Cross-domain Scenarios Research Articles

Cross-Domain Aspect-Based Sentiment Analysis for Enhancing Customer Experience in Electronic Commerce

Cross-Domain ABSA has proved effective in extracting more descriptive sentiment information from the reviews or feedback as an important step in improving the experience of the customers in electronic commerce. This work examines the performance of ABSA in the cross-domain scenario where models trained on the review data of one domain for example electronics domain are applied to another domain such as fashion domain. Here, we put forward a risk mitigation strategy that builds upon transfer learning and domain adversarial training methodologies to enhance the overall resilience and reliability of sentiment estimations across multiple product domains. The proposed model was tested using data obtained from different e-commerce retailers, such as Amazon, eBay, and Alibaba concerning various categories of products including electronics, fashion, and home appliances. The outcome of experiments show better performance over most of the compared methods of single-domain ABSA and cross-domain approaches. The model offered greater accuracy, recall rate, F1- score, and cross-domain efficiency, which demonstrated the model’s effectiveness and versatility. The consequences for e-business companies are significant. Improved sentiment analysis allows businesses to obtain more specific data about customers’ opinions, correct mistakes when developing products, and adjust their advertising approaches. Also, the use of advanced text analytics to measure and monitor such aspects in different product areas offers a competitive edge and improves product innovation decisions. However, there are several limitations to the study itself, such as the variations that may arise among the domain, and the limited availability of the data. Further work is to be done on more complex and sophisticated methods of domain adaptation, using external resources; the main model itself should also be much faster and more efficient in scalability. This work thus indicates an opportunity for cross-domain ABSA to generate practical insights for enhancing customers’ experience within e-commerce

SCFormer: Spectral Coordinate Transformer for Cross-Domain Few-Shot Hyperspectral Image Classification.

Cross-domain (CD) hyperspectral image classification (HSIC) has been significantly boosted by methods employing Few-Shot Learning (FSL) based on CNNs or GCNs. Nevertheless, the majority of current approaches disregard the prior information of spectral coordinates with limited interpretability, leading to inadequate robustness and knowledge transfer. In this paper, we propose an asymmetric encoder-decoder architecture, Spectral Coordinate Transformer (SCFormer), for the CDFSL HSIC task. Several dense Spectral Coordinate blocks (SC blocks) are embedded in the backbone of the encoder to establish feature representation with better generalization, which integrates spectral coordinates via Rotary Position Embedding (RoPE) to minimize spectral position disturbance caused by the convolution operation. Due to a large amount of hyperspectral image data and the high demand for model generalization ability in cross-domain scenarios, we design two mask patterns (Random Mask and Sequential Mask) built on unexploited spectral coordinates within the SC blocks, which are unified with the asymmetric structure to learn high-capacity models efficiently and effectively with satisfactory generalization. Besides, from the perspective of the loss function, we devise an intra-domain loss function founded on the Orthogonal Complement Space Projection (OCSP) theory to facilitate the aggregation of samples in the metric space, which promotes intra-domain consistency and increases interpretability. Finally, the strengthened class expression capacity of the intra-domain loss function contributes to the inter-domain loss function constructed by Wasserstein Distance (WD) for realizing domain alignment. Experimental results on four benchmark data sets demonstrate the superiority of the SCFormer.

Fractal Few-Shot Learning.

Forming deep feature embeddings is an effective method for few-shot learning (FSL). However, in the case of insufficient samples, overcoming the task complexity while improving the accuracy is still a major challenge. To address this problem, this article considers the consistency between similar data from the fractal perspective, introduces a priori knowledge, and proposes a fractal embedding model by combining FSL with fractal dimension theory for the first time. We improve the original fractal dimension algorithm used to describe image texture roughness to suit a neural network. Moreover, in accordance with the improved algorithm, prior knowledge of the quantized image is integrated into the features to reduce the impact of the data distribution on the model. Experimental results obtained on multiple image benchmark datasets show that the performance of the proposed model exceeds or matches that of previous state-of-the-art models. In addition, the proposed model achieves the best performance in cross-domain scenarios, further illustrating its robustness.

Self-Supervised Autoregressive Domain Adaptation for Time Series Data.

Unsupervised domain adaptation (UDA) has successfully addressed the domain shift problem for visual applications. Yet, these approaches may have limited performance for time series data due to the following reasons. First, they mainly rely on the large-scale dataset (i.e., ImageNet) for source pretraining, which is not applicable for time series data. Second, they ignore the temporal dimension on the feature space of the source and target domains during the domain alignment step. Finally, most of the prior UDA methods can only align the global features without considering the fine-grained class distribution of the target domain. To address these limitations, we propose a SeLf-supervised AutoRegressive Domain Adaptation (SLARDA) framework. In particular, we first design a self-supervised (SL) learning module that uses forecasting as an auxiliary task to improve the transferability of source features. Second, we propose a novel autoregressive domain adaptation technique that incorporates temporal dependence of both source and target features during domain alignment. Finally, we develop an ensemble teacher model to align class-wise distribution in the target domain via a confident pseudo labeling approach. Extensive experiments have been conducted on three real-world time series applications with 30 cross-domain scenarios. The results demonstrate that our proposed SLARDA method significantly outperforms the state-of-the-art approaches for time series domain adaptation. Our source code is available at: https://github.com/mohamedr002/SLARDA.

Ships Detection on Aerial Imagery using Transfer Learning and Selective Search

The traffic in the water area such as harbor and sea strait is highly important to be monitored because it helps to minimize unwanted ships accident. As a result, we proposed an automatic detection method to localize ships contained in sattelite image. We examine several deep learning method as the classification backbone, namely MobileNetV2, DenseNet121, VGG16, and ResNet50. Afterwards, we employed the trained model for detecting the ships. To make the detection faster, inspite of using a sliding window, we use selective search to sample the object candidates from the given scene. The experiments was done using Shipsnet dataset and tested on aerial images. We also conducted a cross domain evaluation where the images were taken using Google Earth. The results indicate that MobileNetV2 has the best performance on classification and detection tasks. The MobileNetV2 is also able to detect the ships on cross-domain scenarios.

RLoc

In recent years, decimeter-level accuracy in WiFi indoor localization has become attainable within controlled environments. However, existing methods encounter challenges in maintaining robustness in more complex indoor environments: angle-based methods are compromised by the significant localization errors due to unreliable Angle of Arrival (AoA) estimations, and fingerprint-based methods suffer from performance degradation due to environmental changes. In this paper, we propose RLoc, a learning-based system designed for reliable localization and tracking. The key design principle of RLoc lies in quantifying the uncertainty level arises in the AoA estimation task and then exploiting the uncertainty to enhance the reliability of localization and tracking. To this end, RLoc first manually extracts the underutilized beamwidth feature via signal processing techniques. Then, it integrates the uncertainty quantification into neural network design through Kullback-Leibler (KL) divergence loss and ensemble techniques. Finally, these quantified uncertainties guide RLoc to optimally leverage the diversity of Access Points (APs) and the temporal continuous information of AoAs. Our experiments, evaluating on two datasets gathered from commercial off-the-shelf WiFi devices, demonstrate that RLoc surpasses state-of-the-art approaches by an average of 36.27% in in-domain scenarios and 20.40% in cross-domain scenarios.

A progressive multi-source domain adaptation method for bearing fault diagnosis

Based on massive samples collected from various working conditions, multi-source domain adaptation-based fault diagnosis methods have been a promising way to improve the generalization capability of diagnostic models. However, insufficient multi-domain alignment and category confusion issues lead to unsatisfactory diagnostic performance in practical cross-domain scenarios. This article proposes a progressive multi-source domain adaptation method, which learns domain-uninformative and category-informative features. First, all domains are divided into multiple source-source and source-target domain pairs on different feature spaces to explore rich domain-invariant features, where local maximum mean discrepancy is introduced for domain pairwise alignment. Second, category correlation is reduced to allow ambiguous samples to obtain clear category boundaries. Extensive experimental results show the outstanding performance of our method.

Personalized Federated Learning Based on Bidirectional Knowledge Distillation for WiFi Gesture Recognition

WiFi-based human gesture recognition has a wide range of applications in smart homes. Existing methods train gesture classification models by collecting large amounts of WiFi signal data in a centralized manner. However, centralized training faces challenges, including high communication overhead and the risk of data privacy leakage. Federated learning (FL) provides an opportunity to collaboratively train and share models without compromising data privacy. One of the main challenges FL faces is data that is non-Independent and Identically Distributed (non-IID) across clients. Specifically, in the gesture recognition scenario, since the transmission of WiFi signals is susceptible to cross-environment and cross-person interference, non-IID mainly manifests itself as a cross-domain problem. Cross-domain makes the knowledge learned between client models incompatible. Therefore, in the cross-domain scenario, effectively extracting and combining the knowledge learned by the client is a challenge. To solve this problem, we propose pFedBKD, a novel personalized federated learning scheme via bidirectional distillation. First, the knowledge that is beneficial to the client is extracted from the shared server model through knowledge distillation in the client, which helps train the personalized model of the client. Second, the server adaptively adjusts the aggregation weights according to the deviation between the shared model and the client’s local model so that the server’s shared model can extract more public knowledge. We conduct experiments on multiple open-source datasets. Experimental results show that our method is superior to existing methods and effectively alleviates the problem of reduced model recognition accuracy caused by cross-domain challenges.

General feature spatial location and distance-based unknown Detection: A universal domain adaptation fault diagnosis framework of rotating Machinery

Recently, domain adaptation (DA) based cross-domain fault diagnosis of rotating machinery has gained much attention, where a target domain with only unlabeled data can be diagnosed by transferring diagnosis knowledge from a labeled source domain. In industrial applications, the target domain may occur only limited types of faults contained in the source domain, or even novel faults unseen in the source domain. Moreover, since the target domain is unlabeled in reality, the prior label space relationship between two domains is hard to know. Therefore, not only domain shift, it is also essential to tackle the uncertainty of both the category shift and the unknown space in actual cross-domain scenarios, which can be abstracted as a universal domain adaptation (UniDA) problem. This paper proposes a cross-domain diagnosis framework for rotating machinery under UniDA to handle the uncertainty dilemma by converting it to certainty. Specifically, a feature spatial location principle general for different label space relationships is designed, where the known classes form a hypersphere and the central region of the hypersphere is left for the possible target domain unknown samples. Then, a novel distance-based unknown detection strategy is proposed, which is friendly to different “open” degrees of the unknown space. The diagnosis results of three case studies show the effectiveness of the proposed framework under all possible label space relationships, including close set DA, partial set DA, open set DA and open-partial set DA. Further discussions demonstrate the superiority of the proposed framework in known class recognition and possible unknown detection. The proposed diagnosis framework provides a new insight for solving the uncertainty dilemma laid in actual cross-domain diagnosis scenarios under UniDA setting, which is more potential to practical engineering applications compared with many DA-based diagnosis methods tailored for specific label space relationship.

Learning to Adapt Adversarial Perturbation Consistency for Domain Adaptive Semantic Segmentation of Remote Sensing Images

Semantic segmentation techniques for remote sensing images (RSIs) have been widely developed and applied. However, most segmentation methods depend on sufficiently annotated data for specific scenarios. When a large change occurs in the target scenes, model performance drops significantly. Therefore, unsupervised domain adaptation (UDA) for semantic segmentation is proposed to alleviate the reliance on expensive per-pixel densely labeled data. In this paper, two key issues of existing domain adaptive (DA) methods are considered: (1) the factors that cause data distribution shifts in RSIs may be complex and diverse, and existing DA approaches cannot adaptively optimize for different domain discrepancy scenarios; (2) domain-invariant feature alignment, based on adversarial training (AT), is prone to excessive feature perturbation, leading to over robust models. To address these issues, we propose an AdvCDA method that guides the model to adapt adversarial perturbation consistency. We combine consistency regularization to consider interdomain feature alignment as perturbation information in the feature space, and thus propose a joint AT and self-training (ST) DA method to further promote the generalization performance of the model. Additionally, we propose a confidence estimation mechanism that determines network stream training weights so that the model can adaptively adjust the optimization direction. Extensive experiments have been conducted on Potsdam, Vaihingen, and LoveDA remote sensing datasets, and the results demonstrate that the proposed method can significantly improve the UDA performance in various cross-domain scenarios.

From patch, sample to domain: Capture geometric structures for few-shot learning

Few-shot learning aims to recognize novel concepts with only few samples by using prior knowledge learned from the seen concepts. In this paper, we address the problem of few-shot learning under domain shifts. Traditional few-shot learning methods are not directly applicable to cross-domain scenarios due to the large discrepancy of feature distributions across domains. To this end, we propose a novel Hierarchical Optimal Transport network with Attention (HOTA) for cross-domain few-shot learning. The underlying idea is to learn the transferable and discriminative embeddings by taking advantage of the hierarchical geometric structures among image data, ranging from patch, sample to domain. The HOTA framework utilizes a hierarchical optimal transport network to smooth the domain shifts by domain alignment while enhancing the discrimination and the transferability of the embeddings by aligning the patches of images. To further enhance the transferability, HOTA conducts a mix-up data augmentation based on cross-domain attention to capture the relationships of samples in different domains. The extensive experiments on a variety of few-shot benchmark scenarios demonstrate that HOTA outperforms the state-of-the-art methods under both supervised and unsupervised conditions.

A unified and efficient semi-supervised learning framework for stereo matching

Recently, stereo matching algorithms have made tremendous progress in terms of both accuracy and efficiency. However, it remains a great challenge to train a practical model due to the scarcity of ground truth disparity. In this paper, we propose a Semi-supervised Stereo Matching Framework (SSMF), i.e., a continuous self-training pipeline involving both teacher model and student model. The proposed framework combines the consistency regularization with the entropy minimization to effectively utilize the unlabeled data in large quantities. To enhance the quality of pseudo disparities: (1) we introduce an additional branch in stereo models to regress confidence map and perform adaptive selection; (2) we inject strong data augmentations on unlabeled data to achieve the prediction consistency; (3) we propose a novel loss function to utilize pseudo disparities of different confidence levels. Comprehensive experimental results show that the proposed framework enables to largely improve the disparity accuracy and robustness. Moreover, it also demonstrates competitive performance in cross-domain scenarios. Among all published methods as of August 2023, it achieves 1st on KITTI 2012 benchmark and 4th on KITTI 2015 benchmark. Code is available at https://github.com/Twil-7/semi-supervised-stereo-matching.

A multi-constrained domain adaptation network for remaining useful life prediction of bearings

The prediction of the remaining useful life (RUL) of bearings could reduce maintenance costs and prevent serious consequences. Due to the variable working conditions of bearings, domain adaptation methods for cross-domain RUL prediction have received extensive attention. However, existing research often focuses solely on aligning marginal distributions while overlooking the alignment of conditional distributions and fails to retain domain-specific information during the alignment process. To address these issues, we propose a multi-constrained domain adaptation (MCDA) method for cross-domain RUL prediction. MCDA method is based on an adversarial domain adaptation architecture, where the improved domain discriminator aligns the data features of different degradation stages separately to achieve the alignment of marginal distribution and simultaneously aligns the conditional distribution. The state discrimination module is introduced to align the marginal and conditional distributions while retaining the target domain-specific information. To confirm the effectiveness of the proposed method, we conducted RUL prediction tasks for bearings in various cross-domain scenarios using both publicly and self-built datasets. The experimental results showed that the MCDA method could better handle cross-domain RUL prediction tasks compared to existing methods.

Collective behavior of self-propelled particles with multiple delays in cross-domain scenario * *This document is the results of the research project funded by the National Natural Science Foundation of China (NSFC) under Grant No.62076203.

In cross-domain scenarios, the simultaneous presence of multiple sensing delays exerts a profound influence on collective behavior. Motivated by this, our paper presents a system based on self-propelled particles that consists of two swarms containing two intra-swarm sensing delays and an inter-swarm sensing delay. Three state emerges from the system, namely translating state, ring state, and rotating state. Employing mean field approximation and bifurcation analysis, we theoretically examine the parameter space’s boundaries that govern these three states. Our detailed findings emphasize that within the translating and rotating states, variations in the two intra-swarm delays can lead to the separation of the two swarms. Meanwhile, the extent of separation is influenced by the inter-swarm delay. Finally, we conduct numerical simulations to validate the accuracy of our theoretical derivations.

XAutn: Blockchain-based cross domain authentication for digital certificates in the education sector

Traditional testimony and electronic endorsements are extremely challenging to uphold and defend, and there is a problem with challenging authentication. The identity of the student is typically not recognized when it comes to requirements for access to a student’s academic credentials that are scattered over numerous sites. This is an issue with cross-domain authentication methods. On the one hand, whenever the volume of cross-domain authentication requests increases dramatically, the response time can become intolerable because of the slow throughput associated with blockchain mechanisms. These systems still do not give enough thought to the cross-domain scenario’s anonymity problem. This research proposes an effective cross-domain authentication mechanism called XAutn that protects anonymity and integrates seamlessly through the present Certificate Transparency (CT) schemes. XAutn protects privacy and develops a fast response correctness evaluation method that is based on the RSA (Rivest, Shamir, and Adleman) cryptographic accumulator, Zero Knowledge Proof Algorithm, and Proof of Continuous work consensus Algorithm (POCW). We also provide a privacy-aware computation authentication approach to strengthen the integrity of the authentication messages more securely and counteract the discriminatory analysis of malevolent requests. This research is primarily used to validate identities in a blockchain network, which makes it possible to guarantee their authenticity and integrity while also increasing security and privacy. The proposed technique greatly outperformed the current methods in terms of authentication time, period required for storage, space for storage, and overall processing cost. The proposed method exhibits a speed gain of authentication of roughly 9% when compared to traditional blockchain systems. The security investigation and results from experiments demonstrate how the proposed approach is more reliable and trustworthy.

Anatomically Guided Cross-Domain Repair and Screening for Ultrasound Fetal Biometry.

Ultrasound based estimation of fetal biometry is extensively used to diagnose prenatal abnormalities and to monitor fetal growth, for which accurate segmentation of the fetal anatomy is a crucial prerequisite. Although deep neural network-based models have achieved encouraging results on this task, inevitable distribution shifts in ultrasound images can still result in severe performance drop in real world deployment scenarios. In this article, we propose a complete ultrasound fetal examination system to deal with this troublesome problem by repairing and screening the anatomically implausible results. Our system consists of three main components: A routine segmentation network, a fetal anatomical key points guided repair network, and a shape-coding based selective screener. Guided by the anatomical key points, our repair network has stronger cross-domain repair capabilities, which can substantially improve the outputs of the segmentation network. By quantifying the distance between an arbitrary segmentation mask to its corresponding anatomical shape class, the proposed shape-coding based selective screener can then effectively reject the entire implausible results that cannot be fully repaired. Extensive experiments demonstrate that our proposed framework has strong anatomical guarantee and outperforms other methods in three different cross-domain scenarios.

YOLO-G: Improved YOLO for cross-domain object detection.

Cross-domain object detection is a key problem in the research of intelligent detection models. Different from lots of improved algorithms based on two-stage detection models, we try another way. A simple and efficient one-stage model is introduced in this paper, comprehensively considering the inference efficiency and detection precision, and expanding the scope of undertaking cross-domain object detection problems. We name this gradient reverse layer-based model YOLO-G, which greatly improves the object detection precision in cross-domain scenarios. Specifically, we add a feature alignment branch following the backbone, where the gradient reverse layer and a classifier are attached. With only a small increase in computational, the performance is higher enhanced. Experiments such as Cityscapes→Foggy Cityscapes, SIM10k→Cityscape, PASCAL VOC→Clipart, and so on, indicate that compared with most state-of-the-art (SOTA) algorithms, the proposed model achieves much better mean Average Precision (mAP). Furthermore, ablation experiments were also performed on 4 components to confirm the reliability of the model. The project is available at https://github.com/airy975924806/yolo-G.

A Cross-Domain Multimodal Supervised Latent Topic Model for Item Tagging and Cold-Start Recommendation

Cross-domain data analysis is playing an increasingly important role in media convergence and can be adopted for many applications. Most existing methods consider the domain discrimination as the multimodal representation difference or the imbalanced item classification distribution, which ignore the different tag semantics between domains. To this end, we propose an explainable cross-domain multimodal supervised latent topic model (CDMSLT) and evaluate our model on two applications. First, we learn a common topic space which is capable of explaining both domain specification and commonality. Second, we apply our model to multilabel classification task and put forward a cross-domain item tagging method (CDMSLT-tag). Third, combining user behaviors and CDMSLT model, we propose a cross-domain recommendation algorithm (CDMSLT-rec) which could estimate the user preference on new unseen domains. This paper proves the effectiveness of CDMSLT model by comparing these two applications with existing algorithms in a cross-domain scenario on Douban dataset.

Human Activity Recognition With Low-Resolution Infrared Array Sensor Using Semi-Supervised Cross-Domain Neural Networks for Indoor Environment

Low-resolution infrared-based human activity recognition (HAR) attracted enormous interests due to its low-cost and private. In this paper, a novel semi-supervised crossdomain neural network (SCDNN) based on 8 × 8 low-resolution infrared sensor is proposed for accurately identifying human activity despite changes in the environment at a low-cost. The SCDNN consists of feature extractor, domain discriminator and label classifier. In the feature extractor, the unlabeled and minimal labeled target domain data are trained for domain adaptation to achieve a mapping of the source domain and target domain data. The domain discriminator employs the unsupervised learning to migrate data from the source domain to the target domain. The label classifier obtained from training the source domain data improves the recognition of target domain activities due to the semi-supervised learning utilized in training the target domain data. Experimental results show that the proposed method achieves 92.12% accuracy for recognition of activities in the target domain by migrating the source and target domains. The proposed approach adapts superior to cross-domain scenarios compared to the existing deep learning methods, and it provides a low-cost yet highly adaptable solution for cross-domain scenarios.

CDTA: A Cross-Domain Transfer-Based Attack with Contrastive Learning

Despite the excellent performance, deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples. Besides, these examples are often transferable among different models. In other words, the same adversarial example can fool multiple models with different architectures at the same time. Based on this property, many black-box transfer-based attack techniques have been developed. However, current transfer-based attacks generally focus on the cross-architecture setting, where the attacker has access to the training data of the target model, which is not guaranteed in realistic situations. In this paper, we design a Cross-Domain Transfer-Based Attack (CDTA), which works in the cross-domain scenario. In this setting, attackers have no information about the target model, such as its architecture and training data. Specifically, we propose a contrastive spectral training method to train a feature extractor on a source domain (e.g., ImageNet) and use it to craft adversarial examples on target domains (e.g., Oxford 102 Flower). Our method corrupts the semantic information of the benign image by scrambling the outputs of both the intermediate feature layers and the final layer of the feature extractor. We evaluate CDTA with 16 target deep models on four datasets with widely varying styles. The results confirm that, in terms of the attack success rate, our approach can consistently outperform the state-of-the-art baselines by an average of 11.45% across all target models. Our code is available at https://github.com/LiulietLee/CDTA.