreover, D3Net outperforms by a small margin when detecting deepfakes in the wild. © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Predicting Tertiary Lymphoid Structures in Head and Neck Contrast-Enhanced CT Images using a 3D CNN

Predicting Tertiary Lymphoid Structures in Head and Neck Con...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ting Long Tingguan Sun Juan Liu Hua Chen Zhijun Sun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education & School of Cyber Science and Engineering Wuhan University Wuhan China State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration & Key Laboratory of Oral Biomedicine Ministry of Education Hubei Key Laboratory of Stomatology & School & Hospital of Stomatology Wuhan University & Frontier Science Center for Immunology and Metabolism Wuhan University Wuhan China Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China

Squamous cell carcinoma of the head and neck is currently the eighth most common cancer worldwide. In recent years, medical researchers have proposed a promising immune checkpoint blockade treatment to treat the disease, however, only a small number of patients with advanced head and neck squamous cell carcinoma (about 13%) can benefit from immune checkpoint blockade therapy. Therefore, assessing the patient’s response to immune checkpoint blockade before treatment can help develop a treatment strategy. Tertiary lymphoid structures (TLSs) are complex structures that often appear around tumors, and their presence suggests strong immunoactivity and sensitivity to immunotherapy. Therefore, the presence or absence of TLSs is a valid indicator of the patient’s response to immune checkpoint blockade before surgery. At present, the detection methods for TLSs are all postoperative pathological examinations, but their speed is slow and aggressive. However, few studies have employed non-invasive methods to detect and assess the TLS status of cancer before surgery. In this paper, we propose a 3D convolutional neural network UDNet based on the original U-Net and Dense-Net, which can determine the presence of TLSs by using the patient’s contrast-enhanced CT image. At last, we verify the ability of the model to predict TLSs by comparing the predicting results of the model and the predicting results of three experienced clinicians. The results show that the accuracy of UDNet (72.7%) is much higher than the average accuracy (42.4%) predicted by the three experienced clinicians.

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A Low-profile Quadrifilar Helix Antenna for Beidou Navigation System

A Low-profile Quadrifilar Helix Antenna for Beidou Navigatio...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Jiahong Wang Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

Ahstract-A new low-profile quadrifilar helical antenna (QHA) is proposed and investigated. Four spiral arms of the QHA are divided and connected to for a sleeve structure to reduce the height of the antenna. In this case, four spiral arms on the inner cylindrical sleeve are connected with the ground, while the upper ends of the four spiral arms on the outer cylindrical sleeve are short-circuited, and inner and outer spiral arms are connected in the middle. The QHA has a beam width reaches to above $120 ^{\circ}$ and a cardioid pattern at 1.575 GHz. As a result, the QHA has a very low profile of only 22 mm. The simulation and optimization from the CST show that the center frequency of the QHA is 1.575 GHz, which is used for the satellite navigation system, with axis ratio in the operating band less than 3 dB.

关键词： Satellite antennas Helical antennas Spirals Satellite navigation systems Optimization Antenna radiation patterns

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Family of Mutually Uncorrelated Codes for DNA Storage Address Design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Junhui Li Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Faculty of Science and Engineering University of Bristol Bristol U.K

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate.

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Sparse Linear Array Designed via ULA Fitting

Sparse Linear Array Designed via ULA Fitting

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Wanlu Shi Yingsong Li Zhixiang Huang College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

A sparse array (SA), termed as ATLF3-2BL, is proposed based on the uniform linear array (ULA) fitting (UF) principle. UF is a general SA devising scheme, based on which designers can devise SAs using a series of ULAs. SAs devised upon UF enjoy closed-form expressions for both array configuration and uniform degrees of freedom. Comprised of 7 ULAs, the proposed ATLF3-2BL can provide a long virtual difference coarray than most existing SAs. Besides, the mutual coupling in ATLF3-2BL remains a low level. Therefore, the ATLF3-2BL gives a considerable compromise between mutual coupling and uniform degrees of freedom. Simulations attest the good performance of the proposed ATLF3-2BL.

关键词： Couplings Mutual coupling Closed-form solutions Fitting Sensors Antenna arrays

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A Novel Sparse MIMO Array with Higher Uniform DOF and Sensor Separation

A Novel Sparse MIMO Array with Higher Uniform DOF and Sensor...

引用

IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

In this paper, a novel sparse multiple-input and multiple-output (MIMO) array (SMA) is proposed with closed-form expression. The proposed SMA is devised using the difference-coarray of the sum-coarray concept and the uniform linear array (ULA) fitting (UF) principle. More specifically, the transmitting and receiving arrays of the proposed SMA are sparse arrays designed via the UF principle. Compared with the existing SMAs, the proposed SMA has augmented uniform degrees of freedom (uDOF) and sensor separation. Numerical simulations verify the good capacity of the proposed SMA in terms of uDOF, mutual coupling, and direction finding accuracy.

关键词： Mutual coupling Direction-of-arrival estimation Closed-form solutions Fitting Estimation Numerical simulation MIMO communication

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Hybrid Near- and Far-Field Source Localization Using Symmetric Sparse Array

Hybrid Near- and Far-Field Source Localization Using Symmetr...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Jiawen Zhang Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

Recently, symmetric linear nonuniform arrays (SLNAs) have been promoted for hybrid near-field (NF) and far-field (FF) source localization. A new SLNA based on symmetric nested array (SNA) is presented in the paper. The proposed array and its difference coarray (DCA) configuration can be expressed in closed-form. Comparing to the existing SLNAs with same sensors number, the proposed array can locate more sources and provide longer consecutive lags, which optimizes the detection results. Simulation results prove that the designed array outperforms traditional compressed symmetric nested array (CSNA).

关键词： Location awareness Simulation Sensors Noise measurement Sensor arrays Antenna arrays

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Is Difficulty Calibration All We Need? Towards More Practical Membership Inference Attacks

arXiv

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arXiv 2024年

作者： He, Yu Li, Boheng Wang, Yao Yang, Mengda Wang, Juan Hu, Hongxin Zhao, Xingyu Wuhan University Wuhan China University at Buffalo BuffaloNY United States University of Warwick Warwickshire United Kingdom The Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China

The vulnerability of machine learning models to Membership Inference Attacks (MIAs) has garnered considerable attention in recent years. These attacks determine whether a data sample belongs to the model’s training set or not. Recent research has focused on reference-based attacks, which leverage difficulty calibration with independently trained reference models. While empirical studies have demonstrated its effectiveness, there is a notable gap in our understanding of the circumstances under which it succeeds or fails. In this paper, we take a further step towards a deeper understanding of the role of difficulty calibration. Our observations reveal inherent limitations in calibration methods, leading to the misclassification of non-members and suboptimal performance, particularly on high-loss samples. We further identify that these errors stem from an imperfect sampling of the potential distribution and a strong dependence of membership scores on the model parameters. By shedding light on these issues, we propose RAPID: a query-efficient and computation-efficient MIA that directly Re-leverAges the original membershiP scores to mItigate the errors in Difficulty calibration. Our experimental results, spanning 9 datasets and 5 model architectures, demonstrate that RAPID outperforms previous state-of-the-art attacks (e.g., LiRA and Canary offline) across different metrics while remaining computationally efficient. Our Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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