Deep learning has become a key technique for FDIA detection in power systems; accordingly, studying more effective attacks against detection networks is valuable for security analysis. However, existing black-box neural attacks are often sensitive to noise from surrogates and the data itself, which degrades transferability. To address this issue, this paper proposes the Gradient Detection Iterative Fast Gradient Sign Method (GDI-FGSM), which injects small-angle, multi-direction gradient probing with loss-based selection into iterative updates to suppress noise effects and improve cross-model transferability. Experiments on IEEE 14-bus power flow data with GAF transformation show that GDI-FGSM consistently improves black-box attack success across multiple detectors and generalizes to I-FGSM, DI2-FGSM, and M-DI2-FGSM variants.

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterized by rapid progression and poor prognosis. Early diagnosis remains highly challenging due to nonspecific clinical manifestations, leading to persistently high misdiagnosis rates. This study investigates the feasibility of using resting-state electromyography (EMG) signals for accurate ALS identification. Clinically collected EMG signals vary in length across individuals and muscle regions; further, preprocessing via sliding window methods yields inconsistent numbers of segmented samples, posing challenges for traditional neural networks to adapt to variable-length sequences. To address these issues, we propose a unified time-frequency learning framework called TF-Transformer. Centered on a Transformer-based feature extraction module, the framework treats segmented EMG data as sequence data, enabling efficient processing of variable-length sequences without padding or truncation. It synchronously extracts and fuses time and frequency domain features into a unified EMG representation for ALS identification. Experiments using five-fold cross-validation show that this method effectively utilizes the inherent time-frequency characteristics of resting state electromyographic signals, providing new technical insights and methods for clinical ALS diagnosis.

Crevasse detection is crucial for glacier and climate research, and provides essential guidance for activities in glacier regions. Ground-penetrating radar (GPR) and machine learning are used to automatically detect crevasse. In this study, a Siam-Gabor-ResNet deep learning framework is proposed to detect crevasse automatically using GPR data. A contrast learning with the Siamese network framework is proposed to improve the accuracy of crevasse detection, which aims to increase the feature similarity between crevasses while simultaneously enhancing the feature distinctiveness between crevasse and continuous snow layers. Additionally, a trainable Gabor-ResNet feature extraction module is built by integrating the Gabor filter bank into the ResNet network and used to further reduce the complexity of model training while extracting multiscale features in a real-time manner. Experiments are performed on the Greenland dataset and the 2015 McMurdo dataset, which illustrate the effectiveness of the proposed method. The average accuracy rate (AR) of crevasse detection reaches 94.38%, which can detect the narrowest crevasse (0.6 m) in two datasets, with an average detection time of only 6.8 ms. The experimental results show that the proposed method can detect crevasse in real time, automatically, and accurately.

The structural Magnetic resonance imaging (sMRI) of spinal cord plays a significant role in the clinical diagnosis of Amyotrophic Lateral Sclerosis (ALS). But due to small cross-sectional area in the axial plane and long sagittal/coronal expansion of spinal cord, the diagnosis of ALS using sMRI of spinal cord has remained largely at the stage of morphological observation. In this study, a Multi-branch attention-based deep learning method is proposed to solve this problem. Multi-branch framework is utilized to extract general features of all levels of spinal cord for challenging of long sagittal and coronal expansion of spinal cord, and attention module coupled with multi-scale module in each branch is applied to extract multi-scale features and pay more attention to the important regions of the spinal cord in the axial plane. Experiments show that the proposed method obtains better performance in ALS identification, which implies that the proposed method can extract features of important region in the spinal cord and could be helpful to find more regions sensitive for ALS disease identification.

With the explosive popularity of social media, more and more people, including those with depressive symptoms, are starting to express their emotions online through vlogs recently, which makes it important for video-based depression recognition. As video data contains rich acoustical and visual information, the main challenges faced by existing methods include (1) how to accurately mine features associated with depression in massive data and (2) how to effectively fuse various features from different modalities. In this paper, a multi-domain acoustical-visual information fusion network (MDAVIF) is designed to extract depressive spatio-temporal features from image sequences and audios, and an adaptive feature interaction module is proposed to mix these features. Combined with two autoencoders to retain information and prevent overfitting, the proposed method obtains the state-of-the-art result with the precision of 74.25% and the F1-Score of 75.25% when evaluated on the D-vlog dataset.

The McEliece public key cryptosystem is known for its resistance to quantum attacks. However, its application potential remains limited due to challenges such as large key size and low coding efficiency. In this paper, we proposed a McEliece public-key cryptosystem based on cyclic codes to reduce the key size. The matrix generated by the Goppa codes is replaced with the matrix generated by the cyclic code in encoding, the private key matrix is transformed into the row vector of the generator polynomial. Performance evaluations and security analysis reveal that the refined system retains commendable coding efficacy and security with a condensed key space.

To enhance cryptographic security in the post-quantum era, this paper proposes a hybrid encryption system that combines the strengths of the McEliece and Ring-Learning With Errors (RLWE) algorithms. The system leverages the generalized logistic map to establish a mapping between the public keys of both systems. Inspired by their similar encryption mechanisms, a novel encryption formula is introduced. The Joint McEliece and RLWE Cryptosystem (JMRC) is an asymmetric cryptosystem designed to offer maximum security. Its integrated approach leverages the error correction capabilities of McEliece, reducing the decryption failure rate (DFR) and enhancing performance in Additive White Gaussian Noise (AWGN) channels, ultimately leading to a lower bit error rate (BER). When the signal-to-noise ratio (SNR) reaches 3.4 dB, the BER of the JMRC system drops to 10−3.

In this paper, we have proposed a method to measure the weight efficiency of pointwise convolution (weight sharing index), which finds that the weight efficiency of pointwise convolution decreases rapidly as the network becomes deeper. The redundancy of the feature maps is mainly reflected in the similarity of the feature maps. We achieve the compression of the pointwise convolution layers by quantifying the similar feature maps with the help of structural similarity (SSIM), reducing the input channels of the pointwise convolution by fusing the similar feature maps. The results on the datasets CIFAR-10 and CIFAR-100 demonstrate that the compression method based on fusing similar feature maps can significantly compress the MobileNet-V1(48.74% FLOPs and 13.64% parameters are reduced) with an increase of 0.54% in the top-1 accuracy, improving the top-1 accuracy of ResNet-50 by 0.09% (9.2% of FLOPs and 6.03% of parameters are reduced), and improves the efficiency of the weight utilization of the pointwise convolution layer.

Convolutional neural networks (CNNs) have achieved tremendous success in visual recognition tasks but mainly rely on massive learnable parameters. To solve this problem, many effective and efficient convolution operators have been proposed, such as group-wise convolution, point-wise convolution, and depth-wise convolution. However, the above convolution operations model and optimize the weight relationship within the same convolutional layer. To reduce the network parameters, we explicitly construct the relationship between convolution kernels of adjacent layers. Specifically, we propose an evolutionary kernel convolution, namely EKConv, to generate weight parameters by group-wise convolution efficiently. In particular, EKConv makes the kernel parameters of the current convolutional layer inherit from its preceding adjacent kernel, which promotes the information exchange between convolution kernels. More importantly, EKConv is a novel plug-and-play module that can be easily embedded into mainstream CNNs. Extensive experimental results show that EKConv can compress the parameters of CNNs by a large margin yet barely sacrifice image classification performance.

A joint source-channel code (JSCC) modulation scheme based on variable-length codes (VLCs) and doping modulation is proposed. The parameters of the VLC source encoder are optimized to provide good compression and error-correcting properties. And a doping modulation is designed to match the source encoder with the aid of extrinsic information transfer (EXIT) chart analysis so that a significant coding gain can be achieved by iterative decoding and demodulation at the receiver. Moreover, by using many-to-one mapping we optimize the distribution of the modulation symbols, and further attainable capacity gain can be achieved. The simulation results show that compare to a JSCC modulation scheme based on VLC and unity-rate code, the proposed scheme has 0.5dBgain over AWGN channel and 0.3dBgain over Rayleigh fading channel. And its performance has only 1.2dB and 2dB away from the Shannon capacity of AWGN and Rayleigh channels respectively.

BackgroundIn recent years, the number of people with anxiety disorders has increased worldwide. Methods for identifying anxiety through objective clues are not yet mature, and the reliability and validity of existing modeling methods have not been tested. The objective of this paper is to propose an automatic anxiety assessment model with good reliability and validity.MethodsThis study collected 2D gait videos and Generalized Anxiety Disorder (GAD-7) scale data from 150 participants. We extracted static and dynamic time-domain features and frequency-domain features from the gait videos and used various machine learning approaches to build anxiety assessment models. We evaluated the reliability and validity of the models by comparing the influence of factors such as the frequency-domain feature construction method, training data size, time-frequency features, gender, and odd and even frame data on the model.ResultsThe results show that the number of wavelet decomposition layers has a significant impact on the frequency-domain feature modeling, while the size of the gait training data has little impact on the modeling effect. In this study, the time-frequency features contributed to the modeling, with the dynamic features contributing more than the static features. Our model predicts anxiety significantly better in women than in men (rMale = 0.666, rFemale = 0.763, p < 0.001). The best correlation coefficient between the model prediction scores and scale scores for all participants is 0.725 (p < 0.001). The correlation coefficient between the model prediction scores for odd and even frame data is 0.801~0.883 (p < 0.001).ConclusionThis study shows that anxiety assessment based on 2D gait video modeling is reliable and effective. Moreover, we provide a basis for the development of a real-time, convenient and non-invasive automatic anxiety assessment method.

With the rapid development of wireless communication technology and the emergence of intelligent applications, higher requirements have been put forward for data communication and computing capacity. Multi-access edge computing (MEC) can handle highly demanding applications by users by sinking the services and computing capabilities of the cloud to the edge of the cell. Meanwhile, the multiple input multiple output (MIMO) technology based on large-scale antenna arrays can achieve an order-of-magnitude improvement in system capacity. The introduction of MIMO into MEC takes full advantage of the energy and spectral efficiency of MIMO technology, providing a new computing paradigm for time-sensitive applications. In parallel, it can accommodate more users and cope with the inevitable trend of continuous data traffic explosion. In this paper, the state-of-the-art research status in this field is investigated, summarized and analyzed. Specifically, we first summarize a multi-base station cooperative mMIMO-MEC model that can easily be expanded to adapt to different MIMO-MEC application scenarios. Subsequently, we comprehensively analyze the current works, compare them to each other and summarize them, mainly from four aspects: research scenarios, application scenarios, evaluation indicators and research issues, and research algorithms. Finally, some open research challenges are identified and discussed, and these indicate the direction for future research on MIMO-MEC.

Suicide, as an increasingly prominent social problem, has attracted widespread social attention in the mental health field. Traditional suicide clinical assessment and risk questionnaires lack timeliness and proactivity, and high-risk groups often conceal their intentions, which is not conducive to early suicide prevention. In this study, we used machine-learning algorithms to extract text features from Sina Weibo data and built a suicide risk-prediction model to predict four dimensions of the Suicide Possibility Scale-hopelessness, suicidal ideation, negative self-evaluation, and hostility-all with model validity of 0.34 or higher. Through this method, we can detect the symptoms of suicidal ideation in a more detailed way and improve the proactiveness and accuracy of suicide risk prevention and control.

With the increasing number of depressed patients and the development of computer vision technology, the study of individual automatic depression estimation (ADE) methods based on facial images has attracted much attention in recent years. Most existing works focus on obtaining informative features from the whole images with advancing deep learning models, while the local tiny changes and the fusion of different features have been paid less attention. In this paper, a two-branch predicting model with a elaborate transfomer block NFFT is designed to combine global and local features extracted from whole images and image patches for predicting the depression score precisely. Besides, a classification head is added to guide the regression results for improving accuracy. Experiment results on the AVEC2014 dataset (MAE=5.81, RMSE=7.49) demonstrate that the proposed model outperforms other methods, and the extended experiments on one new dataset (CCPL) are conducted to validate the generalizability and robustness of our model.

In order to improve the security of digital data communication, an asymmetric cryptographic algorithm based on turbo-polar code is proposed in this paper. On the encoding side, the system uses the interleaving of information bits and the puncturing of parity bits to encrypt with the private key. The public key is composed of interleavers, systematic polar code encoders, and puncturers. Generalized logistic map (GLM) is selected to reduce the private key storage space. The security of public-key turbo-polar codes (PKTPC) is analyzed at the end of this paper. Simulation results show the SNR of PKTPC is only 0.1 higher than the SNR of parallel concatenated systematic polar code (PCSP) with the same frames error rates.

Integrating Multiple Input Multiple Output (MIMO) into Multi-access Edge Computing (MEC) as a new computing paradigm can provide users with higher quality of services. In this paper, a cloud-edge-end three-layer collaborative MIMO-MEC system is considered, in which multiple MEC servers can cooperate with each other. We describe the problem as a joint optimization of offloading strategy, computational resource allocation and downlink power allocation to minimize the total delay of all users. A nested algorithm is proposed to address the mixed-integer non-linear programming problem. Therein, a penalty function-based Newton method and the Lagrange multiplier method are used in the inner layer, while a heuristic algorithm is adopted in the outer layer. The results show that the proposed scheme can effectively reduce the total delay of processing tasks compared with the baseline schemes.

Personality affects an individual's academic achievements, occupational tendencies, marriage quality and physical health, so more convenient and objective personality assessment methods are needed. Gait is a natural, stable, and easy-to-observe body movement that is closely related to personality. The purpose of this paper is to propose a personality assessment model based on gait video and evaluate the reliability and validity of the multidimensional model. This study recruited 152 participants and used cameras to record their gait videos. Each participant completed a 44-item Big Five Inventory (BFI-44) assessment. We constructed diverse static and dynamic time-frequency features based on gait skeleton coordinates, interframe differences, distances between joints, angles between joints, and wavelet decomposition coefficient arrays. We established multidimensional personality trait assessment models through machine learning algorithms and evaluated the criterion validity, split-half reliability, convergent validity, and discriminant validity of these models. The results showed that the reliability and validity of the Gaussian process regression (GPR) and linear regression (LR) models were best. The mean values of their criterion validity were 0.478 and 0.508, respectively, and the mean values of their split-half reliability were all greater than 0.8. In the formed multitrait-multimethod matrix, these methods also had higher convergent and discriminative validity. The proposed approach shows that gait video can be effectively used to evaluate personality traits, providing a new idea for the formation of convenient and non-invasive personality assessment methods.

PreviousNext No Access19th International Conference on Ground Penetrating RadarGabor-based U-Net crevasse detection with ground penetrating radar dataAuthors: Min WangDeyuan ChenBo ZhaoYan LiuMin WangUniversity of Chinese Academy of SciencesSearch for more papers by this author, Deyuan ChenUniversity of Chinese Academy of SciencesSearch for more papers by this author, Bo ZhaoAerospace Information Research Institute, Chinese Academy of ScienceSearch for more papers by this author, and Yan LiuUniversity of Chinese Academy of SciencesSearch for more papers by this authorhttps://doi.org/10.1190/gpr2022-091.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract To get rid of human-related risks and further enhance the security of crevasse detection, machine learning methods and Ground Penetrating Radar (GPR) have been used in crevasse detection. In this paper, we proposed a Gabor-based U-Net method to detect crevasses automatically on GPR data. Firstly, we use U-Net as the backbone network of the crevasse detection task. Secondly, we use the Gabor function as convolutional function in the first layer of the U-Net, which is used to extract multi-scale features and reduce the number of model parameters, thus improving the efficiency of the original U-Net model. Finally, three fully connected layers are added at the end of U-Net to identify crevasses and continuous snow layers. Experimental results show that the proposed method can detect crevasses automatically and accurately with the average accuracy rate (AR) of 97.9% and the false alarm rate (FAR) of 1.8%. Keywords: GPR, machine learning, processing, diffraction, offsetPermalink: https://doi.org/10.1190/gpr2022-091.1FiguresReferencesRelatedDetails 19th International Conference on Ground Penetrating RadarISSN (online):2159-6832Copyright: 2022 Pages: 166 publication data© 2022 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 13 Oct 2022 CITATION INFORMATION Min Wang, Deyuan Chen, Bo Zhao, and Yan Liu, (2022), "Gabor-based U-Net crevasse detection with ground penetrating radar data," SEG Global Meeting Abstracts : 102-106. https://doi.org/10.1190/gpr2022-091.1 Plain-Language Summary KeywordsGPRmachine learningprocessingdiffractionoffsetPDF DownloadLoading ...

Visible light communication (VLC) systems have attracted considerable attention in recent years. However, it still faces the challenges of user mobility and random device orientation, which will result in unreliable connection and performance degradation. In this letter, we propose a group-based LED selection aided generalized spatial modulation (GLS-GSM) scheme to tackle this issue by preventing some poor LEDs from communication. Firstly, the LEDs are divided into several equal-sized groups. Then, each group conducts Euclidean distance-based LED selection (ED-LS) and spatial modulation (SM) independently. Moreover, an ED-based grouping criterion is exploited to search for an optimal grouping strategy. The bit error rate (BER) performance of the proposed scheme is evaluated for sitting and walking activities. Numerical simulation results demonstrate the effectiveness of the proposed method.