Domain Information Research Articles

EML: Emotion-Aware Meta Learning for Cross-Event False Information Detection

Modern social media’s development has dramatically changed how people obtain information. However, the wide dissemination of various false information has severely detrimental effects. Accordingly, many deep learning-based methods have been proposed to detect false information and achieve promising results. However, these methods are unsuitable for new events due to the extremely limited labeled data and their discrepant data distribution to existing events. Domain adaptation methods have been proposed to mitigate these problems. However, their performance is suboptimal because they are not sensitive to new events due to they aim to align the domain information between existing events, and they hardly capture the fine-grained difference between real and fake claims by only using semantic information. Therefore, we propose a novel Emotion-aware Meta Learning (EML) approach for cross-event false information early detection, which deeply integrates emotions in meta learning to find event-sensitive initialization parameters that quickly adapt to new events. Emotion-aware meta learning is non-trivial and faces three challenges: 1) How to effectively model semantic and emotional features to capture fine-grained differences. 2) How to reduce the impact of noise in meta learning based on semantic and emotional features. 3) How to detect the false information in a zero-shot detection scenario, i.e., no labeled data for new events. To tackle these challenges, firstly, we construct the emotion-aware meta tasks by selecting claims with similar and opposite emotions to the target claim other than usually used random sampling. Secondly, we propose a task weighting method and event-adaptation meta tasks to further improve the model’s robustness and generalization ability for detecting new events. Finally, we propose a weak label annotation method to extend EML to zero-shot detection according to the calculated labels’ confidence. Extensive experiments on real-world datasets show that the EML achieves superior performances on false information detection for new events.

Noise-Generating and Imaging Mechanism Inspired Implicit Regularization Learning Network for Low Dose CT Reconstrution.

Low-dose computed tomography (LDCT) helps to reduce radiation risks in CT scanning while maintaining image quality, which involves a consistent pursuit of lower incident rays and higher reconstruction performance. Although deep learning approaches have achieved encouraging success in LDCT reconstruction, most of them treat the task as a general inverse problem in either the image domain or the dual (sinogram and image) domains. Such frameworks have not considered the original noise generation of the projection data and suffer from limited performance improvement for the LDCT task. In this paper, we propose a novel reconstruction model based on noise-generating and imaging mechanism in full-domain, which fully considers the statistical properties of intrinsic noises in LDCT and prior information in sinogram and image domains. To solve the model, we propose an optimization algorithm based on the proximal gradient technique. Specifically, we derive the approximate solutions of the integer programming problem on the projection data theoretically. Instead of hand-crafting the sinogram and image regularizers, we propose to unroll the optimization algorithm to be a deep network. The network implicitly learns the proximal operators of sinogram and image regularizers with two deep neural networks, providing a more interpretable and effective reconstruction procedure. Numerical results demonstrate our proposed method improvements of > 2.9 dB in peak signal to noise ratio, > 1.4% promotion in structural similarity metric, and > 9 HU decrements in root mean square error over current state-of-the-art LDCT methods.

EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT.

Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of Things technology will also enable the identification of mental workload remotely based on human physiological signals. In this article, a method based on the spatial and time-frequency domains of electroencephalography (EEG) signals is proposed to improve the classification accuracy of mental workload. Moreover, a hybrid deep learning model is presented. First, the spatial domain features of different brain regions are proposed. Simultaneously, EEG time-frequency domain information is obtained based on wavelet transform. The spatial and time-frequency domain features are input into two types of deep learning models for mental workload classification. To validate the performance of the proposed method, the Simultaneous Task EEG Workload public database is used. Compared with the existing methods, the proposed approach shows higher classification accuracy. It provides a novel means of assessing mental workload.

Joint Variational Inference Network for domain generalization

Domain generalization is a machine learning task that involves training a model on a set of domains with the goal of achieving high performance on unseen domains. While most domain generalization methods focus on extracting domain-invariant features, they may ignore domain-specific information beyond object styles which is label-relevant for classification. In this paper, we first propose a two-step data generation process in which the domain label and observed data are sampled from two distributions sequentially, and accordingly develop an end-to-end Joint Variational Inference Network (JVINet) for domain generalization. JVINet is a framework that admits a variational-based discriminative structure, which the domain-specific latent variable is learned and integrated to enhance the feature for classification. When testing on unseen target domains, the potential beneficial domain information is hence utilized to improve generalization ability. The overall objective is to optimize the variational lower bound of the conditional joint likelihood functions for both class and domain labels. We provide theoretical proof that JVINet can achieve an optimal lower bound using a variational-based discriminative approach. To evaluate the effectiveness of our method, we compare it with state-of-the-art methods on both simulated and real-world datasets.

Social domain integrated semantic self-discovery method for recommendation

Recommender systems effectively improve the convenience for users to access interesting information from vast amounts of data resources. However, the issue of data sparsity significantly impacts recommendation performance. Heterogeneous information network (HIN), as a form of heterogeneous graph data representation, remarkably enhances recommendation performance. Despite this, current HIN-based recommendations insufficiently consider the social attributes and multidimensional interaction information of users. Additionally, the problem of expert experience dependence on extracting semantic features leads to limited generalization performance. To address these issues, we propose the social domain integrated semantic self-discovery method for recommendation (SSRec). This method constructs a social domain information fusion network to fuse multidimensional interaction information of users in the social domain and designs a multidimensional semantic knowledge mining method to extract semantic knowledge of different meta-paths. We also propose a semantic relationship self-discovery method of heterogeneous nodes that utilizes deep reinforcement learning to address the limited efficiency of traditional manual meta-paths selection. Simulation results show that the SSRec can effectively improve the recommendation performance compared with baseline methods, achieving high-precision recommendations.

Sources of Organizational Power among Administrators Working in the Education Directorates at Zarqa Governorate /Jordan

The study aimed to identify the sources of organizational power of administrators working in the directorates of education in Zarqa Governorate / Jordan and the impact of the variables (gender, academic qualification, job title and experience) on that. The study sample consisted of (218) employees. To achieve the aim of the study a questionnaire was developed consisted of (52) items distributed on six domains: official, reward, experience, reference, punishment and information). Validity and reliability of the tool were verified. The results of the study showed that the degree of estimation of the practice of organizational power sources among administrators working in the directorates of education in Zarqa Governorate / Jordan was medium, and the fields were ranked in descending order, respectively (the domain of experience, the domain of formality, the domain of information, the domain of reference, the domain of reward and the domain of punishment). The results also showed that there were statistically significant differences at the level of significance (α ≤ 0.05) due to job title variable, on the domains (reward strength, experience strength, reference strength, and information strength) and overall score, and the differences were in favor of the department member’s name. There were no differences in the impact of the job title on the rest of the fields. The results showed that there were no statistically significant differences at the level of significance (α ≤ 0.05) due to the variables of gender, educational qualification and years of experience. In light of the results, the researchers recommended a set of recommendations, the most important of which are: Strengthening the practice of the source of expertise based on professional reference by increasing the degree of awareness of the requirements of the job role.

A Multi-truth Discovery Algorithm based on Statement Value Grouping and Data Source Information Richness

As the volume of data continues to grow, it is common for data from the same source to contain multiple domains. Combining domain segmentation can enhance the effectiveness of data fusion. This paper presents a multi-truth discovery algorithm that utilises statement value grouping and domain information richness. The data is first grouped based on their similarity, and the resulting groups replace the original data. Then, the reliability of each data source is calculated by domain. The truth value and reliability of each source are iteratively calculated until the end condition is met. Finally, the appropriate value is selected as the final result from the obtained dataset. Experiments were conducted on real datasets to demonstrate the algorithm's effectiveness.

Unraveling the characteristics of Parkinson's Disease through neuroimaging: Insights and future directions

Parkinson's disease (PD) is a neurodegenerative disease with a high degree of patient heterogeneity, and as of 2016 there are approximately 6.1 million PD patients worldwide. PD has a high proportion of patients with intermediate to advanced disease and a high rate of disability, and clinical diagnosis and treatment are difficult due to the lack of neuromarkers to identify disease states and the inability to quantify the effects of treatment in PD. In recent years, many researchers have explored specific changes in brain activity in PD based on electrophysiological and neuroimaging data. Electroencephalography, with its high temporal resolution and rich frequency domain information, and functional magnetic resonance imaging, with its high spatial resolution, have become the main tools to characterize the state of brain activity in PD in recent years. This paper analyses some of the available data on the characteristics of PD through two neuroimaging techniques commonly used in the disease. It concludes with the prospect of being able to establish uniform criteria for determining PD.

Emotion recognition with reduced channels using CWT based EEG feature representation and a CNN classifier

Objective. Although emotion recognition has been studied for decades, a more accurate classification method that requires less computing is still needed. At present, in many studies, EEG features are extracted from all channels to recognize emotional states, however, there is a lack of an efficient feature domain that improves classification performance and reduces the number of EEG channels. Approach. In this study, a continuous wavelet transform (CWT)-based feature representation of multi-channel EEG data is proposed for automatic emotion recognition. In the proposed feature, the time-frequency domain information is preserved by using CWT coefficients. For a particular EEG channel, each CWT coefficient is mapped into a strength-to-entropy component ratio to obtain a 2D representation. Finally, a 2D feature matrix, namely CEF2D, is created by concatenating these representations from different channels and fed into a deep convolutional neural network architecture. Based on the CWT domain energy-to-entropy ratio, effective channel and CWT scale selection schemes are also proposed to reduce computational complexity. Main results. Compared with previous studies, the results of this study show that valence and arousal classification accuracy has improved in both 3-class and 2-class cases. For the 2-class problem, the average accuracies obtained for valence and arousal dimensions are 98.83% and 98.95%, respectively, and for the 3-class, the accuracies are 98.25% and 98.68%, respectively. Significance. Our findings show that the entropy-based feature of EEG data in the CWT domain is effective for emotion recognition. Utilizing the proposed feature domain, an effective channel selection method can reduce computational complexity.

Stress Detection through EEG Signals: Employing a Hybrid Approach integrating Time Domain, Frequency Domain Features and Machine Learning Techniques

Mental stress is a widespread problem that affects people all over the world and can have negative health consequences if not appropriately controlled. The importance of early detection and management in minimizing the detrimental effects of stress cannot be overstated. This study provides a hybrid technique for stress detection that combines time domain and frequency domain information taken from EEG data. Machine learning techniques are used to create a stress detection model that is accurate and dependable. The goal is to increase the accuracy and reliability of stress detection so that prompt intervention and assistance may be provided. The paper opens with an introduction to stress, its effects on mental health, and the need for automated stress detection systems. EEG signals are introduced as a valuable data source for recording stress-related brain activity. To test the model's success in stress detection, performance evaluation criteria such as accuracy, sensitivity, specificity, and F1-score are used. When the result compared to the present approach, the Hybrid Approach has higher accuracy (SVM-98.33% & RF-95%).

Image Enhancement Based on Dual-Branch Generative Adversarial Network Combining Spatial and Frequency Domain Information for Imbalanced Fault Diagnosis of Rolling Bearing

To address the problems of existing 2D image-based imbalanced fault diagnosis methods for rolling bearings, which generate images with inadequate texture details and color degradation, this paper proposes a novel image enhancement model based on a dual-branch generative adversarial network (GAN) combining spatial and frequency domain information for an imbalanced fault diagnosis of rolling bearing. Firstly, the original vibration signals are converted into 2D time–frequency (TF) images by a continuous wavelet transform, and a dual-branch GAN model with a symmetric structure is constructed. One branch utilizes an auxiliary classification GAN (ACGAN) to process the spatial information of the TF images, while the other employs a GAN with a frequency generator and a frequency discriminator to handle the frequency information of the input images after a fast Fourier transform. Then, a shuffle attention (SA) module based on an attention mechanism is integrated into the proposed model to improve the network’s expression ability and reduce the computational burden. Simultaneously, mean square error (MSE) is integrated into the loss functions of both generators to enhance the consistency of frequency information for the generated images. Additionally, a Wasserstein distance and gradient penalty are also incorporated into the losses of the two discriminators to prevent gradient vanishing and mode collapse. Under the supervision of the frequency WGAN-GP branch, an ACWGAN-GP can generate high-quality fault samples to balance the dataset. Finally, the balanced dataset is utilized to train the auxiliary classifier to achieve fault diagnosis. The effectiveness of the proposed method is validated by two rolling bearing datasets. When the imbalanced ratios of the four datasets are 0.5, 0.2, 0.1, and 0.05, respectively, their average classification accuracy reaches 99.35% on the CWRU bearing dataset. Meanwhile, the average classification accuracy reaches 96.62% on the MFS bearing dataset.

Segment all roads: Domain generalized freespace detection by robust surface normal information embedding and edge-aware learning

Freespace detection (FD), also known as drivable area detection, plays a crucial role in autonomous driving. However, existing FD methods, which are based on supervised learning, struggle to perform well when testing on out-of-distribution data. Domain generalization is a prospective solution to address this issue, aiming to improve the generalization ability on out-of-distribution data (unseen domains). In this paper, we explore a new task, domain generalized freespace detection, and design a novel approach, which can generalize to both structured urban on-road scenes and unstructured off-road scenes. First, considering the robustness of surface normal (SN) information in diverse domains, we present a robust information embedding module to embed SN information into RGB features. Then, a cross-scale feature robustness enhancement module is introduced to aggregate RGB-SN features across different scales to obtain more domain-invariant features. Moreover, an RGB-SN edge-aware learning strategy is devised to further improve the generalization ability. Extensive experiments demonstrate that the proposed approach achieves superior performance compared to current approaches.

A dual descriptor combined with frequency domain reconstruction learning for face forgery detection in deepfake videos

Conventional face forgery detectors have primarily relied on image artifacts produced by deepfake video generation models. These methods have performed well when the training and test sets were derived from the same deepfake algorithm, but accuracy and generalizability remain a challenge for diverse datasets. In this study, both supervised and unsupervised approaches are proposed for more accurate detection in in-domain and cross-domain experiments. Specifically, two descriptors are introduced to extract rich information in the spatial domain to achieve higher accuracy. A frequency domain reconstruction module is then included to expand the representation space for facial features. A reconstruction method based on an auto-encoder was also applied to obtain a frequency domain coding vector. In this process, reconstruction learning was sufficient for extracting unknown information, while a combination with classification learning provided essential high-frequency pixel differences between real and fake samples, thus facilitating forgery identification. A series of validation experiments with large-scale benchmark datasets demonstrated that the proposed technique was superior to existing methods.

Domain graph attention neural network: A new mechanical fault diagnosis method with few samples

Mechanical fault diagnosis is currently a highly trending topic, facing two significant challenges. Firstly, the acquisition of an ample number of fault samples proves to be difficult, thereby limiting access to sufficient data samples. Secondly, intricate and non-mathematically describable associations often exist among different faults. Most algorithms treat fault samples as isolated entities, consequently impacting the accuracy of fault diagnosis. This paper proposes a novel machine learning framework called Domain Graph Attention Neural Network (DGAT), which leverages the topological structure of graphs to effectively capture the interrelationships among fault samples. Additionally, this framework incorporates domain information during node updates to obtain richer embeddings, particularly in scenarios with limited available samples. It effectively overcomes the fixed receptive field limitation of the original Graph Attention Network (GAT). In order to validate the effectiveness of the model, we conducted extensive comparative experiments on diverse datasets, which demonstrated the superior performance of the proposed model.

A machine-based corpus optimization method for extracting domain-oriented technical words: an example of COVID-19 corpus data

Although natural language processing (NLP) refers to a process involving the development of algorithms or computational models that empower machines to understand, interpret, and generate human language, machines are still unable to fully grasp the meanings behind words. Specifically, they cannot assist humans in categorizing words with general or technical purposes without predefined standards or baselines. Empirically, prior researches have relied on inefficient manual tasks to exclude these words when extracting technical words (i.e., terminology or terms used within a specific field or domain of expertise) for obtaining domain information from the target corpus. Therefore, to enhance the efficiency of extracting domain-oriented technical words in corpus analysis, this paper proposes a machine-based corpus optimization method that compiles an advanced general-purpose word list (AGWL) to serve as the exclusion baseline for the machine to extract domain-oriented technical words. To validate the proposed method, this paper utilizes 52 COVID-19 research articles as the target corpus and an empirical example. After compared to traditional methods, the proposed method offers significant contributions: (1) it can automatically eliminate the most common function words in corpus data; (2) through a machine-driven process, it removes general-purpose words with high frequency and dispersion rates –57% of word types belonging to general-purpose words, constituting 90% of the total words in the target corpus. This results in 43% of word types representing domain-oriented technical words that makes up 10% of the total words in the target corpus are able to be extracted. This allows future researchers to focus exclusively on the remaining 43% of word types in the optimized word list (OWL), enhancing the efficiency of corpus analysis for extracting domain knowledge. (3) The proposed method establishes a set of standard operation procedure (SOP) that can be duplicated and generally applied to optimize any corpus data.

Fault diagnosis of helicopter tail-drive system using a multi-grained hierarchical message graph convolutional networks

ABSTRACT The fault diagnosis of the tail-drive of helicopter is a crucial task for helicopter system operation and maintenance. Recently, graph convolution network (GCN) has been the focus in fault diagnosis for its powerful representational ability in relationship mining. However, with the difficulty of obtaining node and edge information in the high-order domain, the stable performance of the long-range message-passing process of the deep GCN is unknown limits the application of GCN in fault diagnosis. To address these issues, a multi-grained hierarchical message graph convolutional network (MHGCN) is proposed to diagnose faults of helicopter tail-drive system. First, time-frequency characteristics of the original vibration signals are extracted to construct the graph nodes. The original graph nodes are aggregated by Louvain community detection, which can effectively learn the multi-grained features. Then, the hierarchical graph is introduced to learn the features of high-order neighbourhoods. Finally, a particular message-passing method is used to encode long-range information spanning the graph structure and realise accurate classification. Experiments on a test rig of helicopter tail-drive system are performed to verify the efficacy of the proposed method.

FBN-TCN: Temporal convolutional neural network based on spatial domain fusion brain networks for affective brain–computer interfaces

Background and objective: Electroencephalogram (EEG) emotion recognition is increasingly popular for visualizing and quantifying brain networks, with potential applications in medical cognition and human–computer interaction. However, single brain space is limited, and multi-channel recognition poses challenges with calculation volume and information redundancy. Therefore, this paper focuses on studying the affective brain–computer interface (aBCI) and critical lead acquisition of the fusion brain network. Method: This paper employs two correlation algorithms to build functional and causal networks. A fusion brain network (FBN) was proposed to capture emotional information in the spatial domain. Additionally, a deep network recognition model (FBN-TCN) is introduced to capture the dynamic changes of spatial domain emotional information over time. A critical channel combination with high emotional correlation among multiple channels is obtained by the brain causal networks. All proposed models were performed on the SEED dataset through subject-dependent and subject-independent experiments. Results: Experimental results demonstrate that FBN can cover more emotional information to improve emotional recognition. The critical channel aBCI achieves comparable recognition performance to the full-channel aBCI, with recognition results of 95.51%/3.15% and 96.34%/1.62% in the Total band, respectively. The generalization and effectiveness of the model were verified through ablation experiments, which involved subject-dependent and subject-independent emotion recognition of DEAP, and emotion recognition of SEED on model block transformation. Conclusion and significance: This paper can simplify the electrode cap device and maximizes the utilization of EEG spatial domain emotional information. It is highly significant for enhancing the performance of aBCI in human–computer interaction.

Dual-domain mean-reverting diffusion model-enhanced temporal compressive coherent diffraction imaging

Temporal compressive coherent diffraction imaging is a lensless imaging technique with the capability to capture fast-moving small objects. However, the accuracy of imaging reconstruction is often hindered by the loss of frequency domain information, a critical factor limiting the quality of the reconstructed images. To improve the quality of these reconstructed images, a method dual-domain mean-reverting diffusion model-enhanced temporal compressive coherent diffraction imaging (DMDTC) has been introduced. DMDTC leverages the mean-reverting diffusion model to acquire prior information in both frequency and spatial domain through sample learning. The frequency domain mean-reverting diffusion model is employed to recover missing information, while hybrid input-output algorithm is carried out to reconstruct the spatial domain image. The spatial domain mean-reverting diffusion model is utilized for denoising and image restoration. DMDTC has demonstrated a significant enhancement in the quality of the reconstructed images. The results indicate that the structural similarity and peak signal-to-noise ratio of images reconstructed by DMDTC surpass those obtained through conventional methods. DMDTC enables high temporal frame rates and high spatial resolution in coherent diffraction imaging.

Sparse learning model with embedded RIP conditions for turbulence super-resolution reconstruction

In practical engineering scenarios, constraints arising from sensor placement, quantity, and the limitations of current testing technologies often lead to turbulence data characterized by low resolution and irregular structures. Turbulence super-resolution reconstruction is crucial for extracting finer details from irregularly structured, low-resolution measurement data, thereby facilitating comprehensive flow field analyses. This article introduces a sparse learning model (Embedding Restricted Isometry Property Autoencoder (RIP-AE)) for achieving super-resolution reconstruction of flow fields by obtaining compressed representations and sparse transform domain information. In this model, we embed the Restricted Isometry Property (RIP) condition to ensure the effectiveness of compressed representations and sparse transform domains, thereby enhancing the super-resolution reconstruction accuracy of the model. To assess the performance of the RIP-AE model, we utilized data sets of flow around a cylinder at different Reynolds numbers generated by unsteady Reynolds-Averaged Navier–Stokes simulations. We sequentially validate the effectiveness of the RIP condition at different Reynolds numbers (ranging from 1,000 to 500,000) and compare the reconstruction results of the RIP-AE model with those of the downsampled skip-connection/multi-scale (DSC/MS) and MS-AE models under different sampling ratios. The results indicate that the RIP-AE model excels in terms of L2 relative error and demonstrates the capability to achieve high-precision flow field reconstruction even under high sampling ratios.

Unsupervised cross domain semantic segmentation with mutual refinement and information distillation

Unsupervised cross domain semantic segmentation recently has gained much attention, due to its powerful ability of solving the segmentation problem on unlabeled domains. Traditional methods often employ an adversarial network to confuse the source and target inputs, so as to align them in a new feature space. However, these methods cannot well fuse the source and target domain information because the information of the two domains does not really interact with each other and are passed through separate network branches. To tackle this problem, we propose a real interactive learning framework, named Mutual Refinement and Information Distillation (MURID), to align the two domains. Concretely, MURID introduces a Mutual Refinement module in shallow network layers to enhance information sharing and integration between the source and target domains, which can effectively transfer knowledge from source domain to target domain. In addition, in order to avoid using the same structure for testing as for training, which would result in huge computational requirements, we exploit an Information Distillation module to simplify the testing network while maintaining the powerful inference capability of the training. Moreover, we incorporate Curriculum Learning, a self-training mechanism that iteratively trains the network using pseudo-labels obtained from the target domain, to further improve performance. Extensive experiments were conducted on three popular datasets, i.e., GTA5→Cityscapes and Synthia→Cityscapes, and the results demonstrate the state-of-the-art performance of our method. Additionally, detailed analysis and ablation studies are also carried out to validate the effectiveness of each designed module.